919 lines
30 KiB
Plaintext
919 lines
30 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "708c9745",
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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": "53b10294",
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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 pathlib import Path\n",
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"import sys\n",
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"import os\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 Fahrsimulator_MSY2526_AI.model_training.tools import evaluation_tools, scaler, mad_outlier_removal\n",
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"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
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"from sklearn.svm import OneClassSVM\n",
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"from sklearn.model_selection import GridSearchCV, KFold, ParameterGrid, train_test_split, GroupKFold\n",
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"import matplotlib.pyplot as plt\n",
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"import tensorflow as tf\n",
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"import pickle\n",
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"from sklearn.metrics import (roc_auc_score, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report, balanced_accuracy_score, ConfusionMatrixDisplay) "
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]
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},
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{
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"cell_type": "markdown",
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"id": "68101229",
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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": "24a765e8",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset_path = Path(r\"/home/jovyan/data-paulusjafahrsimulator-gpu/first_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": "471001b0",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_parquet(path=dataset_path)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0fdecdaa",
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"metadata": {},
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"source": [
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"### Load Performance data and Subject Split"
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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": "692d1b47",
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"metadata": {},
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"outputs": [],
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"source": [
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"performance_path = Path(r\"/home/jovyan/data-paulusjafahrsimulator-gpu/subject_performance/3new_au_performance.csv\")\n",
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"performance_df = pd.read_csv(performance_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": "ea617e3f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Subject IDs aus dem Haupt-Dataset nehmen\n",
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"subjects_from_df = df[\"subjectID\"].unique()\n",
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"\n",
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"# Performance-Subset nur für vorhandene Subjects\n",
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"perf_filtered = performance_df[\n",
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" performance_df[\"subjectID\"].isin(subjects_from_df)\n",
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"][[\"subjectID\", \"overall_score\"]]\n",
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"\n",
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"# Merge: nur Subjects, die sowohl im df als auch im Performance-CSV vorkommen\n",
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"merged = (\n",
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" pd.DataFrame({\"subjectID\": subjects_from_df})\n",
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" .merge(perf_filtered, on=\"subjectID\", how=\"inner\")\n",
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")\n",
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"\n",
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"# Sicherstellen, dass keine Scores fehlen\n",
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"if merged[\"overall_score\"].isna().any():\n",
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" raise ValueError(\"Es fehlen Score-Werte für manche Subjects.\")\n"
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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": "ae43df8d",
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"metadata": {},
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"outputs": [],
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"source": [
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"merged_sorted = merged.sort_values(\"overall_score\", ascending=False).reset_index(drop=True)\n",
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"\n",
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"scores = merged_sorted[\"overall_score\"].values\n",
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"n_total = len(merged_sorted)\n",
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"n_small = n_total // 3\n",
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"n_large = n_total - n_small\n",
