update abhaengigkeit
This commit is contained in:
YannAhlgrim
@@ -45,7 +45,7 @@
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" Anzahl starker Bremsvorgänge mit hoher Verzögerung (>0,3 g). Modellierung z.B. als Poisson-verteilte Zufallsvariable mit geringem Mittelwert (wenige Ereignisse pro 100 km). Harte Bremsmanöver korrelieren mit aggressiver Fahrweise und sind prädiktiv für Fahrerwechsel. \n",
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" → Quelle: [Tesla Safety Score Definition](https://www.findmyelectric.com/blog/tesla-safety-score-beta-explained)\n",
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"\n",
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"4. **Geschwindigkeitsüberschreitungen (ordinal):** \n",
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"4. **Geschwindigkeitsüberschreitungen (ordinal, 3 Kategorien):** \n",
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" Häufigkeit bzw. Ausmaß, mit dem Tempolimits überschritten werden. Kategorisierung z.B. in: \n",
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" - *selten*, \n",
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" - *manchmal*, \n",
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@@ -58,24 +58,26 @@
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"\n",
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"#### Nicht erklärende Variablen:\n",
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"\n",
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"5. **Straßentyp (nominal, 3 Kategorien):** \n",
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" - *Autobahn* (~30–33 %) \n",
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" - *Außerorts* (~43 %) \n",
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" - *Innerorts* (~24–27 %) \n",
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" → Modelliert als Multinomialverteilung. Kontextvariable ohne direkte Prädiktionswirkung. \n",
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" → Quelle: [Klimaschutzinstrumente im Verkehr](https://openumwelt.de/server/api/core/bitstreams/07bd23f9-ff0a-41e5-b89b-8d7baf0a5c36/content)\n",
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"\n",
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"6. **Wetterbedingungen (nominal, 3 Kategorien):** \n",
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"5. **Wetterbedingungen (nominal, 3 Kategorien):** \n",
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" - *trocken* (~70–75 %) \n",
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" - *nass* (~20–25 %) \n",
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" - *winterlich* (<5 %) \n",
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" → Modelliert als Multinomial. Kontextvariable. \n",
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" Die Einteilung basiert auf Daten des Statistischen Bundesamtes: Etwa 70–75 % aller Fahrten finden unter trockenen Bedingungen statt, 20–25 % bei Nässe und unter 5 % bei Schnee oder Glätte.\n",
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"\n",
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"7. **Fahrstrecke (metrisch):** \n",
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"6. **Fahrstrecke (metrisch):** \n",
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" Länge der Fahrt (z.B. lognormalverteilt um 12 km). Kontextvariable, nicht direkt erklärend. \n",
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" → Quelle: [Mobilität in Deutschland 2017 – Ergebnisbericht](https://www.bmv.de/SharedDocs/DE/Anlage/G/mid-ergebnisbericht.pdf)\n",
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"\n",
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" \n",
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"7. **Straßentyp (nominal, 3 Kategorien):** \n",
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" - *Autobahn* (~30–33 %) \n",
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" - *Außerorts* (~43 %) \n",
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" - *Innerorts* (~24–27 %) \n",
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" → Modelliert als Multinomialverteilung. Kontextvariable ohne direkte Prädiktionswirkung. \n",
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" → Quelle: [Klimaschutzinstrumente im Verkehr](https://openumwelt.de/server/api/core/bitstreams/07bd23f9-ff0a-41e5-b89b-8d7baf0a5c36/content)\n",
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"\n",
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"8. **Wochentag (nominal, 2–3 Kategorien):** \n",
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" Z.B. *Werktag* vs *Wochenende* oder *Mo–Fr*, *Sa*, *So*. Modellierung als kategorische Variable. Kontextvariable. \n",
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" → Quelle: [Mobilität in Deutschland 2017 – Ergebnisbericht](https://www.bmv.de/SharedDocs/DE/Anlage/G/mid-ergebnisbericht.pdf)\n"
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@@ -91,7 +93,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 50,
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"id": "97ba29a9",
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"metadata": {},
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"outputs": [],
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@@ -102,6 +104,8 @@
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from scipy.stats import norm, multinomial, poisson, lognorm, bernoulli\n",
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"import statsmodels.api as sm\n",
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"from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
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"\n",
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"np.random.seed(11)\n",
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"N = 1_000_000"
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@@ -117,7 +121,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 51,
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"id": "61247da5",
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"metadata": {},
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"outputs": [],
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@@ -131,21 +135,40 @@
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"# 3. Harte Bremsmanöver (metrisch, Poisson)\n",
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"hard_brakes = np.random.poisson(lam=2, size=N)\n",
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"\n",
