YannAhlgrim
1916 lines
658 KiB
Plaintext
1916 lines
658 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7ba05a39",
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"metadata": {},
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"source": [
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"# Simulationsstudie: Diebstahlerkennung eines Autos in Echtzeit\n",
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"\n",
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"**Name:** Yann Ahlgrim \n",
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"\n",
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"In dieser Simulationsstudie wird ein Modell entwickelt, das anhand von Fahrverhaltensdaten erkennt, ob ein anderer Fahrer als üblich am Steuer sitzt. Ziel ist es, eine Wahrscheinlichkeit für einen Fahrerwechsel zu schätzen und bei hoher Wahrscheinlichkeit eine Diebstahlwarnung auszugeben. Die Studie umfasst die Generierung einer synthetischen Grundgesamtheit, die Simulation der Data Scientist Perspektive und die Analyse der Modellgüte."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f04292ef",
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"metadata": {},
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"source": [
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"## Erzeugung der Grundgesamtheit"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a5fe9381",
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"metadata": {},
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"source": [
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"### Datengrundlage\n",
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"\n",
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"#### Erklärende Variablen:\n",
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"\n",
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"1. **Durchschnittsgeschwindigkeit (metrisch):** \n",
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" Die Werte verteilen sich natürlicherweise ungefähr normal um ca. 47 km/h (29 mph) mit großer Streuung. Aggressive Fahrer tendieren zu höheren Durchschnittsgeschwindigkeiten. Diese Variable ist erklärend, da ein anderer Fahrer oft abweichende Geschwindigkeitsprofile aufweist. \n",
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" → Die Verteilung basiert auf: [NHTSA 100-Car Naturalistic Driving Study](https://www.nhtsa.gov/sites/nhtsa.gov/files/100carmain.pdf)\n",
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"\n",
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"2. **Schaltverhalten (ordinal):** \n",
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" Charakteristisches Schaltmuster bei Handschaltgetrieben, insbesondere der Drehzahlbereich beim Hochschalten. Fahrer unterscheiden sich deutlich: \n",
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" - *frueh* (ökonomisches Fahren), \n",
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" - *normal*, \n",
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" - *spaet* (sportliches/aggressives Fahren). \n",
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" Diese Variable dürfte prädiktiv sein, da Schaltgewohnheiten fahrerspezifisch sind. Studien zeigen, dass sie zur Fahreridentifikation genutzt werden können. \n",
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" → Quelle: [Driver Identification Based on Gear Shift Events and Attention-Based Bidirectional Long Short-Term Memory for Manual Transmission System)](https://ieeexplore.ieee.org/document/10034086)\n",
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"\n",
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"3. **Harte Bremsmanöver (metrisch):** \n",
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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, 3 Kategorien):** \n",
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" haeufigkeit 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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" - *haeufig* zu schnell. \n",
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" Diese Variable ist prädiktiv, da ein fremder Fahrer oft ein anderes Risikoverhalten beim Speeding zeigt.\n",
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" Laut einer AAA-Verkehrssicherheitsstudie geben nahezu 50 % der Fahrer an, auf Autobahnen im letzten Monat ~15 mph (~24 km/h) über dem Tempolimit gefahren zu sein.\n",
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" → Quelle: [87 Percent of Drivers Engage in Unsafe Behaviors While Behind the Wheel](https://newsroom.aaa.com/2016/02/87-percent-of-drivers-engage-in-unsafe-behaviors-while-behind-the-wheel/)\n",
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"\n",
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"---\n",
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"\n",
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"#### Nicht erklärende Variablen:\n",
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"\n",
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"\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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"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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]
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},
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{
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"cell_type": "markdown",
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"id": "1468207b",
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"metadata": {},
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"source": [
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"### Bibliotheken Imports"
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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": 163,
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"id": "97ba29a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import der benötigten Bibliotheken\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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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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]
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},
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{
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"cell_type": "markdown",
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"id": "27947656",
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"metadata": {},
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"source": [
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"### Verteilungen der Variablen"
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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": 164,
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"id": "61247da5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 1. Durchschnittsgeschwindigkeit (metrisch, normalverteilt)\n",
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"avg_speed = np.clip(np.random.normal(loc=47, scale=10, size=N), 10, 130)\n",
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"\n",
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"# 2. Schaltverhalten (ordinal, 3 Kategorien)\n",
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"shift_behavior = np.random.choice(['frueh', 'normal', 'spaet'], size=N, p=[0.4, 0.4, 0.2])\n",
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"\n",
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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) – 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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"speed_scaled = (avg_speed - avg_speed.min()) / (avg_speed.max() - avg_speed.min())\n",
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"strength = 0.18\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 # haeufig\n",
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"probs = softmax(logits)\n",
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"speeding = [np.random.choice(['selten', 'manchmal', 'haeufig'], p=p) for p in probs]\n",
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"\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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"# 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) – Abhängigkeit von trip_distance mit Softmax-Funktion\n",
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"# Skaliere trip_distance auf [0, 1]\n",
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"dist_scaled = (trip_distance - trip_distance.min()) / (trip_distance.max() - trip_distance.min())\n",
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"road_strength = 0.18\n",
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"road_logits = np.zeros((N, 3))\n",
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"road_logits[:, 0] = 1 - road_strength * dist_scaled # Innerorts\n",
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"road_logits[:, 1] = 1 # Außerorts\n",
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"road_logits[:, 2] = 1 + road_strength * dist_scaled # Autobahn\n",
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"road_probs = softmax(road_logits)\n",
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"road_type = [np.random.choice(['Innerorts', 'Außerorts', 'Autobahn'], p=p) for p 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])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "defebcc1",
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"metadata": {},
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"source": [
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"### Zusammenführen der Daten in ein DataFrame"
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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": 165,
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"id": "f8e0efb0",
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"metadata": {},
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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>frueh</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>Innerorts</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>frueh</td>\n",
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" <td>3</td>\n",