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"\n",
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"# Schritt 1: zufällige Start-Aufteilung\n",
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"idx = np.arange(n_total)\n",
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"np.random.shuffle(idx)\n",
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"\n",
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"small_idx = idx[:n_small]\n",
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"large_idx = idx[n_small:]\n",
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"\n",
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"def score_diff(small_idx, large_idx):\n",
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" return abs(scores[small_idx].mean() - scores[large_idx].mean())\n",
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"\n",
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"diff = score_diff(small_idx, large_idx)\n",
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"threshold = 0.01\n",
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"max_iter = 100\n",
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"count = 0\n",
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"\n",
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"# Schritt 2: random swaps bis Differenz klein genug\n",
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"while diff > threshold and count < max_iter:\n",
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" # Zwei zufällige Elemente auswählen\n",
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" si = np.random.choice(small_idx)\n",
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" li = np.random.choice(large_idx)\n",
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" \n",
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" # Tausch durchführen\n",
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" new_small_idx = small_idx.copy()\n",
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" new_large_idx = large_idx.copy()\n",
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" \n",
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" new_small_idx[new_small_idx == si] = li\n",
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" new_large_idx[new_large_idx == li] = si\n",
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"\n",
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" # neue Differenz berechnen\n",
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" new_diff = score_diff(new_small_idx, new_large_idx)\n",
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"\n",
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" # Swap akzeptieren, wenn es besser wird\n",
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" if new_diff < diff:\n",
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" small_idx = new_small_idx\n",
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" large_idx = new_large_idx\n",
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" diff = new_diff\n",
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"\n",
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" count += 1\n",
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"\n",
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"# Finalgruppen\n",
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"group_small = merged_sorted.loc[small_idx].reset_index(drop=True)\n",
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"group_large = merged_sorted.loc[large_idx].reset_index(drop=True)\n",
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"\n",
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"print(\"Finale Score-Differenz:\", diff)\n",
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"print(\"Größe Gruppe 1:\", len(group_small))\n",
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"print(\"Größe Gruppe 2:\", len(group_large))\n"
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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": "9d1b414e",
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"metadata": {},
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"outputs": [],
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"source": [
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"group_large['overall_score'].mean()"
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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": "fa71f9a5",
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"metadata": {},
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"outputs": [],
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"source": [
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"group_small['overall_score'].mean()"
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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": "79ecb4a2",
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"metadata": {},
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"outputs": [],
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"source": [
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"training_subjects = group_large['subjectID'].values\n",
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"test_subjects = group_small['subjectID'].values\n",
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"print(training_subjects)\n",
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"print(test_subjects)"
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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": "87f9fe7d",
