iforest training
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"cells": [
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{
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"cell_type": "markdown",
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"id": "74bda07c",
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"metadata": {},
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"source": [
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"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": null,
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"id": "a265b02c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from sklearn.ensemble import IsolationForest\n",
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"from sklearn.model_selection import train_test_split\n",
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"from pathlib import Path\n",
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"import sys\n",
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"import os\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"base_dir = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n",
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"sys.path.append(base_dir)\n",
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"print(base_dir)\n",
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"\n",
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"from tools import evaluation_tools"
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]
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},
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{
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"cell_type": "markdown",
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"id": "092835af",
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"metadata": {},
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"source": [
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"Load dataset"
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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": null,
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"id": "dc788eb4",
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = Path(r\"C:\\Users\\micha\\FAUbox\\WS2526_Fahrsimulator_MSY (Celina Korzer)\\AU_dataset\\output_windowed.parquet\")"
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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": null,
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"id": "a4d39a6e",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_parquet(path=data_path)"
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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": null,
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"id": "a8f36561",
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"metadata": {},
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"outputs": [],
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"source": [
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"subjects = df['subjectID'].unique()\n",
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"print(subjects)\n",
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"print(len(subjects))\n",
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"print(len(subjects)*0.66)\n",
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"print(len(subjects)*0.33)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a46e47dd",
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"metadata": {},
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"source": [
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"Split of subjects and labeling "
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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": null,
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"id": "3fb6dc4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"low_all = df[\n",
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" ((df[\"PHASE\"] == \"baseline\") |\n",
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" ((df[\"STUDY\"] == \"n-back\") & (df[\"PHASE\"] != \"baseline\") & (df[\"LEVEL\"].isin([1,4]))))\n",
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"]\n",
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"print(f\"low all: {low_all.shape}\")\n",
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"\n",
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"high_nback = df[\n",
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" (df[\"STUDY\"]==\"n-back\") &\n",
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" (df[\"LEVEL\"].isin([2, 3, 5, 6])) &\n",
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" (df[\"PHASE\"].isin([\"train\", \"test\"]))\n",
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"]\n",
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"print(f\"high n-back: {high_nback.shape}\")\n",
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"high_kdrive = df[\n",
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" (df[\"STUDY\"] == \"k-drive\") & (df[\"PHASE\"] != \"baseline\")\n",
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"]\n",
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"print(f\"high k-drive: {high_kdrive.shape}\")\n",
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"\n",
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"high_all = pd.concat([high_nback, high_kdrive])\n",
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"print(f\"high all: {high_all.shape}\")"
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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": null,
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"id": "20394aca",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_subjects, test_subjects = train_test_split(\n",
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" subjects, \n",
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" train_size=12, \n",
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" test_size=6, \n",
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" random_state=42\n",
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")\n",
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"\n",
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"# Get all column names that start with 'AU'\n",
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"au_columns = [col for col in low_all.columns if col.startswith('AU')]\n",
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"\n",
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"# Create train set: only normal samples from train subjects, only AU columns\n",
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"X_train = low_all[low_all['subjectID'].isin(train_subjects)][au_columns].copy()\n",
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"y_train = np.ones(len(X_train)) # Label 1 for normal samples\n",
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"\n",
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"# Create test set: both normal and high load from test subjects, only AU columns\n",
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"X_test_normal = low_all[low_all['subjectID'].isin(test_subjects)][au_columns].copy()\n",
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"X_test_high = high_all[high_all['subjectID'].isin(test_subjects)][au_columns].copy()\n",
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"\n",
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"# Combine test sets\n",
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"X_test = pd.concat([X_test_normal, X_test_high], ignore_index=True)\n",
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"\n",
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"# Create labels for test set\n",
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"y_test_normal = np.ones(len(X_test_normal)) # 1 for normal\n",
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"y_test_high = -np.ones(len(X_test_high)) # -1 for anomalies\n",
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"y_test = np.concatenate([y_test_normal, y_test_high])\n",
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"\n",
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"\n",
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"print(f\"Number of AU features: {len(au_columns)}\")\n",
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"print(f\"AU columns: {au_columns}\")\n",
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"print(f\"\\nTrain set: {len(X_train)} normal samples\")\n",
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"print(f\"Test set: {len(X_test_normal)} normal + {len(X_test_high)} high load = {len(X_test)} total samples\")\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": "697b3cf7",
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"metadata": {},
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"source": [
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"Training of Isolation Forest"
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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": null,
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"id": "b5cd4ac6",
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"metadata": {},
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"outputs": [],
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"source": [
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"iforest = IsolationForest(random_state=42)\n",
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"iforest.fit(X_train)\n",
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"iforest_scores = iforest.score_samples(X_test)\n",
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"iforest_predictions = iforest.predict(X_test)"
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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": null,
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"id": "15c45f66",
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"metadata": {},
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"outputs": [],
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"source": [
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"evaluation_tools.plot_confusion_matrix(true_labels=y_test, predictions=iforest_predictions, label_names=['high load', 'low load'])"
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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": null,
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"id": "326fcb47",
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"metadata": {},
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"outputs": [],
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"source": [
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"evaluation_tools.plot_roc_curve_IF(y_test, iforest_scores)"
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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": null,
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"id": "141267e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"iforest.offset_"
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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": null,
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"id": "4bf81d7b",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(classification_report(y_test, iforest_predictions))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -1 +1,23 @@
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import pandas as pd
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import pandas as pd
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from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc
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import matplotlib.pyplot as plt
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def plot_confusion_matrix(true_labels, predictions, label_names):
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for normalize in [None, 'true']:
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cm = confusion_matrix(true_labels, predictions, normalize=normalize)
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cm_disp = ConfusionMatrixDisplay(cm, display_labels=label_names)
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cm_disp.plot(cmap="Blues")
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def plot_roc_curve_IF(true_labels, scores):
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fpr, tpr, thr = roc_curve(true_labels, -scores, pos_label=-1)
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auc_score = auc(fpr, tpr)
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plt.figure()
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plt.plot(fpr, tpr, '-')
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plt.text(0.5, 0.5, f'AUC: {auc_score:.4f}')
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plt.xlabel('False positive rate')
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plt.ylabel('True positive rate')
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plt.show()
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