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" <td>haeufig</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>spaet</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>haeufig</td>\n",
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" <td>trocken</td>\n",
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" <td>5.732684</td>\n",
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" <td>Innerorts</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>spaet</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 frueh 2 manchmal trocken 30.449962 \n",
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"1 44.139270 frueh 3 haeufig trocken 11.911103 \n",
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"2 42.154349 spaet 2 selten trocken 6.086055 \n",
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"3 20.466814 normal 5 haeufig trocken 5.732684 \n",
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"4 46.917154 spaet 2 selten trocken 7.256758 \n",
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"\n",
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" road_type weekday \n",
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"0 Innerorts 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 Innerorts 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": 165,
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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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" '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": 166,
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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 2.570716\n",
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"1 speeding_manchmal 1.819868\n",
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"2 speeding_selten 1.750848\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']\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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||
"cell_type": "code",
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||
"execution_count": 167,
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||
"id": "1d3df64a",
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||
"metadata": {},
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||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"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 trip_distance 1.677148\n",
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"1 road_type_Außerorts 1.342378\n",
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"2 road_type_Innerorts 1.334770\n"
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]
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}
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],
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"source": [
|
||
"vif = pd.DataFrame()\n",
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"columns = ['road_type', 'trip_distance']\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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||
"cell_type": "code",
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||
"execution_count": 168,
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||
"id": "49045ee5",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
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"data": {
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"<div>\n",
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"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
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|
||
"\n",
|
||
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|
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|
||
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|
||
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|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>avg_speed</th>\n",
|
||
" <th>shift_behavior</th>\n",
|
||
" <th>hard_brakes</th>\n",
|
||
" <th>speeding</th>\n",
|
||
" <th>weather</th>\n",
|
||
" <th>trip_distance</th>\n",
|
||
" <th>road_type</th>\n",
|
||
" <th>weekday</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>64.494547</td>\n",
|
||
" <td>frueh</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>manchmal</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>30.449962</td>\n",
|
||
" <td>Innerorts</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>44.139270</td>\n",
|
||
" <td>frueh</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>haeufig</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>11.911103</td>\n",
|
||
" <td>Autobahn</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>42.154349</td>\n",
|
||
" <td>spaet</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>selten</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>6.086055</td>\n",
|
||
" <td>Autobahn</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>20.466814</td>\n",
|
||
" <td>normal</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>haeufig</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>5.732684</td>\n",
|
||
" <td>Innerorts</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>46.917154</td>\n",
|
||
" <td>spaet</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>selten</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>7.256758</td>\n",
|
||
" <td>Außerorts</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" avg_speed shift_behavior hard_brakes speeding weather trip_distance \\\n",
|
||
"0 64.494547 frueh 2 manchmal trocken 30.449962 \n",
|
||
"1 44.139270 frueh 3 haeufig trocken 11.911103 \n",
|
||
"2 42.154349 spaet 2 selten trocken 6.086055 \n",
|
||
"3 20.466814 normal 5 haeufig trocken 5.732684 \n",
|
||
"4 46.917154 spaet 2 selten trocken 7.256758 \n",
|
||
"\n",
|
||
" road_type weekday \n",
|
||
"0 Innerorts Mo-Fr \n",
|
||
"1 Autobahn Mo-Fr \n",
|
||
"2 Autobahn Mo-Fr \n",
|
||
"3 Innerorts Mo-Fr \n",
|
||
"4 Außerorts Mo-Fr "
|
||
]
|
||
},
|
||
"execution_count": 168,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d27de9c5",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Dummies erzeugen"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 169,
|
||
"id": "4ad287b1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
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|
||
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|
||
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|
||
" <th></th>\n",
|
||
" <th>const</th>\n",
|
||
" <th>avg_speed</th>\n",
|
||
" <th>hard_brakes</th>\n",
|
||
" <th>trip_distance</th>\n",
|
||
" <th>shift_frueh</th>\n",
|
||
" <th>shift_normal</th>\n",
|
||
" <th>shift_spaet</th>\n",
|
||
" <th>speeding_haeufig</th>\n",
|
||
" <th>speeding_manchmal</th>\n",
|
||
" <th>speeding_selten</th>\n",
|
||
" <th>weather_nass</th>\n",
|
||
" <th>weather_trocken</th>\n",
|
||
" <th>weather_winterlich</th>\n",
|
||
" <th>road_Autobahn</th>\n",
|
||
" <th>road_Außerorts</th>\n",
|
||
" <th>road_Innerorts</th>\n",
|
||
" <th>weekday_Mo-Fr</th>\n",
|
||
" <th>weekday_Sa</th>\n",
|
||
" <th>weekday_So</th>\n",
|
||
" </tr>\n",
|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
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|
||
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|
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|
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|
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|
||
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|
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|
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
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|
||
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||
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|
||
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|
||
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|
||
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" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" const avg_speed hard_brakes trip_distance shift_frueh shift_normal \\\n",
|
||
"0 1.0 64.494547 2.0 30.449962 1.0 0.0 \n",
|
||
"1 1.0 44.139270 3.0 11.911103 1.0 0.0 \n",
|
||
"2 1.0 42.154349 2.0 6.086055 0.0 0.0 \n",
|
||
"3 1.0 20.466814 5.0 5.732684 0.0 1.0 \n",
|
||
"4 1.0 46.917154 2.0 7.256758 0.0 0.0 \n",
|
||
"\n",
|
||
" shift_spaet speeding_haeufig speeding_manchmal speeding_selten \\\n",
|
||
"0 0.0 0.0 1.0 0.0 \n",
|
||
"1 0.0 1.0 0.0 0.0 \n",
|
||
"2 1.0 0.0 0.0 1.0 \n",
|
||
"3 0.0 1.0 0.0 0.0 \n",
|
||
"4 1.0 0.0 0.0 1.0 \n",
|
||
"\n",
|
||
" weather_nass weather_trocken weather_winterlich road_Autobahn \\\n",
|
||
"0 0.0 1.0 0.0 0.0 \n",
|
||
"1 0.0 1.0 0.0 1.0 \n",
|
||
"2 0.0 1.0 0.0 1.0 \n",
|
||
"3 0.0 1.0 0.0 0.0 \n",
|
||
"4 0.0 1.0 0.0 0.0 \n",
|
||
"\n",
|
||
" road_Außerorts road_Innerorts weekday_Mo-Fr weekday_Sa weekday_So \n",
|
||
"0 0.0 1.0 1.0 0.0 0.0 \n",
|
||
"1 0.0 0.0 1.0 0.0 0.0 \n",
|
||
"2 0.0 0.0 1.0 0.0 0.0 \n",
|
||
"3 0.0 1.0 1.0 0.0 0.0 \n",
|
||
"4 1.0 0.0 1.0 0.0 0.0 "
|
||
]
|
||
},
|
||
"execution_count": 169,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"shift_dummies = pd.get_dummies(shift_behavior, prefix='shift')\n",
|
||
"speeding_dummies = pd.get_dummies(speeding, prefix='speeding')\n",
|
||
"weather_dummies = pd.get_dummies(weather, prefix='weather')\n",
|
||
"road_dummies = pd.get_dummies(road_type, prefix='road')\n",
|
||
"weekday_dummies = pd.get_dummies(weekday, prefix='weekday')\n",
|
||
"\n",
|
||
"X = pd.DataFrame({\n",
|
||
" 'const': np.ones(N), # fuer konstante\n",
|
||
" 'avg_speed': avg_speed,\n",
|
||
" 'hard_brakes': hard_brakes,\n",
|
||
" 'trip_distance': trip_distance\n",
|
||
"})\n",
|
||
"X = pd.concat([X, shift_dummies, speeding_dummies, weather_dummies, road_dummies, weekday_dummies], axis=1)\n",
|
||
"\n",
|
||
"X = X.astype(float)\n",
|
||
"\n",
|
||
"X.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1f6ee727",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Zielvariable erzeugen"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 170,
|
||