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"metadata": {},
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"outputs": [],
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"source": [
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"au_columns = [col for col in df.columns if col.lower().startswith(\"au\")]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "009d268b",
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"metadata": {},
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"source": [
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"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": "4fa79163",
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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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"\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": "82b17d0b",
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"metadata": {},
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"outputs": [],
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"source": [
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"low = low_all.copy()\n",
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"high = high_all.copy()\n",
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"\n",
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"low[\"label\"] = 0\n",
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"high[\"label\"] = 1\n",
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"\n",
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"data = pd.concat([low, high], ignore_index=True)\n",
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"df = data.drop_duplicates()\n",
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"\n",
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"print(\"Label distribution:\")\n",
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"print(df[\"label\"].value_counts())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4353f87c",
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"metadata": {},
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"source": [
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"### Data cleaning with mad"
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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": "c9afaf61",
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"metadata": {},
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"outputs": [],
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"source": [
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"# methode CT\n",
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"def calculate_mad_params(df, columns):\n",
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" \"\"\"\n",
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" Calculate median and MAD parameters for each column.\n",
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" This should be run ONLY on the training data.\n",
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" \n",
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" Returns a dictionary: {col: (median, mad)}\n",
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" \"\"\"\n",
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" params = {}\n",
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" for col in columns:\n",
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" median = df[col].median()\n",
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" mad = np.median(np.abs(df[col] - median))\n",
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" params[col] = (median, mad)\n",
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" return params\n",
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"\n",
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"def apply_mad_filter(df, params, threshold=3.5):\n",
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" \"\"\"\n",
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" Apply MAD-based outlier removal using precomputed parameters.\n",
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" Works on training, validation, and test data.\n",
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" \n",
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" df: DataFrame to filter\n",
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" params: dictionary {col: (median, mad)} from training data\n",
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" threshold: cutoff for robust Z-score\n",
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" \"\"\"\n",
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" df_clean = df.copy()\n",
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"\n",
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" for col, (median, mad) in params.items():\n",
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" if mad == 0:\n",
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" continue # no spread; nothing to remove for this column\n",
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"\n",
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" robust_z = 0.6745 * (df_clean[col] - median) / mad\n",
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" outlier_mask = np.abs(robust_z) > threshold\n",
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"\n",
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" # Remove values only in this specific column\n",
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" df_clean.loc[outlier_mask, col] = np.nan\n",
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" \n",
