diff --git a/1_data_science/simulationsstudie/latex/fig_roc_conf.png b/1_data_science/simulationsstudie/latex/fig_roc_conf.png new file mode 100644 index 0000000..510479b Binary files /dev/null and b/1_data_science/simulationsstudie/latex/fig_roc_conf.png differ diff --git a/1_data_science/simulationsstudie/latex/yann_ahlgrim_ergebnisbericht.pdf b/1_data_science/simulationsstudie/latex/yann_ahlgrim_ergebnisbericht.pdf index 8feabd6..d9ee0ce 100644 Binary files a/1_data_science/simulationsstudie/latex/yann_ahlgrim_ergebnisbericht.pdf and b/1_data_science/simulationsstudie/latex/yann_ahlgrim_ergebnisbericht.pdf differ diff --git a/1_data_science/simulationsstudie/latex/yann_ahlgrim_ergebnisbericht.tex b/1_data_science/simulationsstudie/latex/yann_ahlgrim_ergebnisbericht.tex index 5f956e9..25c67ee 100644 --- a/1_data_science/simulationsstudie/latex/yann_ahlgrim_ergebnisbericht.tex +++ b/1_data_science/simulationsstudie/latex/yann_ahlgrim_ergebnisbericht.tex @@ -31,12 +31,12 @@ \section{Fachthema: Diebstahlerkennung im Auto} Moderne Fahrzeuge erfassen in Echtzeit vielfältige Daten zum Fahrverhalten. Diese Studie untersucht, inwiefern sich anhand dieser Daten mit einem statistischen Modell erkennen lässt, ob statt des gewöhnlichen Fahrers eine andere Person am Steuer sitzt -- was auf einen \emph{Diebstahl} hindeuten würde. Konkret soll ein Modell die -Wahrscheinlichkeit $P(\text{Diebstahl})$ schätzen und bei Überschreiten eines Schwellwerts einen Alarm auslösen. Die \textbf{Zielvariable} (abhängige Variable) ist +Wahrscheinlichkeit $P(\text{Diebstahl})$ schätzen und bei Überschreiten eines Schwellwerts einen Alarm auslösen. Die Zielvariable (abhängige Variable) ist dabei binär (0 = kein Diebstahl, 1 = Diebstahl). Ein Diebstahl wird angenommen, wenn das Fahrverhalten signifikant vom üblichen Fahrerprofil abweicht. \noindent \newline -Für das Fachthema wurden insgesamt \textbf{8 Einflussgrößen} (potenziell erklärende Variablen) definiert, welche typische Aspekte des Fahrverhaltens und Umfelds +Für das Fachthema wurden insgesamt 8 Einflussgrößen (potenziell erklärende Variablen) definiert, welche typische Aspekte des Fahrverhaltens und Umfelds repräsentieren. Davon sind 4 Variablen als tatsächlich erklärungsrelevant für die Zielvariable angenommen, während die übrigen 4 Variablen keinen Einfluss auf einen Fahrerwechsel (Diebstahl) haben. Zur Erfüllung der Aufgabenstellung sind unter den relevanten Prädiktoren zwei Variablen kategorial (ordinal) mit mindestens 3 Ausprägungen, die übrigen relevanten Variablen sind metrisch. Im Folgenden werden alle Variablen beschrieben: @@ -71,13 +71,13 @@ Schlüsselvariablen dar, die einen Fahrerwechsel anzeigen können. Die übrigen besitzen aber per Annahme keinen Erklärungsgehalt für die Zielvariable. \section{Erzeugung der Grundgesamtheit} -Für die Simulation wurde eine \textbf{Grundgesamtheit} von $N = 1.000.000$ Fahrten generiert. Jede Fahrt hat einen binären Indikator (Zielvariable \emph{Diebstahl ja/nein}) und 8 +Für die Simulation wurde eine Grundgesamtheit von $N = 1.000.000$ Fahrten generiert. Jede Fahrt hat einen binären Indikator (Zielvariable \emph{Diebstahl ja/nein}) und 8 zugehörige Merkmalswerte (die oben definierten Variablen). Die Werte wurden mittels geeigneter Zufallsverteilungen erzeugt, basierend auf realistischen Annahmen und empirischen Daten, jedoch so, dass die Abhängigkeitsstruktur kontrolliert vorgegeben ist. Die Zielvariable hängt funktional von genau 4 der 8 Variablen ab (den erklärenden), während die restlichen 4 keinen Einfluss auf den Diebstahl haben. Zudem wurden zwei inhaltlich sinnvolle Korrelationen zwischen ausgewählten Variablen eingeführt, ohne jedoch Multikollinearität zu verursachen (alle Varianzinflationsfaktoren $\text{VIF} < 5$). -\subsubsection*{Verteilungen der Variablen} +\subsection*{Verteilungen der Variablen} \begin{itemize} \item \textbf{Durchschnittsgeschwindigkeit:} Normalverteilung mit $\mu \approx 47$~km/h und $\sigma \approx 10$~km/h, begrenzt auf plausible Werte $[10,130]$~km/h (keine negativen oder extrem hohen Geschwindigkeiten). @@ -106,7 +106,7 @@ zu verursachen (alle Varianzinflationsfaktoren $\text{VIF} < 5$). Durch diese Vorgehensweise bilden die generierten Daten nahezu realistische Verteilungen und Beziehungen der Variablen ab, wobei genau zwei inhaltlich plausible Korrelationen eingebaut wurden. Beide Abhängigkeiten sind so kalibriert, dass im optimalen Regressionsmodell keine Multikollinearität auftritt ($\text{VIF} < 5$ in allen Fällen). -\subsubsection*{Modellierung der Zielvariable und der $\pi$-Werte} +\subsection*{Modellierung der Zielvariable und der $p_i$-Werte} Die Zielvariable \emph{Diebstahl} wurde als Zufallsvariable auf Basis eines logistischen Regressionsmodells generiert. Dazu wurde zunächst für jede Fahrt $i$ die Diebstahl-Wahrscheinlichkeit $p_i = P(Y_i=1)$ berechnet als \[ @@ -119,7 +119,7 @@ Anschließend wurde $Y_i$ durch einen Bernoulli-Zufall mit Parameter $p_i$ reali \noindent \newline -Die \textbf{gewählten Koeffizienten ($\beta$-Werte)} für das Generierungsmodell sind in Tabelle~\ref{tab:true_betas} aufgeführt. Diese wurden so festgelegt, dass sie plausible +Die gewählten Koeffizienten ($\beta$-Werte) für das Generierungsmodell sind in Tabelle~\ref{tab:true_betas} aufgeführt. Diese wurden so festgelegt, dass sie plausible Einflussstärken der erklärenden Variablen widerspiegeln, ohne die Zielvariable extrem zu dominieren. Insbesondere wurde ein relativ niedriger Basiswert (\emph{Intercept} $\beta_0=-2$) gewählt, sodass bei unauffälligen Merkmalen die Diebstahl-Wahrscheinlichkeit sehr gering ist. Die erklärenden Variablen erhöhen bzw.\ verringern diese Grundwahrscheinlichkeit wie folgt: @@ -153,10 +153,10 @@ Durchschnittsgeschwindigkeit & $+0{,}015$ \\ Harte Bremsmanöver & $+0{,}10$ \\ Schaltverhalten (früh) & $-0{,}30$ \\ Schaltverhalten (normal) & $-0{,}30$ \\ -Schaltverhalten (spät) & $+0{,}00$ \\ +Schaltverhalten (spät) & $+0{,}50$ \\ Geschwindigkeitsüberschreitung (selten) & $-0{,}30$ \\ Geschwindigkeitsüberschreitung (manchmal) & $-0{,}30$ \\ -Geschwindigkeitsüberschreitung (häufig) & $+0{,}00$ \\ +Geschwindigkeitsüberschreitung (häufig) & $+0{,}50$ \\ Wetter (alle Kategorien) & $0{,}00$ \\ Straßentyp (alle Kategorien) & $0{,}00$ \\ Wochentag (alle Kategorien) & $0{,}00$ \\ @@ -176,16 +176,16 @@ der $p_i$ über alle Fahrten (siehe Abb.~\ref{fig:fig_pi_dist}). \begin{figure}[H] \centering -\includegraphics[width=0.8\textwidth]{fig_pi_dist.png} -\caption{Verteilung der Diebstahl-Wahrscheinlichkeiten $p_i$} -\label{fig:fig_pi_dist} +\includegraphics[width=0.8\textwidth]{fig_target_variable_dist.png} +\caption{Verteilung der Zielvariable in der Grundgesamtheit} +\label{fig:target_dist} \end{figure} \begin{figure}[H] \centering -\includegraphics[width=0.8\textwidth]{fig_target_variable_dist.png} -\caption{Verteilung der Zielvariable in der Grundgesamtheit} -\label{fig:target_dist} +\includegraphics[width=0.8\textwidth]{fig_pi_dist.png} +\caption{Verteilung der Diebstahl-Wahrscheinlichkeiten $p_i$} +\label{fig:fig_pi_dist} \end{figure} \section{Simulation der Perspektive des Data Scientist} @@ -271,6 +271,26 @@ Somit hat der Data Scientist durch das systematische Vorgehen tatsächlich das z Insbesondere wurden keine falschen Variablen im Endmodell behalten und keine echten Einflüsse übersehen. Die logistische Regressionsgleichung aus der Stichprobe stimmt inhaltlich mit der zur Datengenerierung überein. +\subsection*{Evaluierung auf den Testdaten} +Das finale Modell wurde auf den zurückgehaltenen Testdaten (80:20 Split) evaluiert, um die Generalisierungsfähigkeit zu bewerten. Abbildung~\ref{fig:roc_conf} zeigt die ROC-Kurve +und die Confusion Matrix der Modellvorhersagen. + +\begin{figure}[H] +\centering +\includegraphics[width=\textwidth]{fig_roc_conf.png} +\caption{ROC-Kurve und Confusion Matrix der Modellevaluation auf Testdaten} +\label{fig:roc_conf} +\end{figure} + +\noindent +Die ROC-Kurve zeigt mit einem AUC-Wert deutlich über 0,5 eine gute Diskriminierungsfähigkeit des Modells gegenüber einem Zufallsklassifikator. Die Confusion Matrix offenbart jedoch +ein typisches Klassifikationsproblem: Das Modell erkennt normale Fahrten sehr zuverlässig, hat aber Schwierigkeiten bei der Diebstahlerkennung. + +\noindent +\newline +Diese Missklassifikation resultiert primär aus der begrenzten Stichprobengröße von 20.000 Beobachtungen bei einer unbalancierten Klassenverteilung (ca. 25\,\% Diebstähle). +Das Modell neigt dadurch zur Klassifikation in Richtung der Mehrheitsklasse. + \section{Güte der Modellparameter} In einem letzten Schritt wurde untersucht, wie verlässlich die Modellschätzung bei unterschiedlicher Datenmenge ist. Insbesondere stellt sich die Frage, welchen Einfluss der Stichprobenumfang $n$ auf die Präzision der geschätzten Regressionskoeffizienten hat. Hierzu wurde ein Monte-Carlo-Simulationsansatz gewählt: Aus der Grundgesamtheit wurden für @@ -314,7 +334,7 @@ Präzision der Koeffizientenschätzung erheblich. Noch größere Datenmengen fü \newline \newline Insgesamt zeigt die Simulation, dass zur zuverlässigen Identifikation kleiner Effekte (wie $\beta_{avg\_speed}\approx0{,}015$) eine ausreichend große Stichprobe notwendig ist. -Mit $n \to 50.000$ nähert sich die Streuung einem Wert an, der durch die inhärente Ergebnisvarianz (bedingt durch den Rauschterm $\varepsilon$) begrenzt ist. Für das vorliegende +Mit $n \to 50.000$ nähert sich die Streuung einem Wert an. Für das vorliegende Problem bedeutet dies: Je mehr Fahrten der Data Scientist zur Verfügung hat, desto genauer kann er die wahren Fahrerwechsel-Einflüsse schätzen und desto vertrauenswürdiger sind die vom Modell ausgegebenen Diebstahlwahrscheinlichkeiten. diff --git a/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb b/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb index eb87318..73f26e2 100644 --- a/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb +++ b/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 44, "id": "97ba29a9", "metadata": {}, "outputs": [], @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 45, "id": "61247da5", "metadata": {}, "outputs": [], @@ -156,7 +156,6 @@ "trip_distance = np.random.lognormal(mean=mu, sigma=sigma, size=N)\n", "\n", "# 7. Straßentyp (nominal, Kontext) – Abhängigkeit von trip_distance mit Softmax-Funktion\n", - "# Skaliere trip_distance auf [0, 1]\n", "dist_scaled = (trip_distance - trip_distance.min()) / (trip_distance.max() - trip_distance.min())\n", "road_strength = 0.18\n", "road_logits = np.zeros((N, 3))\n", @@ -180,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 46, "id": "f8e0efb0", "metadata": {}, "outputs": [ @@ -291,7 +290,7 @@ "4 Außerorts Mo-Fr " ] }, - "execution_count": 20, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -321,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 47, "id": "6bfb199a", "metadata": {}, "outputs": [ @@ -352,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 48, "id": "1d3df64a", "metadata": {}, "outputs": [ @@ -383,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 49, "id": "49045ee5", "metadata": {}, "outputs": [ @@ -494,7 +493,7 @@ "4 Außerorts Mo-Fr " ] }, - "execution_count": 23, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -513,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 50, "id": "4ad287b1", "metadata": {}, "outputs": [ @@ -704,7 +703,7 @@ "4 1.0 0.0 1.0 0.0 0.0 " ] }, - "execution_count": 24, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -739,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 51, "id": "75318d77", "metadata": {}, "outputs": [ @@ -807,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 52, "id": "76948d4a", "metadata": {}, "outputs": [ @@ -910,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 53, "id": "706ff5c6", "metadata": {}, "outputs": [], @@ -933,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 54, "id": "9f12d325", "metadata": {}, "outputs": [ @@ -1223,7 +1222,7 @@ "max 1.000000 1.000000 " ] }, - "execution_count": 28, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1240,7 +1239,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 55, "id": "30bfcdde", "metadata": {}, "outputs": [ @@ -1285,12 +1284,14 @@ "source": [ "### Feature Engineering\n", "\n", - "Für die Modellierung werden kategoriale Variablen in numerische Dummy-Variablen umgewandelt. Dies ist notwendig, damit die Regressionsmodelle die Informationen nutzen können. In unserem Fall sind die kategorischen Variablen schon aus dem vorherigen Schritt One-Hot Encoded, weil wir aus den Variablen das logistische Regressionsproblem erstellt haben. Außerdem werden die Daten normalerweise in Trainings- und Testdaten aufgeteilt, jedoch habe ich mich hier nicht dazu entschieden weil 20.000 Instanzen wenig ist zum Trainieren und es die Aufgabe nicht vorgibt. " + "Für die Modellierung werden kategoriale Variablen in numerische Dummy-Variablen umgewandelt. Dies ist notwendig, damit die Regressionsmodelle die Informationen nutzen können. In unserem Fall sind die kategorischen Variablen schon aus dem vorherigen Schritt One-Hot Encoded, weil wir aus den Variablen das logistische Regressionsproblem erstellt haben. \n", + "\n", + "Die Daten werden in Trainings- und Testdaten im Verhältnis 80:20 aufgeteilt" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 56, "id": "b71ab517", "metadata": {}, "outputs": [ @@ -1298,7 +1299,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Trainingsdaten: (20000, 19)\n" + "Trainingsdaten: (16000, 19)\n", + "Testdaten: (4000, 19)\n", + "Anteil Zielvariable in Trainingsdaten:\n", + "0 0.695875\n", + "1 0.304125\n", + "dtype: float64\n", + "Anteil Zielvariable in Testdaten:\n", + "0 0.696\n", + "1 0.304\n", + "dtype: float64\n" ] } ], @@ -1308,15 +1318,14 @@ "# Zielvariable\n", "y = target_sample\n", "\n", - "# One-Hot-Encoding für kategoriale Variablen\n", - "# X_encoded = pd.get_dummies(X_sample)\n", + "X_train, X_test, y_train, y_test = train_test_split(X_sample, y, test_size=0.2, random_state=22, stratify=y)\n", "\n", - "# Aufteilung in Trainings- und Testdaten\n", - "# X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.3, random_state=42)\n", - "X_train = X_sample\n", - "y_train = y\n", "print(\"Trainingsdaten:\", X_train.shape)\n", - "# print(\"Testdaten:\", X_test.shape)" + "print(\"Testdaten:\", X_test.shape)\n", + "print(\"Anteil Zielvariable in Trainingsdaten:\")\n", + "print(pd.Series(y_train).value_counts(normalize=True))\n", + "print(\"Anteil Zielvariable in Testdaten:\")\n", + "print(pd.Series(y_test).value_counts(normalize=True))" ] }, { @@ -1337,13 +1346,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 57, "id": "b748cb80", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1385,7 +1394,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 58, "id": "3ffbfe79", "metadata": {}, "outputs": [ @@ -1395,19 +1404,19 @@ "text": [ "Deskriptive Statistiken für metrische Variablen:\n", " avg_speed hard_brakes trip_distance\n", - "count 20000.000000 20000.000000 20000.000000\n", - "mean 46.955431 1.987300 20.400866\n", - "std 9.983939 1.406321 16.370594\n", + "count 16000.000000 16000.000000 16000.000000\n", + "mean 46.973353 1.981437 20.266509\n", + "std 9.992143 1.400746 16.077625\n", "min 10.000000 0.000000 1.019350\n", - "25% 40.210704 1.000000 9.923435\n", - "50% 46.933676 2.000000 15.896143\n", - "75% 