"id": "75318d77",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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tRx99VMOGDdODDz5Y5+0KCgp0+PBheXt7V2tbfn7+GT+jcePGaevWrfrss88k/XzPpN1u1+jRo82at956SyNHjlTbtm21fPlybd26VVlZWRo3bpyOHz9ebZ91Pff58+frr3/9q3r16qWVK1cqMzNTWVlZGjx4cI1B49TPpWpZWVmZjh49WuMxqj67W265pdr1mTNnjgzDMIdp1qSqHSeHxbqq+qPc6XSetqagoECffPJJtbb5+fnJMIxqn9/proH0v++Hp59+WlOnTtWqVat07bXXqkWLFrrxxhv15ZdfnrHN+/bt09VXX63vvvtOTz31lD788ENlZWWZ98Cd+rnU9H1mt9vrHRTro7KyUgMHDtRbb72lKVOmaP369dq+fbsyMzOrtXH79u3at29fjaGtLm2v78/Ympzp59jJmjZtek6vHfB7xeyRABqNj4+PRo8ereeff155eXl68cUX5efnp1tvvdWsadWqlbp3767HH3+8xn2c+sdifZ6b1qpVK0nStGnTTjtNdn2nprbb7TVOyFH1x2Z9Vf0xVFBQoLZt25rLT5w4Uad9tmzZUtu3b6+2/NSJSM7mWtTnWn/++eeKiYlRp06dtGzZsnp/Ti1btlRaWlqN6/38/GrdfvTo0UpMTNTSpUv1+OOPa9myZbrxxhvdesqWL1+ujh076vXXX3dr2+kmV6lr+5cvX66+fftqyZIlbstPNxFDTRPE5Ofny9vbW82bN69xm6rPbtGiRaed3a+2R2dUbV9bsDud1atXy2az6Zprrql1/z4+PjVOgHHy8auc7hpI//t+aNasmWbMmKEZM2aooKDA7HUbNmyYGc5PZ9WqVTp27Jjeeustt5kWf8lzHBtabm6uPv74Yy1dulRjx441l3/11VfValeuXKmLL764Ts+Tq0l9f8b+UkVFRdU+cwBnRmgD0KjGjx+vZ599Vk888YTWrl2r2NhYt4kpoqKitHbtWl100UV1Ho52qtP9r2+XLl3UuXNnffzxx0pOTj77kzhJhw4d9Mknn7gte//990/bS3ImVX8Mv/7667riiivM5W+++WadHmdw7bXX6t///rdWr17tNoTp1Kndz8W1qHLkyBHddNNNqqysVGpqqtszveoiKipKKSkpqqioUK9evep9/ICAAN1444165ZVXFBERofz8/GpDI202m7y9vd3CWH5+fo2zR9aHzWYzv/6qfPLJJ9q6datCQkKq1b/11lt64oknzF6vI0eO6J133tHVV18tDw+PGo/Rp08fXXDBBdq1a9dZPZi6a9eukn6e+KI+XnrpJa1bt07R0dFuQ/BOFRUVpeTkZLVs2VIdO3Y8437Xr1+vgoICM2hWVFTo9ddf10UXXVTjM8KCgoIUGxurjz/+WAsXLtRPP/0kX19ft+97Hx8fs77qMz75czEMQ88//3zdTvxXUFMbpZ9n1TzVypUrqw2fro+G+BlbVwcPHtTx48d/c4/1AKyA0AagUfXs2VPdu3fXwoULZRhGtWezzZw5UxkZGerdu7fi4+PVpUsXHT9+XHv37tXatWv17LPPnvFhrxdddJF8fHy0YsUKde3aVc2bN5fT6ZTT6dQ///lPDRkyRIMGDVJsbKzatm2rH3/8Ubt379Z///tfvfHGG/U6n5iYGD300EN6+OGHFRkZqV27dmnx4sX1DipVLr30Uo0ePVpPPvmkPDw8dN111+nTTz/Vk08+KYfDoSZNah/lfscdd2jBggW644479Pjjj6tz585au3at3nvvvWq1DX0tTm7D7t27NXnyZB05csQc4nUyu91+2ud73XbbbVqxYoWuv/563Xvvvbryyivl5eWlAwcOaMOGDbrhhhvcHs5dk3Hjxun111/X3XffrXbt2ql///5u66OiovTWW29p4sSJuuWWW7R//349+uijatOmTZ2G3J1OVFSUHn30UT3yyCOKjIzU559/rpkzZ6pjx441hm4PDw8NGDBAiYmJqqys1Jw5c1RcXKwZM2ac9hjNmzfXokWLNHbsWP3444+65ZZbFBgYqEOHDunjjz/WoUOHqvX0naxdu3a68MILlZmZqfj4+GrrS0pK3IblffPNN1q1apXeffddRUZG6tlnn631GiQkJGjlypW65pprdN9996l79+6qrKzUvn37lJ6erqSkJLcw3qpVK1133XV66KGH1KxZMz3zzDP67LPP3Kb979Wrl6KiotS9e3cFBARo9+7dWrZsmSIiIsz/9OnWrZskac6cORoyZIg8PDzUvXt3DRgwQN7e3ho9erSmTJmi48ePa8mSJSoqKqr1PM6Gp6enIiMj631f2yWXXKKLLrpI999/vwzDUIsWLfTOO+8oIyPDrS4nJ0dff/31Wd1fWqUhfsbWVdXX0dnMVAqc9xpzFhQAMAzDeOqppwxJRmhoaI3rDx06ZMTHxxsdO3Y0vLy8jBYtWhjh4eHGAw88YBw9etQwjP/N3vbEE0/UuI/XXnvNuOSSSwwvL69qM6B9/PHHxsiRI43AwEDDy8vLCA4ONq677jrj2WefNWvqOntkaWmpMWXKFCMkJMTw8fExIiMjjZycnNPOHnnqjHo1Hef48eNGYmKiERgYaDRt2tS46qqrjK1btxoOh8O47777aru0hmEYxoEDB4ybb77ZaN68ueHn52fcfPPNxpYtW2qc7a4u1+J0bT8dSWd8tW/f3qw/dQZIwzCM8vJyY968ecZll11mNG3a1GjevLlxySWXGBMmTDC+/PLLWrc1jJ9nCAwJCTEkGQ888ECN7Zw9e7bRoUMHw263G127djWef/75084QOmnSpNOe68lfW6WlpcbkyZONtm3bGk2bNjWuuOIKY9WqVcbYsWPdzrnq63fOnDnGjBkzjHbt2hne3t7G5Zdfbrz33ntuxzjdTIMbN240hg4darRo0cLw8vIy2rZtawwdOtR44403amzryR566CEjICDAOH78uNvyqtkTq17NmjUzLrzwQuOWW24x3njjDXNG0ZOd+rVuGIZx9OhR48EHHzS6dOlieHt7Gw6Hw+jWrZtx3333uc0UWXVtn3nmGeOiiy4yvLy8jEsuucRYsWKF2/7uv/9+o2fPnkZAQIBht9uNCy+80LjvvvuM77//3u3a33XXXUbr1q0Nm83mds3eeecd82upbdu2xt/+9jdzJsyTv/dON3vm6T6/U7+fJNX49Xiqqs9079695rJdu3YZAwYMMPz8/IyAgADj1ltvNfbt2+f2Nfbggw+6teNk7du3N4YOHVpteU3fI3X5GVt1PjXNHlmXn2OGYRgxMTHVZrkEUDc2w6jD1E8AAEvZsmWL+vTpoxUrVlSbCRKor4MHD6pjx4565ZVXanyGH86tp556SgkJCTpy5Mhp712sSWhoqIYMGaInn3zyHLauYRQXF8vpdGrBggV1fv4bgP8htAGAxWVkZGjr1q0KDw+Xj4+PPv74Y82ePVsOh0OffPLJWc36B5xq6tSpWrdunXJycs447BYNw+VyaevWrZo2bZpOnDihnTt3NnaTzpkZM2bo9ddf1yeffFKnx5UAcMd3DQBYnL+/v9LT07Vw4UIdOXJErVq10pAhQzRr1iwCGxrMgw8+KF9fX3333Xc1TpKChrdjxw7ddNNN6t69u1544YXGbs455e/vr6VLlxLYgLNETxsAAAAAWBjjHwAAAADAwghtAAAAAGBhhDYAAAAAsDDuBv2VVVZW6uDBg/Lz85PNZmvs5gAAAABoJIZh6MiRI3I6nbXO3Eto+5UdPHiQWbkAAAAAmPbv36927dqddn2jhrYTJ05o+vTpWrFihfLz89WmTRvFxsbqwQcfNJOmYRiaMWOGnnvuORUVFalXr176xz/+oUsvvdTcT2lpqSZPnqzXXntNJSUl6tevn5555hm3Ey8qKlJ8fLxWr14tSRo+fLgWLVqkCy64wKzZt2+fJk2apPfff18+Pj6Kjo7WvHnz5O3tbdbs3LlTd999t7Zv364WLVpowoQJeuihh+rca+bn5yfp5w/G39//rK8dAAAAgN+24uJihYSEmBnhdBo1tM2ZM0fPPvusXn75ZV166aX66KOPdOedd8rhcOjee++VJM2dO1fz58/X0qVLdfHFF+uxxx7TgAED9Pnnn5snl5CQoHfeeUcpKSlq2bKlkpKSFBUVpezsbHl4eEiSoqOjdeDAAaWlpUmS/vznPysmJkbvvPOOJKmiokJDhw5V69attXnzZv3www8aO3asDMPQokWLJP18UQcMGKBrr71WWVlZ+uKLLxQbG6tmzZopKSmpTudcFe78/f0JbQAAAADO3AFkNKKhQ4ca48aNc1s2YsQIY8yYMYZhGEZlZaURHBxszJ4921x//Phxw+FwGM8++6xhGIZx+PBhw8vLy0hJSTFrvvvuO6NJkyZGWlqaYRiGsWvXLkOSkZmZadZs3brVkGR89tlnhmEYxtq1a40mTZoY3333nVnz2muvGXa73XC5XIZhGMYzzzxjOBwO4/jx42bNrFmzDKfTaVRWVtbpnF0ulyHJ3CcAAACA81Nds0Gjzh75pz/9SevXr9cXX3whSfr444+1efNmXX/99ZKkPXv2KD8/XwMHDjS3sdvtioyM1JYtWyRJ2dnZKi8vd6txOp0KCwsza7Zu3SqHw6FevXqZNVdddZUcDodbTVhYmJxOp1kzaNAglZaWKjs726yJjIyU3W53qzl48KD27t1b4zmWlpaquLjY7QUAAAAAddWowyOnTp0ql8ulSy65RB4eHqqoqNDjjz+u0aNHS5Ly8/MlSUFBQW7bBQUF6dtvvzVrvL29FRAQUK2mavv8/HwFBgZWO35gYKBbzanHCQgIkLe3t1tNhw4dqh2nal3Hjh2rHWPWrFmaMWPGmS8GAAAAANSgUUPb66+/ruXLl+vVV1/VpZdeqpycHCUkJMjpdGrs2LFm3aljPA3DOOO4z1NraqpviBrDME67rSRNmzZNiYmJ5vuqmw0BAACA3wvDMHTixAlVVFQ0dlMsxcPDQ56enr/4UV+NGtr+9re/6f7779dtt90mSerWrZu+/fZbzZo1S2PHjlVwcLAkmTNLViksLDR7uIKDg1VWVqaioiK33rbCwkL17t3brCkoKKh2/EOHDrntZ9u2bW7ri4qKVF5e7lZT1et28nGk6r2BVex2u9twSgAAAOD3pKysTHl5efrpp58auymW5OvrqzZt2rjNSF9fjRrafvrpp2oPkfPw8FBlZaUkqWPHjgoODlZGRoYuv/xyST9/UWzcuFFz5syRJIWHh8vLy0sZGRkaOXKkJCkvL0+5ubmaO3euJCkiIkIul0vbt2/XlVdeKUnatm2bXC6XGewiIiL0+OOPKy8vzwyI6enpstvtCg8PN2v+/ve/q6yszLzo6enpcjqd1YZNAgAAAL93lZWV2rNnjzw8POR0OuXt7f2Le5V+LwzDUFlZmQ4dOqQ9e/aoc+fOtT5AuzaNGtqGDRumxx9/XH/4wx906aWXaseOHZo/f77GjRsn6echhwkJCUpOTlbnzp3VuXNnJScny9fXV9HR0ZIkh8Oh8ePHKykpSS1btlSLFi00efJkdevWTf3795ckde3aVYMHD1ZcXJz++c9/Svp5yv+oqCh16dJFkjRw4ECFhoYqJiZGTzzxhH788UdNnjxZcXFx5tT80dHRmjFjhmJjY/X3v/9dX375pZKTk/Xwww/zxQkAAIDzTllZmSorKxUSEiJfX9/Gbo7l+Pj4yMvLS99++63KysrUtGnTs9pPo4a2RYsW6aGHHtLEiRNVWFgop9OpCRMm6OGHHzZrpkyZopKSEk2cONF8uHZ6errbA+gWLFggT09PjRw50ny49tKlS81ntEnSihUrFB8fb84yOXz4cC1evNhc7+HhoTVr1mjixInq06eP28O1qzgcDmVkZGjSpEnq2bOnAgIClJiY6HbPGgAAAHC+OdsepPNBQ1wbm1E1kwZ+FcXFxXI4HHK5XDxcGwAAAL9px48f1549e9SxY8ez7kX6vavtGtU1GxCJAQAAAMDCGnV4JAAAAIDfp0i/ob/q8TYeWfOrHu/XRE8bAAAAgPPWM888Yw5dDA8P14cfflhrfWlpqR544AG1b99edrtdF110kV588cVz2kZ62gAAAACcl15//XUlJCTomWeeUZ8+ffTPf/5TQ4YM0a5du/SHP/yhxm1GjhypgoICvfDCC+rUqZMKCwt14sSJc9pOQhsAAACA89L8+fM1fvx43XXXXZKkhQsX6r333tOSJUs0a9asavVpaWnauHGjvvnmG7Vo0UKSfpXnNTM8EgAAAMB5p6ysTNnZ2eYjwaoMHDhQW7ZsqXGb1atXq2fPnpo7d67atm2riy++WJMnT1ZJSck5bSs9bQAAAADOO99//70qKioUFBTktjwoKEj5+fk1bvPNN99o8+bNatq0qVJTU/X9999r4sSJ+vHHH8/pfW2ENgAAAADnLZvN5vbeMIxqy6pUVlbKZrNpxYoVcjgckn4eYnnLLbfoH//4h3x8fM5JGxkeCQAAAOC806pVK3l4eFTrVSssLKzW+1alTZs2atu2rRnYJKlr164yDEMHDhw4Z20ltAEAAAA473h7eys8PFwZGRluyzMyMtS7d+8at+nTp48OHjyoo0ePmsu++OILNWnSRO3atTtnbWV4JEy/9gMQAeBc+z0/aBUA8MslJiYqJiZGPXv2VEREhJ577jnt27dPf/nLXyRJ06ZN03fffadXXnlFkhQdHa1HH31Ud955p2bMmKHvv/9ef/vb3zRu3LhzNjRSIrQBAAAAOAd+C/9xNmrUKP3www+aOXOm8vLyFBYWprVr16p9+/aSpLy8PO3bt8+sb968uTIyMnTPPfeoZ8+eatmypUaOHKnHHnvsnLbTZhiGcU6PADfFxcVyOBxyuVzy9/dv7Oa4oacNwO/Nb+EPBgD4LTt+/Lj27Nmjjh07qmnTpo3dHEuq7RrVNRtwTxsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAPhFmNvw9Bri2hDaAAAAAJwVLy8vSdJPP/3UyC2xrqprU3WtzgbPaQMAAABwVjw8PHTBBReosLBQkuTr6yubzdbIrbIGwzD0008/qbCwUBdccIE8PDzOel+ENgAAAABnLTg4WJLM4AZ3F1xwgXmNzhahDQAAAMBZs9lsatOmjQIDA1VeXt7YzbEULy+vX9TDVoXQBgAAAOAX8/DwaJCAguqYiAQAAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwsEYNbR06dJDNZqv2mjRpkiTJMAxNnz5dTqdTPj4+6tu3rz799FO3fZSWluqee+5Rq1at1KxZMw0fPlwHDhxwqykqKlJMTIwcDoccDodiYmJ0+PBht5p9+/Zp2LBhatasmVq1aqX4+HiVlZW51ezcuVORkZHy8fFR27ZtNXPmTBmG0fAXBgAAAAD+v0YNbVlZWcrLyzNfGRkZkqRbb71VkjR37lzNnz9fixcvVlZWloKDgzVgwAAdOXLE3EdCQoJSU1OVkpKizZs36+jRo4qKilJFRYVZEx0drZycHKWlpSktLU05OTmKiYkx11dUVGjo0KE6duyYNm/erJSUFK1cuVJJSUlmTXFxsQYMGCCn06msrCwtWrRI8+bN0/z588/1ZQIAAABwHrMZFuoqSkhI0Lvvvqsvv/xSkuR0OpWQkKCpU6dK+rlXLSgoSHPmzNGECRPkcrnUunVrLVu2TKNGjZIkHTx4UCEhIVq7dq0GDRqk3bt3KzQ0VJmZmerVq5ckKTMzUxEREfrss8/UpUsXrVu3TlFRUdq/f7+cTqckKSUlRbGxsSosLJS/v7+WLFmiadOmqaCgQHa7XZI0e/ZsLVq0SAcOHJDNZqvTORYXF8vhcMjlcsnf379Br98vFek3tLGbAAANauORNY3dBAAATquu2cAy97SVlZVp+fLlGjdunGw2m/bs2aP8/HwNHDjQrLHb7YqMjNSWLVskSdnZ2SovL3ercTqdCgsLM2u2bt0qh8NhBjZJuuqqq+RwONxqwsLCzMAmSYMGDVJpaamys7PNmsjISDOwVdUcPHhQe/fuPe15lZaWqri42O0FAAAAAHVlmdC2atUqHT58WLGxsZKk/Px8SVJQUJBbXVBQkLkuPz9f3t7eCggIqLUmMDCw2vECAwPdak49TkBAgLy9vWutqXpfVVOTWbNmmffSORwOhYSEnP4iAAAAAMApLBPaXnjhBQ0ZMsStt0tStWGHhmGccSjiqTU11TdETdXI0traM23aNLlcLvO1f//+WtsOAAAAACezRGj79ttv9Z///Ed33XWXuSw4OFhS9V6swsJCs4crODhYZWVlKioqqrWmoKCg2jEPHTrkVnPqcYqKilReXl5rTWFhoaTqvYEns9vt8vf3d3sBAAAAQF1ZIrS99NJLCgwM1NCh/5sIo2PHjgoODjZnlJR+vu9t48aN6t27tyQpPDxcXl5ebjV5eXnKzc01ayIiIuRyubR9+3azZtu2bXK5XG41ubm5ysvLM2vS09Nlt9sVHh5u1mzatMntMQDp6elyOp3q0KFDA14NAAAAAPifRg9tlZWVeumllzR27Fh5enqay202mxISEpScnKzU1FTl5uYqNjZWvr6+io6OliQ5HA6NHz9eSUlJWr9+vXbs2KExY8aoW7du6t+/vySpa9euGjx4sOLi4pSZmanMzEzFxcUpKipKXbp0kSQNHDhQoaGhiomJ0Y4dO7R+/XpNnjxZcXFxZs9YdHS07Ha7YmNjlZubq9TUVCUnJysxMbHOM0cCAAAAQH15nrnk3PrPf/6jffv2ady4cdXWTZkyRSUlJZo4caKKiorUq1cvpaeny8/Pz6xZsGCBPD09NXLkSJWUlKhfv35aunSpPDw8zJoVK1YoPj7enGVy+PDhWrx4sbnew8NDa9as0cSJE9WnTx/5+PgoOjpa8+bNM2scDocyMjI0adIk9ezZUwEBAUpMTFRiYuK5uCwAAAAAIMliz2k7H/CcNgD49fCcNgCAlf3mntMGAAAAAKiO0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhjR7avvvuO40ZM0YtW7aUr6+vevTooezsbHO9YRiaPn26nE6nfHx81LdvX3366adu+ygtLdU999yjVq1aqVmzZho+fLgOHDjgVlNUVKSYmBg5HA45HA7FxMTo8OHDbjX79u3TsGHD1KxZM7Vq1Urx8fEqKytzq9m5c6ciIyPl4+Ojtm3baubMmTIMo2EvCgAAAAD8f40a2oqKitSnTx95eXlp3bp12rVrl5588kldcMEFZs3cuXM1f/58LV68WFlZWQoODtaAAQN05MgRsyYhIUGpqalKSUnR5s2bdfToUUVFRamiosKsiY6OVk5OjtLS0pSWlqacnBzFxMSY6ysqKjR06FAdO3ZMmzdvVkpKilauXKmkpCSzpri4WAMGDJDT6VRWVpYWLVqkefPmaf78+ef2QgEAAAA4b9mMRuwmuv/++/V///d/+vDDD2tcbxiGnE6nEhISNHXqVEk/96oFBQVpzpw5mjBhglwul1q3bq1ly5Zp1KhRkqSDBw8qJCREa9eu1aBBg7R7926FhoYqMzNTvXr1kiRlZmYqIiJCn332mbp06aJ169YpKipK+/fvl9PplCSlpKQoNjZWhYWF8vf315IlSzRt2jQVFBTIbrdLkmbPnq1FixbpwIEDstlsZzzn4uJiORwOuVwu+fv7/+Jr2JAi/YY2dhMAoEFtPLKmsZsAAMBp1TUbNGpP2+rVq9WzZ0/deuutCgwM1OWXX67nn3/eXL9nzx7l5+dr4MCB5jK73a7IyEht2bJFkpSdna3y8nK3GqfTqbCwMLNm69atcjgcZmCTpKuuukoOh8OtJiwszAxskjRo0CCVlpaawzW3bt2qyMhIM7BV1Rw8eFB79+6t8RxLS0tVXFzs9gIAAACAumrU0PbNN99oyZIl6ty5s9577z395S9/UXx8vF555RVJUn5+viQpKCjIbbugoCBzXX5+vry9vRUQEFBrTWBgYLXjBwYGutWcepyAgAB5e3vXWlP1vqrmVLNmzTLvo3M4HAoJCTnDVQEAAACA/2nU0FZZWakrrrhCycnJuvzyyzVhwgTFxcVpyZIlbnWnDjs0DOOMQxFPrampviFqqkaXnq4906ZNk8vlMl/79++vtd0AAAAAcLJGDW1t2rRRaGio27KuXbtq3759kqTg4GBJ1XuxCgsLzR6u4OBglZWVqaioqNaagoKCasc/dOiQW82pxykqKlJ5eXmtNYWFhZKq9wZWsdvt8vf3d3sBAAAAQF01amjr06ePPv/8c7dlX3zxhdq3by9J6tixo4KDg5WRkWGuLysr08aNG9W7d29JUnh4uLy8vNxq8vLylJuba9ZERETI5XJp+/btZs22bdvkcrncanJzc5WXl2fWpKeny263Kzw83KzZtGmT22MA0tPT5XQ61aFDh4a4JAAAAADgplFD23333afMzEwlJyfrq6++0quvvqrnnntOkyZNkvTzkMOEhAQlJycrNTVVubm5io2Nla+vr6KjoyVJDodD48ePV1JSktavX68dO3ZozJgx6tatm/r37y/p5967wYMHKy4uTpmZmcrMzFRcXJyioqLUpUsXSdLAgQMVGhqqmJgY7dixQ+vXr9fkyZMVFxdn9o5FR0fLbrcrNjZWubm5Sk1NVXJyshITE+s0cyQAAAAA1JdnYx78j3/8o1JTUzVt2jTNnDlTHTt21MKFC3X77bebNVOmTFFJSYkmTpyooqIi9erVS+np6fLz8zNrFixYIE9PT40cOVIlJSXq16+fli5dKg8PD7NmxYoVio+PN2eZHD58uBYvXmyu9/Dw0Jo1azRx4kT16dNHPj4+io6O1rx588wah8OhjIwMTZo0ST179lRAQIASExOVmJh4Li8TAAAAgPNYoz6n7XzEc9oA4NfDc9oAAFb2m3hOGwAAAACgdoQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAAC2vU0DZ9+nTZbDa3V3BwsLneMAxNnz5dTqdTPj4+6tu3rz799FO3fZSWluqee+5Rq1at1KxZMw0fPlwHDhxwqykqKlJMTIwcDoccDodiYmJ0+PBht5p9+/Zp2LBhatasmVq1aqX4+HiVlZW51ezcuVORkZHy8fFR27ZtNXPmTBmG0bAXBQAAAABO0ug9bZdeeqny8vLM186dO811c+fO1fz587V48WJlZWUpODhYAwYM0JEjR8yahIQEpaamKiUlRZs3b9bRo0cVFRWliooKsyY6Olo5OTlKS0tTWlqacnJyFBMTY66vqKjQ0KFDdezYMW3evFkpKSlauXKlkpKSzJri4mINGDBATqdTWVlZWrRokebNm6f58+ef4ysEAAAA4Hzm2egN8PR0612rYhiGFi5cqAceeEAjRoyQJL388ssKCgrSq6++qgkTJsjlcumFF17QsmXL1L9/f0nS8uXLFRISov/85z8aNGiQdu/erbS0NGVmZqpXr16SpOeff14RERH6/PPP1aVLF6Wnp2vXrl3av3+/nE6nJOnJJ59UbGysHn/8cfn7+2vFihU6fvy4li5dKrvdrrCwMH3xxReaP3++EhMTZbPZfqUrBgAAAOB80ug9bV9++aWcTqc6duyo2267Td98840kac+ePcrPz9fAgQPNWrvdrsjISG3ZskWSlJ2drfLycrcap9OpsLAws2br1q1yOBxmYJOkq666Sg6Hw60mLCzMDGySNGjQIJWWlio7O9usiYyMlN1ud6s5ePCg9u7de9rzKy0tVXFxsdsLAAAAAOqqUUNbr1699Morr+i9997T888/r/z8fPXu3Vs//PCD8vPzJUlBQUFu2wQFBZnr8vPz5e3trYCAgFprAgMDqx07MDDQrebU4wQEBMjb27vWmqr3VTU1mTVrlnkvncPhUEhISO0XBQAAAABO0qihbciQIbr55pvVrVs39e/fX2vWrJH08zDIKqcOOzQM44xDEU+tqam+IWqqJiGprT3Tpk2Ty+UyX/v376+17QAAAABwskYfHnmyZs2aqVu3bvryyy/N+9xO7cUqLCw0e7iCg4NVVlamoqKiWmsKCgqqHevQoUNuNacep6ioSOXl5bXWFBYWSqreG3gyu90uf39/txcAAAAA1JWlQltpaal2796tNm3aqGPHjgoODlZGRoa5vqysTBs3blTv3r0lSeHh4fLy8nKrycvLU25urlkTEREhl8ul7du3mzXbtm2Ty+Vyq8nNzVVeXp5Zk56eLrvdrvDwcLNm06ZNbo8BSE9Pl9PpVIcOHRr+YgAAAACAGjm0TZ48WRs3btSePXu0bds23XLLLSouLtbYsWNls9mUkJCg5ORkpaamKjc3V7GxsfL19VV0dLQkyeFwaPz48UpKStL69eu1Y8cOjRkzxhxuKUldu3bV4MGDFRcXp8zMTGVmZiouLk5RUVHq0qWLJGngwIEKDQ1VTEyMduzYofXr12vy5MmKi4sze8aio6Nlt9sVGxur3NxcpaamKjk5mZkjAQAAAJxTjTrl/4EDBzR69Gh9//33at26ta666iplZmaqffv2kqQpU6aopKREEydOVFFRkXr16qX09HT5+fmZ+1iwYIE8PT01cuRIlZSUqF+/flq6dKk8PDzMmhUrVig+Pt6cZXL48OFavHixud7Dw0Nr1qzRxIkT1adPH/n4+Cg6Olrz5s0zaxwOhzIyMjRp0iT17NlTAQEBSkxMVGJi4rm+TAAAAADOYzajajYN/CqKi4vlcDjkcrksd39bpN/Qxm4CADSojUfWNHYTAAA4rbpmA0vd0wYAAAAAcEdoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwjzrUpSYmFjnHc6fP/+sGwMAAAAAcFen0LZjx4467cxms/2ixgAAAAAA3NUptG3YsOFctwMAAAAAUAPuaQMAAAAAC6tTT9vJjh07ptmzZ2v9+vUqLCxUZWWl2/pvvvmmwRoHAAAAAOe7eoe2u+66Sxs3blRMTIzatGnDfWwAAAAAcA7VO7StW7dOa9asUZ8+fc5FewAAAAAAJ6n3PW0BAQFq0aLFuWgLAAAAAOAU9Q5tjz76qB5++GH99NNP56I9AAAAAICT1Gl45OWXX+5279pXX32loKAgdejQQV5eXm61//3vfxu2hQAAAABwHqtTaLvxxhvPcTMAAAAAADWpU2h75JFHznU7AAAAAAA14OHaAAAAAGBh9Z7yv6KiQgsWLNC///1v7du3T2VlZW7rf/zxxwZrHAAAAACc7+rd0zZjxgzNnz9fI0eOlMvlUmJiokaMGKEmTZpo+vTp56CJAAAAAHD+qndoW7FihZ5//nlNnjxZnp6eGj16tP71r3/p4YcfVmZm5rloIwAAAACct+od2vLz89WtWzdJUvPmzeVyuSRJUVFRWrNmTcO2DgAAAADOc/UObe3atVNeXp4kqVOnTkpPT5ckZWVlyW63N2zrAAAAAOA8V+/QdtNNN2n9+vWSpHvvvVcPPfSQOnfurDvuuEPjxo0764bMmjVLNptNCQkJ5jLDMDR9+nQ5nU75+Piob9+++vTTT922Ky0t1T333KNWrVqpWbNmGj58uA4cOOBWU1RUpJiYGDkcDjkcDsXExOjw4cNuNfv27dOwYcPUrFkztWrVSvHx8dUmWdm5c6ciIyPl4+Ojtm3baubMmTIM46zPGQAAAADOpN6zR86ePdv89y233KKQkBD93//9nzp16qThw4efVSOysrL03HPPqXv37m7L586dq/nz52vp0qW6+OKL9dhjj2nAgAH6/PPP5efnJ0lKSEjQO++8o5SUFLVs2VJJSUmKiopSdna2PDw8JEnR0dE6cOCA0tLSJEl//vOfFRMTo3feeUfSzzNiDh06VK1bt9bmzZv1ww8/aOzYsTIMQ4sWLZIkFRcXa8CAAbr22muVlZWlL774QrGxsWrWrJmSkpLO6rwBAAAA4ExsRiN3FR09elRXXHGFnnnmGT322GPq0aOHFi5cKMMw5HQ6lZCQoKlTp0r6uVctKChIc+bM0YQJE+RyudS6dWstW7ZMo0aNkiQdPHhQISEhWrt2rQYNGqTdu3crNDRUmZmZ6tWrlyQpMzNTERER+uyzz9SlSxetW7dOUVFR2r9/v5xOpyQpJSVFsbGxKiwslL+/v5YsWaJp06apoKDAHAY6e/ZsLVq0SAcOHJDNZqvT+RYXF8vhcMjlcsnf37+hL+cvEuk3tLGbAAANauMR7rUGAFhXXbNBvYdHenh46Nprr632PLaCggKzZ6s+Jk2apKFDh6p///5uy/fs2aP8/HwNHDjQXGa32xUZGaktW7ZIkrKzs1VeXu5W43Q6FRYWZtZs3bpVDofDDGySdNVVV8nhcLjVhIWFmYFNkgYNGqTS0lJlZ2ebNZGRkW737Q0aNEgHDx7U3r17T3t+paWlKi4udnsBAAAAQF3VO7QZhqHS0lL17NlTubm51dbVR0pKiv773/9q1qxZ1dbl5+dLkoKCgtyWBwUFmevy8/Pl7e2tgICAWmsCAwOr7T8wMNCt5tTjBAQEyNvbu9aaqvdVNTWZNWuWeS+dw+FQSEjIaWsBAAAA4FT1Dm02m00rV67UsGHD1Lt3b7399ttu6+pq//79uvfee7V8+XI1bdq01uOdzDCMMx7n1Jqa6huipiqk1taeadOmyeVyma/9+/fX2nYAAAAAONlZ9bR5eHjoqaee0rx58zRq1Cg99thj9e5ly87OVmFhocLDw+Xp6SlPT09t3LhRTz/9tDw9PU/bi1VYWGiuCw4OVllZmYqKimqtKSgoqHb8Q4cOudWcepyioiKVl5fXWlNYWCipem/gyex2u/z9/d1eAAAAAFBX9Q5tJ/vzn/+stLQ0LVy4UDExMfXatl+/ftq5c6dycnLMV8+ePXX77bcrJydHF154oYKDg5WRkWFuU1ZWpo0bN6p3796SpPDwcHl5ebnV5OXlKTc316yJiIiQy+XS9u3bzZpt27bJ5XK51eTm5prPn5Ok9PR02e12hYeHmzWbNm1yewxAenq6nE6nOnToUK9zBwAAAIC6qndoa9++vduEI3379lVmZma1Z6OdiZ+fn8LCwtxezZo1U8uWLRUWFmY+sy05OVmpqanKzc1VbGysfH19FR0dLUlyOBwaP368kpKStH79eu3YsUNjxoxRt27dzIlNunbtqsGDBysuLk6ZmZnKzMxUXFycoqKi1KVLF0nSwIEDFRoaqpiYGO3YsUPr16/X5MmTFRcXZ/aMRUdHy263KzY2Vrm5uUpNTVVycrISExPrNSwUAAAAAOqj3s9p27NnT7VlnTp10o4dO2ochvhLTJkyRSUlJZo4caKKiorUq1cvpaenm89ok6QFCxbI09NTI0eOVElJifr166elS5e6BcsVK1YoPj7enGVy+PDhWrx4sbnew8NDa9as0cSJE9WnTx/5+PgoOjpa8+bNM2scDocyMjI0adIk9ezZUwEBAUpMTFRiYmKDnjMAAAAAnOysn9NWVlamwsJCVVZWui3/wx/+0CAN+73iOW0A8OvhOW0AACurazaod0/bF198ofHjx5vPOKtSNdNiRUVF/VsLAAAAAKhRvUPbnXfeKU9PT7377rtq06YN93MBAAAAwDlU79CWk5Oj7OxsXXLJJeeiPQAAAACAk9R79sjQ0FB9//3356ItAAAAAIBT1Du0zZkzR1OmTNEHH3ygH374QcXFxW4vAAAAAEDDqffwyKrnn/Xr189tORORAAAAAEDDq3do27Bhw2nX7dix4xc1BgAAAADgrt6hLTIy0u29y+XSihUr9K9//Usff/yxEhISGqptAAAAAHDeq/c9bVXef/99jRkzRm3atNGiRYt0/fXX66OPPmrItgEAAADAea9ePW0HDhzQ0qVL9eKLL+rYsWMaOXKkysvLtXLlSoWGhp6rNgIAAADAeavOPW3XX3+9QkNDtWvXLi1atEgHDx7UokWLzmXbAAAAAOC8V+eetvT0dMXHx+uvf/2rOnfufC7bBAAAAAD4/+rc0/bhhx/qyJEj6tmzp3r16qXFixfr0KFD57JtAAAAAHDeq3Noi4iI0PPPP6+8vDxNmDBBKSkpatu2rSorK5WRkaEjR46cy3YCAAAAwHmp3rNH+vr6aty4cdq8ebN27typpKQkzZ49W4GBgRo+fPi5aCMAAAAAnLfOesp/SerSpYvmzp2rAwcO6LXXXmuoNgEAAAAA/r9fFNqqeHh46MYbb9Tq1asbYncAAAAAgP+vQUIbAAAAAODcILQBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsLBGDW1LlixR9+7d5e/vL39/f0VERGjdunXmesMwNH36dDmdTvn4+Khv37769NNP3fZRWlqqe+65R61atVKzZs00fPhwHThwwK2mqKhIMTExcjgccjgciomJ0eHDh91q9u3bp2HDhqlZs2Zq1aqV4uPjVVZW5lazc+dORUZGysfHR23bttXMmTNlGEbDXhQAAAAAOEmjhrZ27dpp9uzZ+uijj/TRRx/puuuu0w033GAGs7lz52r+/PlavHixsrKyFBwcrAEDBujIkSPmPhISEpSamqqUlBRt3rxZR48eVVRUlCoqKsya6Oho5eTkKC0tTWlpacrJyVFMTIy5vqKiQkOHDtWxY8e0efNmpaSkaOXKlUpKSjJriouLNWDAADmdTmVlZWnRokWaN2+e5s+f/ytcKQAAAADnK5thsa6iFi1a6IknntC4cePkdDqVkJCgqVOnSvq5Vy0oKEhz5szRhAkT5HK51Lp1ay1btkyjRo2SJB08eFAhISFau3atBg0apN27dys0NFSZmZnq1auXJCkzM1MRERH67LPP1KVLF61bt05RUVHav3+/nE6nJCklJUWxsbEqLCyUv7+/lixZomnTpqmgoEB2u12SNHv2bC1atEgHDhyQzWar0/kVFxfL4XDI5XLJ39+/oS/fLxLpN7SxmwAADWrjkTWN3QQAAE6rrtnAMve0VVRUKCUlRceOHVNERIT27Nmj/Px8DRw40Kyx2+2KjIzUli1bJEnZ2dkqLy93q3E6nQoLCzNrtm7dKofDYQY2SbrqqqvkcDjcasLCwszAJkmDBg1SaWmpsrOzzZrIyEgzsFXVHDx4UHv37j3teZWWlqq4uNjtBQAAAAB11eihbefOnWrevLnsdrv+8pe/KDU1VaGhocrPz5ckBQUFudUHBQWZ6/Lz8+Xt7a2AgIBaawIDA6sdNzAw0K3m1OMEBATI29u71pqq91U1NZk1a5Z5L53D4VBISEjtFwQAAAAATtLooa1Lly7KyclRZmam/vrXv2rs2LHatWuXuf7UYYeGYZxxKOKpNTXVN0RN1cjS2tozbdo0uVwu87V///5a2w4AAAAAJ2v00Obt7a1OnTqpZ8+emjVrli677DI99dRTCg4OllS9F6uwsNDs4QoODlZZWZmKiopqrSkoKKh23EOHDrnVnHqcoqIilZeX11pTWFgoqXpv4Mnsdrs5O2bVCwAAAADqqtFD26kMw1Bpaak6duyo4OBgZWRkmOvKysq0ceNG9e7dW5IUHh4uLy8vt5q8vDzl5uaaNREREXK5XNq+fbtZs23bNrlcLrea3Nxc5eXlmTXp6emy2+0KDw83azZt2uT2GID09HQ5nU516NCh4S8EAAAAAKiRQ9vf//53ffjhh9q7d6927typBx54QB988IFuv/122Ww2JSQkKDk5WampqcrNzVVsbKx8fX0VHR0tSXI4HBo/frySkpK0fv167dixQ2PGjFG3bt3Uv39/SVLXrl01ePBgxcXFKTMzU5mZmYqLi1NUVJS6dOkiSRo4cKBCQ0MVExOjHTt2aP369Zo8ebLi4uLMnrHo6GjZ7XbFxsYqNzdXqampSk5OVmJiYp1njgQAAACA+vJszIMXFBQoJiZGeXl5cjgc6t69u9LS0jRgwABJ0pQpU1RSUqKJEyeqqKhIvXr1Unp6uvz8/Mx9LFiwQJ6enho5cqRKSkrUr18/LV26VB4eHmbNihUrFB8fb84yOXz4cC1evNhc7+HhoTVr1mjixInq06ePfHx8FB0drXnz5pk1DodDGRkZmjRpknr27KmAgAAlJiYqMTHxXF8mAAAAAOcxyz2n7feO57QBwK+H57QBAKzsN/ecNgAAAABAdYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYZ6N3QAAAGAtkX5DG7sJANCgNh5Z09hN+EXoaQMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALCwRg1ts2bN0h//+Ef5+fkpMDBQN954oz7//HO3GsMwNH36dDmdTvn4+Khv37769NNP3WpKS0t1zz33qFWrVmrWrJmGDx+uAwcOuNUUFRUpJiZGDodDDodDMTExOnz4sFvNvn37NGzYMDVr1kytWrVSfHy8ysrK3Gp27typyMhI+fj4qG3btpo5c6YMw2i4iwIAAAAAJ2nU0LZx40ZNmjRJmZmZysjI0IkTJzRw4EAdO3bMrJk7d67mz5+vxYsXKysrS8HBwRowYICOHDli1iQkJCg1NVUpKSnavHmzjh49qqioKFVUVJg10dHRysnJUVpamtLS0pSTk6OYmBhzfUVFhYYOHapjx45p8+bNSklJ0cqVK5WUlGTWFBcXa8CAAXI6ncrKytKiRYs0b948zZ8//xxfKQAAAADnK5thoW6iQ4cOKTAwUBs3btQ111wjwzDkdDqVkJCgqVOnSvq5Vy0oKEhz5szRhAkT5HK51Lp1ay1btkyjRo2SJB08eFAhISFau3atBg0apN27dys0NFSZmZnq1auXJCkzM1MRERH67LPP1KVLF61bt05RUVHav3+/nE6nJCklJUWxsbEqLCyUv7+/lixZomnTpqmgoEB2u12SNHv2bC1atEgHDhyQzWardk6lpaUqLS013xcXFyskJEQul0v+/v7n9HrWV6Tf0MZuAgA0qI1H1jR2E36T+H0A4PfGqr8PiouL5XA4zpgNLHVPm8vlkiS1aNFCkrRnzx7l5+dr4MCBZo3dbldkZKS2bNkiScrOzlZ5eblbjdPpVFhYmFmzdetWORwOM7BJ0lVXXSWHw+FWExYWZgY2SRo0aJBKS0uVnZ1t1kRGRpqBrarm4MGD2rt3b43nNGvWLHNIpsPhUEhIyFlfHwAAAADnH8uENsMwlJiYqD/96U8KCwuTJOXn50uSgoKC3GqDgoLMdfn5+fL29lZAQECtNYGBgdWOGRgY6FZz6nECAgLk7e1da03V+6qaU02bNk0ul8t87d+//wxXAgAAAAD+x7OxG1Dl7rvv1ieffKLNmzdXW3fqsEPDMGocilhbTU31DVFTNbr0dO2x2+1uPXMAAAAAUB+W6Gm75557tHr1am3YsEHt2rUzlwcHB0uq3otVWFho9nAFBwerrKxMRUVFtdYUFBRUO+6hQ4fcak49TlFRkcrLy2utKSwslFS9NxAAAAAAGkKjhjbDMHT33Xfrrbfe0vvvv6+OHTu6re/YsaOCg4OVkZFhLisrK9PGjRvVu3dvSVJ4eLi8vLzcavLy8pSbm2vWREREyOVyafv27WbNtm3b5HK53Gpyc3OVl5dn1qSnp8tutys8PNys2bRpk9tjANLT0+V0OtWhQ4cGuioAAAAA8D+NGtomTZqk5cuX69VXX5Wfn5/y8/OVn5+vkpISST8POUxISFBycrJSU1OVm5ur2NhY+fr6Kjo6WpLkcDg0fvx4JSUlaf369dqxY4fGjBmjbt26qX///pKkrl27avDgwYqLi1NmZqYyMzMVFxenqKgodenSRZI0cOBAhYaGKiYmRjt27ND69es1efJkxcXFmTO5REdHy263KzY2Vrm5uUpNTVVycrISExPPOFwTAAAAAM5Go97TtmTJEklS37593Za/9NJLio2NlSRNmTJFJSUlmjhxooqKitSrVy+lp6fLz8/PrF+wYIE8PT01cuRIlZSUqF+/flq6dKk8PDzMmhUrVig+Pt6cZXL48OFavHixud7Dw0Nr1qzRxIkT1adPH/n4+Cg6Olrz5s0zaxwOhzIyMjRp0iT17NlTAQEBSkxMVGJiYkNfGgAAAACQZLHntJ0P6voshsbAc3kA/N5Y9bk8VsfvAwC/N1b9ffCbfE4bAAAAAMAdoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCGjW0bdq0ScOGDZPT6ZTNZtOqVavc1huGoenTp8vpdMrHx0d9+/bVp59+6lZTWlqqe+65R61atVKzZs00fPhwHThwwK2mqKhIMTExcjgccjgciomJ0eHDh91q9u3bp2HDhqlZs2Zq1aqV4uPjVVZW5lazc+dORUZGysfHR23bttXMmTNlGEaDXQ8AAAAAOFWjhrZjx47psssu0+LFi2tcP3fuXM2fP1+LFy9WVlaWgoODNWDAAB05csSsSUhIUGpqqlJSUrR582YdPXpUUVFRqqioMGuio6OVk5OjtLQ0paWlKScnRzExMeb6iooKDR06VMeOHdPmzZuVkpKilStXKikpyawpLi7WgAED5HQ6lZWVpUWLFmnevHmaP3/+ObgyAAAAAPAzm2GRriKbzabU1FTdeOONkn7uZXM6nUpISNDUqVMl/dyrFhQUpDlz5mjChAlyuVxq3bq1li1bplGjRkmSDh48qJCQEK1du1aDBg3S7t27FRoaqszMTPXq1UuSlJmZqYiICH322Wfq0qWL1q1bp6ioKO3fv19Op1OSlJKSotjYWBUWFsrf319LlizRtGnTVFBQILvdLkmaPXu2Fi1apAMHDshms9XpPIuLi+VwOORyueTv79+Ql/AXi/Qb2thNAIAGtfHImsZuwm8Svw8A/N5Y9fdBXbOBZe9p27Nnj/Lz8zVw4EBzmd1uV2RkpLZs2SJJys7OVnl5uVuN0+lUWFiYWbN161Y5HA4zsEnSVVddJYfD