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" return df_clean"
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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": "4a286665",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_df = df[df.subjectID.isin(training_subjects)]\n",
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"test_df = df[df.subjectID.isin(test_subjects)]\n",
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"print(train_df.shape, test_df.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": "2671e0f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"params = calculate_mad_params(train_df, au_columns)\n",
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"\n",
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"# Step 2: Apply filter consistently\n",
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"train_outlier_removed = apply_mad_filter(train_df, params, threshold=3.5)\n",
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"test_outlier_removed = apply_mad_filter(test_df, params, threshold=3.5)\n",
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"print(train_outlier_removed.shape, test_outlier_removed.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6c39b37f",
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"metadata": {},
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"source": [
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"Normalisierung der Daten"
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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": "5e6c654f",
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"metadata": {},
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"outputs": [],
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"source": [
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"normalizer = scaler.fit_normalizer(train_df, au_columns=au_columns, method='standard', scope='global')\n",
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"train_df_normal = scaler.apply_normalizer(train_df, au_columns=au_columns, normalizer_dict=normalizer)\n",
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"test_df_normal = scaler.apply_normalizer(test_df, au_columns=au_columns, normalizer_dict=normalizer)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b6d25e7b",
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"metadata": {},
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"source": [
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"to do insert group k fold for train_df_normal"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e826a998",
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"metadata": {},
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"source": [
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"### AE first"
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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": "e6421371",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Beide Klassen für AE und SVM Training\n",
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"X_train_full = train_outlier_removed[au_columns].dropna()\n",
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"y_train_full = train_outlier_removed.loc[X_train_full.index, 'label'].values\n",
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"groups_train = train_outlier_removed.loc[X_train_full.index, 'subjectID'].values\n",
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"\n",
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"print(f\"Training data shape: {X_train_full.shape}\")\n",
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"print(f\"Label distribution in training: {pd.Series(y_train_full).value_counts()}\")\n",
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"\n",
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"# Test data\n",
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"X_test = test_outlier_removed[au_columns].dropna()\n",
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"y_test = test_outlier_removed.loc[X_test.index, 'label'].values\n",
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"\n",
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"print(f\"Test data shape: {X_test.shape}\")\n",
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"print(f\"Label distribution in test: {pd.Series(y_test).value_counts()}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d982e47a",
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"metadata": {},
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"source": [
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"### Custom SVM Layer (differentiable approximation)"
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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": "50fbda1a",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"class DifferentiableSVM(tf.keras.layers.Layer):\n",
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" \"\"\"\n",
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" Differentiable SVM Layer using hinge loss.\n",
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" This allows backpropagation through the SVM to the encoder.\n",
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" \"\"\"\n",
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" def __init__(self, C=1.0, **kwargs):\n",
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" super(DifferentiableSVM, self).__init__(**kwargs)\n",
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" self.C = C\n",
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" \n",