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"# 4. Geschwindigkeitsüberschreitungen (ordinal, 3 Kategorien)\n",
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"speeding = np.random.choice(['selten', 'manchmal', 'häufig'], size=N, p=[0.6, 0.3, 0.1])\n",
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"# 4. Geschwindigkeitsüberschreitungen (ordinal, 3 Kategorien) – Abhängigkeit von avg_speed\n",
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"def softmax(x):\n",
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" e_x = np.exp(x - np.max(x, axis=1, keepdims=True))\n",
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" return e_x / e_x.sum(axis=1, keepdims=True)\n",
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"\n",
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"# 5. Straßentyp (nominal, Kontext)\n",
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"road_type = np.random.choice(['Autobahn', 'Außerorts', 'Innerorts'], size=N, p=[0.32, 0.43, 0.25])\n",
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"speed_scaled = (avg_speed - avg_speed.min()) / (avg_speed.max() - avg_speed.min())\n",
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"strength = 0.01\n",
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"logits = np.zeros((N, 3))\n",
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"logits[:, 0] = 1 - strength * speed_scaled # selten\n",
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"logits[:, 1] = 1 # manchmal\n",
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"logits[:, 2] = 1 + strength * speed_scaled # häufig\n",
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"probs = softmax(logits)\n",
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"speeding = [np.random.choice(['selten', 'manchmal', 'häufig'], p=p) for p in probs]\n",
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"\n",
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"# 6. Wetterbedingungen (nominal, Kontext)\n",
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"# 5. Wetterbedingungen (nominal, Kontext)\n",
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"weather = np.random.choice(['trocken', 'nass', 'winterlich'], size=N, p=[0.75, 0.2, 0.05])\n",
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"\n",
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"# 7. Fahrstrecke (metrisch, lognormal)\n",
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"# 6. Fahrstrecke (metrisch, lognormal)\n",
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"mu, sigma = np.log(12), 0.7\n",
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"trip_distance = np.random.lognormal(mean=mu, sigma=sigma, size=N)\n",
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"\n",
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"# 7. Straßentyp (nominal, Kontext) – schwächere Abhängigkeit von trip_distance\n",
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"road_probs = []\n",
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"for dist in trip_distance:\n",
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" if dist < 5:\n",
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" road_probs.append([0.5, 0.35, 0.15])\n",
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" elif dist < 20:\n",
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" road_probs.append([0.2, 0.5, 0.3])\n",
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" else:\n",
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" road_probs.append([0.1, 0.4, 0.5])\n",
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"road_type = [np.random.choice(['Innerorts', 'Außerorts', 'Autobahn'], p=prob) for prob in road_probs]\n",
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"\n",
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"# 8. Wochentag (nominal, Kontext)\n",
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"weekday = np.random.choice(['Mo-Fr', 'Sa', 'So'], size=N, p=[0.7, 0.15, 0.15])\n"
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"weekday = np.random.choice(['Mo-Fr', 'Sa', 'So'], size=N, p=[0.7, 0.15, 0.15])"
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]
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},
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{
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@@ -158,29 +181,183 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 52,
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"id": "f8e0efb0",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>avg_speed</th>\n",
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" <th>shift_behavior</th>\n",
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" <th>hard_brakes</th>\n",
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" <th>speeding</th>\n",
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" <th>weather</th>\n",
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" <th>trip_distance</th>\n",
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" <th>road_type</th>\n",
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" <th>weekday</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>64.494547</td>\n",
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" <td>früh</td>\n",
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" <td>2</td>\n",
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" <td>manchmal</td>\n",
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" <td>trocken</td>\n",
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" <td>30.449962</td>\n",
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" <td>Außerorts</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>44.139270</td>\n",
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" <td>früh</td>\n",
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" <td>3</td>\n",
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" <td>häufig</td>\n",
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" <td>trocken</td>\n",
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" <td>11.911103</td>\n",
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" <td>Autobahn</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>42.154349</td>\n",
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" <td>spät</td>\n",
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" <td>2</td>\n",
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" <td>selten</td>\n",
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" <td>trocken</td>\n",