4VYTFhZmBjZJGjRokEpLS5WdnW3WREZGmoGtqubgwYPau3fvac+jtLRUxcXFbi8AAAAAqCvLhrb8/HxJUlBQkNvyoKAgc11+fr68vb0VEBBQa01gYGC1/QcGBrrVnHqcgIAAeXt711pT9b6qpiazZs0y76VzOBwKCQmp/cQBAAAA4CSWDW1VTh12aBjGGYcinlpTU31D1FSNLK2tPdOmTZPL5TJf+/fvr7XtAAAAAHAyy4a24OBgSdV7sQoLC80eruDgYJWVlamoqKjWmoKCgmr7P3TokFvNqccpKipSeXl5rTWFhYWSqvcGnsxut8vf39/tBQAAAAB1ZdnQ1rFjRwUHBysjI8NcVlZWpo0bN6p3796SpPDwcHl5ebnV5OXlKTc316yJiIiQy+XS9u3bzZpt27bJ5XK51eTm5iovL8+sSU9Pl91uV3h4uFmzadMmt8cApKeny+l0qkOHDg1/AQAAAABAjRzajh49qpycHOXk5Ej6efKRnJwc7du3TzabTQkJCUpOTlZqaqpyc3MVGxsrX19fRUdHS5IcDofGjx+vpKQkrV+/Xjt27NCYMWPUrVs39e/fX5LUtWtXDR48WHFxccrMzFRmZqbi4uIUFRWlLl26SJIGDhyo0NBQxcTEaMeOHVq/fr0mT56suLg4s2csOjpadrtdsbGxys3NVWpqqpKTk5WYmFjnmSMBAAAAoL48G/PgH330ka699lrzfWJioiRp7NixWrp0qaZMmaKSkhJNnDhRRUVF6tWrl9LT0+Xn52dus2DBAnl6emrkyJEqKSlRv379tHTpUnl4eJg1K1asUHx8vDnL5PDhw92eDefh4aE1a9Zo4sSJ6tOnj3x8fBQdHa158+aZNQ6HQxkZGZo0aZJ69uypgIAAJSYmmm0GAAAAgHPBMs9pO1/wnDYA+PVY9bk8VsfvAwC/N1b9ffCbf04bAAAAAIDQBgAAAACWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGEtrPwzDPPqGPHjmratKnCw8P14YcfNnaTAAAAAPxOEdrq6fXXX1dCQoIeeOAB7dixQ1dffbWGDBmiffv2NXbTAAAAAPwOEdrqaf78+Ro/frzuuusude3aVQsXLlRISIiWLFnS2E0DAAAA8Dvk2dgN+C0pKytTdna27r//frflAwcO1JYtW2rcprS0VKWlpeZ7l8slSSouLj53DT1LJ4zyxm4CADQoK/6s/S3g9wGA3xur/j6oapdhGLXWEdrq4fvvv1dFRYWCgoLclgcFBSk/P7/GbWbNmqUZM2ZUWx4SEnJO2ggA+B+Hw9HYTQAAWIDVfx8cOXKk1jYS2s6CzWZze28YRrVlVaZNm6bExETzfWVlpX788Ue1bNnytNsAv2fFxcUKCQnR/v375e/v39jNAQA0En4fAD/niCNHjsjpdNZaR2irh1atWsnDw6Nar1phYWG13rcqdrtddrvdbdkFF1xwrpoI/Gb4+/vzSxoAwO8DnPfq0gvIRCT14O3trfDwcGVkZLgtz8jIUO/evRupVQAAAAB+z+hpq6fExETFxMSoZ8+eioiI0HPPPad9+/bpL3/5S2M3DQAAAMDvEKGtnkaNGqUffvhBM2fOVF5ensLCwrR27Vq1b9++sZsG/CbY7XY98sgj1YYNAwDOL/w+AOrOZpxpfkkAAAAAQKPhnjYAAAAAsDBCGwAAAABYGKENAAAAACyM0Aag0XXo0EELFy5s7GYAAABYEqENwFmLjY2VzWbT7Nmz3ZavWrVKNputzvvJysrSn//854ZuHgDAQmJjY3XjjTc2djOA3yRCG4BfpGnTppozZ46KiorOeh+tW7eWr69vA7YKAADg94PQBuAX6d+/v4KDgzVr1qzT1mzZskXXXHONfHx8FBISovj4eB07dsxcf+rwSJvNpn/961+66aab5Ovrq86dO2v16tXn8jQAAL+itLQ0/elPf9IFF1ygli1bKioqSl9//XVjNwuwLEIbgF/Ew8NDycnJWrRokQ4cOFBt/c6dOzVo0CCNGDFCn3zyiV5//XVt3rxZd999d637nTFjhkaOHKlPPvlE119/vW6//Xb9+OOP5+o0AAC/omPHjikxMVFZWVlav369mjRpoptuukmVlZWN3TTAkghtAH6xm266ST169NAjjzxSbd0TTzyh6OhoJSQkqHPnzurdu7eefvppvfLKKzp+/Php9xkbG6vRo0erU6dOSk5O1rFjx7R9+/ZzeRoAgF/JzTffrBEjRqhz587q0aOHXnjhBe3cuVO7du1q7KYBlkRoA9Ag5syZo5dffrnaL9zs7GwtXbpUzZs3N1+DBg1SZWWl9uzZc9r9de/e3fx3s2bN5Ofnp8LCwnPWfgDAr+frr79WdHS0LrzwQvn7+6tjx46SpH379jVyywBr8mzsBgD4fbjmmms0aNAg/f3vf1dsbKy5vLKyUhMmTFB8fHy1bf7whz+cdn9eXl5u7202G8NmAOB3YtiwYQoJCdHzzz8vp9OpyspKhYWFqaysrLGbBlgSoQ1Ag5k9e7Z69Oihiy++2Fx2xRVX6NNPP1WnTp0asWUAAKv44YcftHv3bv3zn//U1VdfLUnavHlzI7cKsDaGRwJoMN26ddPtt9+uRYsWmcumTp2qrVu3atKkScrJydGXX36p1atX65577mnElgIAGktAQIBatmyp5557Tl999ZXef/99JSYmNnazAEsjtAFoUI8++qgMwzDfd+/eXRs3btSXX36pq6++WpdffrkeeughtWnTphFbCQD4tVVWVsrT01NNmjRRSkqKsrOzFRYWpvvuu09PPPFEYzcPsDSbcfJfVwAAAMA5MHjwYHXq1EmLFy9u7KYAvzn0tAEAAOCcKSoq0po1a/TBBx+of//+jd0c4DeJiUgAAABwzowbN05ZWVlKSkrSDTfc0NjNAX6TGB4JAAAAABbG8EgAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAFALm82mVatW1bk+NjZWN9544zlrz+lMnz5dPXr0qLWmb9++SkhI+FXaAwBoOIQ2AMB5KTY2VjabTTabTV5eXgoKCtKAAQP04osvqrKy0qzLy8vTkCFDftV2NUboAwBYF6ENAHDeGjx4sPLy8rR3716tW7dO1157re69915FRUXpxIkTkqTg4GDZ7fZGbikA4HxGaAMAnLfsdruCg4PVtm1bXXHFFfr73/+ut99+W+vWrdPSpUslVR8e+d1332nUqFEKCAhQy5YtdcMNN2jv3r3V9j1jxgwFBgbK399fEyZMUFlZmbnuzTffVLdu3eTj46OWLVuqf//+OnbsmKZPn66XX35Zb7/9ttkL+MEHH0iSpk6dqosvvli+vr668MIL9dBDD6m8vLzacZctW6YOHTrI4XDotttu05EjRxrykgEAGgGhDQCAk1x33XW67LLL9NZbb1Vb99NPP+naa69V8+bNtWnTJm3evFnNmzfX4MGD3ULZ+vXrtXv3bm3YsEGvvfaaUlNTNWPGDEk/D7ccPXq0xo0bp927d+uDDz7QiBEjZBiGJk+erJEjR5o9gHl5eerdu7ckyc/PT0uXLtWuXbv01FNP6fnnn9eCBQvc2vf1119r1apVevfdd/Xuu+9q48aNmj179jm8WgCAX4NnYzcAAACrueSSS/TJJ59UW56SkqImTZroX//6l2w2myTppZde0gUXXKAPPvhAAwcOlCR5e3vrxRdflK+vry699FLNnDlTf/vb3/Too48qLy9PJ06c0IgRI9S+fXtJUrdu3cxj+Pj4qLS0VMHBwW7HfvDBB81/d+jQQUlJSXr99dc1ZcoUc3llZaWWLl0qPz8/SVJMTIzWr1+vxx9/vIGuDACgMRDaAAA4hWEYZig7WXZ2tr766iszFFU5fvy4vv76a/P9ZZddJl9fX/N9RESEjh49qv379+uyyy5Tv3791K1bNw0aNEgDBw7ULbfcooCAgFrb9Oabb2rhwoX66quvdPToUZ04cUL+/v5uNR06dHBrW5s2bVRYWFivcwcAWA/DIwEAOMXu3bvVsWPHassrKysVHh6unJwct9cXX3yh6OjoM+7XZrPJw8NDGRkZWrdunUJDQ7Vo0SJ16dJFe/bsOe12mZmZuu222zRkyBC9++672rFjhx544AG3IZmS5OXlVe14J8+ECQD4bSK0AQBwkvfff187d+7UzTffXG3dFVdcoS+//FKBgYHq1KmT28vhcJh1H3/8sUpKSsz3mZmZat68udq1ayfp5zDVp08fzZgxQzt27JC3t7dSU1Ml/Ty0sqKiwu24//d//6f27dvrgQceUM+ePdW5c2d9++235+L0AQAWRGgDAJy3SktLlZ+fr++++07//e9/lZycrBtuuEFRUVG64447qtXffvvtatWqlW644QZ9+OGH2rNnjzZu3Kh7771XBw4cMOvKyso0fvx47dq1S+vWrdMjjzyiu+++W02aNNG2bduUnJysjz76SPv27dNbb72lQ4cOqWvXrpJ+HuL4ySef6PPPP9f333+v8vJyderUSfv27VNKSoq+/vprPf3002bIAwD8/nFPGwDgvJWWlqY2bdrI09NTAQEBuuyyy/T0009r7NixatKk+v9r+vr6atOmTZo6dapGjBihI0eOqG3bturXr5/b/WX9+vVT586ddc0116i0tFS33Xabpk+fLkny9/fXpk2btHDhQhUXF6t9+/Z68sknzQd4x8XF6YMPPlDPnj119OhRbdiwQTfccIPuu+8+3X333SotLdXQoUP10EMPmfsEAPy+2QzDMBq7EQAAAACAmjE8EgAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAAC/t/nNBsNbLEwZIAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"betas = np.array([\n",
|
||
" -2, # konstante\n",
|
||
" 0.015, # avg_speed\n",
|
||
" 0.1, # hard_brakes\n",
|
||
" 0., # trip_distance\n",
|
||
" -0.3, # shift_frueh (erklärend)\n",
|
||
" -0.3, # shift_normal (erklärend)\n",
|
||
" 0.5, # shift_spaet (erklärend)\n",
|
||
" -0.3, # speeding_selten (erklärend)\n",
|
||
" -0.3, # speeding_manchmal (erklärend)\n",
|
||
" 0.5, # speeding_haeufig (erklärend)\n",
|
||
" 0., # weather_nass\n",
|
||
" 0., # weather_trocken\n",
|
||
" 0., # weather_winterlich\n",
|
||
" 0., # road_Außerorts\n",
|
||
" 0., # road_Autobahn\n",
|
||
" 0., # road_Innerorts\n",
|
||
" 0., # weekday_Mo-Fr\n",
|
||
" 0., # weekday_Sa\n",
|
||
" 0. # weekday_So\n",
|
||
"])\n",
|
||
"\n",
|
||
"# Sicherstellen, dass die Dimension passt\n",
|
||
"X_model = X.iloc[:, :len(betas)]\n",
|
||
"\n",
|
||
"X_model = X_model.astype(float)\n",
|
||
"rauschen = np.random.normal(0, 0.2, size=N)\n",
|
||
"\n",
|
||
"# Linearkombination + Rauschen (offset is now in betas)\n",
|
||
"lin_comb = X_model.values @ betas + rauschen\n",
|
||
"\n",
|
||
"# Logistische Funktion → Wahrscheinlichkeiten\n",
|
||
"pi = 1 / (1 + np.exp(-lin_comb))\n",
|
||
"\n",
|
||
"# Zielvariable simulieren\n",
|
||
"target = np.random.binomial(1, pi)\n",
|
||
"\n",
|
||
"# Visualisierung der Verteilung der Zielvariable\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.countplot(x=target, palette='viridis', hue=0.6)\n",
|
||
"plt.title('Verteilung der Zielvariable (Diebstahl: ja/nein)')\n",
|
||
"plt.xlabel('Diebstahl')\n",
|
||
"plt.ylabel('Anzahl')\n",
|
||
"plt.xticks([0, 1], ['Nein', 'Ja'])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "825cc812",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Verteilungen der Variablen\n",
|
||
"\n",
|
||
"- **Durchschnittsgeschwindigkeit (`avg_speed`)**: normalverteilt mit Mittelwert 47 km/h und Standardabweichung 10, begrenzt auf den Bereich [10, 130].\n",
|
||
"- **Schaltverhalten (`shift_behavior`)**: ordinal skaliert mit 3 Kategorien, gezogen aus einer Multinomialverteilung (Wahrscheinlichkeiten: 40% früh, 40% normal, 20% spät).\n",
|
||
"- **Harte Bremsmanöver (`hard_brakes`)**: metrisch, modelliert mit einer Poisson-Verteilung (λ = 2).\n",
|
||
"- **Geschwindigkeitsüberschreitungen (`speeding`)**: ordinal skaliert mit 3 Stufen, abhängig von `avg_speed` über eine Softmax-basierte Wahrscheinlichkeitsverteilung.\n",
|
||
"- **Wetterbedingungen (`weather`)**: nominal skaliert, zufällig verteilt mit 75% trocken, 20% nass und 5% winterlich.\n",
|
||
"- **Fahrstrecke (`trip_distance`)**: lognormal verteilt mit μ = ln(12) und σ = 0.7.\n",
|
||
"- **Straßentyp (`road_type`)**: abhängig von `trip_distance`, mit kategorialer Softmax-Verteilung (Innerorts, Außerorts, Autobahn).\n",
|
||
"- **Wochentag (`weekday`)**: nominal mit festgelegter Verteilung (70% Werktage, je 15% Samstag und Sonntag).\n",
|
||
"\n",
|
||
"### Modellierte Abhängigkeiten\n",
|
||
"\n",
|
||
"- Die Variablen `speeding` und `road_type` wurden nicht rein zufällig erzeugt, sondern basieren auf skalierter Eingangsvariablen (`avg_speed` bzw. `trip_distance`) und einem gewichteten Softmax-Verfahren. Dies erlaubt realistischere Zusammenhänge.\n",
|
||
"\n",
|
||
"### Zielvariable und 𝑝ᵢ-Werte\n",
|
||
"\n",
|
||
"Die Zielvariable, die eine Diebstahlwahrscheinlichkeit beschreibt, wurde aus diesen Features modelliert. Die 𝑝ᵢ-Werte repräsentieren dabei individuelle Wahrscheinlichkeiten für Fahrerwechsel und wurden anhand von Beta-Verteilungen erzeugt.\n",
|
||
"\n",
|
||
"### Wahl und Begründung der Beta-Werte\n",
|
||
"\n",
|
||
"Die Beta-Gewichte wurden bewusst so gewählt, dass sie realistische Zusammenhänge zwischen den Merkmalen und dem Auftreten eines möglichen Diebstahls modellieren, ohne die Zielvariable künstlich zu verzerren. Das Ziel war ein realistischer, aber nicht übermäßig häufiger positiver Klassenanteil.\n",
|
||
"\n",
|
||
"- **Konstante (-2):** Sorgt für eine niedrige Grundwahrscheinlichkeit für Diebstahl, wenn keine auffälligen Merkmale vorliegen. Dadurch bleibt die Zielvariable realistisch balanciert.\n",
|
||
"- **avg_speed (0.015):** Ein leicht positiver Zusammenhang, da eine ungewöhnlich hohe Durchschnittsgeschwindigkeit auf einen anderen Fahrstil und damit auf einen möglichen Fahrerwechsel hindeuten kann.\n",
|
||
"- **hard_brakes (0.1):** Ein deutlicher positiver Effekt, da häufige harte Bremsmanöver ein Indiz für einen ungewohnten oder riskanten Fahrstil sind, der auf einen Fahrerwechsel hindeuten kann.\n",
|
||
"- **trip_distance (0):** Kein Einfluss, da die reine Fahrstrecke keine Aussage über den Fahrerwechsel zulässt.\n",
|
||
"- **shift_frueh (-0.3), shift_normal (-0.3), shift_spaet (0.5):** Das Schaltverhalten ist ein starker Prädiktor für den Fahrer. Besonders „spät“ schalten wird mit einem hohen positiven Beta gewichtet, da es einen aggressiveren Fahrstil beschreibt. „Früh“ und „normal“ schalten erhalten einen negativen Einfluss, da sie auf einen defensiveren oder gewohnten Fahrstil hindeuten.\n",
|
||
"- **speeding_selten (-0.3), speeding_manchmal (-0.3), speeding_haeufig (0.5):** Die Häufigkeit von Geschwindigkeitsüberschreitungen ist ein wichtiger Indikator für den Fahrstil. Besonders „häufig“ zu schnell fahren (0.5) ist ein starkes Signal für einen Fahrerwechsel, da dies ein sehr auffälliges Verhalten ist. „Selten“ und „manchmal“ werden negativ gewichtet, da sie auf einen defensiveren Fahrstil hindeuten.\n",
|
||