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" def build(self, input_shape):\n",
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" # SVM weights: w and bias b\n",
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" self.w = self.add_weight(\n",
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" shape=(input_shape[-1],),\n",
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" initializer='glorot_uniform',\n",
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" trainable=True,\n",
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" name='svm_w'\n",
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" )\n",
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" self.b = self.add_weight(\n",
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" shape=(1,),\n",
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" initializer='zeros',\n",
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" trainable=True,\n",
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" name='svm_b'\n",
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" )\n",
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" \n",
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" def call(self, inputs):\n",
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" # Decision function: w^T * x + b\n",
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" decision = tf.reduce_sum(inputs * self.w, axis=1, keepdims=True) + self.b\n",
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" return decision\n",
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" \n",
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" def compute_loss(self, inputs, labels):\n",
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" \"\"\"\n",
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" Hinge loss for SVM: max(0, 1 - y * (w^T * x + b))\n",
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" labels should be -1 or +1\n",
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" \"\"\"\n",
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" decision = self.call(inputs)\n",
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" \n",
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" # Convert labels from 0/1 to -1/+1\n",
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" labels_svm = tf.where(labels == 0, -1.0, 1.0)\n",
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" labels_svm = tf.cast(labels_svm, tf.float32)\n",
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" labels_svm = tf.reshape(labels_svm, (-1, 1))\n",
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" \n",
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" # Hinge loss\n",
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" hinge_loss = tf.reduce_mean(\n",
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" tf.maximum(0.0, 1.0 - labels_svm * decision)\n",
|
|
" )\n",
|
|
" \n",
|
|
" # L2 regularization\n",
|
|
" l2_loss = 0.5 * tf.reduce_sum(tf.square(self.w))\n",
|
|
" \n",
|
|
" return self.C * hinge_loss + l2_loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e7def811",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class JointAESVM(tf.keras.Model):\n",
|
|
" \"\"\"\n",
|
|
" Joint Autoencoder + SVM Model\n",
|
|
" Loss = reconstruction_loss + svm_loss\n",
|
|
" \"\"\"\n",
|
|
" def __init__(self, input_dim, latent_dim=5, hidden_dim=16, ae_weight=1.0, \n",
|
|
" svm_weight=1.0, svm_C=1.0, reg=0.0001, **kwargs):\n",
|
|
" super(JointAESVM, self).__init__(**kwargs)\n",
|
|
" \n",
|
|
" self.ae_weight = ae_weight\n",
|
|
" self.svm_weight = svm_weight\n",
|
|
" \n",
|
|
" # Encoder\n",
|
|
" self.encoder = tf.keras.Sequential([\n",
|
|
" tf.keras.layers.Dense(input_dim, activation='relu', \n",
|
|
" kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
|
|
" tf.keras.layers.Dense(hidden_dim, activation='relu',\n",
|
|
" kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
|
|
" tf.keras.layers.Dense(latent_dim, activation='relu',\n",
|
|
" kernel_regularizer=tf.keras.regularizers.l2(reg))\n",
|
|
" ], name='encoder')\n",
|
|
" \n",
|
|
" # Decoder\n",
|
|
" self.decoder = tf.keras.Sequential([\n",
|
|
" tf.keras.layers.Dense(latent_dim, activation='relu',\n",
|
|
" kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
|
|
" tf.keras.layers.Dense(hidden_dim, activation='relu',\n",
|
|
" kernel_regularizer=tf.keras.regularizers.l2(reg)),\n",
|
|
" tf.keras.layers.Dense(input_dim, activation='linear',\n",
|
|
" kernel_regularizer=tf.keras.regularizers.l2(reg))\n",
|
|
" ], name='decoder')\n",
|
|
" \n",
|
|
" # SVM Layer\n",
|
|
" self.svm = DifferentiableSVM(C=svm_C, name='svm')\n",
|
|
" \n",
|
|
" def call(self, inputs, training=False):\n",
|
|
" # Encode\n",
|
|
" encoded = self.encoder(inputs, training=training)\n",
|
|
" \n",
|
|
" # Decode (for reconstruction)\n",
|
|
" decoded = self.decoder(encoded, training=training)\n",
|
|
" \n",
|
|
" # SVM decision (for classification)\n",
|
|
" svm_output = self.svm(encoded)\n",
|
|
" \n",
|
|
" return decoded, svm_output, encoded\n",
|
|
" \n",
|
|
" def compute_loss(self, x, y_true):\n",
|
|
" # Forward pass\n",
|
|
" x_reconstructed, svm_decision, encoded = self(x, training=True)\n",
|
|
" \n",
|
|
" # Reconstruction loss (MSE)\n",
|
|
" reconstruction_loss = tf.reduce_mean(\n",
|
|
" tf.square(x - x_reconstructed)\n",
|
|
" )\n",
|
|
" \n",
|
|
" # SVM loss (hinge)\n",
|
|
" svm_loss = self.svm.compute_loss(encoded, y_true)\n",
|
|
" \n",
|
|
" # Total loss\n",
|
|
" total_loss = (self.ae_weight * reconstruction_loss + \n",
|
|
" self.svm_weight * svm_loss)\n",