53.726714 3.000000 25.538578\n", - "max 81.665638 10.000000 228.850160\n" + "25% 40.242019 1.000000 9.951554\n", + "50% 46.994639 2.000000 15.856460\n", + "75% 53.736423 3.000000 25.392592\n", + "max 81.665638 9.000000 218.246601\n" ] }, { "data": { - "image/png": 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kAAD7R2lpafz5z3/eYf2Pf/xjdO3aNYFEAHsulUpFz549W5317NmzxSWzAN4v0eJv1KhRMXXq1Fi/fn3zWm1tbXz5y1+O0aNH7/brvPzyy9G3b98YPHhwnHvuuc0Hd2vWrIna2tqYMGFC83OLiori1FNPjeXLl+/0NefMmROlpaXNtwEDBuzhdwcAkLzHH388vvrVr0bnzp1brJeVlcWrr76aUCoAgP3nzDPPjKlTp8af/vSn5rU//vGP8eUvfznOPPPMBJMB7L5169a1eXLM2rVrY926dR0bCMgaiRZ/3//+92PDhg1RVlYWQ4YMiSFDhsTAgQNj/fr1cfvtt+/Wa1RUVMTdd98dv/zlL+O73/1u1NbWxoknnhhvvPFG1NbWRkREr169WnxNr169mmdtmT59erz11lvNt1deeWXvvkkAgAQ1NTW1eo2bmpoa254DADnpG9/4RnTt2jWGDRsWgwcPjsGDB8cHPvCBOOSQQ+K///u/k44HsFvS6XS75kD+SvQaf0OGDInnn38+li5dGn/4wx8inU7H0UcfHePGjdvtU5UnTpzYfH/EiBExZsyYOOKII+Kuu+6KD33oQxERO7xWOp3e5esXFRVFUVHRHn5HAACZZfz48TF//vxYsGBBRLx3XLRp06a45ppr4h//8R8TTgcAsO+VlpbG8uXLY+nSpfH73/8+unTpEuXl5XHKKackHQ0AYL9LtPiLeO/NpwkTJsQpp5wSRUVF7d6buGvXrjFixIh4+eWX46yzzoqI97YP7dOnT/NzNmzYsMNZgAAAuejGG2+M0047LY4++uior6+PyZMnx8svvxw9e/aMe+65J+l4AAD7xfb3m95/+RcAgHyQaPHX1NQU119/fdx2223x2muvxUsvvRSHH354XH311TFo0KC44IIL9vg1Gxoa4sUXX4yTTz45Bg8eHL17946lS5fGBz/4wYiI2Lp1ayxbtixuuOGGff3tAABknL59+8Zzzz0XixYtimeeeSaampriggsuiPPOOy+6dOmSdDwAgP3i4Ycfjocffjg2bNgQTU1NLWbf//73E0oFsPt2dYJMe0+gAXJXotf4u+666+LOO++MuXPnRufOnZvXR4wYEd/73vd26zWuuOKKWLZsWaxZsyZWrFgRn/zkJ6Ouri6mTJkSqVQqpk6dGrNnz47FixfHCy+8EP/6r/8aJSUlMXny5P31bQEAZIz//d//jS5dusT5558ft9xyS9x6663xuc99Lrp06RL/+Z//mXQ8AIB97tprr40JEybEww8/HK+//nps3LixxQ0gG5SVlcWIESNanZWXl0dZWVkHJwKyRaJn/N19992xYMGCOP300+MLX/hC83p5eXn84Q9/2K3XqKmpiU996lPx+uuvx6GHHhof+tCH4sknn2z+xTdt2rTYsmVLXHTRRbFx48aoqKiIhx56KLp3775fvicAgExyySWXxIEHHhhnnHFGi/UvfelLsWjRovjGN76RUDIAgP3jtttuizvvvDM+85nPJB0FYK+lUqmYMmVKXHHFFTvMtp/0AtCaRIu/V199NYYMGbLDelNTU7z77ru79RqLFi3a6TyVSsXMmTNj5syZexMRACCrLVq0KM4999z46U9/GqecckpERHzxi1+M+++/Px555JGE0wEA7Htbt26NE088MekYAO2STqfj3nvvjVQqFel0unk9lUrFokWL4rjjjlP+Aa1KdKvPY445Jn7zm9/ssP6jH/2o+Zp8AADsvY9+9KNx2223xVlnnRVPP/10XHTRRc2l37Bhw5KOBwCwz33uc5+LhQsXJh0DoF2qq6ujsrKyRekX8V4hWFlZGdXV1QklAzJdomf8XXPNNfGZz3wmXn311Whqaor7778/Vq9eHXfffXf87Gc/SzIaAEDOOPfcc2Pjxo1x0kknxaGHHhrLli1rddcFAIBcUF9fHwsWLIhf/epXUV5eHgcccECL+bx58xJKBrD7Bg4cGKNGjYrKysodZqNHj46BAwcmkArIBokWfx//+Mfj3nvvjdmzZ0cqlYqvfe1rcdxxx8UDDzwQ48ePTzIaAEDWuvzyy1tdP+yww+KDH/xg3Hrrrc1r3vgCAHLN888/H8cee2xERLzwwgstZrbFA7JFKpWK008/vdXi7/TTT/f7DGhTosVfRMRHPvKR+MhHPpJ0DACAnPHss8+2un7EEUdEXV1d89x/KAIAuch1jIFc0NTU1OJDm+/3P//zPzF+/PgoKEj0Sl5Ahkq8+Pvb3/4W9913X/z5z3+OK664Ig4++OD43e9+F7169Yp+/folHQ8AIOt4swsAACC7rVixIurq6lqd1dXVxYoVK2LMmDEdnArIBokWf88//3yMGzcuSktLY+3atfG5z30uDj744Fi8eHGsW7cu7r777iTjAQAAAJCFKisr40c/+lFUV1fH1q1bW8zuv//+hFIB7L6Kioro0aNHq+VfaWlpVFRUJJAKyAaJngt8+eWXx7/+67/Gyy+/HMXFxc3rEydOjMceeyzBZAAAuaOysjKmTZsW5557bkyaNKnFDQAg1yxatCj+4R/+IVatWhWLFy+Od999N1atWhW//vWvo7S0NOl4ALuloKAgvva1r7U6u+aaa2zzCbQp0d8OlZWVceGFF+6w3q9fv6itrU0gEQBAbvHGFwCQb2bPnh033nhj/OxnP4vOnTvHTTfdFC+++GKcc845MXDgwKTjAey23r17t7p+2GGHdXASIJskWvwVFxe3eqry6tWr49BDD00gEQBAbvHGFwCQb/70pz/Fxz72sYiIKCoqinfeeSdSqVR86UtfigULFiScDmD3pNPpuOGGG1qd3XDDDZFOpzs4EZAtEi3+PvGJT8SsWbPi3XffjYiIVCoV1dXVcdVVV8U//dM/JRkNACAneOMLAMg3Bx98cLz99tsR8d6uUi+88EJERPztb3+LzZs3JxkNYLetW7cuqqqqWp1VVVXFunXrOjgRkC0SLf7++7//O/7617/GYYcdFlu2bIlTTz01hgwZEt27d4/rr78+yWgAADnBG18AQL45+eSTY+nSpRERcc4558Rll10W//7v/x6f+tSn4vTTT084HQDA/tUpyT+8R48e8fjjj8evf/3r+N3vfhdNTU1x3HHHxbhx45KMldNanALe+G5yQQDIbe/7N8b2I8na/sbXiBEjmt/4+vWvfx1Lly71xhcAkJNuueWWqK+vj4iI6dOnxwEHHBCPP/54TJo0Ka6++uqE0wHsnoEDB0ZJSUmrH9gsKSlx6QagTYkWf9t9+MMfjg9/+MNJx8gLDQ0Nzfe7/35RgkkAyBcNDQ1RUlKSdIy85Y0vACDfHHzwwc33CwoKYtq0aTFt2rQEEwHsuerq6jZ3adm8eXNUV1fHoEGDOjYUkBUS3eozIuLhhx+OM844I4444ogYMmRInHHGGfGrX/0q6VgAAFlv27Zt8cADD0RBwXuHfNvf+PrpT38a8+bNi4MOOijhhAAA+15hYWFs2LBhh/U33ngjCgsLE0gEsOeampraNQfyV6Jn/N1yyy3xpS99KT75yU/GZZddFhERTz75ZPzjP/5jzJs3Ly655JIk4+WkoqKi5vtvjzw3ovCABNMAkLMa320+s/z9//bQsTp16hT/8R//ES+++GLSUQAAOkxbW803NDRE586dOzgNwN5Zv379LueHH354B6UBskmixd+cOXPixhtvbFHwXXrppfEP//APcf311yv+9oNUKvV/DwoPUPwBsN+1+LeHDldRURHPPvtslJWVJR0FAGC/uvnmmyPivePP733ve9GtW7fmWWNjYzz22GMxbNiwpOIB7JE+ffq0aw7kr0SLv7q6uvjoRz+6w/qECRPiyiuvTCARAEBuueiii+LLX/5y1NTUxPHHHx9du3ZtMS8vL08oGQDAvnXjjTdGxHtn/N12220ttvXs3LlzDBo0KG677bak4gHske2XbNjbOZC/Ei3+zjzzzFi8eHH853/+Z4v1n/zkJ/Hxj388oVQAALnjX/7lXyLivV0VtkulUpFOpyOVSkVjY2NS0QAA9qk1a9ZERMRpp50W999/v+sZA1mtrKwsBg0aFGvXrt1hNnjwYLu6AG1KtPj7wAc+ENdff308+uijMWbMmIh47xp/v/3tb+PLX/5y8xYNES3frAIAYPdsfwMMACBfPPLIIy0eNzY2RlVVVZSVlSkDgayRTqfj9ddfb3X217/+tfnDnAB/L9Hi7/bbb4+DDjooVq1aFatWrWpeP/DAA+P2229vfpxKpRR/AAB7wadAAYB8M3Xq1BgxYkRccMEF0djYGKeccko88cQTUVJSEj/72c9i7NixSUcE2KUVK1bEpk2bWp1t2rQpVqxY0XwyDcD7JVr8+QQ6AMD+t3r16vjWt74VL774YqRSqRg2bFh88YtfjKOOOirpaAAA+9yPfvSj+PSnPx0REQ888ECsXbs2/vCHP8Tdd98dM2bMiN/+9rcJJwTYtdGjR0dhYWGrl2fo1KlTjB49OoFUQDbIqCuANjY2xnPPPRcbN25MOgoAQE647777Yvjw4fHMM8/EyJEjo7y8PH73u9/F8OHD40c/+lHS8QAA9rk33ngjevfuHRERv/jFL+Kf//mf48gjj4wLLrggqqqqEk4HsHtqamravCb7tm3boqampoMTAdki0eJv6tSpzVt6bt964bjjjosBAwbEo48+mmQ0AICcMG3atJg+fXo88cQTMW/evJg3b14sX748vvKVr8SVV16ZdDwAgH2uV69esWrVqmhsbIwHH3wwxo0bFxERmzdvjsLCwoTTAeyefv36tWsO5K9Ei7/77rsvRo4cGREtt16YOnVqzJgxI8loAAA5oba2Nj772c/usP7pT386amtrE0gEALB/nX/++XHOOefE8OHDI5VKxfjx4yPivetlDRs2LOF0ALvn5z//ebvmQP5KtPh7/fXXbb0AALAfjR07Nn7zm9/ssP7444/HySefnEAiAID9a+bMmfG9730vPv/5z8dvf/vbKCoqioiIwsLCuOqqqxJOB7B7zjjjjDbPUi4sLIwzzjijgxMB2aJTkn/49q0X+vTpEw8++GDceuutEWHrBQCA9vjpT3/afP/MM8+MK6+8Mp555pn40Ic+FBERTz75ZPzoRz+Ka6+9NqmIAAD71Sc/+ckd1qZMmZJAEoC9U1BQEAMGDIi1a9fuMBswYEAUFCR6Tg+QwRIt/rZvvdCnTx9bLwAA7CNnnXXWDmu33npr84estrv44ovjC1/4QgelAgDYf26++eb4/Oc/H8XFxXHzzTfv9LmXXnppB6UC2HvV1dWtln4REWvXro3q6uooKyvr2FBAVki0+Js5c2YMHz48Xnnllfjnf/5nWy8AAOwDTU1NSUcAAOhQN954Y5x33nlRXFwcN954Y5vPS6VSij8gK/Tv3z8KCwujsbFxh1lhYWH0798/gVRANki0+IvYva0XRowYEb/4xS9iwIABHRULACCvON4CALLZmjVrWr0PkK1WrFjRaukXEdHY2BgrVqyIE088sYNTAdkgKzYCXrt2bbz77rtJxwAAyFmOtwAAADJH79692zUH8lfiZ/wBAAAAQHtcfvnlu/3cefPm7cckAPtGKpVq1xzIX4o/AAAAALLas88+2+LxM888E42NjXHUUUdFRMRLL70UhYWFcfzxxycRDwCgwyj+AAAAAMhqjzzySPP9efPmRffu3eOuu+6Kgw46KCIiNm7cGOeff36cfPLJSUUE2CPO+AP2VlZc4w8AAAAAdsc3v/nNmDNnTnPpFxFx0EEHxXXXXRff/OY3E0wGsPvKysriyCOPbHV21FFHRVlZWQcnArKF4g8AAACAnFFXVxevvfbaDusbNmyIt99+O4FEAHvn3XffbXV969atHZwEyCZZUfx95zvfiV69eiUdAwAgZzneAgByxdlnnx3nn39+3HfffVFTUxM1NTVx3333xQUXXBCTJk1KOh7Ablm7dm2sWbOm1dmaNWti7dq1HRsIyBqJXuPv5ptvbnU9lUpFcXFxDBkyJE455ZSYPHlyBycDAMhebR1jtebSSy+NiHC8BQDkjNtuuy2uuOKK+PSnP918tkynTp3iggsuiG984xsJpwPYPX/5y192OR88eHAHpQGySaLF34033hh//etfY/PmzXHQQQdFOp2Ov/3tb1FSUhLdunWLDRs2xOGHHx6PPPJIDBgwIMmoAABZ48Ybb2zxePvx1oEHHhgR0Xy8ddhhhzUXfwAAuaKkpCRuvfXW+MY3vhF/+tOfIp1Ox5AhQ6Jr164tnldTUxN9+/aNgoKs2BALyDN9+vRp1xzIX4ke2cyePTtGjRoVL7/8crzxxhvx5ptvxksvvRQVFRVx0003RXV1dfTu3Tu+9KUvJRkTACCrrFmzpvl2/fXXx7HHHhsvvvhivPnmm/Hmm2/Giy++GMcdd1x8/etfTzoqAMB+07Vr1ygvL4+RI0fuUPpFRBx99NG2ygMyViqVatccyF+JFn9f/epX48Ybb4wjjjiieW3IkCHx3//93zF9+vTo379/zJ07N377298mmBIAIHtdffXV8a1vfSuOOuqo5rWjjjoqbrzxxvjqV7+aYDIAgGSl0+ldPuexxx6Lj3/849G3b99IpVLx4x//eIfXmDlzZvTt2ze6dOkSY8eOjZUrV7Z4TkNDQ3zxi1+Mnj17RteuXePMM8+MmpqaffmtADloV7+jdud3GJCfEi3+1q9fH9u2bdthfdu2bVFbWxsREX379o233367o6MBAOSE9evXN1/b5v0aGxvjtddeSyARAED2eOedd2LkyJFxyy23tDqfO3duzJs3L2655ZaorKyM3r17x/jx41u8lzV16tRYvHhxLFq0KB5//PHYtGlTnHHGGdHY2NhR3waQhdavX9+uOZC/Ei3+TjvttLjwwgvj2WefbV579tln4z/+4z/iwx/+cEREVFVVuUgpAMBeOv300+Pf//3f4+mnn27+ROjTTz8dF154YYwbNy7hdAAAmW3ixIlx3XXXxaRJk3aYpdPpmD9/fsyYMSMmTZoUw4cPj7vuuis2b94cCxcujIiIt956K26//fb45je/GePGjYsPfvCD8b//+79RVVUVv/rVr9r8cxsaGqKurq7FDcgvffv2bdccyF+JFn+33357HHzwwXH88cdHUVFRFBUVxQknnBAHH3xw3H777RER0a1bt/jmN7+ZZEwAgKz1/e9/P/r16xejR4+O4uLiKCoqioqKiujTp09873vfSzoeAEDWWrNmTdTW1saECROa14qKiuLUU0+N5cuXR0TEM888E++++26L5/Tt2zeGDx/e/JzWzJkzJ0pLS5tvAwYM2H/fCJCRBg4c2K45kL86JfmH9+7dO5YuXRp/+MMf4qWXXop0Oh3Dhg1rcQ2a0047LcGEAADZK51Ox+bNm+O+++6LV199NV588cVIp9PxgQ98II488sik4wEAJCqVSrXr67dfpqZXr14t1nv16hXr1q1rfk7nzp3joIMO2uE527++NdOnT4/LL7+8+XFdXZ3yD/LMzj4csH1+8sknd1AaIJskesbfsmXLIiJi2LBhceaZZ8YnPvGJFqUfAAB7L51Ox9ChQ+PVV1+NoUOHNh9vtbf0mzNnTqRSqZg6deq+CQoAkIDt26C3198XiOl0epel4q6eU1RUFD169GhxA/LLSy+91K45kL8SLf7Gjx8fAwcOjKuuuipeeOGFJKMAAOScgoKCGDp0aLzxxhv77DUrKytjwYIFUV5evs9eEwBgf3nllVeipqam1dmqVauirKxsr1+7d+/eERE7nLm3YcOG5rMAe/fuHVu3bo2NGze2+RyA1nz2s59t1xzIX4kWf3/5y19i2rRp8Zvf/CbKy8ujvLw85s6d2+YBGQAAe2bu3Lnxn//5n/vkQ1abNm2K8847L7773e/usF0VAECm2LZtW1x99dVRWloagwYNirKysigtLY2vfvWr8e677zY/b8CAAVFYWLjXf87gwYObL2Oz3datW2PZsmVx4oknRkTE8ccfHwcccECL56xfvz5eeOGF5ucAtObVV19t1xzIX4le469nz55xySWXxCWXXBJr1qyJhQsXxt133x1f+cpX4pRTTolf//rXScYDAMh6n/70p2Pz5s0xcuTI6Ny5c3Tp0qXF/M0339zt17r44ovjYx/7WIwbNy6uu+66nT63oaEhGhoamh/X1dXtWXAAgL10ySWXxOLFi2Pu3LkxZsyYiIh44oknYubMmfH666/HbbfdttuvtWnTpvjjH//Y/HjNmjXx3HPPxcEHHxwDBw6MqVOnxuzZs2Po0KExdOjQmD17dpSUlMTkyZMjIqK0tDQuuOCC+PKXvxyHHHJIHHzwwXHFFVfEiBEjYty4cfv2GwdySlNTU7vmQP5KtPh7v8GDB8dVV10VI0eOjKuvvrr5+n8AAOy9+fPn75PXWbRoUfzud7+LysrK3Xr+nDlz4tprr90nfzYAwJ645557YtGiRTFx4sTmtfLy8hg4cGCce+65e1T8Pf3003Haaac1P7788ssjImLKlClx5513xrRp02LLli1x0UUXxcaNG6OioiIeeuih6N69e/PX3HjjjdGpU6c455xzYsuWLXH66afHnXfe2a6zDYHc95e//GWX88MPP7yD0gDZJCOKv9/+9rfxgx/8IO67776or6+PM888M2bPnp10LACArDdlypR2v8Yrr7wSl112WTz00ENRXFy8W18zffr05jfGIt4742/AgAHtzgIAsCvFxcUxaNCgHdYHDRoUnTt33qPXGjt2bKTT6TbnqVQqZs6cGTNnztxpnm9961vxrW99a4/+bCC/7ex3z+7MgfyVaPH3la98Je6555549dVXY/z48TF//vw466yzoqSkJMlYAAA5acuWLS2uaxMR0aNHj11+3TPPPBMbNmyI448/vnmtsbExHnvssbjllluioaFhh0+sFxUVRVFR0b4JDgCwBy6++OL4+te/HnfccUfz8UhDQ0Ncf/31cckllyScDmD3pFKpds2B/JVo8ffoo4/GFVdcEf/yL/8SPXv2TDIKAEBOeuedd+LKK6+MH/7wh/HGG2/sMG9sbNzla5x++ulRVVXVYu3888+PYcOGxZVXXmmbKgAgozz77LPx8MMPR//+/WPkyJEREfH73/8+tm7dGqeffnpMmjSp+bn3339/UjEBdqpPnz7tmgP5K9Hib/ny5RERsWrVqnj66adj69atLeZnnnlmErEAAHLGtGnT4pFHHolbb701PvvZz8b//M//xKuvvhrf+c534r/+67926zW6d+8ew4cPb7HWtWvXOOSQQ3ZYBwBI2oEHHhj/9E//1GLNluNAtqmtrd3l/IgjjuigNEA2SbT4W7NmTZx99tnx/PPPRyqVat6XePtpyrvzCXQAANr2wAMPxN133x1jx46Nf/u3f4uTTz45hgwZEmVlZfGDH/wgzjvvvKQjAgDsU3fccUfSEQDarW/fvu2aA/mrIMk//NJLL41BgwbFa6+9FiUlJbFy5cp47LHH4oQTTohHH300yWgAADnhzTffjMGDB0fEe9fze/PNNyMi4qSTTorHHntsr1/30Ucfjfnz5++LiAAAAPydQYMGxZFHHtnq7KijjopBgwZ1bCAgayR6xt8TTzwRv/71r+PQQw+NgoKCKCgoiJNOOinmzJkTl156aTz77LNJxgMAyHqHH354rF27NsrKyuLoo4+OH/7whzF69Oh44IEH4sADD0w6HgDAPnHcccfFww8/HAcddFB88IMfbN5NqjW/+93vOjAZwN5JpVLx+c9/Pq644oodZp///Od3+nsOyG+JFn+NjY3RrVu3iIjo2bNn/OUvf4mjjjoqysrKYvXq1UlGywuppm2RTjoEZIN0OqJp23v3CzpFOLCCXUpt/5khceeff378/ve/j1NPPTWmT58eH/vYx+Jb3/pWbNu2LebNm5d0PACAfeITn/hEFBUVRUTEWWedlWwYgH0gnU7HggULWp195zvfie985zvKP6BViRZ/w4cPj+effz4OP/zwqKioiLlz50bnzp1jwYIFcfjhh+/Va86ZMye+8pWvxGWXXda8/VQ6nY5rr702FixYEBs3boyKior4n//5nzjmmGP24XeTfbo9d0/SEQCA/exLX/pS8/3TTjst/vCHP8TTTz8dRxxxRIwcOTLBZAAA+84111wTEe99yHzs2LFRXl4eBx10UMKpAPbe2rVr46WXXmp19tJLL8XatWubL+sA8H6JFn9f/epX45133omIiOuuuy7OOOOMOPnkk+OQQw6Je++9d49fr7KyMhYsWBDl5eUt1ufOnRvz5s2LO++8M4488si47rrrYvz48bF69ero3r37PvleAAAy1cMPPxwPP/xwbNiwIZqamlrMvv/97yeUCgBg3yssLIyPfOQj8eKLLyr+gKxWU1Ozy7niD2hNosXfRz7ykeb7hx9+eKxatSrefPPNOOigg/b4NOVNmzbFeeedF9/97nfjuuuua15Pp9Mxf/78mDFjRkyaNCkiIu66667o1atXLFy4MC688MJWX6+hoSEaGhqaH9fV1e1RnkxVXFwcS5YsSToGZJX6+vo4++yzIyJi8eLFUVxcnHAiyC5+ZpJ17bXXxqxZs+KEE06IPn362AoGAMh5I0aMiD//+c/eEAey2l//+td2zYH8lWjx15qDDz54r77u4osvjo997GMxbty4FsXfmjVrora2NiZMmNC8VlRUFKeeemosX768zeJvzpw5ce211+5VlkyWSqWiS5cuSceArFVcXOxnCMgqt912W9x5553xmc98JukoAAAd4vrrr48rrrgivv71r8fxxx8fXbt2bTHv0aNHQskAdt+IESPaNQfyV8YVf3tj0aJF8bvf/S4qKyt3mNXW1kZERK9evVqs9+rVK9atW9fma06fPj0uv/zy5sd1dXUxYMCAfZQYAKBjbN26NU488cSkYwAAdJiPfvSjERFx5plnttjtIJ1ORyqVisbGxqSiAey2qqqqXc6HDh3aQWmAbJL1xd8rr7wSl112WTz00EM73Urs77e12n6w15aioqIoKiraZzkBAJLwuc99LhYuXBhXX3110lEAADrEHXfcEQMGDIjCwsIW601NTVFdXZ1QKoA907Nnz3bNgfyV9cXfM888Exs2bIjjjz++ea2xsTEee+yxuOWWW2L16tUR8d6Zf3369Gl+zoYNG3Y4CxAAIBe8f9eCpqamWLBgQfzqV7+K8vLyOOCAA1o8d968eR0dDwBgv/q3f/u3WL9+fRx22GEt1t94440YN25cTJkyJaFkALtvV9dnd/12oC1ZX/ydfvrpO5z2fP7558ewYcPiyiuvjMMPPzx69+4dS5cujQ9+8IMR8d6WV8uWLYsbbrghicgAAPvVs88+2+LxscceGxERL7zwQot1/6EIAOSitnZ52rRp0053iwLIJE1NTe2aA/kr64u/7t27x/Dhw1usde3aNQ455JDm9alTp8bs2bNj6NChMXTo0Jg9e3aUlJTE5MmTk4gMALBfPfLII0lHAADocNt3PUilUnH11VdHSUlJ86yxsTFWrFjR/IEogExXWVm5y/mpp57aQWmAbJL1xd/umDZtWmzZsiUuuuii2LhxY1RUVMRDDz0U3bt3TzoaAAAAAPvA9l0P0ul0VFVVRefOnZtnnTt3jpEjR8YVV1yRVDyAPXLWWWfFz372s53OAVqTk8Xfo48+2uJxKpWKmTNnxsyZMxPJAwAAAMD+tX3Xg/PPPz9uuumm6NGjR8KJAPbeoEGD2jUH8ldB0gEAAAAAYF+54447lH5A1nvggQfaNQfyl+IPAAAAAAAySM+ePds1B/KX4g8AAAAAADJInz592jUH8pfiDwAAAAAAMkhVVVW75kD+UvwBAAAAAEAGGTFiRLvmQP5S/AEAAAAAQAZ57bXX2jUH8pfiDwAAAAAAMkjv3r3bNQfyl+IPAAAAAAAySG1tbbvmQP5S/AEAAAAAQAbp1atXu+ZA/lL8AQAAAABABnnhhRfaNQfyl+IPAAAAAAAyyMc+9rF2zYH8pfgDAAAAAIAM8tRTT7VrDuQvxR8AAAAAAGSQpqamds2B/KX4AwAAAACADKL4A/aW4g8AAAAAADJIZWVlu+ZA/lL8AQAAAABABjn++OPbNQfyl+IPAAAAAAAyyEsvvdSuOZC/FH8AAAAAAJBB1q1b1645kL8UfwAAAAAAkEFKS0vbNQfyl+IPAAAAAAAyyOjRo9s1B/KX4g8AAAAAADLI66+/3q45kL8UfwAAAAAAkEHWrFnTrjmQvxR/AAAAAACQQRobG9s1B/KX4g8AAAAAADLIQQcd1K45kL8UfwAAAAAAkEE+8IEPtGsO5C/FHwAAAAAAZJBXX321XXMgfyn+AAAAAAAggyxdurRdcyB/Kf4AAAAAACCDbNmypV1zIH8p/gAAAAAAIIMcffTR7ZoD+UvxBwAAAAAAGaRXr17tmgP5S/EHAAAAAAAZ5Mgjj2zXHMhfnZIOAAAA2SKdTkd9fX3SMSCrvP9nxs8P7Lni4uJIpVJJxwCggz311FO7nJ922mkdlAbIJoo/AADYTfX19TFx4sSkY0DWOvvss5OOAFlnyZIl0aVLl6RjANDBXn311XbNgfxlq08AAAAAAMgg/fr1a9ccyF/O+AMAgL2w6dhPRbrA4TTsUjod0bTtvfsFnSJsWQi7lGraFt2euyfpGAAk6IQTToglS5bsdA7QGu9UAADAXkgXdIooPCDpGJAlOicdALJKOukAACTu2Wef3eX89NNP76A0QDax1ScAAAAAAGSQY489tl1zIH8p/gAAAAAAIIM88MAD7ZoD+UvxBwAAAAAAGeStt95q1xzIX4o/AAAAAADIICNHjmzXHMhfij8AAAAAAMggTz31VLvmQP5S/AEAAAAAQAYpKytr1xzIX4o/AAAAAADIIKlUql1zIH8p/gAAAAAAIIOk0+l2zYH8pfgDAAAAAIAM0tDQ0K45kL8UfwAAAAAAkEEOOeSQds2B/KX4AwAAAACADLJx48Z2zYH8pfgDAAAAAIAMUl5e3q45kL8UfwAAAAAAkEEeeOCBds2B/KX4AwAAAACADNKrV692zYH8pfgDAAAAAIAM8sorr7RrDuQvxR8AAAAAAGSQAw44oF1zIH8p/gAAAAAAIIM0NTW1aw7kL8UfAAAAAABkkCFDhrRrDuQvxR8AAAAAAGSQ1atXt2sO5C/FHwAAAAAAZJCtW7e2aw7kr6wv/r797W9HeXl59OjRI3r06BFjxoyJJUuWNM/T6XTMnDkz+vbtG126dImxY8fGypUrE0wMAAAAAABtO+yww9o1B/JX1hd//fv3j//6r/+Kp59+Op5++un48Ic/HJ/4xCeay725c+fGvHnz4pZbbonKysro3bt3jB8/Pt5+++2EkwMAAAAAwI5SqVS75kD+yvri7+Mf/3j84z/+Yxx55JFx5JFHxvXXXx/dunWLJ598MtLpdMyfPz9mzJgRkyZNiuHDh8ddd90VmzdvjoULFyYdHQAAAAAAdlBYWNiuOZC/sr74e7/GxsZYtGhRvPPOOzFmzJhYs2ZN1NbWxoQJE5qfU1RUFKeeemosX758p6/V0NAQdXV1LW4AAAAAALC/vfHGG+2aA/krJ4q/qqqq6NatWxQVFcUXvvCFWLx4cRx99NFRW1sbERG9evVq8fxevXo1z9oyZ86cKC0tbb4NGDBgv+UHAAAAAIDttm3b1q45kL9yovg76qij4rnnnosnn3wy/uM//iOmTJkSq1atap7//X7H6XR6l3sgT58+Pd56663m2yuvvLJfsgMAAAAAwPsdeOCB7ZoD+atT0gH2hc6dO8eQIUMiIuKEE06IysrKuOmmm+LKK6+MiIja2tro06dP8/M3bNiww1mAf6+oqCiKior2X2gAAAAAAGjFpk2b2jUH8ldOnPH399LpdDQ0NMTgwYOjd+/esXTp0ubZ1q1bY9myZXHiiScmmBAAAAAAAFrX0NDQrjmQv7L+jL+vfOUrMXHixBgwYEC8/fbbsWjRonj00UfjwQcfjFQqFVOnTo3Zs2fH0KFDY+jQoTF79uwoKSmJyZMnJx0dAAAAAAB20LVr16ivr9/pHKA1WV/8vfbaa/GZz3wm1q9fH6WlpVFeXh4PPvhgjB8/PiIipk2bFlu2bImLLrooNm7cGBUVFfHQQw9F9+7dE04OAAAAAAA72lnptztzIH9lffF3++2373SeSqVi5syZMXPmzI4JBAAAAAAA7fDuu++2aw7kr5y8xh8AAAAAAADkG8UfAAAAAABkkE6ddr5Z367mQP5S/AEAAAAAQAbZtm1bu+ZA/lL8AQAAAABABmlqamrXHMhfij8AAAAAAMggXbt2bdccyF+KPwAAAAAAyCBFRUXtmgP5S/EHAAAAAAAZZNOmTe2aA/lL8QcAAAAAABlk69at7ZoD+UvxBwAAAAAAGaR79+7tmgP5S/EHAAAAAAAZZOPGje2aA/lL8QcAAAAAABmkU6dO7ZoD+UvxBwAAAAAAGWTbtm3tmgP5S/EHAAAAAAAZ5IADDmjXHMhfij8AAAAAAMggTU1N7ZoD+UvxBwAAAAAAGaSoqKhdcyB/Kf4AAAAAYC/MnDkzUqlUi1vv3r2b5+l0OmbOnBl9+/aNLl26xNixY2PlypUJJgayxebNm9s1B/KX4g8AAAAA9tIxxxwT69evb75VVVU1z+bOnRvz5s2LW265JSorK6N3794xfvz4ePvttxNMDADkMsUfAAA7NWfOnBg1alR07949DjvssDjrrLNi9erVSccCAMgInTp1it69ezffDj300Ih472y/+fPnx4wZM2LSpEkxfPjwuOuuu2Lz5s2xcOHChFMDALmqU9IBAADIbMuWLYuLL744Ro0aFdu2bYsZM2bEhAkTYtWqVdG1a9ek43WodDr9fw8a300uCAC57X3/xrT4t4eM9PLLL0ffvn2jqKgoKioqYvbs2XH44YfHmjVrora2NiZMmND83KKiojj11FNj+fLlceGFF7b5mg0NDdHQ0ND8uK6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" ] @@ -1477,13 +1486,13 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 59, "id": "8a320b4c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1520,7 +1529,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 60, "id": "19c7e28c", "metadata": {}, "outputs": [ @@ -1529,18 +1538,18 @@ "output_type": "stream", "text": [ "Entfernte Variablen und zugehörige p-Werte:\n", - "road_Außerorts: p=1.0000\n", - "weather_winterlich: p=1.0000\n", - "road_Autobahn: p=0.8703\n", - "trip_distance: p=0.6828\n", - "speeding_haeufig: p=1.0000\n", - "road_Innerorts: p=0.3855\n", + "trip_distance: p=0.4639\n", "weekday_Mo-Fr: p=1.0000\n", - "weekday_So: p=0.6575\n", "shift_normal: p=1.0000\n", - "weekday_Sa: p=0.5344\n", - "weather_trocken: p=0.3417\n", - "weather_nass: p=0.0516\n", + "weekday_Sa: p=0.7010\n", + "speeding_haeufig: p=1.0000\n", + "weekday_So: p=0.5605\n", + "weather_winterlich: p=1.0000\n", + "weather_trocken: p=0.4538\n", + "weather_nass: p=0.1143\n", + "road_Autobahn: p=1.0000\n", + "road_Außerorts: p=0.9214\n", + "road_Innerorts: p=0.6182\n", "\n", "Verbleibende Variablen im Modell:\n", "['const', 'avg_speed', 'hard_brakes', 'shift_frueh', 'shift_spaet', 'speeding_manchmal', 'speeding_selten']\n", @@ -1548,23 +1557,23 @@ "Zusammenfassung des finalen Modells:\n", " Logit Regression Results \n", "==============================================================================\n", - "Dep. Variable: y No. Observations: 20000\n", - "Model: Logit Df Residuals: 19993\n", + "Dep. Variable: y No. Observations: 16000\n", + "Model: Logit Df Residuals: 15993\n", "Method: MLE Df Model: 6\n", - "Date: Sat, 05 Jul 2025 Pseudo R-squ.: 0.04322\n", - "Time: 10:31:36 Log-Likelihood: -11755.