"- **weather_nass, weather_trocken, weather_winterlich (je 0):** Wetterbedingungen haben keinen Einfluss auf die Wahrscheinlichkeit eines Fahrerwechsels, da sie unabhängig vom Fahrer sind.\n",
|
||
"- **road_Außerorts, road_Autobahn, road_Innerorts (je 0):** Der Straßentyp ist nicht prädiktiv für einen Fahrerwechsel, da er nicht vom Fahrverhalten abhängt.\n",
|
||
"- **weekday_Mo-Fr, weekday_Sa, weekday_So (je 0):** Der Wochentag hat keinen Einfluss auf die Wahrscheinlichkeit eines Fahrerwechsels.\n",
|
||
"\n",
|
||
"\n",
|
||
"Alle Beta-Werte wurden bewusst reduziert (unterhalb von 0.1), um eine Übergewichtung einzelner Faktoren zu vermeiden und die Zielvariable `Diebstahl` in einem nahe zu realistischen Bereich zu halten. Eine stärkere Gewichtung hätte zu einer zu häufigen Klassifikation als Diebstahl geführt.\n",
|
||
"\n",
|
||
"```visualisierung\n",
|
||
"Die grafische Darstellung zeigt, dass rund 25 % der Fahrten als potenzielle Diebstähle klassifiziert wurden – ein realistischer Wert, der ein geeignetes Trainingsverhältnis für ein Klassifikationsmodell ermöglicht.\n",
|
||
"```\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "adbc166b",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Simulation der Perspektive des Data Scientist"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f8abd5f5",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Stichprobe entnehmen"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 171,
|
||
"id": "706ff5c6",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"sample_size = 20_000\n",
|
||
"sample_indices = np.random.choice(X.index, size=sample_size, replace=False)\n",
|
||
"X_sample = X.loc[sample_indices]\n",
|
||
"target_sample = target[sample_indices]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9b67610e",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Datenexploration und -vorverarbeitung\n",
|
||
"\n",
|
||
"Zunächst verschafft sich der Data Scientist einen Überblick über die Stichprobe, prüft die Verteilung der Zielvariable und der erklärenden Variablen, erkennt potenzielle Ausreißer und prüft auf fehlende Werte. Außerdem werden die Variablentypen für die Modellierung vorbereitet (z.B. One-Hot-Encoding für kategoriale Variablen)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 172,
|
||
"id": "9f12d325",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Form der Stichprobe: (20000, 19)\n",
|
||
"Anteil Zielvariable (Diebstahl):\n",
|
||
"0 0.77085\n",
|
||
"1 0.22915\n",
|
||
"Name: proportion, dtype: float64\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>const</th>\n",
|
||
" <th>avg_speed</th>\n",
|
||
" <th>hard_brakes</th>\n",
|
||
" <th>trip_distance</th>\n",
|
||
" <th>shift_frueh</th>\n",
|
||
" <th>shift_normal</th>\n",
|
||
" <th>shift_spaet</th>\n",
|
||
" <th>speeding_haeufig</th>\n",
|
||
" <th>speeding_manchmal</th>\n",
|
||
" <th>speeding_selten</th>\n",
|
||
" <th>weather_nass</th>\n",
|
||
" <th>weather_trocken</th>\n",
|
||
" <th>weather_winterlich</th>\n",
|
||
" <th>road_Autobahn</th>\n",
|
||
" <th>road_Außerorts</th>\n",
|
||
" <th>road_Innerorts</th>\n",
|
||
" <th>weekday_Mo-Fr</th>\n",
|
||
" <th>weekday_Sa</th>\n",
|
||
" <th>weekday_So</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>20000.0</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" <td>20000.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>46.955431</td>\n",
|
||
" <td>1.987300</td>\n",
|
||
" <td>15.300650</td>\n",
|
||
" <td>0.396750</td>\n",
|
||
" <td>0.405100</td>\n",
|
||
" <td>0.198150</td>\n",
|
||
" <td>0.365450</td>\n",
|
||
" <td>0.328300</td>\n",
|
||
" <td>0.306250</td>\n",
|
||
" <td>0.198100</td>\n",
|
||
" <td>0.753000</td>\n",
|
||
" <td>0.048900</td>\n",
|
||
" <td>0.339150</td>\n",
|
||
" <td>0.334750</td>\n",
|
||
" <td>0.326100</td>\n",
|
||
" <td>0.699600</td>\n",
|
||
" <td>0.147950</td>\n",
|
||
" <td>0.152450</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>9.983939</td>\n",
|
||
" <td>1.406321</td>\n",
|
||
" <td>12.277946</td>\n",
|
||
" <td>0.489236</td>\n",
|
||
" <td>0.490924</td>\n",
|
||
" <td>0.398616</td>\n",
|
||
" <td>0.481568</td>\n",
|
||
" <td>0.469606</td>\n",
|
||
" <td>0.460946</td>\n",
|
||
" <td>0.398578</td>\n",
|
||
" <td>0.431278</td>\n",
|
||
" <td>0.215664</td>\n",
|
||
" <td>0.473433</td>\n",
|
||
" <td>0.471915</td>\n",
|
||
" <td>0.468796</td>\n",
|
||
" <td>0.458443</td>\n",
|
||
" <td>0.355059</td>\n",
|
||
" <td>0.359466</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>10.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.764512</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>40.210704</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>7.442576</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>46.933676</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>11.922107</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>53.726714</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>19.153933</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>81.665638</td>\n",
|
||
" <td>10.000000</td>\n",
|
||
" <td>171.637620</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" const avg_speed hard_brakes trip_distance shift_frueh \\\n",
|
||
"count 20000.0 20000.000000 20000.000000 20000.000000 20000.000000 \n",
|
||
"mean 1.0 46.955431 1.987300 15.300650 0.396750 \n",
|
||
"std 0.0 9.983939 1.406321 12.277946 0.489236 \n",
|
||
"min 1.0 10.000000 0.000000 0.764512 0.000000 \n",
|
||
"25% 1.0 40.210704 1.000000 7.442576 0.000000 \n",
|
||
"50% 1.0 46.933676 2.000000 11.922107 0.000000 \n",
|
||
"75% 1.0 53.726714 3.000000 19.153933 1.000000 \n",
|
||
"max 1.0 81.665638 10.000000 171.637620 1.000000 \n",
|
||
"\n",
|
||
" shift_normal shift_spaet speeding_haeufig speeding_manchmal \\\n",
|
||
"count 20000.000000 20000.000000 20000.000000 20000.000000 \n",
|
||
"mean 0.405100 0.198150 0.365450 0.328300 \n",
|
||
"std 0.490924 0.398616 0.481568 0.469606 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"50% 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"75% 1.000000 0.000000 1.000000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 1.000000 \n",
|
||
"\n",
|
||
" speeding_selten weather_nass weather_trocken weather_winterlich \\\n",
|
||
"count 20000.000000 20000.000000 20000.000000 20000.000000 \n",
|
||
"mean 0.306250 0.198100 0.753000 0.048900 \n",
|
||
"std 0.460946 0.398578 0.431278 0.215664 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 1.000000 0.000000 \n",
|
||
"50% 0.000000 0.000000 1.000000 0.000000 \n",
|
||
"75% 1.000000 0.000000 1.000000 0.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 1.000000 \n",
|
||
"\n",
|
||
" road_Autobahn road_Außerorts road_Innerorts weekday_Mo-Fr \\\n",
|
||
"count 20000.000000 20000.000000 20000.000000 20000.000000 \n",
|
||
"mean 0.339150 0.334750 0.326100 0.699600 \n",
|
||
"std 0.473433 0.471915 0.468796 0.458443 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"50% 0.000000 0.000000 0.000000 1.000000 \n",
|
||
"75% 1.000000 1.000000 1.000000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 1.000000 \n",
|
||
"\n",
|
||
" weekday_Sa weekday_So \n",
|
||
"count 20000.000000 20000.000000 \n",
|
||
"mean 0.147950 0.152450 \n",
|
||
"std 0.355059 0.359466 \n",
|
||
"min 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 \n",
|
||
"50% 0.000000 0.000000 \n",
|
||
"75% 0.000000 0.000000 \n",
|
||
"max 1.000000 1.000000 "
|
||
]
|
||
},
|
||
"execution_count": 172,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Überblick über die Stichprobe\n",
|
||
"print(\"Form der Stichprobe:\", X_sample.shape)\n",
|
||
"print(\"Anteil Zielvariable (Diebstahl):\")\n",
|
||
"print(pd.Series(target_sample).value_counts(normalize=True))\n",
|
||
"\n",
|
||
"# Merkmale\n",
|
||
"X_sample.describe(include='all')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 173,
|
||
"id": "30bfcdde",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Fehlende Werte pro Spalte:\n",
|
||
"const 0\n",
|
||
"avg_speed 0\n",
|
||
"hard_brakes 0\n",
|
||
"trip_distance 0\n",
|
||
"shift_frueh 0\n",
|
||
"shift_normal 0\n",
|
||
"shift_spaet 0\n",
|
||
"speeding_haeufig 0\n",
|
||
"speeding_manchmal 0\n",
|
||
"speeding_selten 0\n",
|
||
"weather_nass 0\n",
|
||
"weather_trocken 0\n",
|
||
"weather_winterlich 0\n",
|
||
"road_Autobahn 0\n",
|
||
"road_Außerorts 0\n",
|
||
"road_Innerorts 0\n",
|
||
"weekday_Mo-Fr 0\n",
|
||
"weekday_Sa 0\n",
|
||
"weekday_So 0\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Prüfung auf fehlende Werte\n",
|
||
"print(\"Fehlende Werte pro Spalte:\")\n",
|
||
"print(X_sample.isnull().sum())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "201bca58",
|
||
"metadata": {},
|
||
"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. Außerdem werden die Daten in Trainings- und Testdaten aufgeteilt, um eine objektive Modellbewertung zu ermöglichen. 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."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 174,
|
||
"id": "b71ab517",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Trainingsdaten: (14000, 19)\n",
|
||
"Testdaten: (6000, 19)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"# Zielvariable\n",
|
||
"y = target_sample\n",
|
||
"\n",
|
||
"# One-Hot-Encoding für kategoriale Variablen\n",
|
||
"X_encoded = pd.get_dummies(X_sample, drop_first=True)\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",
|
||
"print(\"Trainingsdaten:\", X_train.shape)\n",
|
||
"print(\"Testdaten:\", X_test.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "761c0673",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Modellierung der Korrelationen und kategorischen Variablen"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 175,
|
||
"id": "8a320b4c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x1000 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Korrelationsmatrix\n",
|
||
"plt.figure(figsize=(12, 10))\n",
|
||
"sns.heatmap(X_train.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True, cbar_kws={\"shrink\": .8})\n",
|
||
"plt.title('Korrelationsmatrix der Merkmale')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "684d6fee",
|
||
"metadata": {},
|
||
"source": [
|
||
"Man kann erkennen das die kategorischen Variablen natürlich eine stärkere Korrelation haben, weil sie schon in Dummies umgewandelt wurden. Ansonsten gibt es keine stärkeren Korrelationen, sodass wir sehr wahrscheinlich kein Multikolinearitätsproblem haben."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 176,
