CNNs
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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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": "d48f2e13",
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"metadata": {},
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"source": [
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"Importe"
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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": "e34b838d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np \n",
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"import pandas as pd \n",
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"import joblib \n",
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"import seaborn as sns \n",
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"import matplotlib.pyplot as plt \n",
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"\n",
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"from sklearn.metrics import ( \n",
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" confusion_matrix, \n",
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" roc_curve, auc, \n",
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" precision_recall_curve, \n",
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" f1_score, \n",
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" balanced_accuracy_score \n",
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")\n",
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" \n",
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"import tensorflow as tf"
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]
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},
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{
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"cell_type": "markdown",
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"id": "324554b5",
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"metadata": {},
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"source": [
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"Modell und Scaler laden"
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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": "4acc3d2f",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = tf.keras.models.load_model(\"hybrid_fusion_model_V2.keras\") \n",
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"scaler_au = joblib.load(\"scaler_au_V2.joblib\") \n",
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"scaler_eye = joblib.load(\"scaler_eye_V2.joblib\")\n",
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"\n",
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"print(\"Modell & Scaler erfolgreich geladen.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4271cbee",
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"metadata": {},
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"source": [
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"Features laden"
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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": "8342ea10",
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"metadata": {},
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"outputs": [],
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"source": [
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"au_columns = [...] \n",
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"eye_columns = [...]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4a58b20c",
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"metadata": {},
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"source": [
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"Preprocessing"
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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": "b683be47",
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"metadata": {},
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"outputs": [],
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"source": [
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"def preprocess_sample(df, au_columns, eye_columns, scaler_au, scaler_eye):\n",
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" # AUs\n",
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" X_au = df[au_columns].values\n",
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" X_au = scaler_au.transform(X_au).reshape(len(df), len(au_columns), 1)\n",
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"\n",
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" # Eye\n",
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" X_eye = df[eye_columns].values\n",
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" X_eye = scaler_eye.transform(X_eye)\n",
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"\n",
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" return X_au, X_eye"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9dc99a3d",
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"metadata": {},
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"source": [
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"Predict-Funktion"
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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": "00295aa6",
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict_workload(df, model, au_columns, eye_columns, scaler_au, scaler_eye):\n",
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" X_au, X_eye = preprocess_sample(df, au_columns, eye_columns, scaler_au, scaler_eye)\n",
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"\n",
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" probs = model.predict([X_au, X_eye]).flatten()\n",
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" preds = (probs > 0.5).astype(int)\n",
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" \n",
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" return preds, probs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5753516b",
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"metadata": {},
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"source": [
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"Testdaten laden"
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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": "8875b0ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"test_data = pd.read_csv(\"test_data.csv\") # oder direkt aus Notebook 1 exportieren \n",
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"\n",
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"X_au_test = test_data[au_columns].values[..., np.newaxis] \n",
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"X_eye_test = test_data[eye_columns].values \n",
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"y_test = test_data[\"label\"].values \n",
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"groups_test = test_data[\"subjectID\"].values \n",
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"\n",
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"X_au_test_scaled = scaler_au.transform(X_au_test.reshape(len(X_au_test), -1)).reshape(X_au_test.shape) \n",
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"X_eye_test_scaled = scaler_eye.transform(X_eye_test)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "332a3a07",
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"metadata": {},
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"source": [
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"Vorhersagen"
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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": "b5f58ece",
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"metadata": {},
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"outputs": [],
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"source": [
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"y_prob = model.predict([X_au_test_scaled, X_eye_test_scaled]).flatten() \n",
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"y_pred = (y_prob > 0.5).astype(int)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3bc5c66c",
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"metadata": {},
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"source": [
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"Konfusionsmatrix"
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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": "40648dd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"cm = confusion_matrix(y_test, y_pred) \n",
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"plt.figure(figsize=(6,5)) \n",
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"sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
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" xticklabels=[\"Pred 0\", \"Pred 1\"], \n",
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" yticklabels=[\"True 0\", \"True 1\"]) \n",
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"plt.title(\"Konfusionsmatrix - Testdaten\") \n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e79ad8a6",
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"metadata": {},
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"source": [
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"ROC"
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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": "dd93f15c",
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"metadata": {},
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"outputs": [],
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"source": [
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"fpr, tpr, _ = roc_curve(y_test, y_prob) \n",
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"roc_auc = auc(fpr, tpr) \n",
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"\n",
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"plt.figure(figsize=(7,6)) \n",
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"plt.plot(fpr, tpr, label=f\"AUC = {roc_auc:.3f}\") \n",
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"plt.plot([0,1], [0,1], \"k--\") \n",
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"plt.xlabel(\"False Positive Rate\") \n",
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"plt.ylabel(\"True Positive Rate\") \n",
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"plt.title(\"ROC‑Kurve – Testdaten\") \n",
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"plt.legend() \n",
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"plt.grid(True) \n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2eaaf2a0",
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"metadata": {},
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"source": [
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"Precision-Recall"
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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": "601e5dc9",
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"metadata": {},
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"outputs": [],
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"source": [
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"precision, recall, _ = precision_recall_curve(y_test, y_prob) \n",
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"plt.figure(figsize=(7,6)) \n",
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"plt.plot(recall, precision) \n",
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"plt.xlabel(\"Recall\") \n",
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"plt.ylabel(\"Precision\") \n",
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"plt.title(\"Precision‑Recall‑Kurve – Testdaten\")\n",
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"plt.grid(True) \n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "270af771",
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"metadata": {},
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"source": [
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"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": "e2e7da5b",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"F1‑Score:\", f1_score(y_test, y_pred)) \n",
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"print(\"Balanced Accuracy:\", balanced_accuracy_score(y_test, y_pred))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c6e22e1a",
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"metadata": {},
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"source": [
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"Subject-Performance"
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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": "731aaf73",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_eval = pd.DataFrame({ \n",
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" \"subject\": groups_test, \n",
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" \"y_true\": y_test, \n",
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" \"y_pred\": y_pred \n",
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"}) \n",
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"\n",
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"subject_perf = df_eval.groupby(\"subject\").apply( \n",
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" lambda x: balanced_accuracy_score(x[\"y_true\"], x[\"y_pred\"]) \n",
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") \n",
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"\n",
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"print(\"\\n=== Balanced Accuracy pro Proband ===\") \n",
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"print(subject_perf.sort_values())"
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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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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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