\n", - "converged: True LL-Null: -12286.\n", - "Covariance Type: nonrobust LLR p-value: 3.464e-226\n", + "Date: Sat, 05 Jul 2025 Pseudo R-squ.: 0.04383\n", + "Time: 13:10:09 Log-Likelihood: -9398.3\n", + "converged: True LL-Null: -9829.1\n", + "Covariance Type: nonrobust LLR p-value: 7.536e-183\n", "=====================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------------\n", - "const -1.0751 0.085 -12.714 0.000 -1.241 -0.909\n", - "avg_speed 0.0177 0.002 11.111 0.000 0.015 0.021\n", - "hard_brakes 0.0822 0.011 7.401 0.000 0.060 0.104\n", - "shift_frueh -0.8050 0.035 -22.841 0.000 -0.874 -0.736\n", - "shift_spaet -0.7985 0.044 -18.075 0.000 -0.885 -0.712\n", - "speeding_manchmal -0.5367 0.038 -14.142 0.000 -0.611 -0.462\n", - "speeding_selten -0.4607 0.038 -11.994 0.000 -0.536 -0.385\n", + "const -1.1750 0.095 -12.389 0.000 -1.361 -0.989\n", + "avg_speed 0.0189 0.002 10.618 0.000 0.015 0.022\n", + "hard_brakes 0.0966 0.012 7.762 0.000 0.072 0.121\n", + "shift_frueh -0.7953 0.039 -20.180 0.000 -0.873 -0.718\n", + "shift_spaet -0.8065 0.049 -16.304 0.000 -0.903 -0.710\n", + "speeding_manchmal -0.5039 0.042 -11.882 0.000 -0.587 -0.421\n", + "speeding_selten -0.4647 0.043 -10.809 0.000 -0.549 -0.380\n", "=====================================================================================\n" ] } @@ -1623,7 +1632,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "6641f80c", "metadata": {}, "outputs": [ @@ -1633,16 +1642,13 @@ "text": [ "\n", "Modellkoeffizienten:\n", - "const -0.788152\n", - "avg_speed 0.017391\n", - "hard_brakes 0.081678\n", - "shift_frueh -0.814953\n", - "shift_normal -0.808104\n", - "speeding_haeufig -0.838057\n", - "speeding_manchmal -0.834292\n", - "road_Autobahn -0.257555\n", - "road_Außerorts -0.273524\n", - "road_Innerorts -0.257073\n", + "const -1.175040\n", + "avg_speed 0.018919\n", + "hard_brakes 0.096628\n", + "shift_frueh -0.795291\n", + "shift_spaet -0.806477\n", + "speeding_manchmal -0.503908\n", + "speeding_selten -0.464663\n", "dtype: float64\n" ] } @@ -1666,7 +1672,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "0541fa09", "metadata": {}, "outputs": [ @@ -1675,26 +1681,14 @@ "output_type": "stream", "text": [ "Vergleich der geschätzten und wahren Koeffizienten:\n", - " Geschätzte Koeffizienten Wahre Koeffizienten\n", - "avg_speed 0.017391 0.015\n", - "const -0.788152 -2.000\n", - "hard_brakes 0.081678 0.100\n", - "road_Autobahn -0.257555 0.000\n", - "road_Außerorts -0.273524 0.000\n", - "road_Innerorts -0.257073 0.000\n", - "shift_frueh -0.814953 0.500\n", - "shift_normal -0.808104 -0.300\n", - "shift_spaet NaN -0.300\n", - "speeding_haeufig -0.838057 -0.300\n", - "speeding_manchmal -0.834292 -0.300\n", - "speeding_selten NaN 0.500\n", - "trip_distance NaN 0.000\n", - "weather_nass NaN 0.000\n", - "weather_trocken NaN 0.000\n", - "weather_winterlich NaN 0.000\n", - "weekday_Mo-Fr NaN 0.000\n", - "weekday_Sa NaN 0.000\n", - "weekday_So NaN 0.000\n" + " Geschätzte Koeffizienten Wahre Koeffizienten\n", + "const -1.175040 -2.000\n", + "avg_speed 0.018919 0.015\n", + "hard_brakes 0.096628 0.100\n", + "shift_frueh -0.795291 0.000\n", + "shift_spaet -0.806477 0.500\n", + "speeding_manchmal -0.503908 -0.300\n", + "speeding_selten -0.464663 0.000\n" ] } ], @@ -1736,6 +1730,80 @@ "Die Koeffizienten sind nicht deckend gleich aber das erlernte Modell kommt dem idealen sehr nahe." ] }, + { + "cell_type": "markdown", + "id": "a0db711e", + "metadata": {}, + "source": [ + "### Modellevaluation auf Testdaten\n", + "\n", + "Nachdem das optimale Modell auf den Trainingsdaten entwickelt wurde, wird es nun auf den Testdaten evaluiert. Dies gibt uns eine Einschätzung der Modellgüte und zeigt, wie gut das Modell auf ungesehenen Daten performt." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "a1c9fc7f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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k+/bt1KxZk+rVq7Njxw4KFSpErVq1GDduHBkyZCB37txs3779hV5g16lThxw5ctCrVy8CAwMjrYx56aWXGD16NEOHDuXcuXP2PuP//PMPP/30E2nSpGHUqFGxvl8RERERZ1KhQgVmzpxJr169KF26NO+88w5FihThyZMnHD16lNmzZ1O0aFEaN25MwYIF6d69O9OnT8fFxYX69etz4cIFhg0bRs6cORkwYEC85WrQoAG+vr506dKF0aNH4+bmxsKFC7l8+bLDvFmzZrFjxw4aNmxIrly5ePz4MfPnzweMDSejM2LECL777juqV6/O8OHD8fX1ZdmyZXz//feMHz/eYfPH+PbJJ588d07Dhg2ZNGkSbdu2pXv37ty8eZOJEyfa25s8rVixYqxcuZJVq1aRN29evLy8XqjP9YgRI/jvf//Lli1b8Pf357333mPXrl106dKFUqVKxdum7yIizkors0VEnFzfvn3JlSsXo0ePJiwsjDRp0rB161amTp3K4cOHefPNN6lfvz6LFi2ia9euHDt2jIIFCzrchpeXF99//z0LFy4kMDCQHj16UKNGDXr06MGFCxeYP38+jRs3fqF8JUqUYMeOHYSGhlK9enVOnTrFkiVLqFmzJu+//z5vvvkmf//9NytWrIj1bbu4uNChQwf++usvcubMGeVKoiFDhrBmzRrOnDnD22+/Td26dRk8eDAXL16kSpUqL/SYRERERJxNt27d+PnnnyldujSffvopderU4Y033mDFihW0bduW2bNn2+fOnDmTTz75hI0bN9KoUSOGDh1KnTp12LdvX5Q9sl9UunTp2LRpE97e3rRr146ePXtStGhRe8/ocCVLliQ0NJQRI0ZQv3592rdvz/Xr11m/fr2953RUChYsyL59+yhYsCC9e/fmjTfe4Ndff2XBggUMGjQo3h7Hi6pRo4Z9o8jGjRszdOhQWrRowQcffBBp7qhRo6hatSrdunWjbNmyL/Tae+vWrYwbN45hw4Y5vC5euHAh6dKlo1WrVlGu3BcRSU4sNpvNZnYIEREREREREREREZFn0cpsEREREREREREREXF6KmaLiIiIiIiIiIiIiNNTMVtEREREREREREREnJ6K2SIiIiIiIiIiIiLi9FTMFhERERERERERERGnp2K2iIiIiIiIiIiIiDg9N7MDJDar1cqVK1fw9vbGYrGYHUdEREREUhCbzca9e/fIli0bLi5aVyIiIiIiEhsprph95coVcubMaXYMEREREUnBLl++TI4cOcyO4TRSlepjdgQRSWJuH5phdgQRSWK8nKQKmhivex4dTb4/I53kP2Pi8fb2BuDixYukT5/e3DDiVKxWK9evXydz5sxaKSUO9NyQ6Oi5IdHRc0Oic+fOHXLnzm1/TSoiIiIiIjGX4orZ4a1F0qVLR7p06UxOI87EarXy+PFj0qVLp8KDONBzQ6Kj54ZER88NiY7VagVQuzsRERGRlMqivw/iQt89EREREREREREREXF6KW5ltoiIiIiIiIiIiIgp9Am9ONHKbBERERERERERERFxelqZLSIiIiIiIiIiIpIY1DM7TvTdExERERERERERERGnp5XZIiIiIiIiIiIiIolBPbPjRCuzRURERERERERERMTpaWW2iIiIiIiIiIiISGJQz+w40XdPRERERERERERERJyeVmaLiIiIiIiIiIiIJAb1zI4TrcwWEREREREREREREaenldkiIiIiIiIiIiIiiUE9s+NE3z0RERERERERERERcXpamS0iIiIiIiIiIiKSGNQzO060MltEREREREREREREnJ5WZouIiIiIiIiIiIgkBvXMjhN990RERERERERERETE6ZlazN69ezeNGzcmW7ZsWCwW1q1b99zr7Nq1i9KlS+Pl5UXevHmZNWtWwgcVERERERERERERiSuLJeEvyZipxewHDx5QokQJZsyYEaP558+fp0GDBlSuXJmjR4/y4Ycf0q9fP77++usETioiIiIiIiIiIiIiZjK1Z3b9+vWpX79+jOfPmjWLXLlyMWXKFAAKFSrEzz//zMSJE2nevHkCpRQRERERERERERGJB+qZHSdJagPI/fv3U6dOHYexunXrMm/ePJ48eYK7u3uk6wQHBxMcHGw/vnv3LgBWqxWr1ZqwgSVJsVqt2Gw2PS8kEj03JDp6bkh09NyQqGwbf4TJQ/40O4aIiIiISJKVpIrZgYGBZMmSxWEsS5YshIaGcuPGDbJmzRrpOuPGjWPUqFGRxq9fv05ISEiCZZWkx2q1EhQUhM1mw8VF75JJBD03JDp6bkh09NxI2R4/ho8/9ubECWOhhcVmpdLB6YxlKIXxJKfJ+URERETERMm8p3VCS1LFbADLv/6D22y2KMfDDRkyhICAAPvx3bt3yZkzJ5kzZyZ9+vQJllOSHqvVisViIXPmzCo8iAM9NyQ6em5IdPTcSFkeP4YFC+DMGQvTpjm+Js3G3yzibWqxHYB0PDEjooiIiIhIspCkitn+/v4EBgY6jF27dg03NzcyZswY5XU8PT3x9PSMNO7i4qI/LiUSi8Wi54ZESc8NiY6eGxIdPTeStwcP4IcfjH/nzIG9e6Oe14I19kK2FQuTeBeYkmg5RURERMTJqGd2nCSpYnaFChXYsGGDw9iWLVsoU6ZMlP2yRURERETiw8OH8MkncOuWcfz558+/TocOMHZ6X6xNv8Ny+jfC5i+hf9lSjMowJUGzioiIiIgkV6YWs+/fv8+ff0ZsgnP+/HmOHTuGr68vuXLlYsiQIfz9998sXrwYgJ49ezJjxgwCAgLo1q0b+/fvZ968eaxYscKshyAiIiIiydTFizBpEqxbB5cuxew6W5b8Q8EqWciVK3zEBZYvBTc33DNmhDt3EiasiIiIiCQNWpkdJ6YWs3/++WeqV69uPw7vbf3222+zcOFCrl69yqWn/nLIkycPGzduZMCAAXz++edky5aNadOm0bx580TPLiIiIiLJ0+XLPFWMfra0aWHCBGOTx2YXJ5G52zDYvBlyVYmY9K8NzEVERERE5MWYWsyuVq2afQPHqCxcuDDSWNWqVTly5EgCphIRERGRlMRmg06d4OBBuHHDuETH2xt27jS+zp4d/P2BK1fg7bdh2zbjRLt28MsvkCFDgmcXERERkSTGxfL8ORKtJNUzW0REREQkrhYuhB07IHzLlfnznz3/k0+gWzfw9Y3i5LffQpcucPOmcWyxQJs2kCZNfEYWERERERFUzBYRERGRZO7XX6FaNciWDU6ceP58d3d48gTmzYPOnaOZ9PAhBATAl19GjGXLBkuWQI0a8RFbRERERJIj9cyOE333RERERCTZ2r8fihUzFk7HpJD95AmEhBitR6ItZB89CqVLOxaymzaF48dVyBYRERGRJGXcuHG89tpreHt74+fnxxtvvMHp06cd5nTs2BGLxeJwKV++vMOc4OBg+vbtS6ZMmUiTJg1NmjThr7/+cphz+/Zt2rdvj4+PDz4+PrRv3547sdwgXcVsEREREUlWQkPh/n0YOxYqVox83sPD+Hf/fjh50rhcuWIUsN2e97nF5cuhXDn4/XfjOFUqo6j99deQMWO8Pg4RERERSYYsloS/xMKuXbvo3bs3Bw4cYOvWrYSGhlKnTh0ePHjgMK9evXpcvXrVftm4caPD+f79+/PNN9+wcuVK9uzZw/3792nUqBFhYWH2OW3btuXYsWNs2rSJTZs2cezYMdq3bx+rvGozIiIiIiLJwvHjULs2XLsW9fnRo2HYsDjeSalSRsX7yRPj6+XL4ZVX4nijIiIiIiLm2LRpk8PxggUL8PPz4/Dhw1SpUsU+7unpib+/f5S3ERQUxLx581iyZAm1atUCYOnSpeTMmZNt27ZRt25dfvvtNzZt2sSBAwcoV64cAHPmzKFChQqcPn2aggULxiivVmaLiIiISJL255/GApQSJaIvZC9bFg+FbIBChWDqVBg40FjarUK2iIiIiMSGxSXhL3EQFBQEgO+/dj//8ccf8fPz4+WXX6Zbt25ce+qF9+HDh3ny5Al16tSxj2XLlo2iRYuyb98+APbv34+Pj4+9kA1Qvnx5fHx87HNiQiuzRURERCRJK1Ag6vHq1eHRI9i0CXx8XuCGHz6Ezz6DQYPAyytivFu3F8opIiIiIpIYgoODCQ4Odhjz9PTE09Pzmdez2WwEBATwn//8h6JFi9rH69evz5tvvknu3Lk5f/48w4YNo0aNGhw+fBhPT08CAwPx8PAgQ4YMDreXJUsWAgMDAQgMDMTPzy/Sffr5+dnnxIRWZouIiIhIkmCzwY0bxuXyZRg1KuqWgMuXg9UKO3YYi6dfqJB97BiUKQPDh8P778c1uoiIiIiIIRF6Zo8bN86+yWL4Zdy4cc+N1qdPH44fP86KFSscxlu1akXDhg0pWrQojRs35ocffuDMmTN8//33z7w9m82G5akX7JYoXrz/e87zaGW2iIiIiDi9Bw8gbdrnz7PZ4nhHVitMmQJDhkBIiDE2d65R0M6WLY43LiIiIiKS8IYMGUJAQIDD2PNWZfft25f169eze/ducuTI8cy5WbNmJXfu3Pzxxx8A+Pv7ExISwu3btx1WZ1+7do2K/9uR3d/fn3/++SfSbV2/fp0sWbLE6HGBVmaLiIiIiJM7f/75hewMGSJqzy/s6lWoXx/eey/ixkqVgsOHVcgWERERkfiRCD2zPT09SZcuncMlumK2zWajT58+rF27lh07dpAnT57nPoSbN29y+fJlsmbNCkDp0qVxd3dn69at9jlXr17l119/tRezK1SoQFBQED/99JN9zsGDBwkKCrLPiQmtzBYRERERp3P3Lvz3v8ai6HXrIp9v1MioN/v7w6efGv/GyYYN0Lmz0cMk3MCBMGYMPGcVi4iIiIhIUtW7d2+WL1/Ot99+i7e3t71/tY+PD6lSpeL+/fuMHDmS5s2bkzVrVi5cuMCHH35IpkyZaNq0qX1uly5deO+998iYMSO+vr4MHDiQYsWKUatWLQAKFSpEvXr16NatG19++SUA3bt3p1GjRhQsWDDGeVXMFhERERGncueOsdI6OmFh4BJfny98+NAoWs+cGTGWNSssWgS1a8fTnYiIiIiI/E8s+kMnhpn/ex1crVo1h/EFCxbQsWNHXF1dOXHiBIsXL+bOnTtkzZqV6tWrs2rVKry9ve3zJ0+ejJubGy1btuTRo0fUrFmThQsX4urqap+zbNky+vXrR506dQBo0qQJM2bMiFVeFbNFRERExGkMG2Ysho5Kv35GO+t4ff0/b55jIbtJE2MsU6Z4vBMREREREedke86mM6lSpWLz5s3PvR0vLy+mT5/O9OnTo53j6+vL0qVLY53xaSpmi4iIiIjpHj6ENGkij7u7w4wZ0Lo1pEuXAHf8zjuwahUcOQKTJkGPHk63WkZEREREkhGLtjCMCxWzRURERMR0hQtHPf7oETz1ycS4Cw527IHt5gbLlhnV9EKF4vGOREREREQkvumtABERERFJdFevwh9/GJfjx+HiRcfz//wDNls8F7K/+w7y5oWndlAHIHduFbJFREREJHFYLAl/Sca0MltEREREEs2BA1ChwrPnhIbGcxH70SMYNAg+/9w4fustOHoU0qaNxzsREREREZGEpmK2iIiIiCS4v/6C8ePhGfvBAEYL63gtZB8/Dm3awKlTEWOFCkFISDzeiYiIiIhIDKlndpyomC0iIiIiCWLzZvjsM7hxw1gIHZX27Y1/rVbInx9GjoynO7daYdo0eP/9iMK1l5exyWPPnsn+45ciIiIiIsmRitkiIiIiEu/GjIFhw6I/3749LF6cQHceGAgdOxrV9HAlSsDy5dHvNCkiIiIikhi0qCJOVMwWERERkXgTGgoFCsCFC1Gff/dd6NIFihZNoAA7dkDr1nD9esTYgAEwbhx4eibQnYqIiIiISGJQMVtERERE4sxmg/nzoWvXyOe2bYNq1cDFJREWovj6QlCQ8XWWLLBoEdStm8B3KiIiIiISQ+qZHScqZouIiIjIC7tzBxYsgICAqM+fOWOs1E40JUsaq7B37jSq65kzJ+Kdi4iIiIhIQlIxW0RERERixWqFsWONViLz50c/7+FDSJUqAYPYbLBsGbRsCR4eEeP9+xutRdSPUEREREScjVZmx4mK2SIiIiISYyEh4O1t/BsVT0+jbXXFigkc5J9/oFMn+OEHOHnSWI0dzkV/IIiIiIiIJEd6pS8iIiIi0bpxA/buNVZiWyxGsTqqQvYrr8C5c/D4cSIUsjduhOLFjUI2wPjxcPZsAt+piIiIiEg8sFgS/pKMaWW2iIiIiNiFhsK6dfDXX/DrrzBv3rPnnzgB6dNDjhyJEO7xYxg8GKZPjxgL3+QxX75ECCAiIiIiImZSMVtERERE7Ly8ICzs2XMyZDBqx4sXQ6FCiZOLEyegbVujwh6uUSNt8igiIiIiSYt6ZseJvnsiIiIiKdzFi/DZZ8YnEqMrZLdoAQEBxqaPt27BoUOJVMi22WDaNHjttYhCtpcXzJgB69erkC0iIiIikoJoZbaIiIhICmW1Qo0asGtX1OdXrjT+LV8ecudOvFwO5s+Hd9+NOC5WDFasgCJFTAokIiIiIhIHybyndUJTMVtEREQkBapUCfbti/783r2JsJFjTLRvD198AUeOGEXtTz4xVmaLiIiIiEiKo2K2iIiISApis4FLNI3mRo6Eli0TsQ92VGw2x9UqHh6wfDmcOwf165uXS0REREQkPqhndpzouyciIiKSzNls0KkT5MgRdSG7VCm4dg1GjDC5kP3rr1CuHBw/7jhesKAK2SIiIiIiopXZIiIiIsndtGmwcGHU50JCwN09UeNEZrPB55/DoEHw+DG0aQM//wypUpkcTEREREQknqlndpyomC0iIiKSTFitsGQJnD0b8Rr5/Hlj7Glp0hgrsH/80QkK2deuQefO8P33EWOursa4abtOioiIiIiIM1IxW0RERCQZePQIUqd+/rxff4UiRRI+T4xs2gQdO8I//0SMaZNHEREREUnGLFqZHSfqmS0iIiKSxJ06FbNC9vTpTlLIfvwY+vc3+mCHF7L9/GDjRpgyRYVsERERERGJklZmi4iIiCRBNhv07AmzZ0d9/ttvHWvCZcqAr2/iZHumU6eMnthPb/LYoAHMnw9ZspiXS0REREQkEWhldtyomC0iIiKSRJw+DdWrGy1F7tyJek7evLB/v7HQ2SndvQsnTxpfe3rChAnQp482whERERERkedSmxERERERJ2e1woYN8MorcPVq9IXsuXONzR+dtpANUL48jBwJRYvCoUPQt68K2SIiIiKSclgS4ZKMqZgtIiIi4uRGjIAmTRzHUqUyVmGXLGkUt2026NLFjHTPsW8fhIU5jg0ZYhSyixUzJ5OIiIiIiCRJKmaLiIiIOLGwMBgzxnGsbVt4+NBYhX30KPj4mJPtmYKDYcAAqFQJxo1zPOfqqk0eRURERCRFslgsCX5JzlTMFhEREXFSYWHg9q8dTjZsgKVLzckTY6dOQblyMGWKcTxyZESfbBERERERkRekYraIiIiIEwoMhOzZI483auTELaZtNvjiCyhdGn75xRjz9IRJk6BwYXOziYiIiIg4Aa3Mjhu3508RERERkcR04wZkzRp5/OHDxM8SY9evG027N2yIGCtSBFasUG9sERERERGJFypmi4iIiDgBm83412qFzJkjn79zx9j00Slt2QJvv20sJw/Xpw+MH+/EoUVEREREEl9yXzmd0NRmRERERMQE9+7Bxo1GDdhiARcX4/LvHtllysDVq066ySPA2rVQt25EITtzZvjuO5g+XYVsEREREZF/UZuRuNHKbBEREZFEcuUKvPoqhIbCzZvPn58qFRw8aBS5nVb9+kY/7FOnoF49WLAA/P3NTiUiIiIiIsmQitkiIiIiCeiXX+DLL+HoUThw4PnzK1SAsDBjgfO33zp5IRuMivuKFbBzJ/TtmwQCy4uYNm1ajOf269cvAZOIiIiIJHHJe+F0glMxW0RERCSB3LoFJUs+e07r1lCjhrHAOUeORIn14q5fNwrWo0ZBwYIR48WLGxdJtiZPnhyjeRaLRcVsEREREUkwKmaLiIiIJICwMMiaNepVynPmQNeuiRworrZuhQ4djN7Yf/4J+/aBh4fZqSSRnD9/3uwIIiIiIslCcu9pndD0OVARERGReBQcbCxgzpHDsW/0K6/A778b/bKTVCE7OBjeew/q1InY5PHiRThzxtxcIiIiIiKS4mhltoiIiEg8qVPHWMD87/UCvr7G/ohJbhHGb79B27Zw7FjEWN26sHChNnlMwcLCwli4cCHbt2/n2rVrWK1Wh/M7duwwKZmIiIiI89PK7LhRMVtEREQkDh4+hNWroWPH6OfcuJHECtk2m7FrZUAAPHpkjHl4wKefQr9+2uQxhXv33XdZuHAhDRs2pGjRovqDTEREREQSjYrZIiIiIi/g9Gm4cAGGDoXDhyOfr1o1mPLlPRg92pK0Ctk3bhh9UL79NmKscGFYvhxKlDAvlziNlStX8tVXX9GgQQOzo4iIiIgkOVoIEDcqZouIiIjE0JMnsHYtjBkDv/4a/bzr162Eht7Gz88PF5ck9mL15ElYvz7iuFcvmDABUqc2L5M4FQ8PD/Lnz292DBERERFJgfQZUREREZHnCAqCadOMThutW0dfyN65E6xWo0d2klW1Krz/PmTKZKzO/vxzFbLFwXvvvcfUqVOx2WxmRxERERFJciwWS4JfkjOtzBYRERGJhs1m1HObNo1+zpAhkCYNNGsGhQpFXC/JOH8ecud27IM9ejS8+642eRS7Zs2aORzv2LGDH374gSJFiuDu7u5wbu3atYkZTURERERSEBWzRURERP5lyRKYNQv27Yt+zvr1UK0aeHsnWqz4ZbPB7NkwYACMHWv8G87dXYVsceDj4+Nw3PRZ7/CIiIiISPSS98LpBKditoiIiKR4NhscPw49e8KBA9HPq18fRo6E114jaW3q+G//3uTxgw+gZk0oXtzcXOK0FixYYHYEEREREREVs0VERCRlOnjQ2Nfw9m3YsePZc93dYcsWYyV2krdtG3ToAFevRox16QLa0E9EREREJMEl957WCU3FbBEREUnWwsLg//4PPvkE0qePGL9z59nXS50aDh0yarweHgmZMJGEhMDQoTBxYsRYxowwfz40aWJeLkmS1qxZw1dffcWlS5cICQlxOHfkyBGTUomIiIhIcufy/CkiIiIiSZPNBm5uRiEbjAJ2+CUq2bPDxx9DaCg8eACFCyeTQvbvv0P58o6F7Nq14cQJFbIl1qZNm0anTp3w8/Pj6NGjlC1blowZM3Lu3Dnq169vdjwRERERp2axWBL8kpypmC0iIiLJhs0G+/cbva8tFnCJ4pVOxozw8svGpWhRWLwY7t6FJ0/gr79gyBBwdU387Anmxx/h1Vfh6FHj2N0dPvsMNm2CrFlNjSZJ0xdffMHs2bOZMWMGHh4eDB48mK1bt9KvXz+CgoLMjiciIiIiyZjajIiIiEiyEVXx+mlBQZAuXeJkcRqlS0O2bHD2LLzyCixfDqVKmZ1KkrBLly5RsWJFAFKlSsW9e/cAaN++PeXLl2fGjBlmxhMRERFxasl95XRC08psERERSRY+/TT6c6VKpdBCNoC3t1HAfucdOHxYhWyJM39/f27evAlA7ty5OXDgAADnz5/HZrOZGU1EREREkjkVs0VERCTJ++Yb+OADx7Fp0+DGDaP1yJEjKaSQHRJi7HZ5/rzjeNmy8MUXxq6WInFUo0YNNmzYAECXLl0YMGAAtWvXplWrVjRt2tTkdCIiIiJOzpIIl2RMbUZEREQkSbt3D5o1cxzbuxf+1wUh5Th9Gt56y1h9/eOPxsVNL/Uk/s2ePRur1QpAz5498fX1Zc+ePTRu3JiePXuanE5EREREkjOtzBYREZEk6cIFox30v1dc79yZwgrZNhvMmWNs8nj4sDH2009w6JC5uSTZ+uuvv3B9apfUli1bMm3aNPr27UtgYKCJyUREREScn8ViSfBLcqblOiIiIpKk3Ltn7Gd4/37kc5UqQbVqiR7JPDdvQrduRp+VcAULGj2yX33VvFySrOXJk4erV6/i5+fnMH7r1i3y5MlDWFiYSclEREREJLnTymwRERFJMq5dM1ZiR1XIHj4cNm1K/Eym2bEDSpRwLGR3726szlYhWxKQzWaLcsXP/fv38fLyMiGRiIiISNKhldlxo5XZIiIikiSsWQNvvhl5fNo06Ns38fOYJiQEhg2DCROMFiMAvr4wdy5o8z1JQAEBAYDxB9iwYcNI/dSGomFhYRw8eJCSJUualE5EREREUgIVs0VERMSpPXkC27ZFLmSnSRP1Cu1kb98+GD8+4rhmTVi82Oi9IpKAjh49Chgrs0+cOIGHh4f9nIeHByVKlGDgwIFmxRMRERFJEpL7yumEpmK2iIiIOJ1ff4W9e6Fnz6jPd+liLEROkapVM5aiz5oFY8fCe++BizrHScLbuXMnAJ06dWLq1Kmk+/fuqyIiIiIiCUzFbBEREXEqz1uosHIltGqVOFmcwt274O3t+I0ZP96o6JcoYV4uSbEWLFjgcHz37l127NjBK6+8wiuvvGJSKhEREZGkQSuz40bLeERERMR0gYGwcOGzC9klSsDXX6ewQvbOnVC4MMyZ4zju5aVCtpimZcuWzJgxA4BHjx5RpkwZWrZsSbFixfj6669NTiciIiIiyZmK2SIiIpJo7tyBixeNTRu7dAFXV6OAnTUrdOoUeX5AAOzfb+xzeOwYNGuW2IlNEhICH3xg9MP++2/o3x9+/93sVCIA7N69m8qVKwPwzTffYLPZuHPnDtOmTWPMmDEmpxMRERFxcpZEuCRjajMiIiIiCerhQ9i+HZo0id317twBH58EieTczpyBtm3h8OGIsQoVjFYjIk4gKCgIX19fADZt2kTz5s1JnTo1DRs2ZNCgQSanExEREZHkTCuzRUREJMFcugRp0jy/kB2+j1z//sbGjzZbCixk22wwbx6UKhVRyHZ3N/pjb90K2bObm0/kf3LmzMn+/ft58OABmzZtok6dOgDcvn0bLy8vk9OJiIiIODeLxZLgl+RMK7NFREQk3u3fDxUrRn++cmUoXhzq1IGqVVNg4frfbt2C7t2NpuDhXn4Zli+H0qXNyyUShf79+/PWW2+RNm1acufOTbVq1QCj/UixYsXMDSciIiIiyZqK2SIiIhJvfvwRqleP+lz69LBkCTRqlJiJkoBffjG+KX/9FTHWrRtMnmwsaxdxMr169aJs2bJcvnyZ2rVr4+JifNgzb9686pktIiIi8hzJfeV0QlMxW0REROLF2bPRF7L/7//go48SN0+SkSuXsQsmgK8vzJ0LTZuam0nkOcqUKUOZMmWw2WzYbDYsFgsNGzY0O5aIiIiIJHPqmS0iIiJxFhYG+fNHHt+zx2gFrUL2M2TIAEuXQu3acPy4CtmSJMybN4+iRYvi5eWFl5cXRYsWZe7cuWbHEhEREXF66pkdNypmi4iISJwMHw5u//qsV4sWRhG7UiVzMjktmw0WL4YrVxzHq1SBzZu1yaMkCcOGDePdd9+lcePGrF69mtWrV9O4cWMGDBjA//3f/5kdT0RERESSMbUZERERkRfSowfMnh31udWrEzdLknD7tvFNW70aatUyitcuT60rSOYrKCT5mDlzJnPmzKFNmzb2sSZNmlC8eHH69u2rvtkiIiIiz6KX/XFi+srsL774gjx58uDl5UXp0qX573//+8z5y5Yto0SJEqROnZqsWbPSqVMnbt68mUhpRUREBGDcuKgL2QUKQEhI4udxert2QYkSEVX+bdtgyxZzM4m8oLCwMMqUKRNpvHTp0oSGhpqQSERERERSClOL2atWraJ///4MHTqUo0ePUrlyZerXr8+lS5einL9nzx46dOhAly5dOHnyJKtXr+bQoUN07do1kZOLiIikTFeuQMmS8OGHjuOZM8OtW3DmDLi7mxLNOT15AkOHGjtjXr5sjGXIAGvWQL165mYTeUHt2rVj5syZkcZnz57NW2+9ZUIiERERkaRDPbPjxtQ2I5MmTaJLly72YvSUKVPYvHkzM2fOZNy4cZHmHzhwgJdeeol+/foBkCdPHnr06MH48eMTNbeIiEhyZrPBTz/BH3/ApUuwaBFkyQLRfXjqyhXImjVxMyYFrufPY2nSBA4dihisXt3omZ0jh3nBRF5AQECA/WuLxcLcuXPZsmUL5cuXB4zX6ZcvX6ZDhw5mRRQRERGRFMC0YnZISAiHDx/mgw8+cBivU6cO+/bti/I6FStWZOjQoWzcuJH69etz7do11qxZQ8OGDRMjsoiISLJ18SLcv298Xbo0BAc7nj9zJurrrVqlQnYkNhssWEDGfv2wPHxojLm5wZgxMHAguLqam0/kBRw9etThuHTp0gCcPXsWgMyZM5M5c2ZOnjyZ6NlEREREkpLkvnI6oZlWzL5x4wZhYWFkyZLFYTxLliwEBgZGeZ2KFSuybNkyWrVqxePHjwkNDaVJkyZMnz492vsJDg4m+Km/yO/evQuA1WrFarXGwyOR5MJqtWKz2fS8kEj03JDoJIfnxt9/Q65cse869t57Nj75xIaLCyThh58w9u/H5akWaLYCBbAtWQKvvWYM6BuWoiXVnxc7d+40O4KIiIiIiLltRiDyuxE2my3adyhOnTpFv379GD58OHXr1uXq1asMGjSInj17Mm/evCivM27cOEaNGhVp/Pr164Rohyp5itVqJSgoCJvNhouL6XujihPRc0Oik9SeGxs2ePLf/3ri6moDIDTUwtKlqZ95nY8/vovFYqNy5RBy5gzDYonoiX3jRkInTqLy5cO7TRvSrFjBg9atuT9mDLY0aeDaNbOTiRMICgoyO0K8+fPPPzl79ixVqlQhVapUz3wdLynXwM51eKNGCV5+KQuPgp9w8JdzDJ36LX9cdPyZWDBPFsa8+waVX82Pi4uF385epd3787kceBuAPDky8cmAplQolRdPdze27vuNgE9Xc+3WPfttDO5Sl/qVi1D85RyEhIaStcrgRH2sIpJ4Hjy4z+fTprJj+zZu3brJK4UKM/iDDylarDgAMz+fzqYfvicwMBB3d3cKFy5Cn3cHULx4CZOTi2hldlyZVszOlCkTrq6ukVZhX7t2LdJq7XDjxo2jUqVKDBo0CIDixYuTJk0aKleuzJgxY8gaxeechwwZ4tDj7+7du+TMmZPMmTOTPn36+HtAkuRZrVYsFguZM2dOEkUpSTx6bkh0ksJz4+FD2LABevWycOfO8180de5sFLozZ4bevW1kz542oSMmfaGhRhuRp1hnzuRW3bqke+stUjnpc0PM4eHhYXaEOLt58yYtW7Zk586dWCwW/vjjD/LmzUvXrl1Jnz49n332mdkRxYlUfjU/s1bt5vDJi7i5uTKyd2O+m9mHUs3G8PCxsbgoT45MbJ8fwKJ1+xgz83uC7j/ilTz+PA5+AkBqLw+++6I3J878Tf3uxqdyR/RqyNdTe1Clw2fYbMbvLg93V9ZuPcrB4+d5+40K5jxgEUkUI4f/H3/+8QdjPxlP5sx+fP/denp07cTa9RvJkiULuXO/xJChw8mRIyePgx+zdPFC3unWmQ0/bMXX19fs+CISB6YVsz08PChdujRbt26ladOm9vGtW7fy+uuvR3mdhw8f4vavPxZd/9d3MvwFzL95enri6ekZadzFxcVpCw9iHovFoueGREnPDYmOMz83bDbw9o7Z3IEDYcIEgKcL3lox8Fx//glvvQV9+0K7dhHj3t6E1K3rtM8NMU9yeD4MGDAAd3d3Ll26RKFChezjrVq1YsCAASpmi4PX+3zhcNxj5FIu7/iEUoVzsveI0XN9VJ/GbN5zkqFTv7XPu/D3TfvXFUrmJXe2jJRv8yn3HjwGoPuIpVzdPYFqZV9m58HTAIyZtRGAdo3LJehjEhFzPX78mO1btzBl+heULmO0cXund192bt/G6pXL6fPuABo0auxwnYGDh/DN12v448xpypXXm11iLq3MjhtT24wEBATQvn17ypQpQ4UKFZg9ezaXLl2iZ8+egLGq+u+//2bx4sUANG7cmG7dujFz5kx7m5H+/ftTtmxZsmXLZuZDERERcQo2G+zcCadOGfXVqGzeDJkyRRz7+4N+jcaSzQaLFhnf5Pv3oVcvqFgR8uY1O5lIgtuyZQubN28mR44cDuMFChTg4sWLJqWSpCJdWi8AbgcZG+RaLBbq/acIkxZtY/3nvSnxSg4u/n2TCfO3sOHH4wB4erhhs9kIDgm1387jkFDCwqxULJnPXswWkZQhLCyUsLCwSAsXPb28OHr0SKT5T0JC+Hr1Kry9vXm5YMHEiikiCcTUYnarVq24efMmo0eP5urVqxQtWpSNGzeSO3duAK5evcqlS5fs8zt27Mi9e/eYMWMG7733HunTp6dGjRp8+umnZj0EERERp7B7N8ycCStXRj9nzRpo2BC8vBIvV7J0+zb07AlffRUxliUL3LsX/XVEkpEHDx6QOnXkfvs3btyI8hORIk/79L3m7D3yJ6fOXgXAzzct3mm8GNipNqM+/47/m7qOOpUKs/KzrtTtPo09h//kpxMXePAohLHvvs7wGeuxYGHsu6/j6uqCf6Z0Jj8iEUlsadKkpUTJUsye9QV58uYlY8ZM/LDxO04c/4Vc/6snAez6cSfvDwzg8eNHZMqcmVlz5pMhg1qMiBPQwuw4MX0DyF69etGrV68ozy1cuDDSWN++fekb3VIzERGRFMJqhXPnjFXWffo8f35oKPyvM5fExe7dRjuRy5cjxjp3hqlTIa36i0vKUKVKFRYvXsxHH30EGCtrrVYrEyZMoHr16s+9fnBwMMHBwQ5jNmsYFhf9kEruJn/QkmIFslGz02T7WHjrne9+PMH0ZTsBOH7mb8qVyEu3Fv9hz+E/uXH7Pm8Nnse0D1vRq01VrFYbX206zJFTlwizWk15LCJirrHjxjNi2IfUrl4FV1dXXilUmPoNG/H7qVP2Oa+VLcdXX6/jzp3bfL3mKwa915+lK1aTMWNGE5OLSFyZXswWERGRmFu6FMaNM9qIPM/ChVCiBJQsmdCpUoAnT2D0aPj4Y+OdBID06WH2bHjzTVOjiSS2CRMmUK1aNX7++WdCQkIYPHgwJ0+e5NatW+zdu/e51x83bhyjRo1yGHPN8hruWcsmVGRxApPef5NGVYtRq8sU/r52xz5+4/Z9njwJ47dzVx3mnz4XSMVSEa2bth/4nSJNRpExfRpCQ60E3X/E+a0fc/Gp3toiknLkzJWL+YuW8vDhQx48uE/mzH4Meq8/2Z9qgZU6dWpy5c5Nrty5KV6iJI3r12Hd2jV06dbDxOQi6pkdV0l/BxoREZFk7Nw5qFED/PzAYoH27Z9dyF67Fq5cMVo6v/22Ctnx4tIlqFwZxoyJKGRXrQrHj6uQLSlS4cKFOX78OGXLlqV27do8ePCAZs2acfToUfLly/fc6w8ZMoSgoCCHi1uW0omQXMwy+f03eb1GCer1mMbFK47F5yehYRw+dZGXc2dxGC+Q249LV29Huq2bdx4QdP8RVV97GT/ftHy360SCZhcR55Y6dWoyZ/bjblAQ+/fuoVr1mtHOtdlshISEJGI6EUkIWpktIiLipM6ehfz5nz0nf36j60XNmlCpklHwlnjm5QUXLhhfu7nBqFHw/vvq2yIpmr+/f6TV1THl6ekZqbe2WowkX1OGtKRV/TK8OWA29x88JktGbwCC7j/mcfATACYv2saSTzuz58if7Pr5DHUqFqZBlaLU7TbVfjvtm5Tn9PlArt++T7nieZg4qAXTl+3kj4vX7HNy+mcgQ7rU5MyaAVcXF4q/nB2As5ev8+CRClgiycnePf8Fm43cefJw+dIlJk8cT+6X8vB602Y8fPiQubNnUa16DTJlzkzQnTusWrmcf/4JpHbdemZHF9HK7DhSMVtERMRJHDgAFStC3v99qvrs2chzPD0hOBj27oVy5VRPTRR+frBoEfTta/R5KatWCJLyHD9+nKJFi+Li4sLx48efObd48eKJlEqSgh4tqwCwdW5/h/Fuw5ewdMNBANbvPE7fsSsZ1LkOnw1uwZmL12gzaC77jp2zz3/5JT9G922Cr09qLl65xfh5m5m2dIfDbQ57pyHtm5S3Hx9cNQSAOl2n8t/DfyTEwxMRk9y/f49pUybxT2AgPj7pqVm7Dn3fHYC7uztWq5Xz58+x/ttvuHP7NunTp6dI0WIsWLyM/PkLmB1dROLIYrPZbGaHSEx3797Fx8eH2//7gSYSzmq1cu3aNfz8/Owb0YiAnhsSvbg+N2w2+Pln+P136NDh2XN79IDp08Hd/QXDSszt2QMFC0LmzI7jT57E+D+Afm5IdO7cuUOGDBkICgoiXbp0ZseJMRcXFwIDA+3PaYvFQlR/RlgsFsLCwmJ9+6lKxWAnWxGRp9w+NMPsCCKSxHg5yZLe/AN/SPD7+HNi/RjPHTduHGvXruX3338nVapUVKxYkU8//ZSCBQva59hsNkaNGsXs2bO5ffs25cqV4/PPP6dIkSL2OcHBwQwcOJAVK1bw6NEjatasyRdffEGOp3rZ3759m379+rF+/XoAmjRpwvTp02NVo3WS/4wiIiLJ28OHxqLeGzeM4+3bYceOZ18nY0a4fRveegtmzUr4jCne05s8NmwI337r2LdF7yRICnb+/Hky/+8NnvPnz5ucRkRERETiy65du+jduzevvfYaoaGhDB06lDp16nDq1CnSpEkDwPjx45k0aRILFy7k5ZdfZsyYMdSuXZvTp0/j7W20EOvfvz8bNmxg5cqVZMyYkffee49GjRpx+PBhXP/3keK2bdvy119/sWnTJgC6d+9O+/bt2bBhQ4zzqpgtIiKSAGw2mDoVdu0yjteti/l1N26E+jF/I13iw9mzxrsGB42PvLNhA3z9NbRoYW4uESeRO3fuKL8WERERkdhxtp7Z4YXlcAsWLMDPz4/Dhw9TpUoVbDYbU6ZMYejQoTRr1gyARYsWkSVLFpYvX06PHj0ICgpi3rx5LFmyhFq1agGwdOlScubMybZt26hbty6//fYbmzZt4sCBA5QrVw6AOXPmUKFCBU6fPu2wEvxZVMwWERGJR4GB8MYbETXRmMiRw2gz0qgRVKiQYNEkKjYbLFkCvXvD/fvGmKursclj06bmZhNxUjt27GDt2rVcuHABi8VCnjx5aNGiBVWqVDE7moiIiIhgtPwIDg52GItqE+6oBAUFAeDr6wsYn8oLDAykTp06DrdVtWpV9u3bR48ePTh8+DBPnjxxmJMtWzaKFi3Kvn37qFu3Lvv378fHx8deyAYoX748Pj4+7Nu3L8bFbDVxFBERiUdZsz6/kL1+vXHZutXYzPHyZRg7VoXsRHfnDrRtC2+/HVHIzpvX2F1z6FDtrikShZ49e1KrVi1WrFjBzZs3uX79OsuWLaN69er07dvX7HgiIiIiTs9iSfjLuHHj8PHxcbiMGzfuudlsNhsBAQH85z//oWjRogAEBgYCkCVLFoe5WbJksZ8LDAzEw8ODDBkyPHOOn59fpPv08/Ozz4kJrcwWERGJI5sNVqww6qL/VqYMLFgAmTJBqlTg45P4+SQKe/ZAu3Zw8WLE2NtvG7ts/q/nm4g4+uabb1iwYAHz58/n7bfftn9E1mq1snDhQt555x1q165NkyZNTE4qIiIikrINGTKEgIAAh7GYrMru06cPx48fZ8+ePZHO/bs9is1me27LlH/PiWp+TG7naSpmi4iIxFGFChYOHYo8brMlfhaJgePHoWpVsFqNYx8f+PJLaNXK3FwiTm7BggUEBATQsWNHh3EXFxc6d+7M6dOnmTdvnorZIiIiIs+QGD2zY9pS5Gl9+/Zl/fr17N69mxw5ctjH/f39AWNlddasWe3j165ds6/W9vf3JyQkhNu3bzuszr527RoVK1a0z/nnn38i3e/169cjrfp+FrUZEREReUGbNsH776fj0KHIL0Zu3DAhkMRMsWLQsqXxdeXK8MsvKmSLxMCRI0do+oxe8s2bN+fw4cOJmEhERERE4spms9GnTx/Wrl3Ljh07yJMnj8P5PHny4O/vz9atW+1jISEh7Nq1y16oLl26NO7u7g5zrl69yq+//mqfU6FCBYKCgvjpp5/scw4ePEhQUJB9TkxoZbaIiEgsPHoEq1cbHSmM94RTO5w/eBBee83oUyZOymKBmTOhbFno10+9sUVi6MaNG2TPnj3a89mzZ+fmzZuJmEhEREQk6XG2vxV79+7N8uXL+fbbb/H29rb3r/bx8SFVqlRYLBb69+/Pxx9/TIECBShQoAAff/wxqVOnpu3/em36+PjQpUsX3nvvPTJmzIivry8DBw6kWLFi1KpVC4BChQpRr149unXrxpdffglA9+7dadSoUYw3fwQVs0VERJ7LZoNjx+DVV589b84coz4qTiQoCHr1gubNoVmziPH06WHAANNiiSRFISEheHh4RHvezc2NkJCQREwkIiIiInE1c+ZMAKpVq+YwvmDBAnt7ucGDB/Po0SN69erF7du3KVeuHFu2bMH7qf2GJk+ejJubGy1btuTRo0fUrFmThQsX4vrU4qFly5bRr18/6tSpA0CTJk2YMWNGrPJabLaU1dHz7t27+Pj4cPv2bdKnT292HHEiVquVa9eu4efnh4uLOvBIBD03UjabDapXh127op+zeLGVevVcyJw58XJJDOzdC2+9ZWzy6Otr9Mp+xqrS+KSfGxKdO3fukCFDBoKCgkiXLp3ZcWLFxcWF7t27kzp16ijPP3z4kDlz5hAWFhbr205Vqk9c44lICnP7UOyKHyIiXk6ypLfwh1sS/D5OfVwnwe/DLE7yn1FERMS5zJ1r1D6nT4/6/BtvwNChVnLkCC9YJmo8eZbQUPjoIxgzJmKTx7Aw+P33RCtmiyRHVapU4fTp08+dIyIiIiKSUFTMFhERecqGDdCkSfTn582Dzp2Nr61WuHYtcXJJDJ0/b6zG3r8/Yuw//4GlSyF3bvNyiSQDP/74o9kRRERERJI8Z+uZndSomC0iIgI8eGBs3visQvbvv0Ms9qWQxLZ0qdEf+94949jVFUaMgCFDwE0veURERERERJI6/WUnIiIpks0Gu3fDH38Y/y5ZEvW8H3+El17Sol6nFhQEvXvDsmURY3nyGMcVKpiXS0RERERE5F8sWpodJypmi4hIirB6NbRsGfGRrudtf/x//2e0XZYk4P59+OGHiOP27WHGDEhim+uJiIiIiIjIs6mYLSIiydrFi8bK6nDPK2I3agStWkG7dgkaS+JT9uzGjp0dO8KsWdCmjdmJREREREREoqSF2XGjYraIiCQ7Nhv88gts2waDBkU+X7QopE5tbOB4754xx9UV6taFrFkTP6/E0oULkD69cQnXtClUqQIZM5oUSkRERERERBKaitkiIpJs/Pe/0Lo1XLny7Dn/+U/iZZJ4tnw5vPMO1K8PK1Y4LmtQIVsk0dy5c4effvqJa9euYbVaHc516NDBpFQiIiIizk89s+NGxWwREUkWbt0yFuZGJ00ao7WyJFF37xqbPC5dahyvWgVvvGG8eyEiiWrDhg289dZbPHjwAG9vb4c/yCwWi4rZIiIiIpJgXMwOICIi8qJCQqBqVXBxiXpRbt680K8f/PyzCtlJ2v79ULJkRCEbjE0eGzQwLZJISvbee+/RuXNn7t27x507d7h9+7b9cuvWLbPjiYiIiDg1i8WS4JfkTCuzRUQkybl4ETp3hh07oj5fowZs3564mSQBhIbCxx/D6NEQFmaMpUsHM2dC27bmZhNJwf7++2/69etH6tSpzY4iIiIiIimMitkiIpKkHDwI5ctHf37cOOjbN/HySAK5cAHatYO9eyPGKlY0VmfnyWNaLBGBunXr8vPPP5M3b16zo4iIiIgkOcl84XSCUzFbRESShH37oFKlqM+9+abRQlkvCpKJP/+E0qWNPtlg9JEZPhyGDgU3vXQRMVvDhg0ZNGgQp06dolixYri7uzucb9KkiUnJRERERCS501+EIiLi9NauhebNI4/nzGks4HXRDhDJS758UK0arF8PL70Ey5YZq7JFxCl069YNgNGjR0c6Z7FYCAtvCyQiIiIikST3ntYJTX/+i4iIU7p9GyZPNlZbR1XI3rEDLl1SITtZslhg3jzo3RuOHVMhW8TJWK3WaC8qZIuIiIhIQtLKbBERcSr37hl7/EVn5EgYMSLR4khCCwszNnksWxbq1o0Yz5QJZswwL5eIiIiIiEgC0MLsuFExW0RETHXtGnTuHPEL/bvvop975gwUKJA4uSQRXLxobPK4Zw9kyQLHj4Ofn9mpRCQK06ZNo3v37nh5eTFt2rRnzu3Xr18ipRIRERGRlEbFbBERMUVwMOTIATduPHtet27GpUwZvYOdrKxcCT17QlCQcXz9utE7pnVrc3OJSJQmT57MW2+9hZeXF5MnT452nsViUTFbRERE5BnUMztuVMwWERFTlC377EJ2oUJw6lTi5ZFEcu8e9OkDixdHjL30EixdCpUqmRZLRJ7t/PnzUX4tIiIiIpKYtG2WiIgkOqvV6CjxtE6djMW516/D/fsqZCdLBw9CyZKOhey33jI2eVQhWyTJCQkJ4fTp04SGhpodRURERCTJsFgS/pKcqZgtIiKJbtMmx+MnT2D+fGPPv0yZIE0ac3JJAgkLgzFjjIL1uXPGmLc3LFlirMj28TE3n4jEysOHD+nSpQupU6emSJEiXLp0CTB6ZX/yyScmpxMRERGR5EzFbBERSTQ2G7zyCjRsGDHm6wtuanqVvF25AhMmGEVtgAoVjNXY7dqZGktEXsyQIUP45Zdf+PHHH/Hy8rKP16pVi1WrVpmYTERERMT5WSyWBL8kZypmi4hIolm8GE6fdhz74gtzskgiypnT+A/t4gLDh8Pu3ZA3r9mpROQFrVu3jhkzZvCf//zH4Y+lwoULc/bsWROTiYiIiEhyp7VwIiKSIGw2o6NEcLDxdc2a8M8/jnM2bIAGDczJJwno3j2jcP10v5i33oIyZaBgQfNyiUi8uH79On5+fpHGHzx4kOxXAomIiIjElV4uxY1WZouISLwLDTVqmfnzQ5EiULRo5EL2wYPQqJExT5KRn36CUqXg3Xcjn1MhWyRZeO211/j+++/tx+EF7Dlz5lChQgWzYomIiIgkCWozEjdamS0iIvHmv/+FDh3gwoVnz9u5E8qWTZRIkljCwuCTT2DECOPrs2ehXj1o0cLsZCISz8aNG0e9evU4deoUoaGhTJ06lZMnT7J//3527dpldjwRERERScZUzBYRkXgRFgZVqkR9rnNn418fH5g4Uauxk51Ll6B9e6MXdrjy5eHVV83LJCIJpmLFiuzdu5eJEyeSL18+tmzZwquvvsr+/fvJnTu32fFEREREnFoyXzid4FTMFhGRF/bwobGv3//9n9Eb+2mZM0PDhjB/vn5ZJ2tffQU9esCdO8axiwsMHQrDhoG7u6nRRCR+TZw4kYEDBwJQrFgxFi1a5HD+7t271KlThwMHDpgRT0RERERSABWzRUQkVmw2mDkTxoyBq1ejn3ftWuJlEhPcuwf9+sHChRFjuXLB0qVQubJpsUQk4QwbNoyMGTPSqVOnSOfu379P3bp1uXv3rgnJRERERJKO5N7TOqHpg94iIhIjISHQq5ex8LZ37+gL2c2aGS1HJBm7csXY5PHpQnbr1vDLLypkiyRjS5YsoVevXqxbt85h/P79+9SpU4dbt26xc+dOc8KJiIiISIqgldkiIvJMly/D9OkwYULU511cIG9e2LgRChRI3GxiEn9/4z/62bOQNi18/rnRM1srDESStRYtWnDnzh3atm3L999/T/Xq1bl//z716tXjxo0b7Nq1iyxZspgdU0RERMSp6c+muFExW0REojV7ttEOOTo3b4Kvb+LlESfh4gKLFkG3bjB1KuTLZ3YiEUkkXbt25datW7zxxht8++23DBs2jMDAQHbt2kXWrFnNjiciIiIiyZyK2SIi4sBmM2qUX31ltEX+t2rVYPFiyJkz0aOJWVavhixZoEqViLGsWeG778zLJCKmGTx4MLdv36ZmzZq89NJL7Nq1i+zZs5sdS0RERCRJUM/suFExW0REHCxfDvPmRR4fORLeeQf8/BI9kpjl/n1jk8cFCyBHDjh+HDJkMDuViJikWbNmDsfu7u5kypSJfv36OYyvXbs2MWOJiIiISAqiYraIiNidPAnt2kUe37oVatVK/DxiokOHoG1b+PNP4/ivv2DJEqO4LSIpko+Pj8NxmzZtTEoiIiIiknRpYXbcqJgtIiIAhIVB0aKOY8eOQYkSpsQRs4SFwfjxMHw4hIYaY2nTwowZ0KGDudlExFQLFiwwO4KIiIiIpHAqZouICAD16zseBwSokJ3iXL4M7dvDrl0RY2XLwrJlkD+/eblERERERESSCfXMjhsXswOIiIj5pk83Wok8bcIEc7KISdasMd69CC9kWywwdCjs2aNCtoiIiIiIiDgFrcwWEUnBbDbo2hXmz3ccDwoCF73dmXIEBhotRB49Mo5z5oSlS6FKFXNziYiIiIiIJDNamR03KlWIiKRQjx9DmjSRC9lHjkC6dOZkEpP4+8PkycbXLVvCL7+okC0iIiIiIiJORyuzRURSqH37Ihbihhs4EEqVMiePJKKwMOPi4REx1r075M0LtWppe20REREREZEEoj+34kbFbBGRFCo01PH47l3w9jYniySiv/4yNnksVgymTYsYt1igdm3zcomIiIiIiIg8h4rZIiIp0J078M47EcfDh6uQnSJ8/TV06wa3b8OPP0K9etCggdmpREREREREUgz1zI4b9cwWEUlBbt+Gvn0hQwY4dy5i/MED8zJJIrh/39jps0UL40kAkCOH3sEQERERERGRJEUrs0VEkrHly2HECPjzT0iVKnKP7HA9eyZuLklEP/8MbdvCH39EjL35Jnz5pfGuhoiIiIiIiCQaLcyOGxWzRUSSIasVXF0dx6IqZOfMCSdPaoFusmS1woQJ8H//F9EgPU0amD4dOnbUKygRERERERFJctRmREQkGblwwahR/ruQHa5gQfDyggED4Pp1uHRJhexk6fZtqFULPvggopBdpgwcPQqdOqmQLZJC/fnnn2zevJlH/3t302azmZxIREREJOWxWCwJfknOtDJbRCQZ6dIl6vHdu6FSJXDRW5gpg7c3hIQYX1ssRlF71Chwdzc3l4iY4ubNm7Rq1YodO3ZgsVj4448/yJs3L127diV9+vR89tlnZkcUEREREYkRlTVERJKRHTscj6tWNTZ3rFxZhewUxc0Nli6FIkWMJ8XHH6uQLZKCDRgwADc3Ny5dukTq1Knt461atWLTpk0mJhMRERFJeSyWhL8kZ1qZLSKSRNlssH8/nDoFY8bAxYuO52/d0v5+Kcbhw8a/pUtHjL30Ehw/rncxRIQtW7awefNmcuTI4TBeoEABLv77l4eIiIiIiBNTMVtEJIm5exf+/BMqVIjoJPFvvr4qZKcIVitMnGhs8pgrl9ET++km6Cpkiwjw4MEDhxXZ4W7cuIGnp6cJiURERERSLpfkvnQ6gb3QX7mhoaFs27aNL7/8knv37gFw5coV7t+/H6/hREQkwqNHRoHax8dYgBtdITt7djh7NnGziQn+/htq14b334cnT4z/6JMmmZ1KRJxQlSpVWLx4sf3YYrFgtVqZMGEC1atXNzGZiIiIiEjsxHpl9sWLF6lXrx6XLl0iODiY2rVr4+3tzfjx43n8+DGzZs1KiJwiIinaH3/Ayy9Hf/7LL6FECShbNvn3xxLgm2+ga1ejlwwY/9EHD4YhQ8zNJSJOacKECVSrVo2ff/6ZkJAQBg8ezMmTJ7l16xZ79+41O56IiIhIiqK/2eMm1iuz3333XcqUKcPt27dJlSqVfbxp06Zs3749XsOJiAh89VXUhez27Y3uEn/9Bd27Q7ly+qWY7D14YPzHbtYsopCdPTts3w6ffAIeHubmExGnVLhwYY4fP07ZsmWpXbs2Dx48oFmzZhw9epR8+fKZHU9EREREJMZivTJ7z5497N27F49//cGcO3du/v7773gLJiKSkp07B+vXw4ABkc+lTg3376twneIcOQJt28Lp0xFjzZvD7NlGk3QRkWfw9/dn1KhRZscQERERSfEs+mM+TmK9MttqtRIWFhZp/K+//sL76U2nRETkheTLZ1yiKmQHBMC9eypkpzi3b0PVqhGF7NSpYe5cWL1ahWwRea5NmzaxZ88e+/Hnn39OyZIladu2Lbdv3zYxmYiIiIhI7MS6mF27dm2mTJliP7ZYLNy/f58RI0bQoEGD+MwmIpLinD9vrMqOyqFD8Nln4PJCW/dKkpYhA4webXxdujQcPQpduuhdDRGJkUGDBnH37l0ATpw4QUBAAA0aNODcuXMEBASYnE5EREQkZXGxJPwlOYt1m5HJkydTvXp1ChcuzOPHj2nbti1//PEHmTJlYsWKFQmRUUQkxcib1/G4f39o08bY2FFSGJvNsVj97ruQNi28/bZ6Y4tIrJw/f57ChQsD8PXXX9O4cWM+/vhjjhw5osUoIiIiIpKkxLqYnS1bNo4dO8bKlSs5fPgwVquVLl268NZbbzlsCCkiIrHj9q+fyL17w+TJ5mQREz14YPST8fWFceMixl1coFs383KJSJLl4eHBw4cPAdi2bRsdOnQAwNfX175iW0REREQSh3pmx02si9m7d++mYsWKdOrUiU6dOtnHQ0ND2b17N1WqVInXgCIiKcH9+/Dv7QjGjzcni5jo6FFjKf7p08aq7Dp1oHp1s1OJSBL3n//8h4CAACpVqsRPP/3EqlWrADhz5gw5cuQwOZ2IiIiISMzFuvNq9erVuXXrVqTxoKAgqusPbhGRF9Knj+NxSIixx5+kEFar0RC9XLmITR5TpYLAQHNziUiyMGPGDNzc3FizZg0zZ84ke/bsAPzwww/Uq1fP5HQiIiIiKYvFkvCX5CzWK7NtNluUy+Fv3rxJmjRp4iWUiEhKMW4cfPih49ibb4K7uzl5xARXrhh9sLdtixh79VVYvhwKFjQvl4gkG7ly5eK7776LND5ZvaxEREREJImJcTG7WbNmgNHXpWPHjnh6etrPhYWFcfz4cSpWrBj/CUVEkhmbDQYMgF274NixyOcnTkz0SGKWb7+FLl3g5k3j2GKBQYPgo4+0yaOIxJsjR47g7u5OsWLFAPj2229ZsGABhQsXZuTIkXjo542IiIhIorGQzJdOJ7AYtxnx8fHBx8cHm82Gt7e3/djHxwd/f3+6d+/O0qVLEzKriEiS9tVX8Prrxj5+U6dGLmQXKAAHD0KuXKbEk8T06BG88w688UZEITtbNmN19qefqpAtIvGqR48enDlzBoBz587RunVrUqdOzerVqxk8eLDJ6UREREREYi7GK7MXLFgAwEsvvcTAgQPVUkREJAb+/BM6dID9+5897/Bho7OEpBCurnDoUMRx06YwZw5kzGheJhFJts6cOUPJkiUBWL16NVWqVGH58uXs3buX1q1bM2XKFFPziYiIiKQkLlqYHSex3gByxIgRKmSLiETjwQMoUcLoee3hYay2jq6QnScPXL4MwcEqZKc4Hh6wbBlkzgyzZ8PXX6uQLSIJxmazYbVaAdi2bRsNGjQAIGfOnNy4ccPMaCIiIiIisRLrDSAB1qxZw1dffcWlS5cICQlxOHfkyJF4CSYiktTYbJA27fPnbdwI1apBqlQJHkmcxdWrcPeu44aOBQvChQuQOrVpsUQkZShTpgxjxoyhVq1a7Nq1i5kzZwJw/vx5smTJYnI6ERERkZTFYtHS7LiI9crsadOm0alTJ/z8/Dh69Chly5YlY8aMnDt3jvr16ydERhGRJGH8+MhjefMa/y5aBPfuGQXv+vVVyE5R1q+HYsWgWTOjV/bTVMgWkUQwZcoUjhw5Qp8+fRg6dCj58+cHjAUq2sBdRERERJKSWK/M/uKLL5g9ezZt2rRh0aJFDB48mLx58zJ8+HBu3bqVEBlFRJxaaCi8/z5MmuQ4brWC3nBNwR4+hIED4X8rILl5E0aPhnHjzM0lIilO8eLFOXHiRKTxCRMm4OrqakIiERERkZRLdYK4iXUx+9KlS/YVHKlSpeLevXsAtG/fnvLlyzNjxoz4TSgi4sSuXoVs2aIe1y+oFOyXX6BNG/jtt4ixN94witsiIk7Cy8vL7AgiIiIiIrES6zYj/v7+3Lx5E4DcuXNz4MABwOi5Z7PZ4jediIiTOnLEKFZHVcieNQv8/RM/kzgBqxUmT4ayZSMK2alSwZdfwtq12uRRREwRFhbGxIkTKVu2LP7+/vj6+jpcRERERCTxuFgsCX5JzmJdzK5RowYbNmwAoEuXLgwYMIDatWvTqlUrmjZtGusAX3zxBXny5MHLy4vSpUvz3//+95nzg4ODGTp0KLlz58bT05N8+fIxf/78WN+viMiLOHcOuneH0qWjPn/9OvTokbiZxElcvWo0RA8IgPDNkUuVMt756N5dS/VFxDSjRo1i0qRJtGzZkqCgIAICAmjWrBkuLi6MHDnS7HgiIiIiIjEW6zYjs2fPxmq1AtCzZ098fX3Zs2cPjRs3pmfPnrG6rVWrVtG/f3+++OILKlWqxJdffkn9+vU5deoUuXLlivI6LVu25J9//mHevHnkz5+fa9euERoaGtuHISISa48fQ758UZ8bPx769gV9YjuFevDAeIfj6tWIsYEDYcwY8PQ0L5eICLBs2TLmzJlDw4YNGTVqFG3atCFfvnwUL16cAwcO0K9fP7MjioiIiKQYWucUN7EuZru4uODiErGgu2XLlrRs2RKAv//+m+zZs8f4tiZNmkSXLl3o2rUrYOy0vnnzZmbOnMm4KDbI2rRpE7t27eLcuXP2j0S+9NJLsX0IIiIxEhRkrMS+fx/mzUvHokWRP8wyeTL075/42cTJpEkDvXrBsGGQNSssXgy1apmdSkQEgMDAQIoVKwZA2rRpCQoKAqBRo0YMGzbMzGgiIiIiIrES62J2VAIDAxk7dixz587l0aNHMbpOSEgIhw8f5oMPPnAYr1OnDvv27YvyOuvXr6dMmTKMHz+eJUuWkCZNGpo0acJHH31EqlSporxOcHAwwcHB9uO7d+8CYLVa7SvMRcB4TthsNj0vBIBVq6Bt2/DitQuQOtKcu3etpEljtEmWlMnh58b772MJDsbWty9kyqQnRgqn3ykSHTOeEzly5ODq1avkypWL/Pnzs2XLFl599VUOHTqEpz49IiIiIpKoLFqaHScxLmbfuXOH3r17s2XLFtzd3fnggw/o06cPI0eOZOLEiRQpUiRWvatv3LhBWFgYWbJkcRjPkiULgYGBUV7n3Llz7NmzBy8vL7755htu3LhBr169uHXrVrT3PW7cOEaNGhVp/Pr164SE9zQVwfjjMigoCJvN5vDpA0l5bt600LZtlmjP58wZyv79N3jwwOguISmM1UrqOXOwPHnCvV69HH9u9O5tFLGvXTM7pZhMv1MkOuGrohNT06ZN2b59O+XKlePdd9+lTZs2zJs3j0uXLjFgwIBEzyMiIiIi8qJiXMz+8MMP2b17N2+//TabNm1iwIABbNq0icePH/PDDz9QtWrVFwrw73cjbDZbtO9QWK1WLBYLy5Ytw8fHBzBalbRo0YLPP/88ytXZQ4YMISAgwH589+5dcubMSebMmUmfPv0LZZbkKfz5lTlzZhUeUphff4Wn954dNMjxZ1Dr1lbc3R+RP78XLVtaePllF8AvcUOKc7h6FUunTli2bsXm6kqqunWx5M+vnxsSiX6nSHQ8PDwS/T4/+eQT+9ctWrQgR44c7Nu3j/z589OkSZNEzyMiIiKSkmlhdtzEuJj9/fffs2DBAmrVqkWvXr3Inz8/L7/8MlOmTHmhO86UKROurq6RVmFfu3Yt0mrtcFmzZiV79uz2QjZAoUKFsNls/PXXXxQoUCDSdTw9PaP8+OS/e3+LgPHmip4bKUuOHPD339GfT58eli2Da9fu4eeXSs+NlOy776BTJ7hxAwBLWBgu+/djKVBAPzckSvqdIlFxhudD+fLlKV++vNkxRERERERiLcbF7CtXrlC4cGEA8ubNi5eXl33jxhfh4eFB6dKl2bp1K02bNrWPb926lddffz3K61SqVInVq1dz//590qZNC8CZM2dwcXEhR44cL5xFRFKO4GBYtw7++QfefffZc11c7HVLSckePYJBg+DzzyPG/P1h0SJjk0e1FBERJ7R+/foYz9XqbBEREZHE46Kl2XES42K21WrF3d3dfuzq6kqaNGnidOcBAQG0b9+eMmXKUKFCBWbPns2lS5fo2bMnYLQI+fvvv1m8eDEAbdu25aOPPqJTp06MGjWKGzduMGjQIDp37hztBpAiIuHmzIHu3aM/P22asRIbwM0N6tQBV1ft45eiHT8ObdvCyZMRY02awLx52uRRRJzaG2+8EaN5FouFsLCwhA0jIiIiIhJPYlzMttlsdOzY0d6y4/Hjx/Ts2TNSQXvt2rUxvvNWrVpx8+ZNRo8ezdWrVylatCgbN24kd+7cAFy9epVLly7Z56dNm5atW7fSt29fypQpQ8aMGWnZsiVjxoyJ8X2KSMp069azC9mPH0MUHYkkpbLZjHc33n/fWM4PkCoVTJoEPXqoyZmIOD2r3mwTERERcUr6azJuYlzMfvvttx2O27VrFy8BevXqRa9evaI8t3Dhwkhjr7zyClu3bo2X+xaR5O/ePUiXLvJ47dpG++P8+aFMGdUm5V+Cg42l/OGF7BIlYMUKKFTI3FwiIrHw66+/UrRo0WfO+eSTT/jggw8SKZGIiIiISNzEuJi9YMGChMwhIhLvvvwS/te1yEHLlrBqVeLnkSTEywuWL4dy5aBXL/j4Yy3dF5Ekp27duuzdu5eXXnopyvOffvopI0aMUDFbREREJBFZtJouTszfTl1EJJ49fAj/+U/UheyBA1XIlig8egR//eU4Vrw4/PknfPaZCtkikiRVrlyZ2rVrcy2KjWonTJjAsGHDWLp0qQnJRERERERejIrZIpKshIZC4cKwd6/j+IcfwpMnMGGCObnEiR0/Dq+9Bq+/DiEhjueyZzcnk4hIPFi6dCn58+enTp06BAUF2cc/++wzPvzwQxYvXsybb75pYkIRERGRlMfFkvCX5EzFbBFJVvbuhYsXHcd++gnGjgW3GDdWkhTBZoOpU6FsWTh5Eo4cgREjzE4lIhJv3NzcWLt2LWnTpqVRo0Y8fvyYKVOm8MEHH7Bo0SJat25tdkQRERERkVhRMVtEkpUaNRyP//nHWHQr4uCff6BBA+jfP2KTx+LFIZ42NxYRcRapUqXi+++/5969e5QuXZpBgwaxYMEC2rZta3Y0ERERkRTJYrEk+CU2du/eTePGjcmWLRsWi4V169Y5nO/YsWOk2y9fvrzDnODgYPr27UumTJlIkyYNTZo04a9/tfK8ffs27du3x8fHBx8fH9q3b8+dO3di/f1TMVtEko1//gGrNeL422/Bz8+8POKkvv8eihWDTZsixvr3h4MHoUgR02KJiMS39evXs379enbt2sU777zD2bNnadq0KenSpbOfW79+vdkxRURERMREDx48oESJEsyYMSPaOfXq1ePq1av2y8aNGx3O9+/fn2+++YaVK1eyZ88e7t+/T6NGjQgLC7PPadu2LceOHWPTpk1s2rSJY8eO0b59+1jnfaEP3S9ZsoRZs2Zx/vx59u/fT+7cuZkyZQp58uTh9ddff5GbFBGJs39/WrpxY3NyiJN69AgGD4anf0FnyQILF0K9eqbFEhFJKG+88UaksTVr1rBmzRr7scVicfgjQ0REREQSViwXTie4+vXrU79+/WfO8fT0xN/fP8pzQUFBzJs3jyVLllCrVi3A2LslZ86cbNu2jbp16/Lbb7+xadMmDhw4QLly5QCYM2cOFSpU4PTp0xQsWDDGeWO9MnvmzJkEBATQoEED7ty5Y3/xmz59eqZMmRLbmxMRibOHD+HLL+HHHyPG2rVzvl8QYqLgYKhQwbGQ3agRnDihQraIJFtWq/W5FxWyRURERJKf4OBg7t6963AJDm+x+QJ+/PFH/Pz8ePnll+nWrRvXrl2znzt8+DBPnjyhTp069rFs2bJRtGhR9u3bB8D+/fvx8fGxF7IBypcvj4+Pj31OTMW6mD19+nTmzJnD0KFDcXV1tY+XKVOGEydOxPbmRETixGYzOkb07Ok4Pn++OXnESXl6Gj2yAby8jKL2+vWQObO5uURERERERCRFSYye2ePGjbP3pg6/jBs37oXy1q9fn2XLlrFjxw4+++wzDh06RI0aNezF8cDAQDw8PMiQIYPD9bJkyUJgYKB9jl8UfWD9/Pzsc2Iq1m1Gzp8/T6lSpSKNe3p68uDBg9jenIhInHzxBZw75zg2bx64u5uTR5zYqFFw5QoMGqTe2CIiIiIiIpJsDRkyhICAAIcxT0/PF7qtVq1a2b8uWrQoZcqUIXfu3Hz//fc0a9Ys2uvZbDaHzSij2pjy33NiItbF7Dx58nDs2DFy587tMP7DDz9QuHDh2N6ciEisHDwI778Pu3aBtzfcu+d4/uhRKFnSlGjiTH74Af76C7p1ixhzdzf6Y4uIiIiIiIiYxCURWqJ6enq+cPH6ebJmzUru3Ln5448/APD39yckJITbt287rM6+du0aFStWtM/5559/It3W9evXyZIlS6zuP9ZtRgYNGkTv3r1ZtWoVNpuNn376ibFjx/Lhhx8yaNCg2N6ciMhz2WxGEXvmTChf3ihkQ+RC9rlzKmSneI8fw7vvGi1F+vSBY8fMTiQiIiIiIiKSbNy8eZPLly+TNWtWAEqXLo27uztbt261z7l69Sq//vqrvZhdoUIFgoKC+Omnn+xzDh48SFBQkH1OTMV6ZXanTp0IDQ1l8ODBPHz4kLZt25I9e3amTp1K69atY3tzIiLP9c030Lx51OcKFTJaIPftC3nyJG4ucTK//gpt2xqbOgKEhBjN06dNMzeXiIiIiIiIyP/Etq1GQrt//z5//vmn/fj8+fMcO3YMX19ffH19GTlyJM2bNydr1qxcuHCBDz/8kEyZMtG0aVMAfHx86NKlC++99x4ZM2bE19eXgQMHUqxYMWrVqgVAoUKFqFevHt26dePLL78EoHv37jRq1IiCBQvGKm+si9kA3bp1o1u3bty4cQOr1RplA28RkfgQGhp1IbtpU/jqK3B7oZ9ikqzYbPD55zBwIITvzuzpCRMnQu/e5mYTEXESd+7cYc2aNZw9e5ZBgwbh6+vLkSNHyJIlC9mzZzc7noiIiIiY5Oeff6Z69er24/Be22+//TYzZ87kxIkTLF68mDt37pA1a1aqV6/OqlWr8Pb2tl9n8uTJuLm50bJlSx49ekTNmjVZuHAhrq6u9jnLli2jX79+1KlTB4AmTZowY8aMWOeNdRlo1KhRtGvXjnz58pEpU6ZY36GISExs22bUJ9etcxzv0AG6d4eKFcHJ3swUM1y7Bp07w/ffR4wVLQorVhj/iogIx48fp1atWvj4+HDhwgW6deuGr68v33zzDRcvXmTx4sVmRxQRERFJMZytlFGtWjVsNlu05zdv3vzc2/Dy8mL69OlMnz492jm+vr4sXbr0hTI+LdY9s7/++mtefvllypcvz4wZM7h+/XqcQ4iIPG3iRKhdO3IhG2DBAqhUSYVsATZtguLFHQvZ/frBoUMqZIuIPCUgIICOHTvyxx9/4OXlZR+vX78+u3fvNjGZiIiIiEjsxLqYffz4cY4fP06NGjWYNGkS2bNnp0GDBixfvpyHDx8mREYRSUF274ao9pItVw5u3gSXWP/UkmTpyROjcB2+G7KfH2zcCFOnGk3URUTE7tChQ/To0SPSePbs2QkMDDQhkYiIiEjK5WKxJPglOXuhslCRIkX4+OOPOXfuHDt37iRPnjz0798ff3//+M4nIinE6tXw0ktQtarj+Ny58OgRHDgAvr6mRBNn5O4Oy5YZTdMbNIDjx6F+fbNTiYg4JS8vL+7evRtp/PTp02TOnNmERCIiIiIiLybOW6elSZOGVKlS4eHhwb179+Ijk4ikID//DK+9FvW5zZvhf/sCSEpns8Hdu+DjEzH22mvGuxyvvqq+MyIiz/D6668zevRovvrqKwAsFguXLl3igw8+oHlUuyyLiIiISILRn69x80Irs8+fP8/YsWMpXLgwZcqU4ciRI4wcOVIfUxSRGAsJgTlzoi5kp05tbPKoQrYAxiaPTZoYK7BDQx3PlS6tVwIiIs8xceJErl+/jp+fH48ePaJq1arkz58fb29vxo4da3Y8EREREZEYi/XK7AoVKvDTTz9RrFgxOnXqRNu2bcmePXtCZBORZOzjj2HUqMjjP/0U/UptSYE2b4a3347ojT1mDIwcaWokEZGkJl26dOzZs4cdO3Zw5MgRrFYrr776KrVq1TI7moiIiEiKY9GCrDiJdTG7evXqzJ07lyJFiiREHhFJAd59F6ZNcxwbPRqGDTMnjzihx49hyBCYMiVizM8PypY1LZKISFJXo0YNatSoYXYMEREREZEXFuti9scff5wQOUQkmQsNhYMHoW1buHTJ8dwvv0Dx4ubkEid06hS0aWNs6hiufn1YsACyZDEvl4hIErZ9+3a2b9/OtWvXsFqtDufmz59vUioRERGRlEcLs+MmRsXsgIAAPvroI9KkSUNAQMAz506aNClegolI0hcWBl26wH//C+fORT3nww9VyJb/sdlg5kx47z1jZTaApydMmAB9+ug3vojICxo1ahSjR4+mTJkyZM2aVR9tFREREZEkK0bF7KNHj/LkyRP71yIiz7N2LTRv/uw5Dx4Ymz2KEBpqPGHWr48YK1IEVqyAYsXMyyUikgzMmjWLhQsX0r59e7OjiIiIiKR4LlpYECcxKmbv3Lkzyq9FRKITXSG7UiVo0ADatVMhW57i5gYvvRRx3KcPjB8PqVKZFklEJLkICQmhYsWKZscQEREREYkzl9heoXPnzty7dy/S+IMHD+jcuXO8hBKRpC0kxPH47bfh1i2ji8SePUZrkVy5zMkmTuzTT6FmTfjuO5g+XYVsEZF40rVrV5YvX252DBERERHB6KCZ0JfkLNYbQC5atIhPPvkEb29vh/FHjx6xePFibSAjItSo4Xi8cKEpMcSZnToFv/3muITfywu2bTMvk4hIMvL0PjdWq5XZs2ezbds2ihcvjru7u8Nc7XkjIiIiIklFjIvZd+/exWazYbPZuHfvHl5eXvZzYWFhbNy4ET8/vwQJKSJJQ2AgrF4Ne/dGjJUubV4ecUI2G8yaBQEBxtvFhQtDoUJmpxIRSXb+vc9NyZIlAfj1118dxrUZpIiIiEji0uuvuIlxMTt9+vRYLBYsFgsvv/xypPMWi4VRo0bFazgRSTo++wwGDow8vnt34mcRJ3X9OnTt6rjJ4+jRxiaPIiISr7TPjYiIiIgkRzEuZu/cuRObzUaNGjX4+uuv8fX1tZ/z8PAgd+7cZMuWLUFCiojzslrB1TXqc8uWaZNH+Z+tW6FDB2P5frjevWHCBPMyiYikEEFBQYSFhTm8fge4desWbm5upEuXzqRkEabNGmR2BBFJYqw2m9kRRCTJcY4V0bHewFAcxLiYXbVqVQDOnz9Prly5tCReRAAoXjzyWJ8+0KQJ1KqV+HnEyQQHGzt+Pt2PNXNmmD8fGjUyL5eISArSunVrGjduTK9evRzGv/rqK9avX8/GjRtNSiYiIiIiEjsxKmYfP36cokWL4uLiQlBQECdOnIh2bvGoKlsikiytXg0nTzqOnTxptEEW4bffoG1bOHYsYqxePViwAPz9TYslIpLSHDx4MMpNHqtVq8bQoUNNSCQiIiKScmmBcNzEqJhdsmRJAgMD8fPzo2TJklgsFmxRfKTHYrEQFhYW7yFFxHkEBcHBg7BxI0yd6nguNDT6liOSwlit0KwZ/P67cezhAZ9+Cv36gYs+VCUikpiCg4MJDQ2NNP7kyRMePXpkQiIRERGRlMtFtew4iVEx+/z582TOnNn+tYikPKtXQ+vWRo0yKkeOqJAtT3FxgS+/hOrV4ZVXYPlyKFHC7FQiIinSa6+9xuzZs5k+fbrD+KxZsyhdurRJqUREREREYi9GxezcuXNH+bWIpAzXrkHLltGfb9UKSpVKvDzipEJDwe2pXytVqsC330LNmpAqlXm5RERSuLFjx1KrVi1++eUXatasCcD27ds5dOgQW7ZsMTmdiIiISMqildlxE+vPei9atIjvv//efjx48GDSp09PxYoVuXjxYryGExFz/fUXDBgAWbJEPteiBcyeDZcvw8qViZ9NnEhwMAwcCA0aRF6636iRCtkiIiarVKkS+/fvJ2fOnHz11Vds2LCB/Pnzc/z4cSpXrmx2PBERERGRGIvRyuynffzxx8ycOROA/fv3M2PGDKZMmcJ3333HgAEDWLt2bbyHFJHEd+QIRPXJ4969YcaMxM8jTur3341NHo8eNY4/+wwGDTI3k4iIRFKyZEmWLVtmdgwRERGRFE8bQMZNrIvZly9fJn/+/ACsW7eOFi1a0L17dypVqkS1atXiO5+ImCSqQvbAgTBmTOJnESdksxlL8wcMgPDNwzw8wMvL3FwiIgLA3bt3SZcunf3rZwmfJyIiIiLi7GJdzE6bNi03b94kV65cbNmyhQEDBgDg5eWl3dBFkokNGxyPW7SAjz4y9vET4eZN6NoV1q2LGCtUyNjksWRJs1KJiMhTMmTIwNWrV/Hz8yN9+vRRrgCy2WxYLBbCwsJMSCgiIiKSMqlndtzEuphdu3ZtunbtSqlSpThz5gwNGzYE4OTJk7z00kvxnU9EEtGTJ5ApE/x7Adfq1ebkESe0fTt06ABXrkSMvfMOTJwIqVObl0tERBzs2LEDX19fAHbu3GlyGhERERGR+BHrYvbnn3/O//3f/3H58mW+/vprMmbMCMDhw4dp06ZNvAcUkcTj4RF57MMPEz+HOCGbDd5/3yha22zGWKZMMG8eNGlibjYREYmkatWqUX4tIiIiIuZSy+y4iXUxO3369MyIYve3UaNGxUsgEUlcjx/DN9/Ali2Rz+3cCWqFL4Dx2/b27YhCdu3asGgRZM1qbi4REYnS8ePHYzy3ePHiCZhERERERCT+xLqYDXDnzh3mzZvHb7/9hsVioVChQnTp0gUfH5/4ziciCSgkBFKlivpceM1SxG7yZDhwADp1gv79wcXF7EQiIhKNkiVLYrFYsD3nF7p6ZouIiIgkLhctzY6TWBezf/75Z+rWrUuqVKkoW7YsNpuNyZMn8/HHH7NlyxZeffXVhMgpIvHo4UM4fRoqVIj6/P79iZtHnNDNm3D8OFSvHjGWNi0cPQpuL/Q+qIiIJKLz58+bHUFEREREJN7FuiIxYMAAmjRpwpw5c3D7X0EjNDSUrl270r9/f3bv3h3vIUUk7kJDYelSY1FtdJYvN9qKqHNEChe+yeO9e3DsGOTNG3FOhWwRkSQhd+7cZkcQERERkSjoM85x80Irs58uZAO4ubkxePBgypQpE6/hRCR+HDwI5cs/e05wcNQbQEoKEhIC//d/jps89usH331nbi4REYmzM2fO8OOPP3Lt2jWsVqvDueHDh5uUSkREREQkdmJdzE6XLh2XLl3ilVdecRi/fPky3t7e8RZMROLHtm3GXn1RKVECunUzFuGqkJ3CnT4NbdvCkSMRY7VqwezZ5mUSEZF4MWfOHN555x0yZcqEv78/lqf6NFosFhWzRURERBKRWmbHTayL2a1ataJLly5MnDiRihUrYrFY2LNnD4MGDaJNmzYJkVFE4qBvX8fjAgVg1SooVcqcPOJkbDaYNw/efddopg7g7g7jxsGAAdrkUUQkGRgzZgxjx47l/fffNzuKiIiIiEicxLqYPXHiRCwWCx06dCA0NBQAd3d33nnnHT755JN4DygiL+677+D33yOOV6+GFi3MyyNO5tYtY2n+2rURY6+8YjRP17sdIiLJxu3bt3nzzTfNjiEiIiIigIuWZsdJrJfceXh4MHXqVG7fvs2xY8c4evQot27dYvLkyXh6eiZERhGJJZvN+NhK48aO46+/bk4ecUI2G9St61jI7tEDDh9WIVtEJJl588032bJli9kxRERERETiLMYrsx8+fMigQYNYt24dT548oVatWkybNo1MmTIlZD4ReQHDhkUea9jQ6B4hAhjvdowZA/XqQcaMMHcuvPGG2alERCSeTJs2zf51/vz5GTZsGAcOHKBYsWK4/+sFQb9+/RI7noiIiEiKpYXZcRPjYvaIESNYuHAhb731Fl5eXqxYsYJ33nmH1atXJ2Q+EYmFS5cgd+7I44cOQZkyiZ9HnFzdusYGjw0bQrZsZqcREZF4NHnyZIfjtGnTsmvXLnbt2uUwbrFYVMwWERERkSQjxsXstWvXMm/ePFq3bg1Au3btqFSpEmFhYbi6uiZYQBF5PqsV8ueH8+cjn7t0CXLmTPxM4kTCN3ncvt3oh/3028DdupmXS0REEsz5qF4UiIiIiIjpXLQyO05i3DP78uXLVK5c2X5ctmxZ3NzcuHLlSoIEE5Fns1ph1SoYMgRcXaMuZO/fr0J2infrlrHrZ7dusHIlzJxpdiIRERERERERkRcS45XZYWFheHh4OF7ZzY3Q0NB4DyUiz3buHOTLF/35oUONdsiSwu3cCe3bw99/R4ydPm1eHhERMUWLFi0oU6YMH3zwgcP4hAkT+Omnn9Q2UERERCQRuahpdpzEuJhts9no2LEjnp6e9rHHjx/Ts2dP0qRJYx9bu3Zt/CYUEQc227ML2ffvw1P/S0pKFBICw4fD+PHGEwbA19fY5LFpU3OziYhIotu1axcjRoyINF6vXj0mTpxoQiIRERERkRcT42L222+/HWmsXbt28RpGRJ5vz57IY99+C5kzQ9myRssRScHOnIG2beHw4YixGjVg8WLInt28XCIiYpr79+9H+oQlgLu7O3fv3jUhkYiIiEjKpYXZcRPjYvaCBQsSMoeIPMcvv0Dz5nD2rOO41aofhIKxAnv+fOjXDx4+NMbc3WHsWHjvPXCJ8RYJIiKSzBQtWpRVq1YxfPhwh/GVK1dSuHBhk1KJiIiIiMRejIvZImKeX36BkiUjj+/bp0K2PGX79ohC9ssvw/LlULq0uZlERMR0w4YNo3nz5pw9e5YaNWoAsH37dlasWKF+2SIiIiKJzEV1nDhRMVvEif36KxQrFvW5wYOhfPnEzSNOzGKBmTONdzjq1IHJk9U8XUREAGjSpAnr1q3j448/Zs2aNaRKlYrixYuzbds2qlatanY8EREREZEYUzFbxEn98AM0aBB5fMQI46IV2Snckyfw229QvHjEmI8PHD0KGTKYl0tERJxSw4YNadiwodkxRERERFI8CyroxIWaqIo4oT/+iLqQvXgxjBypQnaK98cfULEiVKsGf/3leE6FbBERERERERFJplTMFnFC/24fMnOmsb9f+/bm5BEnEb7JY6lS8PPPcPs2dOlidioREXFyYWFhTJw4kbJly+Lv74+vr6/DRUREREQSj4sl4S/J2QsVs5csWUKlSpXIli0bFy9eBGDKlCl8++238RpOJCX69Ve4dSviuH9/6NnTtDjiLG7fhlatjOL1gwfGWIECMHasublERMQpNWvWjLt37wIwatQoJk2aRMuWLQkKCiIgIIBmzZrh4uLCyJEjzQ0qIiIiIhILsS5mz5w5k4CAABo0aMCdO3cICwsDIH369EyZMiW+84mkGMePGx0i/r3h4+TJ5uQRJ7Jrl9Ebe/XqiLEuXeDIEShTxrxcIiLitHx8fLD8ry/ZsmXLmD17NgMHDsTNzY02bdowd+5chg8fzoEDB0xOKiIiIpKyaGV23MS6mD19+nTmzJnD0KFDcXV1tY+XKVOGEydOxGs4kZTg8WN4+WUoUQLu3HE8N3WqKZHEWTx5Ah9+CNWrR/TGzpAB1qyBuXMhbVpz84mIiNNasGAB3t7eAFy9epXi/9swOG3atAQFBQHQqFEjvv/+e9MyioiIiIjEVqyL2efPn6dUqVKRxj09PXkQ/tF3EYmRU6cgVSpjP79/W70a+vVL/EziRF5/HcaNM3plg7Hh4y+/QPPmpsYSERHn5+rqyrVr1wDIkSMHgYGBAOTPn58tW7YAcOjQITw9PU3LKCIiIpISWSyWBL8kZ7EuZufJk4djx45FGv/hhx8oXLhwfGQSSfZsNli4EIoUiXxu3jyjJXKLFokeS5xN9+7Gv25u8MknsG0b5MxpbiYREUkS1q5dS4YMGQBo3LgxmzdvBuDdd99l2LBhFChQgA4dOtC5c2czY4qIiIiIxIpbbK8waNAgevfuzePHj7HZbPz000+sWLGCcePGMXfu3ITIKJKsnD4Nr7wSefy11+C778DPL/EziZN64w0YPRrq1TOeICIiIjH0+uuv27/+7LPP7F+3aNGCHDlysG/fPvLnz0+TJk3MiCciIiKSYiX3ntYJLdbF7E6dOhEaGsrgwYN5+PAhbdu2JXv27EydOpXWrVsnREaRZKN+fdi0KfL4lCnw7ruJHkecye7d8M03MGkSPP2RoGHDzMskIiLJUvny5SlfvrzZMUREREREYi3WbUYAuv1/e/cdHkX1tnH8u+lACjWFGjpIkyJVBKRJBwu9CaIISAdFREAUFOlSRSAgXQH1FUSKdBAh9CLSm4kBhNDTdt4/9seGJQkmpGzK/fGay50zZ2ae2UzC7rNnn9OjBxcvXiQkJITg4GAuX75M9+7dkzo2kXSlW7fYE9m//65EdoYWEQHDh1vqYU+ZAgsX2jsiERFJZxYuXGgz0ePQoUPJmjUr1atX5+LFi3aMTERERCTjMZmSf0nPnimZ/UjOnDnxVk0Ekae6c8fyh2TBAtv2OXMstbOrVLFPXJIKnD0LL74IY8dGT/K4alX0YxERkSQwduxYMmXKBMCePXuYPn0648ePJ2fOnAwYMMDO0YmIiIiIxF+Cy4wULFjwqbNinjt3LlEBiaQXhgGvvWapHPGkf/+F/83JJBmRYcCiRdCnD9y9a2lzcoIxY2DIkPT/MaqIiKSoy5cvU6RIEQB++OEHXn/9dd5++21q1KhB7dq17RuciIiISAbjoPf8iZLgZHb//v1t1iMiIjh48CDr169nyJAhSRWXSJo3fHjsiew9e5TIztBu3YKePWHFiui2IkVg6VJN8igiIsnC3d2dGzdukD9/fjZs2GAdje3m5saDBw/sHJ2IiIiISPwlOJndL47ivjNmzGD//v2JDkgkPbhxA8aNs23r3RumT7dPPJJK7NgBHTvCpUvRbd26wdSp4O5uv7hERCRdq1+/Pm+99Rbly5fnr7/+okmTJgAcP34cf39/+wYnIiIiksE4aGB2oiSqZvbjGjVqxKpVq5LqcCJp0tGjkC8f5Mxp2x4crES2YJng8VEiO2tWWLkS5s1TIltERJLVjBkzqFatGteuXWPVqlXkyJEDgMDAQNq1a2fn6ERERERE4i/BI7Pj8v3335M9e/akOpxImlS2bOztPj4pG4ekUnPmWOrMFCsG335r+eRDREQkmd2+fZtp06bh4GA7jmXUqFFcvnzZTlGJiIiIZEwqmZ04CU5mly9f3mYCSMMwCA4O5tq1a8ycOTNJgxNJK/78E0qWjNk+aBCMH5/y8UgqYBgQFAS5c0e35cxpKTXi7w+OjnYLTUREMpaCBQsSFBSEt7e3Tfu///5LwYIFiYqKslNkIiIiIiIJk+BkdsuWLW3WHRwcyJUrF7Vr16ZEiRJJFZdImtKtW8w2w0j5OCSVeDTJ45YtcOSI7dD8woXtFpaIiGRMRhwvSu7evYubm1sKRyMiIiKSsTmgodmJkaBkdmRkJP7+/jRs2BBfX9/kikkkzXjwADJntm3LnBmuX7dPPJIKPDnJ45tvwtq1+h6RiIikuIEDBwJgMpn4+OOPyfzYi5aoqCj27t3L888/b6foREREREQSLkHJbCcnJ959911OnjyZXPGIpCnVq9uuZ88OISGqIJEhRUbCJ5/AZ5+B2Wxpy5oVunZVIltEROzi4MGDgGVk9tGjR3FxcbFuc3FxoVy5cgwePNhe4YmIiIhkSEoRJE6Cy4xUqVKFgwcPUqBAgeSIRyTNOHECDh2ybfvxRyWyM6Rz56BDB/j99+i2l16yTPKYP7/94hIRkQxty5YtALz55ptMnToVT09PO0ckIiIiIpI4CU5m9+rVi0GDBnHlyhUqVqxIlixZbLaXLVs2yYITSa169IBvvrFti4xUIjvDMQxYvBh694Y7dyxtjo6WEdrvv68bQkREUoUFCxbYOwQRERER+R8HjcxOlHgns7t168aUKVNo06YNAH379rVuM5lMGIaByWTSbOiSrt24ATlzxmyvVk15ywypVy+YPTt6vXBhWLoUKle2X0wiIiKx2LdvH9999x2XLl0iPDzcZtvq1avtFJWIiIiISMI4xLfjwoULefjwIefPn4+xnDt3zvr/hJo5cyYFCxbEzc2NihUrsmPHjnjtt2vXLpycnDRpjaSYjz+OPZH93XeWOf8kA6pZM/px165w8KAS2SIikuosX76cGjVqcOLECdasWUNERAQnTpzgt99+w8vLy97hiYiIiGQoDiZTsi/pWbxHZhuGAZCktbJXrFhB//79mTlzJjVq1GDOnDk0atSIEydOkP8pdWZDQ0Pp3LkzdevW5Z9//kmyeETiEhUFn34as/3IEShTJuXjkVSifXvYvduS1P7ft1ZERERSm7FjxzJ58mR69+6Nh4cHU6dOpWDBgrzzzjv4+fnZOzwRERERkXiL98hssJQTSUqTJk2ie/fuvPXWW5QsWZIpU6aQL18+Zs2a9dT93nnnHdq3b0+1atWSNB6RuFy6ZCmP/MjYsRARoUR2RuJ48SJ8+WXMDdOnK5EtIiKp2tmzZ2nSpAkArq6u3Lt3D5PJxIABA/j666/tHJ2IiIhIxmIyJf+SniUomV2sWDGyZ8/+1CW+wsPDCQwMpEGDBjbtDRo0YPfu3XHut2DBAs6ePcvIkSMTErpIohQqFP24YEEYNgycEjx9qqRZixeTo149HD74wFJXRkREJA3Jnj07d/43UXGePHk4duwYALdu3eL+/fv2DE1EREREJEESlI4bPXp0ktXVu379OlFRUfj4+Ni0+/j4EBwcHOs+p0+f5oMPPmDHjh04xTOTGBYWRlhYmHX99u3bAJjNZsxm8zNGL+mR2WzGMIwY98W//8Ljn/sMGmRGt04GERqKqXdvHJYtszYZX3yB8eqr6f+jTomXuP5uiOjekLik5D3RrVs3pk6dSs2aNdm4cSNlypShdevW9OvXj99++42NGzdSt27dFItHREREREj3Na2TW4KS2W3btsXb2ztJA3iydIlhGLGWM4mKiqJ9+/aMHj2aYsWKxfv448aNY/To0THar127FmMmd8nYzGYzoaGhGIaBg4MleX35sgOVK9ve86+9FkJIiD0ilJTk/McfePXpg9Ply9a2+6+/zp2xYzGuXbNjZJKaxPZ3QwR0b0jcQkNDU+xcCxcu5PPPP2f69Ok8fPgQgGHDhuHs7MzOnTt59dVXGTFiRIrFIyIiIiKSWPFOZid1veycOXPi6OgYYxR2SEhIjNHaAHfu3GH//v0cPHiQPn36ANGjnpycnNiwYQMvv/xyjP2GDRvGwIEDreu3b98mX7585MqVi6xZsybpNUnaZjabMZlMZM2ai337HJg1y8Ty5bb3/fjx5iT/QEdSmchITJ99Bp9+iul/o+cMLy9ujRuHR48e5FJSSh7z6O9Grly5lLAUG7o3JC4uLi4pdq5HE7g/XgrQwcGBoUOHMnTo0BSLQ0RERESiaWB24sQ7mW08PvtdEnBxcaFixYps3LiRVq1aWds3btxIixYtYvT39PTk6NGjNm0zZ87kt99+4/vvv6dgwYKxnsfV1RVXV9cY7Q4ODnpzKTbMZujfPysrV8b9a/Heew7otknHzp+Hjh3h8br9L76IsWgRYZky4aW/GxILk8mkf1MkVro3JDYpfT8k9YAUERERERF7incyOznq+w0cOJBOnTpRqVIlqlWrxtdff82lS5fo2bMnYBlVffXqVRYtWoSDgwOlS5e22d/b2xs3N7cY7SLPwtnZAcgU67bu3eGbb1I2HrGDIUOiE9mOjjBqlGW2T5MJ1ZYREZG0qFixYv+Z0P7XMkGIiIiIiKQADXVJnATVzE5qbdq04caNG3zyyScEBQVRunRp1q1bR4ECBQAICgri0qVL9gxR0rl79yAgAP5XucbGiy9C8+bw9tuQRPOeSmr31VewdSt4esLSpVC1qqVdE7iJiEgalZQTuIuIiIiI2JvJSOr6Ianc7du38fLy4ubNm6qZncH98gs0bhz7ttu3wcMjZeMRO7h/HzJntm3btw+KF7cktP/HbDYTEhKCt7e3ygWIDd0bEhfdGxKXW7dukS1bNkJDQ/F87N+a5ODg4EBwcHCamO9j7t6L9g5BRNKYDhXy2zsEEUljMjunjvJrC/dfTvZzdKmUL9nPYS96dyUZjtkMx4/Hnci+f9+sRHZ6FxlpKSFSqhQ8+dXqF16wSWSLiIikVaqXLSIiIiLpjZLZkqHcvWvJXz5ZZr1RI9i61czVq8HEMl+opCcXLkCtWjB6tOXxO+9AxvqCioiIZBAZ7AuYIiIiImmCKQWW9MyuNbNFUlrz5vDnn7Ztr7wC69ZZRmxrjr90bulSePddSx0ZsEzyWKaMJZmt0WsiIpLOJMcE7iIiIiIi9qRktmQY4eGwZYtt29dfQ48e9olHUtDt29C7NyxeHN1WsCAsWQLVqtkvLhERERERERHJUBw0mC5RlMyWDCMszHb9+nXIkcM+sUgK2r0bOnaE8+ej2zp2hBkzVBtbRERERERERCQNUc1syRCuXbPNW5Ypo0R2hjB+PLz0UnQi29PTMhr722+VyBYRERERERGRFJfaamZv376dZs2akTt3bkwmEz/88IPNdsMwGDVqFLlz5yZTpkzUrl2b48eP2/QJCwvjvffeI2fOnGTJkoXmzZtz5coVmz43b96kU6dOeHl54eXlRadOnbh161YCo1UyWzIIb2/b9YIF7ROHpLBs2SAqyvK4enU4dAjat7drSCIiIiIiIiIiqcW9e/coV64c06dPj3X7+PHjmTRpEtOnT2ffvn34+vpSv3597ty5Y+3Tv39/1qxZw/Lly9m5cyd3796ladOmRD3KyQDt27fn0KFDrF+/nvXr13Po0CE6deqU4HhVZkTSvQMHbNdNJvjyS/vEIinsrbdg40YoVQqGDwcn/ckTEREREREREftJbSWzGzVqRKNGjWLdZhgGU6ZMYfjw4bz66qsALFy4EB8fH5YuXco777xDaGgo8+bN49tvv6VevXoALF68mHz58rFp0yYaNmzIyZMnWb9+Pb///jtVqlQBYO7cuVSrVo1Tp05RvHjxeMerkdmSrkVGQsWKtm0REVCsmH3ikWR0+7alhMjjTCZYsQJGjlQiW0REREREREQkAc6fP09wcDANGjSwtrm6ulKrVi12794NQGBgIBERETZ9cufOTenSpa199uzZg5eXlzWRDVC1alW8vLysfeJL2R1Jty5fhvz5bdu++w4cHe0TjySjPXugQwdLbeysWaFJk+htqe0jTxERERERERHJsEwpkKcICwsjLCzMps3V1RVXV9cEHSc4OBgAHx8fm3YfHx8uXrxo7ePi4kK2bNli9Hm0f3BwMN5P1gAGvL29rX3iSyOzJV3asSNmIrt4cXj9dfvEI8kkKgrGjIGaNaMneezXzzIkX0REREREREQkAxo3bpx1osVHy7hx4575eE8m4A3D+M+k/JN9Yusfn+M8SSOzJd15+BBeesm2LXt2OHnSPvFIMrl4ETp2hJ07o9uqVYPFi1VSRERERERERERSpZQYWTxs2DAGDhxo05bQUdkAvr6+gGVktZ+fn7U9JCTEOlrb19eX8PBwbt68aTM6OyQkhOrVq1v7/PPPPzGOf+3atRijvv+LRmZLujNokO364MFw44aqTaQry5dDuXLRiWwHB0td7O3boVAh+8YmIiIiIiIiImJHrq6ueHp62izPkswuWLAgvr6+bNy40doWHh7Otm3brInqihUr4uzsbNMnKCiIY8eOWftUq1aN0NBQ/vjjD2ufvXv3Ehoaau0TXxq+KOlGWBj8/DPMnBndVqwYfPml/WKSJHbnDvTpA4sWRbcVKGCZ+LFGDfvFJSIiIiIiIiISDylRMzsh7t69y5kzZ6zr58+f59ChQ2TPnp38+fPTv39/xo4dS9GiRSlatChjx44lc+bMtG/fHgAvLy+6d+/OoEGDyJEjB9mzZ2fw4MGUKVOGevXqAVCyZEleeeUVevTowZw5cwB4++23adq0KcWLF09QvEpmS7rwyy/QuHHM9sc+8JH0oFcvSxmRR9q3t3x64eVlv5hERERERERERNKo/fv3U6dOHev6o/IkXbp0ISAggKFDh/LgwQN69erFzZs3qVKlChs2bMDDw8O6z+TJk3FycqJ169Y8ePCAunXrEhAQgKOjo7XPkiVL6Nu3Lw0aNACgefPmTJ8+PcHxmgzDMJ71YtOi27dv4+Xlxc2bN8maNau9w5EkcPQolC0bs71JE8tI7fgym82EhITg7e2Ng4Mq8KRKFy9ayouYzZYkdseOKXJa3RsSF90bEhfdGxKXW7dukS1bNkJDQ/H09LR3OKnG3L0X7R2CiKQxHSrkt3cIIpLGZHZOHSOivzv0d7Kf443ncyf7OexF764kzTp5Evr2jZnIrl7dMiI7IYlsSaWe/KytQAFYuRIOHUqxRLaIiIiIiIiIiKQOKjMiac6tW1C6NFy9GnPbl19aJnyUdGDFCssPdMsWeOyrK/zv6ygiIiIiIiIiImlNaquZndZoZLakKefOQbZssSeyq1WDQYNSPiZJYnfuQNeu0LYtBAZaJnwUEREREREREZEMT8lsSTP27IHChWO2v/8+3LwJu3eDPtxK4/buhfLlYeHC6LaICMsiIiIiIiIiIpLGOaTAkp6pzIikGdWr266//DJs3myfWCSJRUXB55/DyJGWx2ApLTJjhqU2tj6lEBERERERERHJ8JTMljThzTdt1wcMgEmT7BOLJLFLl6BTJ9i+PbqtalVYsgQKFbJfXCIiIiIiIiIiSUw1sxMnvY88l3SgbVsICLBtUyI7nfj+eyhXLjqR7eAAI0ZY1pXIFhERERERERGRx2hktqRqEybAihW2bSEh9olFksGVK3DrluVx/vyweDHUrGnXkEREREREREREkovGZSeOktmSap09C0OG2LYdPQq5ctknHkkGffvC+vWQLRvMmgVZs9o7IhERERERERERSaWUzJZU6cEDeOcd27aTJ6FECfvEI0kgKspSPqROneg2BwdYswbc3DTJo4iIiIiIiIike0p/JI5qZkuqM2cOZM4MmzdHt02ZokR2mnb5MtSta1l++812W6ZM+ksuIiIiIiIiIhmCA6ZkX9IzJbMlVYmMhJ49Y7Z37pzysUgS+e47KFsWtm0Dw4CuXSEszN5RiYiIiIiIiIhIGqMyI5KqPFky+bXXoF07S0llSWPu3rXUxF6wILrt0SSPrq72i0tERERERERExE705fTEUTJbUo2oKLh3L3q9fn34/nv7xSOJsG8ftG8PZ85Et7VpA7Nna5JHERERERERERF5JiozIqnCP/9AtWq2bT/9ZJ9YJBGiouDzz6F69ehEtrs7BATAsmVKZIuIiIiIiIhIhmZKgf/SM43MFrsym6FYMTh71rbd3x/c3OwSkiTGgAHw1VfR61WqwJIlULiw/WISERGRJDFw4MB49500aVIyRiIiIiIiGZWS2WJXjo6xt//6a8rGIUnkvfdg3jx48AA+/BBGjgRnZ3tHJSIiIkng4MGD8epnUiFIERERkTjppVLiKJktdmEY4BBLkZvRo2HgQEtlCkmDiha1TPjo6wsvvWTvaERERCQJbdmyxd4hiIiIiEgGp2S2pCjDgF27oGbNmNvMZn06labs3w+ffgpLl0LmzNHtrVvbLyYRERERERERkVTMIZ3XtE5uSmZLivr++9hzndeuKZGdZkRFwZdfwogREBkJgwfDzJn2jkpERERS0L179/j888/ZvHkzISEhmM1mm+3nzp2zU2QiIiIikp4pmS0paseOmG2RkXHXzpZU5soV6NQJtm6NbgsMtNTIzpTJbmGJiIhIynrrrbfYtm0bnTp1ws/PT3WyRUREROJJL5sSR8lsSVFz5kQ/HjIExo5VIjvNWLUKevSAmzct6yYTDBsGo0ZpkkcREZEM5pdffmHt2rXUqFHD3qGIiIiISAaiZLakqPDw6MfDhoGT7sDU79496N8fvvkmui1vXli8GGrVsltYIiIiYj/ZsmUje/bs9g5DREREJM3RyOzEcbB3AJJxXLliu54tm33ikAQIDIQKFWwT2W+8AUeOKJEtIiKSgY0ZM4aPP/6Y+/fv2zsUEREREclANC5WUoRhQIcO9o5CEmzDBvjrL8vjLFngq6+ga1d9jCgiIpIBlS9f3qY29pkzZ/Dx8cHf3x/nJ0qOHThwIKXDExEREUkTTCinkhhKZkuKGD0atm+PXu/Xz36xSAIMHQrr11smeFyyBIoWtXdEIiIiYictW7a0dwgiIiIiksEpmS3Jzmy2JLMf9/779olF/sOpU1C8ePS6o6Nl4kcvL03yKCIiksGNHDnS3iGIiIiIpHkOGpidKKqZLcnq8GFLPvRxf/4Jfn72iUficO8e9OgBpUrBnj2223LmVCJbRERERERERETsTiOzJVl17Gi7Xreu7cBfSQUCA6F9++ja2O3bw7FjlhrZIiIiIrGIiopi8uTJrFy5kkuXLhEeHm6z/d9//7VTZCIiIiKpm2pmJ45GZkuyiYy05EQfKVIEJkywXzzyBLMZxo+HatWiE9mZM8NHH1n+LyIiIhKH0aNHM2nSJFq3bk1oaCgDBw7k1VdfxcHBgVGjRtk7PBERERFJpzQyW5JN7ty263/+GbPkiNjJ1avQpQts3hzdVqmSZZLHYsXsF5eIiIikCUuWLGHu3Lk0adKE0aNH065dOwoXLkzZsmX5/fff6du3r71DFBEREUmVTBqYnSgamS3J5tq16McvvaREdqrxww9Qtmx0Ittkgg8+gF27lMgWERGReAkODqZMmTIAuLu7ExoaCkDTpk1Zu3atPUMTERERkXRMyWxJcg8fwu7dtm1bt9olFHnS559Dq1bwqI5lnjyWpPa4ceDiYt/YREREJM3ImzcvQUFBABQpUoQNGzYAsG/fPlxdXe0ZmoiIiEiqZkqB/9IzJbMlSf36K2TKBDVqRLe5u+srFKlG06bw6A3ma6/BkSNQp459YxIREZE0p1WrVmz+37e8+vXrx4gRIyhatCidO3emW7dudo5ORERERNIr1cyWJHHtGtSvD4cPx9w2aFDKxyNxKF0apk2z1Hzp1k2fMoiIiMgz+fzzz62PX3/9dfLly8euXbsoUqQIzZs3t2NkIiIiIqmbg1IxiaJktiRKRAR88w306hVzW/Xq0KgRvP9+ysclWCZ5/PxzmDAhejQ2wNtv2y8mERERSRe2b99O9erVcXKyvJ2oUqUKVapUITIyku3bt/PSSy/ZOUIRERERSY+UzJZEadAg9nrYP/8MTZqkeDjyyA8/QPfultrYbm7w5Zf2jkhERETSkTp16hAUFIS3t7dNe2hoKHXq1CEqKspOkYmIiIikbum9pnVyU81seWaffBIzkd26NYSHK5FtN/fuwTvv2E7yuHw53L5t37hEREQkXTEMA1Ms5cpu3LhBlixZ7BCRiIiIiGQEGpktCfL335Anj2VSx7t3bbft3g3VqtknLgEOHoR27eDUqei2Vq1g7lzw9LRfXCIiIpJuvPrqqwCYTCa6du2K62OlzKKiojhy5AjVq1e3V3iSCl3+8wj71n3HPxdOc+/Wv7ToN5KiFaNnizcMg91rvuXI1nWE3buLb+ES1Ovch5x5/a19bv3zN1uXf83Vv44TFRGBf9lK1O3Umyxe2ax91kz+mJCLZ7l/5xZumT0oUKo8L7V5C/dsOVLyckUkGQTu38eiBfM4ceI4169dY9LU6dSpWw+AiIgIZn41lZ07tnHlyhXc3d2pUrU6fQcMxNvbx3qMT0d/zN49e7h2LYRMmTNT7vny9BswmIKFCtnrsiQD0/RliaOR2ZIgefJY/v9kIvvoUSWy7cZsttTFrlIlOpGdOTN8/TWsWgU59AJeREREkoaXlxdeXl4YhoGHh4d13cvLC19fX95++20WL15s7zAlFYkIe4h3/kLU7dQn1u1/rF1J4PrV1O3Uhw6jvyKLVza+G/8B4Q/uAxAe9oDvvhyGCROtPxhPuxGTMUdGsGbyxxhms/U4+UqWo1mfj+j2xXya9x3BrZAgfvpqTIpco4gkrwcPHlCseAk++HBEjG0PHz7k5IkT9HinF8tWrmLilK+4dPEC/fvYTuxV8rlSjPp0LKt/WsvMOd9gGAa93u6uslgiaZBGZku8bdwYs61yZRg+HEqXTvl4BMtQ+S5dYNOm6LYKFWDpUihe3H5xiYiISLq0YMECAPz9/Rk8eLBKish/KlSuMoXKVY51m2EYHPh1DVWat6PYCy8C0OjtIcx6rw0n9/xGuZeb8vdfx7l97R86j5mJaybL/fZKj8FMf/c1Lp04RIHSFQCo9Mpr1uN65fShctM2/DB1FFGRkTg66W2vSFr2Ys2XeLFm7BMLe3h4MPub+TZt7w/7iI7t3iAo6G/8/HID8Nobbazbc+fJS+/3+tPmtRb8ffUq+fLnT77gRWKhgdmJo5HZ8lRmsyVP+sYblskeH2cYsHcvNG9un9gEmDcvOpFtMsHQobBnjxLZIiIikqyGDh1qUzP74sWLTJkyhQ0bNtgxKklrQq8Fcy/0X/xLV7S2OTm7kLd4Wa6ePgFAVGQEmMDRydnax9HZBZPJgSt/HYv1uA/u3ubk7t/IU+Q5JbJFMqA7d+9gMpnw8Ii93OaD+/f56YfV5MmbF18/3xSOTkQSS/+yS5zmz4fu3WPf9uTEj2InH3wAP/8MV67At9/Cyy/bOyIRERHJAFq0aMGrr75Kz549uXXrFpUrV8bFxYXr168zadIk3n33XXuHKGnAvVDLhOWP1762rGfl9vUQAPwKl8TZ1Y3tK+ZR8403MTDYvmIehmG27v/IthXfcHDjj0SGh+FXuCSvDlSZEZGMJiwsjGmTJ9KocVPc3d1ttq1cvpQpEyfw4MF9ChYsxKyv5+Ps7GKnSCUjc1DR7ETRyGyJ1YEDcSeyu3WDWrVSNh75n5s3bdednWHlSjhyRIlsERERSTEHDhygZs2aAHz//ff4+vpy8eJFFi1axLRp0566b1hYGLdv37ZZIsLDUiJsSa2eeE9vGFhnx8rsmZXmfT7i7KHfmfp2C756pxXh9+/h418Ek8n27ewLjd+g85hZvD50HA4ODqz7ejyGYaTQRYiIvUVERPDBkIEYhsGwESNjbG/UpBnLvl/NNwHfkq9AAd4f3J+wMP37I5LWKJktMbRoARUr2raVKwfLl8O9e5bKFpLCzGaYOBHy54eDB223FSigSR5FREQkRd2/fx8PDw8ANmzYwKuvvoqDgwNVq1bl4sWLT9133LhxNhNHenl58cvCmSkRtqQyWbyyA3Dvlu2Ajfu3b5HZM6t13b9MJXpMWEiv6SvpPeN7Gvd8nzs3b+CVy7Y8QGYPL7L75cW/dEWa9v6Q84f/IOjMyWS/DhGxv4iICN4fNICrV64wa+68GKOywVJfu0ABfypWeoEJk6dy/vx5ftscy+RgIsnMlAJLeqZkttg4cQJ++sm2bepUOHQI2rSBzJntElbGFhQEr7wCgwfD3bvQvj3cv2/vqERERCQDK1KkCD/88AOXL1/m119/pcH/JlcJCQnB0zP2GqWPDBs2jNDQUJulUZdeKRG2pDJeuXzJ4pWdi8cPWNuiIiO4cuoIeYo+F6N/Zg8v3LK4c+nEQe7fvkWRCtXiPPajEdmRkRFJH7iIpCqPEtmXLl1k9jcLyJo123/vBGAYRISHJ29wIpLkVDNbbLzxhu36xo1Qr559YhEsnyx06wY3bkS3NW0Kjo72i0lEREQyvI8//pj27dszYMAAXn75ZapVsyQVN2zYQPny5Z+6r6urK66urjZtzi434+gtaV34wwfc+udv63rotWBCLp7FLYsHnjm9qdCwFXv/bxnZfHKT1TcPe39ajpOLKyWrRZfQO7r9V3Lkzk9mDy/+PnOC3xbPomLDV8nulw+AoLN/EnTuFHmLlcY1izuhIUHsWr2IrN65yV2kZIpfs4gkrfv373H50iXr+tWrVzj150k8vbzIlcubIQP78eeJE0ydMRuzOYrr168B4OXlhbOzC1cuX+bX9euoVr0G2bJnJ+SffwiY/w2urq68WFM1VMUO0vvQ6WSmZLZYGYZlZPYjM2cqkW039+9bRmLPmhXd5ucHixbphyIiIiJ29/rrr/Piiy8SFBREuXLlrO1169alVatWdoxMUpvg83+xctwQ6/rWpXMAKPVifRq9PYTKTVoTGR7GpoXTeXj/Dn6FSvD60HG4ZIr+SujNoCvs+G4+D+/ewSunD1Wbt6PiK69Ztzu5uHJ6/052r15ERPhDsnhlp2DZF2ja60OcNLmbSJp34tgxenTrYl2fOP5zAJq1aEnPXn3YtuU3ANq+3tJmv7nzF1KpchVcXF04eCCQpd8u4vbt2+TIkYMKlSoRsHgZ2VWyUyTNMRkZbEaM27dv4+Xlxc2bN8maNau9w0k1DMMy4eOCBdFtt26Bl5fdQkpxZrOZkJAQvL29cXCwYwWew4ehXTs4+Vh9vxYt4JtvIGdO+8WVgaWae0NSHd0bEhfdGxKXW7dukS1bNkJDQ/+zHEdacfnyZUwmE3nz5n3mY8zd+/Q62yIiT+pQIb+9QxCRNCazc+oYEr33bGiyn6NK4fSb0NO7KyE0FBwcbBPZ+fNnrER2qrFoEVSuHJ3IzpQJZs+GNWuUyBYREZFUIzIykhEjRuDl5YW/vz8FChTAy8uLjz76iIgI1SgWERERkeShMiMZ3D//gK9vzPZ9+1I+FgFKlgSz2fL4+edh6VJLm4iIiEgq0qdPH9asWcP48eOt9bL37NnDqFGjuH79OrNnz7ZzhCIiIiKpkyl1DBBPs5TMzqBu3YKVK+Gdd2JuO3oUvL1TPCQBeOEF+OwzCAmx/P+JyZFEREREUoNly5axfPlyGjVqZG0rW7Ys+fPnp23btkpmi4iIiEiyUDI7g2rdGjZutG0rVw4OHLCUHJEUcP++ZZbN/v3B6bFfxaFD7RaSiIiISHy4ubnh7+8fo93f3x8XF024JyIiIhIXDcxOHKUtM5gJEyxfZ3gykQ0QGKhEdoo5fBgqVYIhQ2DMGHtHIyIiIpIgvXv3ZsyYMYSFhVnbwsLC+Oyzz+jTp48dIxMRERGR9EwjszOQTz+FESNitv/wA9SpA46OKR5SxmM2w9Sp8MEHEB5uaZs0Cd57TxM8ioiISKr26quv2qxv2rSJvHnzUq5cOQAOHz5MeHg4devWtUd4IiIiImmDhmYnipLZGcS+fbEnsvfvh4oVUz6eDCk4GLp2hV9/jW57NMmjEtkiIiKSynl5edmsv/baazbr+fLlS8lwRERERCQDUjI7AwgLg8qVbdv++MMy16CkkJ9/hm7d4Nq16LZBgzTJo4iIiKQZCxYssHcIIiIiImmeSUOzE0UVktO5wYPBzc22bfFiJbJTzIMH0KcPNGsWncj284MNGywFzJXIFhERkTQqMjKSTZs2MWfOHO7cuQPA33//zd27d+0cmYiIiIikVxqZnY4dOwYTJ8Zsb98+5WPJsKZOhRkzotebN4d581RWRERERNK0ixcv8sorr3Dp0iXCwsKoX78+Hh4ejB8/nocPHzJ79mx7hygiIiKSKpk0MDtRNDI7ndqyBcqUsW2rWxcMQ780Kap/fyhbFjJlglmzLLNtKpEtIiIiaVy/fv2oVKkSN2/eJFOmTNb2Vq1asXnzZjtGJiIiIiLpmUZmp1Mvv2y7vno1tGpln1gylIgIcHaOXndzgxUrLJ8ilCxpv7hEREREktDOnTvZtWsXLi4uNu0FChTg6tWrdopKREREJPXTGNPE0cjsdCQiAnr2jDnyevx4aNnSLiFlLGvXQtGicOKEbXuJEkpki4iISLpiNpuJioqK0X7lyhU8PDzsEJGIiIiIZARKZqcjfn4wZ07M9iFDVFokWT14AO+9B02bwsWLlqLkYWH2jkpEREQk2dSvX58pU6ZY100mE3fv3mXkyJE0btzYfoGJiIiIpHamFFjSMZUZSSf+/Rdu3LBtc3GxTAIpyejoUUvy+vEnOl8+uH8fXF3tF5eIiIhIMpo8eTJ16tThueee4+HDh7Rv357Tp0+TM2dOli1bZu/wRERERCSdUjI7Hbh7F3LksG27dw8yZ7ZPPBmCYcBXX8HQodGjsN3cYOJEePddDYUXERGRdC137twcOnSIZcuWceDAAcxmM927d6dDhw42E0KKiIiIiC1Teh86ncyUzE4Hsma1XR86VInsZPXPP/Dmm/DLL9FtZcvC0qVQqpT94hIRERFJQZkyZaJbt25069bN3qGIiIiISAahZHYad+4cPD73jrs7fPGF/eJJ9zZtgg4dICQkum3AABg3TmVFREREJF376aefaNSoEc7Ozvz0009P7du8efMUikpEREQkbdGX+RNHyew07NAhKF/etu32bbuEknG4ucH165bHPj6wcCE0bGjfmERERERSQMuWLQkODsbb25uWLVvG2c9kMhH1+GgLEREREZEkomR2Glavnu36vHn6dCfZvfgiDB8OBw/C/PmQK5e9IxIRERFJEWazOdbHIiIiIhJ/St0ljpLZadSxY3DjRvR6376gcoVJzDDg++/htdfAwSG6feRIy7o+ORAREZEMyGw2ExAQwOrVq7lw4QImk4lChQrx2muv0alTJ0x6jSQiIiIiycThv7skr5kzZ1KwYEHc3NyoWLEiO3bsiLPv6tWrqV+/Prly5cLT05Nq1arx66+/pmC0qUeZMrbrU6faJ450659/oEkTaN0aJkyw3eboqES2iIiIZEiGYdC8eXPeeustrl69SpkyZShVqhQXLlyga9eutGrVyt4hioiIiKRuphRY0jG7JrNXrFhB//79GT58OAcPHqRmzZo0atSIS5cuxdp/+/bt1K9fn3Xr1hEYGEidOnVo1qwZBw8eTOHI7WvlStv1GTPsE0e6tW4dlC0Lv/xiWR8xAq5etW9MIiIiIqlAQEAA27dvZ/PmzRw8eJBly5axfPlyDh8+zKZNm/jtt99YtGiRvcMUERERkXTKZBiGYa+TV6lShQoVKjBr1ixrW8mSJWnZsiXjxo2L1zFKlSpFmzZt+Pjjj+PV//bt23h5eXHz5k2yZs36LGHb1f37kCWLbZv9foLpi/n+fR707UuWefOiG318ICAAXnnFbnGJ/ZnNZkJCQvD29sbBwe5faJFURPeGxEX3hsTl1q1bZMuWjdDQUDw9Pe0dToI1aNCAl19+mQ8++CDW7WPHjmXbtm0J/vbk3L0XkyI8EclAOlTIb+8QRCSNyeycOoYsH7l8N9nPUTafe7Kfw17sVjM7PDycwMDAGC+EGzRowO7du+N1DLPZzJ07d8iePXucfcLCwggLC7Ou375927pvWpy4ZsAA2+8LHDliJg1eRupz7BimDh3IcuyYtclo3Bhj3jzw9kZPcsZmNpsxDCNN/s2Q5KV7Q+Kie0PiktbviSNHjjB+/Pg4tzdq1Ihp06alYEQiIiIikpHYLZl9/fp1oqKi8PHxsWn38fEhODg4XseYOHEi9+7do3Xr1nH2GTduHKNHj47Rfu3aNcLDwxMWtJ1dvuzA1197W9ezZjWTK1cIISF2DCqtMwwyL1iAxyefYPrfhx6Gmxt3Pv6Y+127WvroCc7wzGYzoaGhGIahEZZiQ/eGxEX3hsQlNDTU3iEkyr///hvj9fvjfHx8uHnzZgpGJCIiIpK2aBq2xLFbMvuRJ2c7NwwjXjOgL1u2jFGjRvHjjz/i7e0dZ79hw4YxcOBA6/rt27fJly8fuXLlSnNlRvz8bN8M//MPODnFfe0SDzNm4DB8uHU1omRJTMuW4V6mDOn3CxmSUGazGZPJRK5cuZSUEhu6NyQuujckLi4uLvYOIVGioqJwcor7LYSjoyORkZEpGJGIiIiIZCR2S2bnzJkTR0fHGKOwQ0JCnjraAywTR3bv3p3vvvuOevXqPbWvq6srrq6uMdodHBzS1JvLIUNs1/v3BxeXtBN/qtWtm2UGzVOnMPr25caAAXjnz5+m7g1JGSaTKc393ZCUoXtD4qJ7Q2KT1u8HwzDo2rVrrK+vAZvyfiIiIiISkwZmJ47dktkuLi5UrFiRjRs30qpVK2v7xo0badGiRZz7LVu2jG7durFs2TKaNGmSEqGmCseP265PnmyfONI8w7D9PkeWLLB0KfzzD0bDhiopIiIiIvIUXbp0+c8+nTt3ToFIRERERCQjsmuZkYEDB9KpUycqVapEtWrV+Prrr7l06RI9e/YELCVCrl69yqJFiwBLIrtz585MnTqVqlWrWkd1Z8qUCS8vL7tdR0r45Zfox3//bb840rRjx6BnT1i4EAoXjm6vUMHy/zQ+IZOIiIhIcluwYIG9QxARERFJ2zQ0O1Hs+j3HNm3aMGXKFD755BOef/55tm/fzrp16yhQoAAAQUFBXLp0ydp/zpw5REZG0rt3b/z8/KxLv3797HUJKeLJHKuHh33iSLMMA6ZPhxdegF27oEMHiIiwd1QiIiIiIiIiIiKSAHafALJXr1706tUr1m0BAQE261u3bk3+gFKhpk1t1901M2H8hYRY6mKvXRvddu+epT1PHvvFJSIiIiIiIiIiGY5JQ7MTJW3PQJMBGIZtiZFKlewXS5qzfj2ULWubyH7vPfjjDyWyRURERERERERE0hi7j8yWpzt40HY9gw5OT5iHD+GDD2Dq1Og2b29YsAAaN7ZfXCIiIiIiIiIikqGZNDA7UZTMToXu3YN334Vz5ywlnh/x94csWewWVtpw/Di0bw9HjkS3NWpkSWT7+NgvLhEREREREREREUkUJbNTmYsXLUnr2AwZkqKhpE1//x2dyHZ1hfHjLaVF9LGXiIiIiIiIiIjYmTJUiaNkdiqzcGHs7c2aQc+eKRtLmlS/PgwcCL/+CsuWQZky9o5IREREREREREREkoAmgExlLl+OflysGFy7BhER8NNP4KCfVkz79llmyXzc2LGWdiWyRUREREREREQkNTGlwJKOKT2aynzzTfTjWbMgZ05w0vj5mMLCYMAAqFwZZs603ebqCpky2ScuERERERERERGRNGLUqFGYTCabxdfX17rdMAxGjRpF7ty5yZQpE7Vr1+b48eM2xwgLC+O9994jZ86cZMmShebNm3PlypVkiVfJ7FTk1Cnb9YoV7RNHqnfiBFSpAlOmWNYHD7bMlikiIiIiIiIiIpKKmVLgv4QqVaoUQUFB1uXo0aPWbePHj2fSpElMnz6dffv24evrS/369blz5461T//+/VmzZg3Lly9n586d3L17l6ZNmxIVFZUkz9njlMxORUqUsF339LRPHKmWYViGq1esCIcPW9pcXODzz+OeNVNERERERERERETi5OTkhK+vr3XJlSsXYBmVPWXKFIYPH86rr75K6dKlWbhwIffv32fp0qUAhIaGMm/ePCZOnEi9evUoX748ixcv5ujRo2zatCnJY1UyO5U4c8Z2fdkyMKXzGjcJcu0atGgBvXrBw4eWtuees9TG7tdPBcVFRERERERERCTVM5mSfwkLC+P27ds2S1hYWJwxnT59mty5c1OwYEHatm3Luf9VQDh//