|
||
"id": "b748cb80",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_7304\\146936503.py:6: FutureWarning: \n",
|
||
"\n",
|
||
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
|
||
"\n",
|
||
" sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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nJ9vVOnqf/s2JEyfk6up61yM03ulzlNHP150+R3f63N7u85nm7NmzkqR+/fql+8x1795d0v99D0ycOFHvv/++VqxYodq1a6tAgQJq0aLFbX+0AvBgMBohAIeaNWuWDMPQ0qVLtXTp0nTzZ8+erY8//tiuJyujQkJCNGPGDIWFhalBgwZ28w4dOqQffvhB4eHhateunTn9TgNK3E9eXl5KSUnRhQsX7A5ao6Oj7ery588vJycnBQcH6+23377tukqWLGn3/HaDAPyTTp06KTQ0VHPmzFGZMmV07tw5jRkzJl1bz58/bxe4DMNQdHR0ulHw/mn7rVu3lq+vrwYNGqTU1FRzsIS07UjSmTNn0gWx06dPq2DBgpnaLysVLFhQbm5ut/0hIG2+ldIO4hMTE83AL915VL/MfCYqVKigRYsWyTAM/fjjjwoPD9ewYcPk5uamDz744N4a/oD89ddf2r9/v2rWrGmOxJnZ1+xhkvZ5GjBggFq2bHnbmrJly0qSPDw8NHToUA0dOlRnz541e7maNWumw4cPP7A2A/g/hC0ADnP9+nXNnj1bpUuX1owZM9LNX716tcaMGaO1a9emO+Xr38ycOVNDhw7VsGHDbjuyWdoB6M0HXpI0ffr0TG3nbtSuXVujR4/W/Pnz1bNnT3P6ggUL7Orc3d1Vu3ZtHTx4UBUrVjR/Rb+fqlWrpoCAAIWFhalMmTLy9PS0OxWxbt26Gj16tObNm6c+ffqY07/66itdvXpVdevWzdT2Bg8erDx58qhPnz66evWqRo4cKUmqU6eOJGnevHl2AW7v3r2KiorSoEGD7mU376ugoCCNGDFCXl5e6cLug5B2r60ff/zR7rX6+uuv79s2bDabKlWqpHHjxik8PFwHDhy4b+u+1d32EN9OQkKCOnfurJSUFPXv39+c/iBeM6uULVtWfn5++uGHHzRixIgML+fj46P27dvrhx9+0Pjx4xUfH293CiSAB4OwBcBh1q5dq9OnT+uTTz657c1MAwICNHnyZM2cOTNTYWvnzp3q1q2bnn32WdWvXz/ddU3Vq1dXuXLlVLp0aX3wwQcyDEMFChTQ119/rfXr19/rbv2rBg0a6IUXXlD//v119epVVa1aVd9//73mzp2brnbChAl67rnn9Pzzz+utt95SiRIldPnyZR09elRff/21eU3VvejYsaP69u2rI0eO6M0337S7B1P9+vXVsGFDvf/++4qLi9Ozzz6rH3/8UUOGDFGVKlXu6qbHvXr1Uu7cudW1a1dduXJFEydOVNmyZdW1a1dNmjRJOXLkUOPGjfXHH3/of//7n4oVK2YX9Bytd+/e+uqrr/TCCy+oT58+qlixolJTU/Xnn39q3bp1evfddy29R1yTJk1UoEABderUScOGDZOzs7PCw8N18uTJe1rv6tWr9fnnn6tFixYqVaqUDMPQsmXLdOnSJdWvX/8+tT69PHnyqHjx4lq5cqXq1q2rAgUKqGDBgmZAupM///xTu3btUmpqqmJjY82bGp84cUJjxoyx68226jV7UKZPn67GjRurYcOGat++vYoWLaqLFy8qKipKBw4c0Jdffinpxo8nQUFBqlixovLnz6+oqCjNnTtXgYGBBC3AQQhbABxm5syZypkzpzkYw60KFiyol156SUuXLjWvW8iII0eOKCUlRd9//70CAwPTzTcMQy4uLvr666/Vq1cvvfnmm3J2dla9evW0YcMGPf7443e9TxmRI0cOrVq1Sn379tXo0aOVlJSkZ599Vt98843KlStnV1u+fHkdOHBAH330kQYPHqxz584pX7588vPzs7sA/14EBwfrgw8+UFJSknlvrTQ2m00rVqxQSEiIwsLCNHz4cBUsWFDBwcEaMWJEup7BjOrUqZM8PDwUHBysq1evasaMGZo6dapKly6tmTNnasqUKfL09FSjRo00cuTI214j5CgeHh7atm2bRo0apS+++ELHjx+Xm5ubHn/8cdWrV+9fQ8K9yps3ryIiItS7d2/997//Vb58+dS5c2c1btxYnTt3vuv1+vn5KV++fBo9erROnz6tnDlzqmzZsulOtbXCzJkz9d5776l58+ZKTExUu3btFB4e/o/LTJo0SZMmTZKTk5Py5s2rUqVKqVmzZurSpYvKly9vV2vVa/ag1K5dW3v27NHw4cPVu3dvxcTEyMvLS+XLl1erVq3Mujp16mjVqlUaN26c4uPjVbRoUbVt2/ah6hkGHjU2416H7AEAAAAApMNohAAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgPtsZVBqaqpOnz6tPHnyyGazObo5AAAAABzEMAxdvnxZRYoUUY4cd+6/Imxl0OnTp1WsWDFHNwMAAADAQ+LkyZN67LHH7jifsJVBefLkkXTjBc2bN6+DWwMAAADAUeLi4lSsWDEzI9wJYSuD0k4dzJs3L2ELAAAAwL9eXsQAGQAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABZwdnQDkF6D1sMc3QQgy1m3+ENHNwEAAMAOPVsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFnB2dAMAAOlV/jjE0U0AsqTIwSGObgIAmOjZAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALEDYAgAAAAALELYAAAAAwAKELQAAAACwAGELAAAAACxA2AIAAAAACxC2AAAAAMAChC0AAAAAsABhCwAAAAAsQNgCAAAAAAsQtgAAAADAAoQtAAAAALAAYQsAAAAALODQsBUSEiKbzWb38PX1NecbhqGQkBAVKVJEbm5uqlWrln7++We7dSQmJqpHjx4qWLCgPDw81Lx5c506dcquJiYmRsHBwfL09JSnp6eCg4N16dKlB7GLAAAAAB5RDu/Z+s9//qMzZ86Yj59++smcN3r0aI0dO1aTJ0/W3r175evrq/r16+vy5ctmTe/evbV8+XItWrRI27dv15UrVxQUFKTr16+bNW3atFFkZKQiIiIUERGhyMhIBQcHP9D9BAAAAPBocXZ4A5yd7Xqz0hiGofHjx2vQoEFq2bKlJGn27Nny8fHRggUL9Oabbyo2NlYzZ87U3LlzVa9ePUnSvHnzVKxYMW3YsEENGzZUVFSUIiIitGvXLlWrVk2SFBoaqsDAQB05ckRly5Z9cDsLAAAA4JHh8J6t3377TUWKFFHJkiX12muv6ffff5ckHT9+XNHR0WrQoIFZ6+rqqpo1a2rHjh2SpP379ys5OdmupkiRIgoICDBrdu7cKU9PTzNoSVL16tXl6elp1txOYmKi4uLi7B4AAAAAkFEODVvVqlXTnDlz9O233yo0NFTR0dGqUaOGLly4oOjoaEmSj4+P3TI+Pj7mvOjoaOXMmVP58+f/xxpvb+902/b29jZrbmfkyJHmNV6enp4qVqzYPe0rAAAAgEeLQ8NW48aN9fLLL6tChQqqV6+e1qxZI+nG6YJpbDab3TKGYaSbdqtba25X/2/rGTBggGJjY83HyZMnM7RPAAAAACA9BKcR3szDw0MVKlTQb7/9Zl7HdWvv07lz58zeLl9fXyUlJSkmJuYfa86ePZtuW+fPn0/Xa3YzV1dX5c2b1+4BAAAAABn1UIWtxMRERUVFqXDhwipZsqR8fX21fv16c35SUpK2bNmiGjVqSJKeeuopubi42NWcOXNGhw4dMmsCAwMVGxurPXv2mDW7d+9WbGysWQMAAAAA95tDRyPs16+fmjVrpscff1znzp3Txx9/rLi4OLVr1042m029e/fWiBEj5OfnJz8/P40YMULu7u5q06aNJMnT01OdOnXSu+++Ky8vLxUoUED9+vUzT0uUJH9/fzVq1EhdunTR9OnTJUldu3ZVUFAQIxECAAAAsIxDw9apU6f0+uuv6++//1ahQoVUvXp17dq1S8WLF5ck9e/fXwkJCerevbtiYmJUrVo1rVu3Tnny5DHXMW7cODk7O6tVq1ZKSEhQ3bp1FR4eLicnJ7Nm/vz56tmzpzlqYfPmzTV58uQHu7MAAAAAHik2wzAMRzciK4iLi5Onp6diY2Mtv36rQethlq4fyI7WLf7Q0U24ryp/HOLoJgBZUuTgEEc3AcAjIKPZ4KG6ZgsAAAAAsgvCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAAWIGwBAAAAgAUIWwAAAABgAcIWAAAAAFiAsAUAAAAAFiBsAQAAAIAFCFsAAAAAYAHCFgAAAABYgLAFAAAAABYgbAEAAACABQhbAAAAAGABwhYAAAAAWICwBQAAAAA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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_7304\\146936503.py:6: FutureWarning: \n",
|
||
"\n",
|
||
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
|
||
"\n",
|
||
" sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_7304\\146936503.py:6: FutureWarning: \n",
|
||
"\n",
|
||
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
|
||
"\n",
|
||
" sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_7304\\146936503.py:6: FutureWarning: \n",
|
||
"\n",
|
||
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
|
||
"\n",
|
||
" sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_7304\\146936503.py:6: FutureWarning: \n",
|
||
"\n",
|
||
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
|
||
"\n",
|
||
" sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def plot_dummy_counts(df, prefix):\n",
|
||
" dummy_cols = [col for col in df.columns if col.startswith(prefix)]\n",
|
||
" counts = df[dummy_cols].sum().sort_values(ascending=False)\n",
|
||
" \n",
|
||
" plt.figure(figsize=(10, 6))\n",
|
||
" sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n",
|
||
" plt.title(f'Anzahl der Vorkommen für {prefix} Dummies')\n",
|
||
" plt.xlabel('Dummy Variablen')\n",
|
||
" plt.ylabel('Anzahl')\n",
|
||
" plt.xticks(rotation=45)\n",
|
||
" plt.show()\n",
|
||
" \n",
|
||
"plot_dummy_counts(X_train, 'shift')\n",
|
||
"plot_dummy_counts(X_train, 'speeding')\n",
|
||
"plot_dummy_counts(X_train, 'weather')\n",
|
||
"plot_dummy_counts(X_train, 'road')\n",
|
||
"plot_dummy_counts(X_train, 'weekday')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2127272a",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Schrittweise Variablenselektion: Backward Selection\n",
|
||
"\n",
|
||
"Um herauszufinden, welche Variablen tatsächlich erklärend für die Zielvariable sind, wird eine schrittweise Rückwärtsselektion (Backward Selection) durchgeführt. Dabei startet man mit allen potenziellen Prädiktoren und entfernt schrittweise die am wenigsten signifikanten Variablen, bis nur noch signifikante Prädiktoren übrig bleiben. Dies hilft, ein einfaches und interpretierbares Modell zu erhalten. Da die Zielvariable binär ist, wird eine logistische Regression verwendet. Das Modell wird auf den Trainingsdaten trainiert und die Modellgüte auf den Testdaten evaluiert."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 177,
|
||
"id": "19c7e28c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Entfernte Variablen und zugehörige p-Werte:\n",
|
||
"road_Autobahn: p=1.0000\n",
|
||
"weekday_Mo-Fr: p=1.0000\n",
|
||
"weather_winterlich: p=1.0000\n",
|
||
"road_Innerorts: p=0.9915\n",
|
||
"road_Außerorts: p=0.7254\n",
|
||
"trip_distance: p=0.6943\n",
|
||
"weekday_So: p=0.6043\n",
|
||
"speeding_selten: p=1.0000\n",
|
||
"shift_spaet: p=1.0000\n",
|
||
"weekday_Sa: p=0.4605\n",
|
||
"weather_nass: p=0.2285\n",
|
||
"weather_trocken: p=0.6093\n",
|
||
"\n",
|
||
"Verbleibende Variablen im Modell:\n",
|
||
"['const', 'avg_speed', 'hard_brakes', 'shift_frueh', 'shift_normal', 'speeding_haeufig', 'speeding_manchmal']\n",
|
||
"\n",
|
||
"Zusammenfassung des finalen Modells:\n",
|
||
" Logit Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: y No. Observations: 14000\n",
|
||
"Model: Logit Df Residuals: 13993\n",
|
||
"Method: MLE Df Model: 6\n",
|
||
"Date: Fri, 13 Jun 2025 Pseudo R-squ.: 0.04892\n",
|
||
"Time: 15:26:41 Log-Likelihood: -7127.3\n",
|
||
"converged: True LL-Null: -7493.9\n",
|
||
"Covariance Type: nonrobust LLR p-value: 4.293e-155\n",
|
||
"=====================================================================================\n",
|
||
" coef std err z P>|z| [0.025 0.975]\n",
|
||
"-------------------------------------------------------------------------------------\n",
|
||
"const -1.0511 0.113 -9.323 0.000 -1.272 -0.830\n",
|
||
"avg_speed 0.0174 0.002 8.369 0.000 0.013 0.021\n",
|
||
"hard_brakes 0.0817 0.015 5.584 0.000 0.053 0.110\n",
|
||
"shift_frueh -0.8151 0.053 -15.281 0.000 -0.920 -0.711\n",
|
||
"shift_normal -0.8082 0.053 -15.242 0.000 -0.912 -0.704\n",
|
||
"speeding_haeufig -0.8379 0.050 -16.906 0.000 -0.935 -0.741\n",
|
||
"speeding_manchmal -0.8340 0.051 -16.288 0.000 -0.934 -0.734\n",
|
||
"=====================================================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import statsmodels.api as sm\n",
|
||
"\n",
|
||
"def backward_selection(X, y, significance_level=0.05):\n",
|
||
" X_ = X.copy().astype(float)\n",
|
||
" removed_features = []\n",
|
||
" while True:\n",
|
||
" X_with_const = sm.add_constant(X_)\n",
|
||
" model = sm.Logit(y, X_with_const).fit(disp=0)\n",
|
||
" pvalues = model.pvalues.drop('const')\n",
|
||
" max_pval = pvalues.max()\n",
|
||
" if max_pval > significance_level:\n",
|
||
" excluded_feature = pvalues.idxmax()\n",
|
||
" removed_features.append((excluded_feature, max_pval))\n",
|
||
" X_ = X_.drop(columns=[excluded_feature])\n",
|
||
" else:\n",
|
||
" break\n",
|
||
" return X_, removed_features, model\n",
|
||
"\n",
|
||
"X_selected, removed_features, final_model = backward_selection(X_train, y_train)\n",
|
||
"\n",
|
||
"print(\"Entfernte Variablen und zugehörige p-Werte:\")\n",
|
||
"for feat, pval in removed_features:\n",
|
||
" print(f\"{feat}: p={pval:.4f}\")\n",
|
||
"\n",
|
||
"print(\"\\nVerbleibende Variablen im Modell:\")\n",
|
||
"print(list(X_selected.columns))\n",
|
||
"\n",
|
||
"print(\"\\nZusammenfassung des finalen Modells:\")\n",
|
||
"print(final_model.summary())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "257e622b",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Interpretation der Modellkoeffizienten\n",