|
|
" \n",
|
|
" return total_loss, reconstruction_loss, svm_loss\n",
|
|
"\n",
|
|
"print(\"Joint AE-SVM Model class defined\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "541085f3",
|
|
"metadata": {},
|
|
"source": [
|
|
"Train function"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d0bf18e3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def train_joint_model(X_train, y_train, groups, model_params, \n",
|
|
" epochs=200, batch_size=64, learning_rate=0.0001):\n",
|
|
" \"\"\"\n",
|
|
" Train joint model on given data\n",
|
|
" \"\"\"\n",
|
|
" # Build model\n",
|
|
" model = JointAESVM(\n",
|
|
" input_dim=X_train.shape[1],\n",
|
|
" latent_dim=model_params['latent_dim'],\n",
|
|
" hidden_dim=model_params['hidden_dim'],\n",
|
|
" ae_weight=model_params['ae_weight'],\n",
|
|
" svm_weight=model_params['svm_weight'],\n",
|
|
" svm_C=model_params['svm_C'],\n",
|
|
" reg=model_params['reg']\n",
|
|
" )\n",
|
|
" \n",
|
|
" optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
|
|
" \n",
|
|
" # Training history\n",
|
|
" history = {\n",
|
|
" 'total_loss': [],\n",
|
|
" 'recon_loss': [],\n",
|
|
" 'svm_loss': []\n",
|
|
" }\n",
|
|
" \n",
|
|
" # Convert to tensors\n",
|
|
" X_train_tf = tf.constant(X_train.values, dtype=tf.float32)\n",
|
|
" y_train_tf = tf.constant(y_train, dtype=tf.float32)\n",
|
|
" \n",
|
|
" # Create dataset\n",
|
|
" dataset = tf.data.Dataset.from_tensor_slices((X_train_tf, y_train_tf))\n",
|
|
" dataset = dataset.shuffle(buffer_size=1024).batch(batch_size)\n",
|
|
" \n",
|
|
" # Training loop\n",
|
|
" for epoch in range(epochs):\n",
|
|
" epoch_loss = 0.0\n",
|
|
" epoch_recon = 0.0\n",
|
|
" epoch_svm = 0.0\n",
|
|
" n_batches = 0\n",
|
|
" \n",
|
|
" for x_batch, y_batch in dataset:\n",
|
|
" with tf.GradientTape() as tape:\n",
|
|
" total_loss, recon_loss, svm_loss = model.compute_loss(x_batch, y_batch)\n",
|
|
" \n",
|
|
" # Backpropagation\n",
|
|
" gradients = tape.gradient(total_loss, model.trainable_variables)\n",
|
|
" optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
|
|
" \n",
|
|
" epoch_loss += total_loss.numpy()\n",
|
|
" epoch_recon += recon_loss.numpy()\n",
|
|
" epoch_svm += svm_loss.numpy()\n",
|
|
" n_batches += 1\n",
|
|
" \n",
|
|
" # Average losses\n",
|
|
" history['total_loss'].append(epoch_loss / n_batches)\n",
|
|
" history['recon_loss'].append(epoch_recon / n_batches)\n",
|
|
" history['svm_loss'].append(epoch_svm / n_batches)\n",
|
|
" \n",
|
|
" if (epoch + 1) % 20 == 0:\n",
|
|
" print(f\"Epoch {epoch+1}/{epochs} - \"\n",
|
|
" f\"Total: {history['total_loss'][-1]:.4f}, \"\n",
|
|
" f\"Recon: {history['recon_loss'][-1]:.4f}, \"\n",
|
|
" f\"SVM: {history['svm_loss'][-1]:.4f}\")\n",
|
|
" \n",
|
|
" return model, history\n",
|
|
"\n",
|
|
"print(\"Training function defined\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b6a04540",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Parameter Grid\n",
|
|
"param_grid = {\n",
|
|
" 'latent_dim': [5, 8],\n",
|
|
" 'hidden_dim': [10, 16],\n",
|
|
" 'ae_weight': [0.5, 1.0],\n",
|
|
" 'svm_weight': [0.5, 1.0, 2.0],\n",
|
|
" 'svm_C': [0.1, 1.0, 10.0],\n",
|
|
" 'reg': [0.0001, 0.001]\n",
|
|
"}\n",
|
|
"\n",
|
|
"n_splits = 5 # Weniger Splits wegen Rechenzeit\n",
|
|
"gkf = GroupKFold(n_splits=n_splits)\n",
|
|
"\n",
|
|
"print(f\"Starting Grid Search with {n_splits}-fold GroupKFold\")\n",
|
|
"print(f\"Parameter combinations: {len(list(ParameterGrid(param_grid)))}\")\n",
|
|
"print(\"This will take a while...\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "228463ce",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def evaluate_model(model, X, y):\n",
|
|
" \"\"\"Evaluate joint model\"\"\"\n",
|
|
" X_tf = tf.constant(X, dtype=tf.float32)\n",
|
|
" _, svm_decision, _ = model(X_tf, training=False)\n",
|
|
" \n",
|
|
" # Predict: decision > 0 -> class 1, else class 0\n",
|
|
" y_pred = (svm_decision.numpy().flatten() > 0).astype(int)\n",
|
|
" \n",
|
|
" bal_accuracy = balanced_accuracy_score(y, y_pred)\n",
|
|
" return bal_accuracy, y_pred\n",
|
|
"\n",
|
|
"print(\"Evaluation function defined\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c945fc87",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Grid Search\n",
|
|
"best_score = -np.inf\n",
|
|
"best_params = None\n",
|
|
"best_model = None\n",
|
|
"all_results = []\n",
|
|
"\n",
|
|
"X_train_array = X_train_full.values\n",
|
|
"y_train_array = y_train_full\n",
|
|
"\n",
|
|
"for param_idx, params in enumerate(ParameterGrid(param_grid)):\n",
|
|
" print(f\"\\n{'='*60}\")\n",
|
|
" print(f\"Testing parameters {param_idx + 1}/{len(list(ParameterGrid(param_grid)))}\")\n",
|
|
" print(f\"Params: {params}\")\n",
|
|
" print(f\"{'='*60}\")\n",
|
|
" \n",
|
|
" fold_scores = []\n",
|
|
" \n",
|
|
" for fold, (train_idx, val_idx) in enumerate(gkf.split(X_train_array, y_train_array, groups_train)):\n",
|
|
" print(f\"\\nFold {fold + 1}/{n_splits}\")\n",
|
|
" \n",
|
|
" X_fold_train = pd.DataFrame(X_train_array[train_idx], columns=X_train_full.columns)\n",
|
|
" y_fold_train = y_train_array[train_idx]\n",
|
|
" X_fold_val = X_train_array[val_idx]\n",
|
|
" y_fold_val = y_train_array[val_idx]\n",
|
|
" \n",
|
|
" # Train model\n",
|
|
" model, history = train_joint_model(\n",
|
|
" X_fold_train, y_fold_train, groups_train[train_idx],\n",
|
|
" model_params=params,\n",
|
|
" epochs=100, # Weniger Epochen für Grid Search\n",
|
|
" batch_size=64,\n",
|
|
" learning_rate=0.0001\n",
|
|
" )\n",
|
|
" \n",
|
|
" # Validate\n",
|
|
" val_bal_acc, _ = evaluate_model(model, X_fold_val, y_fold_val)\n",
|
|
