max dist calculation for eye tracking, new notebook for model training
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c7295f310c
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72
dataset_creation/maxDist.py
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72
dataset_creation/maxDist.py
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import math
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def fixation_radius_normalized(theta_deg: float,
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distance_cm: float,
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screen_width_cm: float,
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screen_height_cm: float,
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resolution_x: int,
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resolution_y: int,
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method: str = "max"):
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"""
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Berechnet den PyGaze-Fixationsradius für normierte Gaze-Daten in [0,1].
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"""
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# Schritt 1: visueller Winkel → physische Distanz (cm)
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delta_cm = 2 * distance_cm * math.tan(math.radians(theta_deg) / 2)
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# Schritt 2: physische Distanz → Pixel
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delta_px_x = delta_cm * (resolution_x / screen_width_cm)
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delta_px_y = delta_cm * (resolution_y / screen_height_cm)
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# Pixelradius
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if method == "max":
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r_px = max(delta_px_x, delta_px_y)
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else:
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r_px = math.sqrt(delta_px_x**2 + delta_px_y**2)
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# Schritt 3: Pixelradius → normierter Radius
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r_norm_x = r_px / resolution_x
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r_norm_y = r_px / resolution_y
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if method == "max":
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return max(r_norm_x, r_norm_y)
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else:
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return math.sqrt(r_norm_x**2 + r_norm_y**2)
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# Beispiel: 55" 4k Monitor
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screen_width_cm = 3*121.8
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screen_height_cm = 68.5
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resolution_x = 3*3840
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resolution_y = 2160
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distance_to_screen_cm = 120
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method = 'max'
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max_angle= 1.0
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maxdist_px = fixation_radius_normalized(theta_deg=max_angle,
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distance_cm=distance_to_screen_cm,
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screen_width_cm=screen_width_cm,
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screen_height_cm=screen_height_cm,
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resolution_x=resolution_x,
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resolution_y=resolution_y,
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method=method)
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print("PyGaze max_dist (max):", maxdist_px)
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method = 'euclid'
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maxdist_px = fixation_radius_normalized(theta_deg=max_angle,
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distance_cm=distance_to_screen_cm,
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screen_width_cm=screen_width_cm,
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screen_height_cm=screen_height_cm,
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resolution_x=resolution_x,
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resolution_y=resolution_y,
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method=method)
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print("PyGaze max_dist (euclid):", maxdist_px)
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# Passt noch nicht zu der Breite
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# https://osdoc.cogsci.nl/4.0/de/visualangle/
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# https://reference.org/facts/Visual_angle/LUw29zy7
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323
model_training/VAE_SVM/vaesvm.ipynb
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323
model_training/VAE_SVM/vaesvm.ipynb
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{
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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": 61,
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"id": "53b10294",
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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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"/home\n"
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]
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},
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{
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"ename": "ImportError",
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"evalue": "cannot import name 'mad_outlier_removal' from 'Fahrsimulator_MSY2526_AI.model_training.tools' (unknown location)",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mImportError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[61]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m 8\u001b[39m sys.path.append(base_dir)\n\u001b[32m 9\u001b[39m \u001b[38;5;28mprint\u001b[39m(base_dir)\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mFahrsimulator_MSY2526_AI\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodel_training\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtools\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m evaluation_tools, scaler, mad_outlier_removal\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpreprocessing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m StandardScaler, MinMaxScaler\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msvm\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m OneClassSVM\n",
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"\u001b[31mImportError\u001b[39m: cannot import name 'mad_outlier_removal' from 'Fahrsimulator_MSY2526_AI.model_training.tools' (unknown location)"
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]
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}
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],
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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\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, \n",
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" recall_score, f1_score, confusion_matrix, classification_report) "
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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": 12,
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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": 13,
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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": 32,
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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": 33,
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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": 52,
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"id": "ae43df8d",
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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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"Finale Score-Differenz: 0.0020961590397180485\n",
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"Größe Gruppe 1: 6\n",
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"Größe Gruppe 2: 12\n"
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]
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}
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],
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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": 53,
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"id": "9d1b414e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.7895307985978888"
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]
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},
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"execution_count": 53,
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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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"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": 54,
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"id": "fa71f9a5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.7874346395581707"
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]
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},
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"execution_count": 54,
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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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"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": 55,
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"id": "79ecb4a2",
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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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"[22 4 26 16 3 11 18 14 24 13 9 28]\n",
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"[ 5 6 29 0 7 17]\n"
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]
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}
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],
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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": "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": 60,
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"id": "76610052",
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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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"(7115, 25)\n",
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"(7320, 25)\n"
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]
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}
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],
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"source": [
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"# SET\n",
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"threshold_mad = 100\n",
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"column_praefix ='AU'\n",
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"\n",
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"au_columns = [col for col in df.columns if col.startswith(column_praefix)]\n",
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"cleaned_df = mad_outlier_removal(df,columns=au_columns, threshold=threshold_mad)\n",
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"print(cleaned_df.shape)\n",
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"print(df.shape)"
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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": "Python 3 (ipykernel)",
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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": "3.12.10"
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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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