|
||
"\n",
|
||
"Die geschätzten Koeffizienten der logistischen Regression geben Aufschluss darüber, welche Variablen einen Einfluss auf die Wahrscheinlichkeit eines Diebstahls haben. Diese werden mit den tatsächlichen, zur Generierung verwendeten Koeffizienten verglichen."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 178,
|
||
"id": "6641f80c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Modellkoeffizienten:\n",
|
||
"const -1.051133\n",
|
||
"avg_speed 0.017392\n",
|
||
"hard_brakes 0.081747\n",
|
||
"shift_frueh -0.815083\n",
|
||
"shift_normal -0.808237\n",
|
||
"speeding_haeufig -0.837906\n",
|
||
"speeding_manchmal -0.833977\n",
|
||
"dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Modellkoeffizienten anzeigen\n",
|
||
"coefficients = final_model.params\n",
|
||
"print(\"\\nModellkoeffizienten:\")\n",
|
||
"print(coefficients)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4194adef",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Vergleich mit dem Generierungsmodell (Verlassen der Anwenderperspektive)\n",
|
||
"\n",
|
||
"Abschließend wird das trainierte Modell mit dem tatsächlich zur Generierung der Zielvariable verwendeten Modell verglichen. Die geschätzten Koeffizienten sollten – abgesehen von Zufallsschwankungen – mit den wahren Koeffizienten übereinstimmen. Dies zeigt, dass das systematische Vorgehen des Data Scientist erfolgreich war und das zugrundeliegende Modell korrekt rekonstruiert wurde."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "0541fa09",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Vergleich der geschätzten und wahren Koeffizienten:\n",
|
||
" Geschätzte Koeffizienten Wahre Koeffizienten\n",
|
||
"avg_speed 0.017392 0.015\n",
|
||
"const -1.051133 -2.000\n",
|
||
"hard_brakes 0.081747 0.100\n",
|
||
"road_Autobahn NaN 0.000\n",
|
||
"road_Außerorts NaN 0.000\n",
|
||
"road_Innerorts NaN 0.000\n",
|
||
"shift_frueh -0.815083 0.500\n",
|
||
"shift_normal -0.808237 0.300\n",
|
||
"shift_spaet NaN 0.300\n",
|
||
"speeding_haeufig -0.837906 0.300\n",
|
||
"speeding_manchmal -0.833977 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"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Tatsächliche Betas aus der Generierung (siehe oben)\n",
|
||
"true_betas = pd.Series([\n",
|
||
" -2, # konstante\n",
|
||
" 0.015, # avg_speed\n",
|
||
" 0.1, # hard_brakes\n",
|
||
" 0., # trip_distance\n",
|
||
" 0.5, # shift_frueh (erklärend)\n",
|
||
" -0.3, # shift_normal (erklärend)\n",
|
||
" -0.3, # shift_spaet (erklärend)\n",
|
||
" -0.3, # speeding_selten (erklärend)\n",
|
||
" -0.3, # speeding_manchmal (erklärend)\n",
|
||
" 0.5, # speeding_haeufig (erklärend)\n",
|
||
" 0., # weather_nass\n",
|
||
" 0., # weather_trocken\n",
|
||
" 0., # weather_winterlich\n",
|
||
" 0., # road_Außerorts\n",
|
||
" 0., # road_Autobahn\n",
|
||
" 0., # road_Innerorts\n",
|
||
" 0., # weekday_Mo-Fr\n",
|
||
" 0., # weekday_Sa\n",
|
||
" 0. # weekday_So\n",
|
||
"], index=X_train.columns[:19])\n",
|
||
"\n",
|
||
"print(\"Vergleich der geschätzten und wahren Koeffizienten:\")\n",
|
||
"comparison = pd.DataFrame({\n",
|
||
" 'Geschätzte Koeffizienten': coefficients,\n",
|
||
" 'Wahre Koeffizienten': true_betas\n",
|
||
"})\n",
|
||
"print(comparison)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7516554c",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Güte der Modellparameter"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "18f9a07d",
|
||
"metadata": {},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c1bb79ce",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Güte der Modellparameter: Einfluss des Stichprobenumfangs\n",
|
||
"\n",
|
||
"In diesem Abschnitt wird untersucht, wie sich der Stichprobenumfang auf die Schätzgüte der Modellparameter auswirkt. Dazu werden jeweils k = 1.000 zufällige Stichproben aus der Grundgesamtheit gezogen, mit Stichprobenumfängen n von 1.000 bis 50.000 (Schrittweite 5.000). Für jede Stichprobe wird das optimale Modell aus Abschnitt 3 trainiert und der geschätzte Beta-Wert einer erklärenden Variable (z.B. `avg_speed`) gespeichert. Die Verteilungen der geschätzten Beta-Werte werden für drei ausgewählte Stichprobenumfänge verglichen und der funktionale Zusammenhang zwischen n und der Standardabweichung der Schätzungen analysiert."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 180,
|
||
"id": "616ae484",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Stichprobenumfang: 0%| | 0/10 [00:00<?, ?it/s]c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation for alternative solver options:\n",
|
||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||
" n_iter_i = _check_optimize_result(\n",
|
||
"c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation for alternative solver options:\n",
|
||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||
" n_iter_i = _check_optimize_result(\n",
|
||
"c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation for alternative solver options:\n",
|
||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||
" n_iter_i = _check_optimize_result(\n",
|
||
"c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation for alternative solver options:\n",
|
||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||
" n_iter_i = _check_optimize_result(\n",
|
||
"c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation for alternative solver options:\n",
|
||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||
" n_iter_i = _check_optimize_result(\n",
|
||
"c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation for alternative solver options:\n",
|
||
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
||
" n_iter_i = _check_optimize_result(\n",
|
||
"c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
||
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
||
"\n",
|
||
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
||
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
||
"Please also refer to the documentation 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 [00:25<00:00, 2.52s/it]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from tqdm import tqdm\n",
|
||
"from sklearn.linear_model import LogisticRegression\n",
|
||
"\n",
|
||
"k = 10\n",
|
||
"n_values = np.arange(1000, 50001, 5000)\n",
|
||
"beta_name = 'avg_speed'\n",
|
||
"\n",
|
||
"betas_by_n = {}\n",
|
||
"\n",
|
||
"for n in tqdm(n_values, desc=\"Stichprobenumfang\"):\n",
|
||
" betas = []\n",
|
||
" for _ in range(k):\n",
|
||
" idx = np.random.choice(X.index, size=n, replace=False)\n",
|
||
" X_sub = X.iloc[idx]\n",
|
||
" y_sub = target[idx]\n",
|
||
" X_sub_enc = pd.get_dummies(X_sub, drop_first=True)\n",
|
||
" # Modell mit denselben Features wie das finale Modell aus Abschnitt 3\n",
|
||
" model = LogisticRegression(max_iter=200, solver='lbfgs')\n",
|
||
" model.fit(X_sub_enc, y_sub)\n",
|
||
" # Finde Index der gewünschten Variable\n",
|
||
" try:\n",
|
||
" beta_idx = list(X_sub_enc.columns).index(beta_name)\n",
|
||
" betas.append(model.coef_[0][beta_idx])\n",
|
||
" except ValueError:\n",
|
||
" betas.append(np.nan) # Falls Feature in kleiner Stichprobe fehlt\n",
|
||
" betas_by_n[n] = np.array(betas)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "28e0cb42",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Vergleich der Verteilungen des geschätzten Beta-Werts für drei Stichprobenumfänge\n",
|
||
"\n",
|
||
"Im Folgenden werden die Verteilungen des geschätzten Beta-Werts für die erklärende Variable `avg_speed` für drei verschiedene Stichprobenumfänge (n = 1.000, 10.000, 50.000) graphisch verglichen."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 181,
|
||
"id": "b0bb7329",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(12, 6))\n",
|
||
"# Use only available keys from betas_by_n\n",
|
||
"for n in [1000, 11000, 46000]:\n",
|
||
" sns.kdeplot(betas_by_n[n][~np.isnan(betas_by_n[n])], label=f\"n={n}\")\n",
|
||
"plt.title(\"Verteilung des geschätzten Beta-Werts für avg_speed bei verschiedenen Stichprobenumfängen\")\n",
|
||
"plt.xlabel(\"Geschätzter Beta-Wert für avg_speed\")\n",
|
||
"plt.ylabel(\"Dichte\")\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "990bb2a0",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Interpretation:**\n",
|
||
"\n",
|
||
"Mit wachsendem Stichprobenumfang werden die Verteilungen der geschätzten Beta-Werte schmaler und konzentrieren sich stärker um den wahren Wert. Kleine Stichproben führen zu einer größeren Streuung und damit zu unsichereren Schätzungen."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "50ea2928",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Funktionaler Zusammenhang zwischen n und der Standardabweichung des geschätzten Beta-Werts\n",
|
||
"\n",
|
||
"Für jeden Stichprobenumfang wird die Standardabweichung der geschätzten Beta-Werte berechnet und gegen n aufgetragen. Zusätzlich wird die theoretische Entwicklung der Standardabweichung nach der Formel $\\sigma(\\hat{\\beta}) \\propto 1/\\sqrt{n}$ eingezeichnet."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 182,
|
||
"id": "cf7a7e61",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"stds = np.array([np.nanstd(betas_by_n[n]) for n in n_values])\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"plt.plot(n_values, stds, marker='o', label='Empirische Standardabweichung')\n",
|
||
"plt.plot(n_values, stds[0]*np.sqrt(n_values[0]/n_values), '--', label=r'$\\propto 1/\\sqrt{n}$')\n",
|
||
"plt.xlabel('Stichprobenumfang n')\n",
|
||
"plt.ylabel('Standardabweichung des geschätzten Beta-Werts')\n",
|
||
"plt.title('Zusammenhang zwischen Stichprobenumfang und Schätzgenauigkeit')\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "10afcd69",
|
||
"metadata": {},
|
||
"source": [
|
||
"Die Standardabweichung der Schätzung eines Modellparameters nimmt mit wachsendem Stichprobenumfang näherungsweise proportional zu $1/\\sqrt{n}$ ab. Das bedeutet: Je mehr Daten zur Verfügung stehen, desto präziser und stabiler werden die Parameterschätzungen. Die Lernqualität eines Modells steigt also mit der Datenmenge, wobei der Zugewinn mit zunehmender Datenmenge abnimmt (abnehmender Grenznutzen)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ca39ff07",
|
||
"metadata": {},
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "hft_ml",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.10"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|