" fold_scores.append(val_bal_acc)\n",
|
|
" print(f\"Fold {fold + 1} Validation balanced Accuracy: {val_bal_acc:.4f}\")\n",
|
|
" \n",
|
|
" mean_score = np.mean(fold_scores)\n",
|
|
" std_score = np.std(fold_scores)\n",
|
|
" \n",
|
|
" result = {\n",
|
|
" **params,\n",
|
|
" 'mean_cv_bal_accuracy': mean_score,\n",
|
|
" 'std_cv_bal_accuracy': std_score\n",
|
|
" }\n",
|
|
" all_results.append(result)\n",
|
|
" \n",
|
|
" print(f\"\\nMean CV bal. Accuracy: {mean_score:.4f} ± {std_score:.4f}\")\n",
|
|
" \n",
|
|
" if mean_score > best_score:\n",
|
|
" best_score = mean_score\n",
|
|
" best_params = params\n",
|
|
" print(\"*** NEW BEST PARAMETERS ***\")\n",
|
|
"\n",
|
|
"print(f\"\\n{'='*60}\")\n",
|
|
"print(\"GRID SEARCH COMPLETED\")\n",
|
|
"print(f\"{'='*60}\")\n",
|
|
"print(f\"Best parameters: {best_params}\")\n",
|
|
"print(f\"Best CV bal. accuracy: {best_score:.4f}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "0a0606f5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"results_df = pd.DataFrame(all_results)\n",
|
|
"results_df = results_df.sort_values('mean_cv_accuracy', ascending=False)\n",
|
|
"\n",
|
|
"print(\"\\nTop 10 configurations:\")\n",
|
|
"print(results_df.head(10))\n",
|
|
"\n",
|
|
"# Plot\n",
|
|
"plt.figure(figsize=(12, 6))\n",
|
|
"plt.barh(range(min(10, len(results_df))), \n",
|
|
" results_df['mean_cv_accuracy'].head(10))\n",
|
|
"plt.yticks(range(min(10, len(results_df))), \n",
|
|
" [f\"Config {i+1}\" for i in range(min(10, len(results_df)))])\n",
|
|
"plt.xlabel('Mean CV Accuracy')\n",
|
|
"plt.title('Top 10 Configurations')\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "87906b05",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(\"Training final model on all training data...\")\n",
|
|
"print(f\"Best parameters: {best_params}\")\n",
|
|
"\n",
|
|
"final_model, final_history = train_joint_model(\n",
|
|
" X_train_full, y_train_full, groups_train,\n",
|
|
" model_params=best_params,\n",
|
|
" epochs=300, # Mehr Epochen für finales Training\n",
|
|
" batch_size=64,\n",
|
|
" learning_rate=0.0001\n",
|
|
")\n",
|
|
"\n",
|
|
"print(\"\\nFinal model training completed!\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "718137a8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
|
|
"\n",
|
|
"axes[0].plot(final_history['total_loss'])\n",
|
|
"axes[0].set_title('Total Loss')\n",
|
|
"axes[0].set_xlabel('Epoch')\n",
|
|
"axes[0].set_ylabel('Loss')\n",
|
|
"axes[0].grid(True, alpha=0.3)\n",
|
|
"\n",
|
|
"axes[1].plot(final_history['recon_loss'])\n",
|
|
"axes[1].set_title('Reconstruction Loss')\n",
|
|
"axes[1].set_xlabel('Epoch')\n",
|
|
"axes[1].set_ylabel('Loss')\n",
|
|
"axes[1].grid(True, alpha=0.3)\n",
|
|
"\n",
|
|
"axes[2].plot(final_history['svm_loss'])\n",
|
|
"axes[2].set_title('SVM Loss')\n",
|
|
"axes[2].set_xlabel('Epoch')\n",
|
|
"axes[2].set_ylabel('Loss')\n",
|
|
"axes[2].grid(True, alpha=0.3)\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "02fbc5a2",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Get predictions\n",
|
|
"test_acc, y_pred = evaluate_model(final_model, X_test.values, y_test)\n",
|
|
"\n",
|
|
"# Get SVM decision values for ROC-AUC\n",
|
|
"X_test_tf = tf.constant(X_test.values, dtype=tf.float32)\n",
|
|
"_, svm_decision, _ = final_model(X_test_tf, training=False)\n",
|
|
"y_pred_decision = svm_decision.numpy().flatten()\n",
|
|
"\n",
|
|
"# Metrics\n",
|
|
"print(\"=\" * 50)\n",
|
|
"print(\"TEST SET EVALUATION\")\n",
|
|
"print(\"=\" * 50)\n",
|
|
"print(f\"\\nAccuracy: {accuracy_score(y_test, y_pred):.4f}\")\n",
|
|
"print(f\"Precision: {precision_score(y_test, y_pred):.4f}\")\n",
|
|
"print(f\"Recall: {recall_score(y_test, y_pred):.4f}\")\n",
|
|
"print(f\"F1-Score: {f1_score(y_test, y_pred):.4f}\")\n",
|
|
"\n",
|
|
"# ROC-AUC (decision values as probability proxy)\n",
|
|
"decision_scaled = MinMaxScaler().fit_transform(y_pred_decision.reshape(-1, 1)).flatten()\n",
|
|
"print(f\"ROC-AUC: {roc_auc_score(y_test, decision_scaled):.4f}\")\n",
|
|
"\n",
|
|
"print(\"\\nConfusion Matrix:\")\n",
|
|
"cm = confusion_matrix(y_test, y_pred)\n",
|
|
"print(cm)\n",
|
|
"\n",
|
|
"print(\"\\nClassification Report:\")\n",
|
|
"print(classification_report(y_test, y_pred))\n",
|
|
"\n",
|
|
"# Visualize Confusion Matrix\n",
|
|
"fig, ax = plt.subplots(figsize=(8, 6))\n",
|
|
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Low Load (0)', 'High Load (1)'])\n",
|
|
"disp.plot(cmap='Blues', ax=ax, colorbar=True, values_format='d')\n",
|
|
"ax.set_title('Confusion Matrix - Test Set', fontsize=14, fontweight='bold')\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "4c524bce",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Save entire model\n",
|
|
"final_model.save_weights('joint_ae_svm_weights.h5')\n",
|
|
"print(\"Model weights saved as 'joint_ae_svm_weights.h5'\")\n",
|
|
"\n",
|
|
"# Save encoder separately\n",
|
|
"final_model.encoder.save('encoder_joint.keras')\n",
|
|
"print(\"Encoder saved as 'encoder_joint.keras'\")\n",
|
|
"\n",
|
|
"# Save best parameters\n",
|
|
"with open('best_params_joint.pkl', 'wb') as f:\n",
|
|
" pickle.dump(best_params, f)\n",
|
|
"print(\"Best parameters saved as 'best_params_joint.pkl'\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "792c658d",
|
|
"metadata": {},
|
|
"source": [
|
|
"* doch mal svm ae pipeline?\n",
|
|
"* einfach mal mit 20 13 5\n",
|
|
"* label hinzufügen\n",
|
|
"* mad von CT verwenden oder wert anpassen, ggf. vergleich welches label wie oft vorkommt vorher und nachher. --> labelling schritt von CT übernehmen\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|