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" <td>6.086055</td>\n",
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" <td>Autobahn</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>20.466814</td>\n",
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" <td>normal</td>\n",
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" <td>5</td>\n",
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" <td>häufig</td>\n",
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" <td>trocken</td>\n",
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" <td>5.732684</td>\n",
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" <td>Außerorts</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>46.917154</td>\n",
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" <td>spät</td>\n",
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" <td>2</td>\n",
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" <td>selten</td>\n",
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" <td>trocken</td>\n",
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" <td>7.256758</td>\n",
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" <td>Außerorts</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" avg_speed shift_behavior hard_brakes speeding weather trip_distance \\\n",
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"0 64.494547 früh 2 manchmal trocken 30.449962 \n",
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"1 44.139270 früh 3 häufig trocken 11.911103 \n",
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"2 42.154349 spät 2 selten trocken 6.086055 \n",
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"3 20.466814 normal 5 häufig trocken 5.732684 \n",
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"4 46.917154 spät 2 selten trocken 7.256758 \n",
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"\n",
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" road_type weekday \n",
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"0 Außerorts Mo-Fr \n",
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"1 Autobahn Mo-Fr \n",
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"2 Autobahn Mo-Fr \n",
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"3 Außerorts Mo-Fr \n",
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"4 Außerorts Mo-Fr "
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]
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},
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"execution_count": 52,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.DataFrame({\n",
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" 'avg_speed': avg_speed,\n",
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" 'shift_behavior': shift_behavior,\n",
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" 'hard_brakes': hard_brakes,\n",
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" 'speeding': speeding,\n",
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" 'road_type': road_type,\n",
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" 'weather': weather,\n",
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" 'trip_distance': trip_distance,\n",
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" 'road_type': road_type,\n",
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" 'weekday': weekday\n",
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"})\n",
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"\n",
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"df.head()\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f06e7c4d",
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"metadata": {},
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"source": [
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"### VIF der Abhängigkeiten prüfen"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"id": "6bfb199a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Variance Inflation Factor (VIF) für ausgewählte Variabeln:\n",
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" features VIF Factor\n",
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"0 avg_speed 5.768491\n",
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"1 trip_distance 2.469313\n",
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"2 road_type_Außerorts 2.238422\n",
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"3 speeding_manchmal 1.900947\n",
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"4 speeding_selten 1.893981\n",
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"5 road_type_Innerorts 1.587059\n"
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]
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}
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],
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"source": [
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"vif = pd.DataFrame()\n",
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"columns = ['avg_speed', 'speeding', 'trip_distance', 'road_type']\n",
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"vif_df = pd.get_dummies(df[columns], drop_first=True)\n",
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"vif_df = vif_df.astype(float)\n",
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"vif['features'] = vif_df.columns\n",
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"vif['VIF Factor'] = [variance_inflation_factor(vif_df.values, i) for i in range(vif_df.shape[1])]\n",
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"vif = vif.sort_values('VIF Factor', ascending=False).reset_index(drop=True)\n",
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"print(\"\\nVariance Inflation Factor (VIF) für ausgewählte Variabeln:\")\n",
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"print(vif)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"display_name": "hft_ml",
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"language": "python",
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"name": "python3"
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},
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@@ -194,7 +371,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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"version": "3.11.10"
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}
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},
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"nbformat": 4,
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Reference in New Issue
Block a user