jzBwcE0aNDA2tfV1ZVatWqxe/duAAIDA4mIiLDpkzt3bkqXLm3tk5SUAUwFDANWrLBta9vWPrGkShs2QNmy8H//F93Wuzfs329pFxEREREREREREQDGjRuHl5eXzTJu3LhY+1apUoVFixbx66+/MnfuXIKDg6levTo3btwgODgYAB8fH5t9fHx8rNuCg4NxcXEhW7ZscfZJSppaMBUoVsx2ZHb37vaLJdVZtgzat49ez5kTFiyApk3tF5OIiIiIiIiIiMgzSIlCDMOGDWPgwIE2ba6urrH2bdSokfVxmTJlqFatGoULF2bhwoVUrVoVANMT5SMMw4jR9qT49HkWGpltZ3Pnxiwx8vrr9oklVWrcOLoedsOGcPSoEtkiIiIiIiIiIiJxcHV1xdPT02aJK5n9pCxZslCmTBlOnz6Nr68vQIwR1iEhIdbR2r6+voSHh3Pz5s04+yQlJbPtbOZM2/WTJ+GVV+wTS6rk5QWLF8PkybBuHfzvl0hERERERERERCTNMaXAkghhYWGcPHkSPz8/ChYsiK+vLxs3brRuDw8PZ9u2bVSvXh2AihUr4uzsbNMnKCiIY8eOWfskJZUZsaNz5+DQoej1Q4egRAl7RZMKXL8OAwfCZ59BvnzR7TVqWBYRERERERERERFJMoMHD6ZZs2bkz5+fkJAQPv30U27fvk2XLl0wmUz079+fsWPHUrRoUYoWLcrYsWPJnDkz7f9XFtjLy4vu3bszaNAgcuTIQfbs2Rk8eDBlypShXr16SR6vktl2NGuW7XqGnstw40bo0gWCguDyZdi0CRwd7R2ViIiIiIiIiIhIkjGlSNXs+Lty5Qrt2rXj+vXr5MqVi6pVq/L7779ToEABAIYOHcqDBw/o1asXN2/epEqVKmzYsAEPDw/rMSZPnoyTkxOtW7fmwYMH1K1bl4CAAByTIbdnMgzDSPKjpmK3b9/Gy8uLmzdvkjVrVrvG8ngN9IEDYeJE+8ViN2FhMHy47cXnzAm7dllmxkxBZrOZkJAQvL29cXBQBR6JpntD4qJ7Q+Kie0PicuvWLbJly0ZoaCienp72DifVmLv3or1DEJE0pkOF/PYOQUTSmMzOqSOJfPqfB8l+jqI+mZL9HPaikdl2Mm6c7XqfPvaJw67+/BPatbOttdKwIQQEqDa2iIiIiIiIiIikO6bUkVNPszRUyA4OHoQPP7RtK1jQPrHYhWHAnDlQoUJ0ItvFRZM8ioiIiIiIiIiISJw0MtsOata0XT9zxj5x2MX16/DWW/Djj9FtJUvC0qXw/PN2C0tERERERERERCS5aWB24mhkth0UKhT9eMkSKFzYfrGkuD17bBPZ774L+/crkS0iIiIiIiIiIumfKQWWdEzJbDtr397eEaSwZs3gnXcgRw5LUnvmTMic2d5RiYiIiIiIiIiISCqnZHYKu3kTjh61PHbICM/+lSuWGtmPmzQJjhyB5s3tE5OIiIiIiIiIiIgdmFLgv/QsI6RTU5Xs2aMfm832iyPZGQZ8/TUUKwYLF9puy5wZcue2T1wiIiIiIiIiIiKSJimZnYJOnbJdf+cd+8SR7G7cgNdes1zggwfQpw+cPWvvqEREREREREREROzKZEr+JT1zsncAGcW9e1CihG3brFn2iSVZbd4MnTvD339Ht3XuDH5+9otJRERERERERERE0jwls1OIu7vt+sSJ6eyTkvBw+OgjmDAhukZ2jhwwf75qY4uIiIiIiIiIiEA6r2id/JTMtpMePewdQRI6dQrat4cDB6Lb6te31MrWiGwRERERERERERFJAkpmp4AHD2zXHw1cThc2bIBWreD+fcu6szN8/jn07w8OKskuIiIiIiIiIiJipaHZiaJkdgrIl8/eESSjChXAy8uSzC5RApYuhfLl7R2ViIiIiIiIiIiIpDNKZiezAQPgxo3o9d697RdLssiZE779Fr77DiZNgsyZ7R2RiIiIiIiIiIhIqmTS0OxEUR2IZBQSAlOm2LZ99pldQkka4eEwejT8849te926MHu2EtkiIiIiIiIiIiKSbDQyO5lERYGPj23b9euWihxp0l9/WSZ5DAyEP/6An38Gkz5JEhERERERERERiS+l0xJHI7OTSb9+tuvdukGOHPaJJVEMA775xlIHOzDQ0rZxIxw6ZNewREREREREREREJGNRMjsZbNsGM2ZEr5coAV9/bb94ntm//8Ibb0CPHpYJHgGKF4fff9ckjyIiIiIiIiIiIglkSoElPVMyO4mdOwe1a9u27dwJjo52CefZbdkCZcvCqlXRbW+/bRmdXaGC/eISERERERERERGRDEk1s5PYtGm266+9lsbKi4SHw8cfw/jxlhIjANmzW0qNtGpl39hEROSpzGYz4eHh9g5DsPwsIiIiePjwIQ4OGjuQkTg7O+OY5kYxiIiIiEhKUc3sxFEyO4llyRL9+L33Yia3U70NG+CLL6LXX34ZFi2CPHnsF5OIiPyn8PBwzp8/j9lstncoAhiGgdls5s6dO5j0ajXDyZo1K76+vvrZi4iIiIgkMSWzk9BXX8HYsdHrzZvbL5Zn1rQpdOwIK1bAZ5/BoEGgEWUiIqmaYRgEBQXh6OhIvnz5NBI4FTAMg8jISJycnJTQzEAMw+D+/fuEhIQA4OfnZ+eIRERERCT10fuDxFAyO4ns3Qt9+9q2eXvbJ5YEuXfPdjg5WGavHDBAtbFFRNKIyMhI7t+/T+7cucmcObO9wxGUzM7IMmXKBEBISAje3t4qOSIiIiIikoQ0dCsJhIdD1aq2bR99ZJk/MVXbuhVKlICVK23bPT2VyBYRSUOioqIAcHFxsXMkIgJYP1SKiIiwcyQiIiIiktqYTMm/pGdKZieSYYCrq23bli0wZox94omXiAgYNsxSD/vKFXjnHbh0yd5RiYhIImkEsEjqoN9FEREREZHkoTIjiTRqVMy22rVTOooEOH0a2reH/fuj2ypUACfdCiIiIiIiIiIiIslJwx4SRyOzE+HaNfjkE9s2w7BPLP/JMGD+fChfPjqR7eQEX3wBGzdC7tz2jU9EREQS5LfffqNEiRKYzWZ7h5KhDB48mL5PTpQiIiIiIiIpQsnsRGjQwHY9ONg+cfynmzehTRvo3t0y4SNAsWLw++8wdCg46DYQEZGU17VrV0wmEyaTCScnJ/Lnz8+7777LzZs3Y/TdvXs3jRs3Jlu2bLi5uVGmTBkmTpxorRf+uC1bttC4cWNy5MhB5syZee655xg0aBBXr16NMxZ/f3+mTJliXTcMg0GDBuHh4cFvv/2WJNeb1IYOHcrw4cNxeOLf8QcPHpAtWzayZ8/OgwcPYuxnMpn44YcfYrT379+f2k98vSw4OJj33nuPQoUK4erqSr58+WjWrBmbN29OykuJYdu2bVSsWBE3NzcKFSrE7Nmz47VfQEAAZcuWxc3NDV9fX/r06WPddurUKerUqYOPj4/1uB999JFNXeutW7da78nHlz///NPaZ+jQoSxYsIDz588n3QWLiIiISIahmtmJoyzmMzIMOHQoen34cPDxsVs4cTtwAMqVg+++i2576y1Le8WK9otLREQEeOWVVwgKCuLChQt88803/N///R+9evWy6bNmzRpq1apF3rx52bJlC3/++Sf9+vXjs88+o23bthiPfS1qzpw51KtXD19fX1atWsWJEyeYPXs2oaGhTJw4MV4xRUVF0b17dxYtWsRvv/3Gyy+//EzXFh4e/kz7xcfu3bs5ffo0b7zxRoxtq1atonTp0jz33HOsXr36mc9x4cIFKlasyG+//cb48eM5evQo69evp06dOvTu3Tsx4T/V+fPnady4MTVr1uTgwYN8+OGH9O3bl1WrVj11v0mTJjF8+HA++OADjh8/zubNm2nYsKF1u7OzM507d2bDhg2cOnWKKVOmMHfuXEaOHBnjWKdOnSIoKMi6FC1a1LrN29ubBg0axDvBLiIiIiIiSUeFkp9RSIjt+qef2ieO/+TnB49GZWXLBnPnwmuv2TcmERGR/3F1dcXX1xeAvHnz0qZNGwICAqzb7927R48ePWjevDlff/21tf2tt97Cx8eH5s2bs3LlStq0acOVK1fo27cvffv2ZfLkyda+/v7+vPTSS9y6des/4wkLC6Ndu3bs27eP7du3U7JkSQBq167N888/bzN6u2XLlmTNmtUar7+/P2+99RZnzpxhzZo1NG/enDNnzlCrVi0+//xz637Xrl0jd+7cbNiwgTp16hAeHs5HH33EkiVLuHXrFqVLl+aLL76IMUr6ccuXL6dBgwa4ubnF2DZv3jw6duyIYRjMmzePDh06/Od1x6ZXr16YTCb++OMPsmTJYm0vVaoU3bp1e6Zjxsfs2bPJnz+/9bkuWbIk+/fvZ8KECbwWx2uYmzdv8tFHH/F///d/1K1b1ybWRwoVKkShQoWs6wUKFGDr1q3s2LEjxvG8vb3JmjVrnDE2b96cESNG8MUXXyTw6kREREQkozOpanaiaGT2MwoMjH5cpoz94vhPfn6WWtl16sCRI0pki4hkEJUqQd68Kb9UqvTsMZ87d47169fj7OxsbduwYQM3btxg8ODBMfo3a9aMYsWKsWzZMgC+++47wsPDGTp0aKzHf1pyEuDu3bs0adKE48ePs2vXLmsiOyG+/PJLSpcuzf79+/nwww9p3749y5Ytsxk9vmLFCnx8fKhVqxYAb775Jrt27WL58uUcOXKEN954g1deeYXTp0/HeZ7t27dTKZYn++zZs+zZs4fWrVvTunVrdu/ezblz5xJ8Hf/++y/r16+nd+/eNonsR572XC5ZsgR3d/enLkuWLIlz/z179tDgiVpuDRs2ZP/+/TYlQR63ceNGzGYzV69epWTJkuTNm5fWrVtz+fLlOM9z5swZ1q9fb/05PK58+fL4+flRt25dtmzZEmN75cqVuXz5MhcvXozz+CIiIiIikvQ0MvsZbdwY/ThXLvvFYcMwYOlSaNQIsmePbm/WDJo2Tf9Fc0RExCo4GJ5SIjrV+Pnnn3F3dycqKoqHDx8ClnIRj/z1118AcSaWS5QoYe1z+vRpPD098fPze6ZYxowZg4eHBydOnMDb2/uZjvHyyy8zePBgDMMgMjKSHDlyMHDgQHbu3EnNmjUBWLp0Ke3bt8fBwYGzZ8+ybNkyrly5Qu7/TcY8ePBg1q9fz4IFCxg7dmys57lw4YK1/+Pmz59Po0aNyJYtG2Ap4zJ//nw+TeBXyM6cOYNhGJQoUSJB+4Fl1HKVKlWe2sfnKbXZgoODY2z38fEhMjKS69evx/rzPXfuHGazmbFjxzJ16lS8vLz46KOPqF+/PkeOHMHFxcXat3r16hw4cICwsDDefvttPnlsNm8/Pz++/vprKlasSFhYGN9++y1169Zl69atvPTSS9Z+efLkASw/hwIFCjz9CREREREReZzSc4miZPYzeuxbxtSvb7cwot28Ce+8Y6mN/dprlv8/nrxWIltEJEP5X+WOVH/eOnXqMGvWLO7fv88333zDX3/9xXvvvRej3+Mjm59sN/3v37jHHz+LBg0asGnTJsaOHWtTTiQhnhwtnStXLurXr8+SJUuoWbMm58+fZ8+ePcyaNQuAAwcOYBgGxYoVs9kvLCyMHDlyxHmeBw8exCgxEhUVxcKFC5k6daq1rWPHjgwYMIDRo0fj6OgY7+t49Hw/y/Pp4eGBh4dHgvd73JPn/a94zGYzERERTJs2zTqqe9myZfj6+rJlyxab2tkrVqzgzp07HD58mCFDhjBhwgTraP7ixYtTvHhxa99q1apx+fJlJkyYYJPMzpQpEwD3799P1HWKiIiIiEjCKJn9DP74w3a9aVP7xGG1bRt06gSPvkq7ahXs2AGPvekSEZGMZf9+e0cQP1myZKFIkSIATJs2jTp16jB69GjGjBkDYE3ynjx5kurVq8fY/88//+S5556z9g0NDSUoKOiZRmfXrVuXvn370qJFC6Kiovjqq6+s2xwcHGIk1GMreRFbSY4OHTrQr18/vvrqK5YuXUqpUqUoV64cYEnCOjo6EhgYGCPZ7O7uHmesOXPm5ObNmzZtv/76K1evXqVNmzY27VFRUWzYsIFGjRoBlmRzaGhojGPeunULLy8vAIoWLYrJZOLkyZO0bNkyzjhis2TJEt55552n9pkzZ06ctbx9fX0JDg62aQsJCcHJySnOBP+jn/ejewEsHyTkzJmTS5cu2fTNly+ftW9UVBRvv/02gwYNijPZX7VqVRYvXmzT9u+//1rPISIiIiKSEBpumjiqmZ1A16/Dk9+cLV3aPrEQEQHDh1vqYT9KZGfNahmVrUS2iIikQSNHjmTChAn8/fffgGW0dPbs2Zk4cWKMvj/99BOnT5+mXbt2ALz++uu4uLgwfvz4WI8dnwkg69evz88//8z8+fPp3bu3NYGdK1cugoKCrP2ioqI4duxYvK6pZcuWPHz4kPXr17N06VI6duxo3Va+fHmioqIICQmhSJEiNovvU4a5ly9fnhMnTti0zZs3j7Zt23Lo0CGbpUOHDsybN8/ar0SJEuzbt89mX8MwCAwMtI5Kzp49Ow0bNmTGjBncu3cvxvmf9lw2b948RgxPLs2bN49z/2rVqrHx8XpuWGqnV6pUyaae+uNq1KgBwKlTp6xt//77L9evX39qGRDDMIiIiIhz5D/AwYMHY3w4cuzYMZydnW0mmBQRERERkeSnkdkJdOiQ7fr8+XYJA86cgQ4dbIeJ164NixbB/0YciYiIpDW1a9emVKlSjB07lunTp5MlSxbmzJlD27Ztefvtt+nTpw+enp5s3ryZIUOG8Prrr9O6dWvAMuJ28uTJ9OnTh9u3b9O5c2f8/f25cuUKixYtwt3dPdak+JNefvll1q5dS9OmTTEMgxkzZvDyyy8zcOBA1q5dS+HChZk8eXK8kuNgGa3dokULRowYwcmTJ2nfvr11W7FixejQoQOdO3dm4sSJlC9fnuvXr/Pbb79RpkwZGjduHOsxGzZsyMKFC63r165d4//+7//46aefKP3Ep+xdunShSZMmXLt2jVy5cjF48GC6dOlCiRIlaNCgAQ8ePODrr7/m7Nmz9O7d27rfzJkzqV69OpUrV+aTTz6hbNmyREZGsnHjRmbNmsXJkydjjS2xZUZ69uzJ9OnTGThwID169GDPnj3MmzfPOtEnwJo1axg2bBh//vmn9Xls0aIF/fr14+uvv8bT05Nhw4ZRokQJ6tSpA1hGjDs7O1OmTBlcXV0JDAxk2LBhtGnTBicny0viKVOm4O/vT6lSpQgPD2fx4sWsWrWKVatW2cS4Y8cOatasaS03IiIiIiISX6oEnDgamZ1Aj72PonlzePPNFA7AMCAgAMqXj05kOznBuHGwaZMS2SIikuYNHDiQuXPncvl/3zp6/fXX2bJlC5cvX+all16iePHiTJo0ieHDh7N8+XKbOsq9evViw4YNXL16lVatWlGiRAneeustPD09GTx4cLxjqF27NuvWrePbb7/l3XffpVu3bnTp0oXOnTtTq1YtChYsaE2SxkeHDh04fPgwNWvWJH/+/DbbFixYQOfOnRk0aBDFixenefPm7N2711oOIzYdO3bkxIkT1pHIixYtIkuWLNStWzdG3zp16uDh4cG3334LQOvWrQkICGDhwoW88MILNGjQgLNnz7Jjxw6bUcwFCxbkwIED1KlTh0GDBlG6dGnq16/P5s2brTW/k0PBggVZt24dW7du5fnnn2fMmDFMmzaN1157zdonNDTUZhQ2WJ6DKlWq0KRJE2rVqoWzszPr16+3juZ2cnLiiy++oHLlypQtW5ZRo0bRu3dvvvnmG+sxwsPDGTx4MGXLlqVmzZrs3LmTtWvX8uqrr9qca9myZfTo0SPZngMREREREYmdyXja9yrTodu3b+Pl5cXNmzfJmjVrgvY9cQIe/zbpkCEQxzeZk8+mTbYzThYpAkuXwgsvpHAg6Y/ZbCYkJARvb28cHPQ5j0TTvSFxSS33xsOHDzl//jwFCxaMMSmg2IdhGERGRuLk5JSoSSmfZujQoYSGhjJnzpxkOb7Ebu3atQwZMoQjR45YR3Q/6Wm/k7du3SJbtmyEhobi6emZEiGnCXP3XrR3CCKSxnSokP+/O4mIPCazc+oYEn3tTmSynyOXR/otxqGsTDxFRtomsgE++MAOgdStC49GB3XrBgcPKpEtIiKSAQ0fPpwCBQoQFRVl71AylHv37rFgwYI4E9kiIiIiIpJ89Co8niZMsF3v3h2yZ0+BE5vN8PhoP5MJ5s611Mt+4iuvIiIiknF4eXnx4Ycf2juMDOdRjXYRERERkWeSOgaIp1kamR1PP/xgu/5YecXkc/YsVK8OP/9s2549uxLZIiIiIiIiIiIikqEomR1Pe/dGPz53LplPZhiwcCE8/7zlxG++CcHByXxSERERERERERERSU6mFFjSMyWz4+HKFdv1bNmS8WS3bkG7dtC1K9y9a2nz8oLr15PxpCIiIiIiIiIiIiKpm2pmx0PjxrbrWbMm04l27ICOHeHSpei2rl1h2jTw8Eimk4qIiIiIiIiIiEhKMKX3odPJTCOz4+Ho0ejHyTLPUkQEjBgBtWtHJ7K9vGDFCliwQIlsERERERERERERyfA0Mvs/dOliu57kyewLF6BtW9ui3DVrwuLFkD9/Ep9MRERERERERERE7MWU7qtaJy+NzP4P338f/bh8eciSJYlP4OAAf/5peezoCJ9+Clu2KJEtIiIiIiIiIiIi8hgls58iIgLu349e//HHZDhJ/vwwZw4ULgy7dsHw4ZaktoiIiMTb119/Tb58+XBwcGDKlCnx2sff39+mr8lk4ocffnjqPjdu3MDb25sLFy48c6wZSUhICLly5eLq1av2DkVEREREJFUwmZJ/Sc+UzH6K8eOjHzs7Q758SXDQPXvg9m3btjZt4NgxqFIlCU4gIiKS+m3duhWTyRTnUqdOnXgf6/bt2/Tp04f333+fq1ev8vbbbydb3OPGjaNZs2b4+/vH2NagQQMcHR35/fffY2yrXbs2/fv3j9H+ww8/YHri1WZ4eDjjx4+nXLlyZM6cmZw5c1KjRg0WLFhAREREUl1KDJcuXaJZs2ZkyZKFnDlz0rdvX8LDw5+6T+3atWP87Nq2bWvd7u3tTadOnRg5cmSyxS0iIiIiIhmHambHITISPvooej1z5iQ44JgxljIiHTvCwoW2293cEnkCERGRtKN69eoEBQXFaP/pp5/o2bMnvXr1ivexLl26REREBE2aNMHPzy8pw7Tx4MED5s2bx7p162KNYc+ePfTp04d58+ZR5Rk/oA4PD6dhw4YcPnyYMWPGUKNGDTw9Pfn999+ZMGEC5cuX5/nnn0/klcQUFRVFkyZNyJUrFzt37uTGjRt06dIFwzD46quvnrpvjx49+OSTT6zrmTJlstn+5ptvUrlyZb788kuyZcuW5LGLiIiIiEjGoZHZcXhy4sdEfZv43DnLpI6ffAJmMyxaBOvXJyY8ERGRNM3FxQVfX1+b5ebNmwwZMoQPP/yQN954A4CAgACyZs1qs+/jo5kDAgIoU6YMAIUKFcJkMnHhwgXOnj1LixYt8PHxwd3dnRdeeIFNmzYlKuZffvkFJycnqlWrFmPbggULaNq0Ke+++y4rVqzg3r17z3SOKVOmsH37djZv3kzv3r15/vnnKVSoEO3bt2fv3r0ULVo0UdcQlw0bNnDixAkWL15M+fLlqVevHhMnTmTu3LncfvIbZU/InDmzzc/Ry8vLZnuZMmXw9fVlzZo1yRK7iIiIiIhkHEpmx2HpUtv1J95Hx9/ixfD88/DoK8ePJnmsXz8R0YmIiMTDpEmQN+9/L82bx9y3efP47TtpUpKEeuvWLVq2bEmtWrUYM2ZMvPdr06aNNUn9xx9/EBQURL58+bh79y6NGzdm06ZNHDx4kIYNG9KsWTMuXbr0zDFu376dSpUqxWg3DIMFCxbQsWNHSpQoQbFixVi5cuUznWPJkiXUq1eP8uXLx9jm7OxMljhmor506RLu7u5PXXr27Bnneffs2UPp0qXJnTu3ta1hw4aEhYURGBj4nzHnzJmTUqVKMXjwYO7cuROjT+XKldmxY8dTjyMiIiIikhGoZnbiqMxILB4+tF2/e/cZDhIaCr162WbFCxWyrKs2toiIpITbtyE+E+/FNinEtWvx2/c/Ru3Gh9lspn379jg6OrJ48eIYNaSfJlOmTOTIkQOAXLly4evrC0C5cuUoV66ctd+nn37KmjVr+Omnn+jTp88zxXnhwgWbZO8jmzZt4v79+zRs2BCAjh07Mn/+fDp16pTgc5w+fZratWsneL/cuXNz6NChp/bx9PSMc1twcDA+Pj42bdmyZcPFxYXg4OA49+vQoQMFCxbE19eXY8eOMWzYMA4fPszGjRtt+uXJk4eDBw/+94WIiIiIiIg8hZLZsfj8c9v1OAZBxW3XLujQAS5ejG7r0gW++go8PBIdn4iISLx4ekKePP/dL1eu2Nvis+9TEqTx9eGHH7Jnzx7++OOPpyZcE+LevXuMHj2an3/+mb///pvIyEgePHiQqJHZDx48wC2WOS7mzZtHmzZtcHKyvKxq164dQ4YM4dSpU5QqVSpB5zAMI0HJ/EecnJwoUqRIgvd7XGzn/a94evToYX1cunRpihYtSqVKlThw4AAVKlSwbsuUKRP3799PVHwiIiIiIumBiXQ+dDqZKZkdi++/j35cp04Cd967F156yVIbG8DLC+bMgTZtkiw+ERGReBk40LI8i59+StpY4rBixQomTJjA2rVrY60H7eDggGEYNm0RERH/edwhQ4bw66+/MmHCBIoUKUKmTJl4/fXXCQ8Pf+ZYc+bMyc2bN23a/v33X3744QciIiKYNWuWtT0qKoqAgAC+/PJLwDIqOjQ0NMYxb926ZZPAL1asGCdPnkxwbJcuXeK55557ap+OHTsye/bsWLf5+vqyd+9em7abN28SERERY8T201SoUAFnZ2dOnz5tk8z+999/yRXbhyYiIiIiIiIJoGR2LB4fPL1iRQJ3rlwZGjSwTPD44ouWmtkFCiRpfCIiIunBoUOH6NatG59//rm1RMeTcuXKxZ07d7h37561XvR/ldMA2LFjB127dqVVq1YA3L17lwuJms0Zypcvz+LFi23alixZQt68efnhhx9s2jdt2sTnn3/OuHHjcHZ2pkSJEvzyyy8xjrlv3z6KFy9uXW/fvj0ffvghBw8ejFE3OzIykrCwsFjrZie2zEi1atX47LPPCAoKws/PD7BMCunq6krFihWfetzHHT9+nIiICOsxHjl27NgzlU8REREREUlv0ntN6+SmCSCfcPNm9FyNEPs3r5/KZIKAABg/HrZuVSJbREQkFtevX6dly5bUrl2bjh07EhwcbLNcu3YNgCpVqpA5c2Y+/PBDzpw5w9KlSwkICPjP4xcpUoTVq1dz6NAhDh8+TPv27TE/+tbUM2rYsCHHjx+3GZ09b948Xn/9dUqXLm2zdOvWjVu3brF27VoAevXqxdmzZ+nduzeHDx/mr7/+YsaMGcybN48hQ4ZYj9e/f39q1KhB3bp1mTFjBocPH+bcuXOsXLmSKlWqcPr06Vhje1Rm5GmLt7d3nNfWoEEDnnvuOTp16sTBgwfZvHkzgwcPpkePHtYk+NWrVylRogR//PEHAGfPnuWTTz5h//79XLhwgXXr1vHGG29Qvnx5atSoYT32/fv3CQwMpEGDBs/+5IuIiIiIiKBkto2tWyF79gTsEBoKnTvD5s227T4+MGQIODomZXgiIiLpxtq1a7l48SLr1q3Dz88vxvLCCy8AkD17dhYvXsy6desoU6YMy5YtY9SoUf95/MmTJ5MtWzaqV69Os2bNaNiwoU3Zi2dRpkwZKlWqxMqVKwEIDAzk8OHDvPbaazH6enh4UK9ePebPnw+Av78/O3bs4OzZszRo0IAXXniBgIAAAgICeOONN6z7ubq6snHjRoYOHcqcOXOoWrUqL7zwAtOmTaNv376ULl06UdcQF0dHR9auXYubmxs1atSgdevWtGzZkgkTJlj7REREcOrUKWvtaxcXFzZv3kzDhg0pXrw4ffv2pUGDBmzatAnHx14D/fjjj+TPn5+aNWsmS+wiIiIiImmJKQWW9MxkPFmIMp27ffs2Xl5e3Lx5k6xZs1rbL14Ef3/bvu3awdKlcRxo927LJI8XLkDu3HDkCOTIkUxRS0owm82EhITg7e2Ng4M+55FoujckLqnl3nj48CHnz5+nYMGCsU5QKEln3bp1DB48mGPHjj31Z24YBpGRkTg5OT3ThI7pSeXKlenfvz/t27e3dygp5mm/k7du3SJbtmyEhoYm2YSn6cHcvRf/u5OIyGM6VMhv7xBEJI3J7Jw6XpffeZi4b4zGh4db+s1dpN8rS6Bly2K2xZrIjoyE0aMtkzw+qr157x4cP56c4YmIiEgq0LhxY9555x2uXr1q71DShJCQEF5//XXatWtn71BERERERFIHDc1OFCWz/+fxiiAtW0Ks49UvXIBatWDUKIiKsrS9+CIcPmxJbouIiEi6169fP/Lly2fvMNIEb29vhg4dmuFHp4uIiIiISNJQMhvLpI9Dh0avxzp4aOlSKFfOUl4ELNnvTz6BLVs0yaOIiIiIiIiIiIj8J1MK/JeeOdk7gNTgyy9t120GW92+Db17w+LF0W0FC8KSJVCtWorEJyIiIiIiIiIiIpLRKZkNjBtnu1616mMr//wDa9ZEr3fqBNOngybsERGRVCaDzekskmrpd1FERERE4qIKfImjMiNPuH79iZuqaFGYNs2SvF66FBYtUiJbRERSFcf/TfwQHh5u50hEBOD+/fsAODs72zkSEREREZH0JcOPzP76a9v1HHcvQqZckDlzdOObb0KTJuDjk7LBiYiIxIOTkxOZM2fm2rVrODs74+Cgz6rtzTAMIiMjcXJy0uSHGYhhGNy/f5+QkBCyZs1q/aBJREREROQRvTtInAydzL5wAd55J3q9LcugbE/o0AFmzozeYDIpkS0iIqmWyWTCz8+P8+fPc/HiRXuHI1iSmmazGQcHByWzM6CsWbPi6+tr7zBERERERNKdDJvMvnXLMo8jgAe3mU4fOvMt3AZmzYKmTaFxY3uGKCIiEm8uLi4ULVpUpUZSCbPZzI0bN8iRI4dGymcwzs7OGpEtIiIiInHTWJdEsXsye+bMmXz55ZcEBQVRqlQppkyZQs2aNePsv23bNgYOHMjx48fJnTs3Q4cOpWfPngk+b4UKljunCr+zhA4U5lz0xg4doEaNBB9TRETEnhwcHHBzc7N3GIIlme3s7Iybm5uS2SIiIiIiIknEru+uVqxYQf/+/Rk+fDgHDx6kZs2aNGrUiEuXLsXa//z58zRu3JiaNWty8OBBPvzwQ/r27cuqVasSfO5bN818xBh28mJ0ItvDAxYvtixeXom5NBEREREREREREREbphT4Lz2zazJ70qRJdO/enbfeeouSJUsyZcoU8uXLx6xZs2LtP3v2bPLnz8+UKVMoWbIkb731Ft26dWPChAkJPvc6GjOGj3EiytJQrRocPmwZlS0iIiIiIiIiIiIiqYrdktnh4eEEBgbSoEEDm/YGDRqwe/fuWPfZs2dPjP4NGzZk//79REREJOj81fkdAMPBAUaOhO3bo4toi4iIiIiIiIiIiCQxkyn5l/TMbjWzr1+/TlRUFD4+PjbtPj4+BAcHx7pPcHBwrP0jIyO5fv06fn5+MfYJCwsjLCzMuh4aGgpY5nn82zEfvj9/DVWrwt27ibwiSevMZjO3b9/GxcVF9U3Fhu4NiYvuDYmL7g2Jy61btwAwDMO+gYiIiIiIpEF2nwDS9MTHBYZhxGj7r/6xtT8ybtw4Ro8eHaM9H0DUZWjUKGEBi4iIiIgk0o0bN/DSHC1WPaoUsHcIkgqFhYUxbtw4hg0bhqurq73DEZE0QH83JC1ws3s2Nm2z29OXM2dOHB0dY4zCDgkJiTH6+hFfX99Y+zs5OZEjR45Y9xk2bBgDBw60rt+6dYsCBQpw6dIlvYEQG7dv3yZfvnxcvnwZT09Pe4cjqYjuDYmL7g2Ji+4NiUtoaCj58+cne/bs9g5FJNULCwtj9OjRDBw4UEkpEYkX/d0QSf/slsx2cXGhYsWKbNy4kVatWlnbN27cSIsWLWLdp1q1avzf//2fTduGDRuoVKkSzs7Ose7j6uoa6x8wLy8vvbmUWHl6eurekFjp3pC46N6QuOjekLio/IyIiIiISMLZ9VX0wIED+eabb5g/fz4nT55kwIABXLp0iZ49ewKWUdWdO3e29u/ZsycXL15k4MCBnDx5kvnz5zNv3jwGDx5sr0sQERERERERERERkRRg1yotbdq04caNG3zyyScEBQVRunRp1q1bR4EClpp5QUFBXLp0ydq/YMGCrFu3jgEDBjBjxgxy587NtGnTeO211+x1CSIiIiIiIiIiIiKSAuxecrxXr1706tUr1m0BAQEx2mrVqsWBAwee+Xyurq6MHDlStZMkBt0bEhfdGxIX3RsSF90bEhfdGyLxp98XEUko/d0QSf9MhmEY9g5CRERERERERERERORpNPOMiIiIiIiIiIiIiKR6SmaLiIiIiIiIiIiISKqnZLaIiIiIiIjEyt/fnylTpqTIuS5cuIDJZOLQoUPx3qd27dr0798/2WKKS9euXWnZsuVT+6TkcyeSVplMJn744Yd494/P715yGDVqFM8///xT+9jr75FIRpMuk9kzZ86kYMGCuLm5UbFiRXbs2PHU/tu2baNixYq4ublRqFAhZs+enUKRSkpLyL2xevVq6tevT65cufD09KRatWr8+uuvKRitpKSE/t14ZNeuXTg5Of3nCxtJuxJ6b4SFhTF8+HAKFCiAq6srhQsXZv78+SkUraSkhN4bS5YsoVy5cmTOnBk/Pz/efPNNbty4kULRSkrZvn07zZo1I3fu3PF+g67XopJcYkv6fP/997i5uTF+/Ph4HWPfvn28/fbbiYqjdu3amEwmTCYTrq6u5MmTh2bNmrF69Wqbfvny5SMoKIjSpUsn6nwJjU3JJ5Gk17VrV+vvvbOzMz4+PtSvX5/58+djNput/YKCgmjUqFGKxmWPZLiIJJ10l8xesWIF/fv3Z/jw4Rw8eJCaNWvSqFEjLl26FGv/8+fP07hxY2rWrMnBgwf58MMP6du3L6tWrUrhyCW5JfTe2L59O/Xr12fdunUEBgZSp04dmjVrxsGDB1M4ckluCb03HgkNDaVz587UrVs3hSKVlPYs90br1q3ZvHkz8+bN49SpUyxbtowSJUqkYNSSEhJ6b+zcuZPOnTvTvXt3jh8/znfffce+fft46623UjhySW737t2jXLlyTJ8+PV799VpUUtI333xDhw4dmD59OkOHDo3XPrly5SJz5syJPnePHj0ICgrizJkzrFq1iueee462bdvaJModHR3x9fXFyckp0ecTEft75ZVXCAoK4sKFC/zyyy/UqVOHfv360bRpUyIjIwHw9fXF1dXVzpGKSJpipDOVK1c2evbsadNWokQJ44MPPoi1/9ChQ40SJUrYtL3zzjtG1apVky1GsY+E3huxee6554zRo0cndWhiZ896b7Rp08b46KOPjJEjRxrlypVLxgjFXhJ6b/zyyy+Gl5eXcePGjZQIT+wooffGl19+aRQqVMimbdq0aUbevHmTLUaxP8BYs2bNU/votagkpy5duhgtWrQwDMMwvvjiC8PV1dX4/vvvbfrs2rXLqFmzpuHm5mbkzZvXeO+994y7d+9atxcoUMCYPHmydR0w5s6da7Rs2dLIlCmTUaRIEePHH398ahy1atUy+vXrF6N9/vz5BmBs3LjRMAzDOH/+vAEYBw8etPY5fvy40ahRIyNLliyGt7e30bFjR+PatWs2x+7du7fRu3dvw8vLy8iePbsxfPhww2w2W/vMmDHDKFKkiOHq6mp4e3sbr732mvX5AWyW8+fPG5GRkUa3bt0Mf39/w83NzShWrJgxZcqUWJ/bL7/80vD19TWyZ89u9OrVywgPD4/zuRPJSB7/+/O4zZs3W/+OGEbMfyuvXLlitG7d2siaNauRPXt2o3nz5sb58+djHHfUqFFGrly5DA8PD+Ptt982wsLCrH2+++47o3Tp0oabm5uRPXt2o27dusbdu3eNkSNHxvid37Jli2EYln+PixYtamTKlMkoWLCg8dFHH9n8Pj96z7do0SKjQIEChqenp9GmTRvj9u3b1j5x/a0TkaSVrkZmh4eHExgYSIMGDWzaGzRowO7du2PdZ8+ePTH6N2zYkP379xMREZFssUrKepZ740lms5k7d+6QPXv25AhR7ORZ740FCxZw9uxZRo4cmdwhip08y73x008/UalSJcaPH0+ePHkoVqwYgwcP5sGDBykRsqSQZ7k3qlevzpUrV1i3bh2GYfDPP//w/fff06RJk5QIWVIxvRaVlPDBBx8wZswYfv75Z1577TVr+9GjR2nYsCGvvvoqR44cYcWKFezcuZM+ffo89XijR4+mdevWHDlyhMaNG9OhQwf+/fffBMfVpUsXsmXLFqPcyCNBQUHUqlWL559/nv3797N+/Xr++ecfWrdubdNv4cKFODk5sXfvXqZNm8bkyZP55ptvANi/fz99+/blk08+4dSpU6xfv56XXnoJgKlTp1KtWjXrqPGgoCDy5cuH2Wwmb968rFy5khMnTvDxxx/z4YcfsnLlSpvzbtmyhbNnz7JlyxYWLlxIQEAAAQEBCX4eRDKSl19+mXLlysX6e3///n3q1KmDu7s727dvZ+fOnbi7u/PKK68QHh5u7bd582ZOnjzJli1bWLZsGWvWrGH06NGA5e9Gu3bt6NatGydPnmTr1q28+uqrGIbB4MGDad26tXXEeFBQENWrVwfAw8ODgIAATpw4wdSpU5k7dy6TJ0+2ie/s2bP88MMP/Pzzz/z8889s27aNzz//PBmfLRGJTbr6/tb169eJiorCx8fHpt3Hx4fg4OBY9wkODo61f2RkJNevX8fPzy/Z4pWU8yz3xpMmTpzIvXv3Yrx4lrTtWe6N06dP88EHH7Bjxw59DTYde5Z749y5c+zcuRM3NzfWrFnD9evX6dWrF//++6/qZqcjz3JvVK9enSVLltCmTRsePnxIZGQkzZs356uvvkqJkCUV02tRSW6//PILP/74I5s3b+bll1+22fbll1/Svn17a83ookWLMm3aNGrVqsWsWbNwc3OL9Zhdu3alXbt2AIwdO5avvvqKP/74g1deeSVBsTk4OFCsWDEuXLgQ6/ZZs2ZRoUIFxo4da22bP38++fLl46+//qJYsWKApdb25MmTMZlMFC9enKNHjzJ58mR69OjBpUuXyJIlC02bNsXDw4MCBQpQvnx5ALy8vHBxcSFz5sz4+vpaz+Ho6GhNjAEULFiQ3bt3s3LlSpv3AtmyZWP69Ok4OjpSokQJmjRpwubNm+nRo0eCngeRjKZEiRIcOXIkRvvy5ctxcHDgm2++wWQyAZZBRFmzZmXr1q3WD39dXFyYP38+mTNnplSpUnzyyScMGTKEMWPGEBQURGRkJK+++ioFChQAoEyZMtZzZMqUibCwMJvfeYCPPvrI+tjf359BgwaxYsUKm5JMZrOZgIAAPDw8AOjUqRObN2/ms88+S6JnRkTiI12NzH7k0R+9RwzDiNH2X/1ja5e0L6H3xiPLli1j1KhRrFixAm9v7+QKT+wovvdGVFQU7du3Z/To0dY3UJK+JeTvhtlsxmQysWTJEipXrkzjxo2ZNGkSAQEBGp2dDiXk3jhx4gR9+/bl448/JjAwkPXr13P+/Hl69uyZEqFKKqfXopKcypYti7+/Px9//DF37tyx2RYYGEhAQADu7u7WpWHDhpjNZs6fP//UYz6SJUsWPDw8CAkJeab4nva3MzAwkC1bttjE92geirNnz1r7Va1a1eYY1apV4/Tp00RFRVG/fn0KFChAoUKF6NSpE0uWLOH+/fv/Gdfs2bOpVKkSuXLlwt3dnblz58aYF6FUqVI4Ojpa1/38/J75eRDJSOL6vQ8MDOTMmTN4eHhYf+ezZ8/Ow4cPbX7nH02o/Ui1atW4e/culy9fply5ctStW5cyZcrwxhtvMHfuXG7evPmfMX3//fe8+OKL+Pr64u7uzogRI2L8zvv7+1sT2aDfeRF7SVfJ7Jw5c+Lo6BhjVFRISEiMES+P+Pr6xtrfycmJHDlyJFuskrKe5d54ZMWKFXTv3p2VK1dSr1695AxT7CCh98adO3fYv38/ffr0wcnJCScnJz755BMOHz6Mk5MTv/32W0qFLsnsWf5u+Pn5kSdPHry8vKxtJUuWxDAMrly5kqzxSsp5lntj3Lhx1KhRgyFDhlC2bFkaNmzIzJkzmT9/PkFBQSkRtqRSei0qyS1Pnjxs27aNoKAgXnnlFZuEttls5p133uHQoUPW5fDhw5w+fZrChQvHeUxnZ2ebdZPJhNlsTnBsUVFRnD59moIFC8a63Ww206xZM5v4Dh06xOnTp62lQv6Lh4cHBw4cYNmyZfj5+fHxxx9Trlw5bt26Fec+K1euZMCAAXTr1o0NGzZw6NAh3nzzTZsyB5B0z4NIRnPy5MlYf+/NZjMVK1aM8Tv/119/0b59+/88rslkwtHRkY0bN/LLL7/w3HPP8dVXX1G8ePGnfkD3+++/07ZtWxo1asTPP//MwYMHGT58uH7nRVKpdJXMdnFxoWLFimzcuNGmfePGjdY6SE+qVq1ajP4bNmygUqVKMf5QSdr1LPcGWEZkd+3alaVLl6quaTqV0HvD09OTo0eP2ry46tmzJ8WLF+fQoUNUqVIlpUKXZPYsfzdq1KjB33//zd27d61tf/31Fw4ODuTNmzdZ45WU8yz3xv3793FwsH3Z9Wg036NRuJIx6bWopIT8+fOzbds2QkJCaNCgAbdv3wagQoUKHD9+nCJFisRYXFxckj2uhQsXcvPmTZs63o97FJ+/v3+M+LJkyWLt9/vvv9vs9/vvv1O0aFHr31knJyfq1avH+PHjOXLkCBcuXLAOQHBxcSEqKspm/x07dlC9enV69epF+fLlKVKkiM2oUBF5dr/99htHjx6N9fe+QoUKnD59Gm9v7xi/848PFjl8+LDNtx5///133N3dra+3TSYTNWrUYPTo0Rw8eBAXFxfWrFkDxP47v2vXLgoUKMDw4cOpVKkSRYsW5eLFi8lx+SKSBNJVMhtg4MCBfPPNN8yfP5+TJ08yYMAALl26ZP0a77Bhw+jcubO1f8+ePbl48SIDBw7k5MmTzJ8/n3nz5jF48GB7XYIkk4TeG8uWLaNz585MnDiRqlWrEhwcTHBwMKGhofa6BEkmCbk3HBwcKF26tM3i7e2Nm5sbpUuXtnljJWlfQv9utG/fnhw5cvDmm29y4sQJtm/fzpAhQ+jWrRuZMmWy12VIMkjovdGsWTNWr17NrFmzOHfuHLt27aJv375UrlyZ3Llz2+syJBncvXvX+mEnwPnz5zl06JD1q8p6LSr2kjdvXrZu3cqNGzdo0KABoaGhvP/+++zZs4fevXtbRzz/9NNPvPfee0l+/vv37xMcHMyVK1fYu3cv77//Pj179uTdd9+lTp06se7Tu3dv/v33X9q1a8cff/zBuXPn2LBhA926dbNJRl2+fJmBAwdy6tQpli1bxldffUW/fv0A+Pnnn5k2bRqHDh3i4sWLLFq0CLPZTPHixQFL2YC9e/dy4cIFrl+/jtlspkiRIuzfv59ff/2Vv/76ixEjRrBv374kf05E0ruwsDCCg4O5evUqBw4cYOzYsbRo0YKmTZva/Fv4SIcOHciZMyctWrRgx44dnD9/nm3bttGvXz+bbzmGh4fTvXt3Tpw4wS+//MLIkSPp06cPDg4O7N27l7Fjx7J//34uXbrE6tWruXbtGiVLlgQsv/NHjhzh1KlTXL9+nYiICIoUKcKlS5dYvnw5Z8+eZdq0adbkt4ikPulu5rI2bdpw48YNPvnkE4KCgihdujTr1q2zFv4PCgqyqXtUsGBB1q1bx4ABA5gxYwa5c+dm2rRpcY4OkLQroffGnDlziIyMpHfv3vTu3dva3qVLF81Sns4k9N6QjCOh94a7uzsbN27kvffeo1KlSuTIkYPWrVvz6aef2usSJJkk9N7o2rUrd+7cYfr06QwaNIisWbPy8ssv88UXX9jrEiSZ7N+/3yYxN3DgQCD69YNei4o9PSo5UqdOHerXr8+GDRvYtm0bw4cPp2bNmhiGQeHChWnTpk2Sn3vu3LnMnTsXFxcXcuTIQcWKFVmxYgWtWrWKc5/cuXOza9cu3n//fRo2bEhYWBgFChTglVdesfm2S+fOnXnw4AGVK1fG0dGR9957j7fffhuArFmzsnr1akaNGsXDhw8pWrQoy5Yto1SpUgAMHjyYLl268Nxzz/HgwQPrfAaHDh2iTZs2mEwm2rVrR69evfjll1+S/HkRSc/Wr1+Pn58fTk5OZMuWjXLlyjFt2jS6dOkS4xtrAJkzZ2b79u28//77vPrqq9y5c4c8efJQt25dPD09rf3q1q1L0aJFeemllwgLC6Nt27aMGjUKsHyTdvv27UyZMoXbt29ToEABJk6cSKNGjQDo0aMHW7dupVKlSty9e5ctW7bQokULBgwYQJ8+fQgLC6NJkyaMGDHCekwRSV1Mhr7bKiIiIiIiIiIiIiKpXLorMyIiIiIiIiIiIiIi6Y+S2SIiIiIiIiIiIiKS6imZLSIiIiIiIiIiIiKpnpLZIiIiIiIiIiIiIpLqKZktIiIiIiIiIiIiIqmektkiIiIiIiIiIiIikuopmS0iIiIiIiIiIiIiqZ6S2SIiIiIiIiIiIiKS6imZLSKSwgICAsiaNau9w3hm/v7+TJky5al9Ro0axfPPP58i8YiIiIjIf0vrr0HTmgsXLmAymTh06JC9QxERSVeUzBYReQZdu3bFZDLFWM6cOWPv0AgICLCJyc/Pj9atW3P+/PkkOf6+fft4++23resmk4kffvjBps/gwYPZvHlzkpwvLk9ep4+PD82aNeP48eMJPo7e2ImIiEhq0KxZM+rVqxfrtj179mAymThw4EAKR5V+dO3alZYtWyb6OLElqu/cuUPt2rUpUaIEly9fTvQ5REQkdkpmi4g8o1deeYWgoCCbpWDBgvYOCwBPT0+CgoL4+++/Wbp0KYcOHaJ58+ZERUUl+ti5cuUic+bMT+3j7u5Ojhw5En2u//L4da5du5Z79+7RpEkTwsPDk/3cIiIiIkmte/fu/Pbbb1y8eDHGtvnz5/P8889ToUKFBB83OV8bRUREJNux04pr165Rp04d7t69y86dO8mXL5+9QxIRSbeUzBYReUaurq74+vraLI6OjkyaNIkyZcqQJUsW8uXLR69evbh7926cxzl8+DB16tTBw8MDT09PKlasyP79+63bd+/ezUsvvUSmTJnIly8fffv25d69e0+NzWQy4evri5+fH3Xq1GHkyJEcO3bMOnJ81qxZFC5cGBcXF4oXL863335rs/+oUaPInz8/rq6u5M6dm759+1q3PV5mxN/fH4BWrVphMpms64+XGfn1119xc3Pj1q1bNufo27cvtWrVSrLrrFSpEgMGDODixYucOnXK2udpP4+tW7fy5ptvEhoaah3hPWrUKMDypm/o0KHkyZOHLFmyUKVKFbZu3frUeEREREQSo2nTpnh7exMQEGDTfv/+fVasWEH37t0BWLVqFaVKlcLV1RV/f38mTpxo09/f359PP/2Url274uXlRY8ePazbfv31V0qWLIm7u7t1cMbjFixYQMmSJXFzc6NEiRLMnDnTuu3RiOSVK1dSu3Zt3NzcWLx4MZGRkfTt25esWbOSI0cO3n//fbp06WIzCtowDMaPH0+hQoXIlCkT5cqV4/vvv7duv3nzJh06dCBXrlxkypSJokWLsmDBAuv2999/n2LFipE5c2YKFSrEiBEjYiTSP/30U7y9vfHw8OCtt97igw8+sL4mHTVqFAsXLuTHH3+0vu579Nru6tWrtGnThmzZspEjRw5atGjBhQsX4vUzu3z5MjVr1sTDw4MtW7aQM2fOWPtFRUXRvXt3ChYsSKZMmShevDhTp0616bN161YqV65MlixZyJo1KzVq1LB+sJEc7xlERNIiJbNFRJKYg4MD06ZN49ixYyxcuJDffvuNoUOHxtm/Q4cO5M2bl3379hEYGMgHH3yAs7MzAEePHqVhw4a8+uqrHDlyhBUrVrBz50769OmToJgyZcoEWEbOrFmzhn79+jFo0CCOHTvGO++8w5tvvsmWLVsA+P7775k8eTJz5szh9OnT/PDDD5QpUybW4+7btw+wvOkJCgqyrj+uXr16ZM2alVWrVlnboqKiWLlyJR06dEiy67x16xZLly4FsD5/8PSfR/Xq1ZkyZYp1hHdQUBCDBw8G4M0332TXrl0sX76cI0eO8MYbb/DKK69w+vTpeMckIiIikhBOTk507tyZgIAADMOwtn/33XeEh4fToUMHAgMDad26NW3btuXo0aOMGjWKESNGxEiAf/nll5QuXZrAwEBGjBgBWJLiEyZM4Ntvv2X79u1cunTJ+toHYO7cuQwfPpzPPvuMkydPMnbsWEaMGMHChQttjv3+++/Tt29fTp48ScOGDfniiy9YsmQJCxYsYNeuXdy+fTtGGbqPPvqIBQsWMGvWLI4fP86AAQPo2LEj27ZtA2DEiBGcOHGCX375hZMnTzJr1iybxLCHhwcBAQGcOHGCqVOnMnfuXCZPnmzdvmTJEj777DO++OILAgMDyZ8/P7NmzbJuHzx4MK1bt7b5dmX16tW5f/8+derUwd3dne3bt7Nz505rov+/RrSfOnWKGjVqUKJECdavX4+Hh0ecfc1mM3nz5mXlypWcOHGCjz/+mA8//JCVK1cCEBkZScuWLalVqxZHjhxhz549vP3225hMJiBl3jOIiKQJhoiIJFiXLl0MR0dHI0uWLNbl9ddfj7XvypUrjRw5cljXFyxYYHh5eVnXPTw8jICAgFj37dSpk/H222/btO3YscNwcHAwHjx4EOs+Tx7/8uXLRtWqVY28efMaYWFhRvXq1Y0ePXrY7PPGG28YjRs3NgzDMCZOnGgUK1bMCA8Pj/X4BQoUMCZPnmxdB4w1a9bY9Bk5cqRRrlw563rfvn2Nl19+2br+66+/Gi4uLsa///6bqOsEjCxZshiZM2c2AAMwmjdvHmv/R/7r52EYhnHmzBnDZDIZV69etWmvW7euMWzYsKceX0RERCQxTp48aQDGb7/9Zm176aWXjHbt2hmGYRjt27c36tevb7PPkCFDjOeee866XqBAAaNly5Y2fR69djpz5oy1bcaMGYaPj491PV++fMbSpUtt9hszZoxRrVo1wzAM4/z58wZgTJkyxaaPj4+P8eWXX1rXIyMjjfz58xstWrQwDMMw7t69a7i5uRm7d++22a979+7W62rWrJnx5ptvPuWZsTV+/HijYsWK1vUqVaoYvXv3tulTo0YNm9ekXbp0scb0yLx584zixYsbZrPZ2hYWFmZkypTJ+PXXX2M996PnwcXFxahdu7YRGRkZZ5+DBw/GeQ29evUyXnvtNcMwDOPGjRsGYGzdujXWvkn9nkFEJK3SyGwRkWdUp04dDh06ZF2mTZsGwJYtW6hfvz558uTBw8ODzp07c+PGjTi/5jdw4EDeeust6tWrx+eff87Zs2et2wIDAwkICMDd3d26NGzYELPZ/NQJHUNDQ3F3d7eW1ggPD2f16tW4uLhw8uRJatSoYdO/Ro0anDx5EoA33niDBw8eUKhQIXr06MGaNWuIjIxM1HPVoUMHtm7dyt9//w1YRs40btyYbNmyJeo6PTw8OHToEIGBgcyePZvChQsze/Zsmz4J/XkAHDhwAMMwKFasmE1M27Zts/n5iIiIiCS1EiVKUL16debPnw/A2bNn2bFjB926dQOI87Xc6dOnbeZHqVSpUoxjZ86cmcKFC1vX/fz8CAkJASx1ny9fvkz37t1tXv98+umnMV7/PH7s0NBQ/vnnHypXrmxtc3R0pGLFitb1EydO8PDhQ+rXr29z7EWLFlmP/e6777J8+XKef/55hg4dyu7du23O+f333/Piiy/i6+uLu7s7I0aM4NKlS9btp06dsokBiLEem8DAQM6cOYOHh4c1ruzZs/Pw4cP/fN3XokULdu7cafMNxKeZPXs2lSpVIleuXLi7uzN37lzrNWTPnp2uXbvSsGFDmjVrxtSpU21KwCTHewYRkbTIyd4BiIikVVmyZKFIkSI2bRcvXqRx48b07NmTMWPGkD17dnbu3En37t3jnBxn1KhRtG/fnrVr1/LLL78wcuRIli9fTqtWrTCbzbzzzjs2NasfyZ8/f5yxeXh4cODAARwcHPDx8SFLliw22x99XfERwzCsbfny5ePUqVNs3LiRTZs20atXL7788ku2bdtmU74jISpXrkzhwoVZvnw57777LmvWrLGpgfis1+ng4GD9GZQoUYLg4GDatGnD9u3bgWf7eTyKx9HRkcDAQBwdHW22ubu7J+jaRURERBKqe/fu9OnThxkzZrBgwQIKFChA3bp1AdvXbY8Yj5UkeeTJ139AjNdyJpPJuq/ZbAYspUaqVKli0+/J10OxHftpMT069tq1a8mTJ49NP1dXVwAaNWrExYsXWbt2LZs2baJu3br07t2bCRMm8Pvvv9O2bVtGjx5Nw4YN8fLyYvny5TFqhcfneXmS2WymYsWKLFmyJMa2XLlyPXXfDz/8kLJly9KhQwcMw6BNmzZx9l25ciUDBgxg4sSJVKtWDQ8PD7788kv27t1r7bNgwQL69u3L+vXrWbFiBR999BEbN26katWqyfKeQUQkLVIyW0QkCe3fv5/IyEgmTpyIg4Plyy+P6uA9TbFixShWrBgDBgygXbt2LFiwgFatWlGhQgWOHz8eI2n+Xx5P8j6pZMmS7Ny5k86dO1vbdu/eTcmSJa3rmTJlonnz5jRv3pzevXtTokQJjh49SoUKFWIcz9nZ2WYUUFzat2/PkiVLyJs3Lw4ODjRp0sS67Vmv80kDBgxg0qRJrFmzhlatWsXr5+Hi4hIj/vLlyxMVFUVISAg1a9ZMVEwiIiIiCdW6dWv69evH0qVLWbhwIT169LAmap977jl27txp03/37t0UK1YsRtI5IXx8fMiTJw/nzp2zzmsSH15eXvj4+PDHH39YXzdFRUVx8OBB6+SLzz33HK6urly6dMlmAvAn5cqVi65du9K1a1dq1qzJkCFDmDBhArt27aJAgQIMHz7c2vfRxIiPFC9enD/++INOnTpZ2x6fIBFif91XoUIFVqxYgbe3N56envG+7kc++ugjnJyc6NChA2azmXbt2sXab8eOHVSvXp1evXpZ22Ib+V2+fHnKly/PsGHDqFatGkuXLqVq1apA0r9nEBFJi1RmREQkCRUuXJjIyEi++uorzp07x7fffhuj7MXjHjx4QJ8+fdi6dSsXL15k165d7Nu3z5pYfv/999mzZw+9e/fm0KFDnD59mp9++on33nvvmWMcMmQIAQEBzJ49m9OnTzNp0iRWr15tnfwnICCAefPmcezYMes1ZMqUiQIFCsR6PH9/fzZv3kxwcDA3b96M87wdOnTgwIEDfPbZZ7z++uu4ublZtyXVdXp6evLWW28xcuRIDMOI18/D39+fu3fvsnnzZq5fv879+/cpVqwYHTp0oHPnzqxevZrz58+zb98+vvjiC9atW5egmEREREQSyt3dnTZt2vDhhx/y999/07VrV+u2QYMGsXnzZsaMGcNff/3FwoULmT59us1Ejs9q1KhRjBs3jqlTp/LXX39x9OhRFixYwKRJk56633vvvce4ceP48ccfOXXqFP369ePmzZvWBLyHhweDBw9mwIABLFy4kLNnz3Lw4EFmzJhhnVzy448/5scff+TMmTMcP36cn3/+2fqauEiRIly6dInly5dz9uxZpk2bxpo1a2LEMG/ePBYuXMjp06f59NNPOXLkiM1obX9/f44cOcKpU6e4fv06ERERdOjQgZw5c9KiRQt27NjB+fPn2bZtG/369ePKlSvxet4++OADxo0bR6dOnWId4f3oGvbv38+vv/7KX3/9xYgRI2wmTz9//jzDhg1jz549XLx4kQ0bNvDXX39RsmRJu7xnEBFJtexWrVtEJA2LbfKYRyZNmmT4+fkZmTJlMho2bGgsWrTIAIybN28ahmE74WBYWJjRtm1bI1++fIaLi4uRO3duo0+fPjYTtfzxxx9G/fr1DXd3dyNLlixG2bJljc8++yzO2GKb0PBJM2fONAoVKmQ4OzsbxYoVMxYtWmTdtmbNGqNKlSqGp6enkSVLFqNq1arGpk2brNufnADyp59+MooUKWI4OTkZBQoUMAwj5gSQj7zwwgsxJjRK6uu8ePGi4eTkZKxYscIwjP/+eRiGYfTs2dPIkSOHARgjR440DMMwwsPDjY8//tjw9/c3nJ2dDV9fX6NVq1bGkSNH4oxJREREJKns3r3bAIwGDRrE2Pb9998bzz33nOHs7Gzkz5/fZvJFw4j5es0wYn/ttGbNGuPJtMCSJUuM559/3nBxcTGyZctmvPTSS8bq1asNw4h7UsOIiAijT58+hqenp5EtWzbj/fffN9544w2jbdu21j5ms9mYOnWqUbx4ccPZ2dnIlSuX0bBhQ2Pbtm2GYVgmmixZsqSRKVMmI3v27EaLFi2Mc+fOWfcfMmSIkSNHDsPd3d1o06aNMXny5BjX88knnxg5c+Y03N3djW7duhl9qKcKdAAAAdNJREFU+/Y1qlatat0eEhJifb0JGFu2bDEMwzCCgoKMzp07Gzlz5jRcXV2NQoUKGT169DBCQ0NjPPdPex4mTpxoODo6GosWLYrR5+HDh0bXrl0NLy8vI2vWrMa7775rfPDBB9bXzMHBwUbLli0NPz8/w8XFxShQoIDx8ccfG1FRUcnynkFEJK0yGUY8ikiJiIiIiIiIiMSD2WymZMmStG7dmjFjxtgtjvr16+Pr68u3335rtxhERCRpqWa2iIiIiIiIiDyzR2UxatWqRVhYGNOnT+f8+fO0b98+xWK4f/8+s2fPpmHDhjg6OrJs2TI2bdrExo0bUywGERFJfkpmi4iIiIiIiMgzc3BwICAggMGDB2MYBqVLl2bTpk02E4wnN5PJxLp16/j0008JCwujePHirFq1inr16qVYDCIikvxUZkREREREREREREREUj0HewcgIiIiIiIiIiIiIvJflMwWERERERERERERkVRPyWwRERERERERERERSfWUzBYRERERERERERGRVE/JbBERERERERERERFJ9ZTMFhEREREREREREZFUT8lsEREREREREREREUn1lMwWERERERERERERkVRPyWwRERERERERERERSfX+H8rX+YCib7xWAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "X_test_selected = X_test[X_selected.columns]\n", + "X_test_with_const = sm.add_constant(X_test_selected)\n", + "y_pred_proba = final_model.predict(X_test_with_const)\n", + "y_pred = (y_pred_proba > 0.5).astype(int)\n", + "auc_score = roc_auc_score(y_test, y_pred_proba)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n", + "# ROC-Kurve\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n", + "axes[0].plot(fpr, tpr, color='blue', lw=2, label=f'ROC Kurve (AUC = {auc_score:.3f})')\n", + "axes[0].plot([0, 1], [0, 1], color='red', lw=2, linestyle='--', label='Zufall (AUC = 0.5)')\n", + "axes[0].set_xlim([0.0, 1.0])\n", + "axes[0].set_ylim([0.0, 1.05])\n", + "axes[0].set_xlabel('False Positive Rate')\n", + "axes[0].set_ylabel('True Positive Rate')\n", + "axes[0].set_title('ROC-Kurve')\n", + "axes[0].legend(loc=\"lower right\")\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Confusion Matrix\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[1],\n", + " xticklabels=['Kein Diebstahl', 'Diebstahl'],\n", + " yticklabels=['Kein Diebstahl', 'Diebstahl'])\n", + "axes[1].set_xlabel('Vorhergesagte Klasse')\n", + "axes[1].set_ylabel('Tatsächliche Klasse')\n", + "axes[1].set_title('Confusion Matrix')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2bff1bcb", + "metadata": {}, + "source": [ + "Die ROC-Kurve zeigt mit einem AUC-Wert deutlich über 0.5 eine gute Diskriminierungsfähigkeit des Modells. Die Confusion Matrix offenbart jedoch ein Klassifikationsproblem: Das Modell erkennt normale Fahrten sehr zuverlässig, hat aber Schwierigkeiten bei der Diebstahlerkennung. \n", + "\n", + "Diese Missklassifikation resultiert aus der Stichprobengröße von nur 20.000 Beobachtungen bei einer unbalancierten Klassenverteilung (ca. 25% Diebstähle im Datensatz). Das Modell neigt dadurch zur Mehrheitsklasse und klassifiziert konservativ." + ] + }, { "cell_type": "markdown", "id": "7516554c", @@ -1756,7 +1824,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "id": "616ae484", "metadata": {}, "outputs": [ @@ -8436,7 +8504,495 @@ "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", - "Stichprobenumfang: 10%|█ | 1/10 [01:21<12:12, 81.40s/it]c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -8940,7 +9496,7 @@ "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", - "Stichprobenumfang: 20%|██ | 2/10 [02:54<11:45, 88.21s/it]c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -8948,7 +9504,7568 @@ "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", - "Stichprobenumfang: 100%|██████████| 10/10 [32:08<00:00, 192.82s/it]\n" + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "Stichprobenumfang: 10%|█ | 1/10 [01:15<11:21, 75.70s/it]c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "Stichprobenumfang: 10%|█ | 1/10 [01:15<11:21, 75.70s/it]c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "Stichprobenumfang: 100%|██████████| 10/10 [34:23<00:00, 206.36s/it]\n", + "Stichprobenumfang: 100%|██████████| 10/10 [34:23<00:00, 206.36s/it]\n" ] } ], @@ -8997,13 +17114,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "b0bb7329", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -9046,13 +17163,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "cf7a7e61", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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