{ "cells": [ { "cell_type": "markdown", "id": "708c9745", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": null, "id": "53b10294", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from pathlib import Path\n", "import sys\n", "import os\n", "\n", "base_dir = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n", "sys.path.append(base_dir)\n", "print(base_dir)\n", "\n", "from Fahrsimulator_MSY2526_AI.model_training.tools import evaluation_tools, scaler, mad_outlier_removal\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "from sklearn.svm import OneClassSVM\n", "from sklearn.model_selection import GridSearchCV, KFold, ParameterGrid, train_test_split, GroupKFold\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "import pickle\n", "from sklearn.metrics import (roc_auc_score, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report, balanced_accuracy_score, ConfusionMatrixDisplay) " ] }, { "cell_type": "markdown", "id": "68101229", "metadata": {}, "source": [ "### load Dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "24a765e8", "metadata": {}, "outputs": [], "source": [ "dataset_path = Path(r\"/home/jovyan/data-paulusjafahrsimulator-gpu/first_AU_dataset/output_windowed.parquet\")" ] }, { "cell_type": "code", "execution_count": null, "id": "471001b0", "metadata": {}, "outputs": [], "source": [ "df = pd.read_parquet(path=dataset_path)" ] }, { "cell_type": "markdown", "id": "0fdecdaa", "metadata": {}, "source": [ "### Load Performance data and Subject Split" ] }, { "cell_type": "code", "execution_count": null, "id": "692d1b47", "metadata": {}, "outputs": [], "source": [ "performance_path = Path(r\"/home/jovyan/data-paulusjafahrsimulator-gpu/subject_performance/3new_au_performance.csv\")\n", "performance_df = pd.read_csv(performance_path)" ] }, { "cell_type": "code", "execution_count": null, "id": "ea617e3f", "metadata": {}, "outputs": [], "source": [ "# Subject IDs aus dem Haupt-Dataset nehmen\n", "subjects_from_df = df[\"subjectID\"].unique()\n", "\n", "# Performance-Subset nur für vorhandene Subjects\n", "perf_filtered = performance_df[\n", " performance_df[\"subjectID\"].isin(subjects_from_df)\n", "][[\"subjectID\", \"overall_score\"]]\n", "\n", "# Merge: nur Subjects, die sowohl im df als auch im Performance-CSV vorkommen\n", "merged = (\n", " pd.DataFrame({\"subjectID\": subjects_from_df})\n", " .merge(perf_filtered, on=\"subjectID\", how=\"inner\")\n", ")\n", "\n", "# Sicherstellen, dass keine Scores fehlen\n", "if merged[\"overall_score\"].isna().any():\n", " raise ValueError(\"Es fehlen Score-Werte für manche Subjects.\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "ae43df8d", "metadata": {}, "outputs": [], "source": [ "merged_sorted = merged.sort_values(\"overall_score\", ascending=False).reset_index(drop=True)\n", "\n", "scores = merged_sorted[\"overall_score\"].values\n", "n_total = len(merged_sorted)\n", "n_small = n_total // 3\n", "n_large = n_total - n_small\n", "\n", "# Schritt 1: zufällige Start-Aufteilung\n", "idx = np.arange(n_total)\n", "np.random.shuffle(idx)\n", "\n", "small_idx = idx[:n_small]\n", "large_idx = idx[n_small:]\n", "\n", "def score_diff(small_idx, large_idx):\n", " return abs(scores[small_idx].mean() - scores[large_idx].mean())\n", "\n", "diff = score_diff(small_idx, large_idx)\n", "threshold = 0.01\n", "max_iter = 100\n", "count = 0\n", "\n", "# Schritt 2: random swaps bis Differenz klein genug\n", "while diff > threshold and count < max_iter:\n", " # Zwei zufällige Elemente auswählen\n", " si = np.random.choice(small_idx)\n", " li = np.random.choice(large_idx)\n", " \n", " # Tausch durchführen\n", " new_small_idx = small_idx.copy()\n", " new_large_idx = large_idx.copy()\n", " \n", " new_small_idx[new_small_idx == si] = li\n", " new_large_idx[new_large_idx == li] = si\n", "\n", " # neue Differenz berechnen\n", " new_diff = score_diff(new_small_idx, new_large_idx)\n", "\n", " # Swap akzeptieren, wenn es besser wird\n", " if new_diff < diff:\n", " small_idx = new_small_idx\n", " large_idx = new_large_idx\n", " diff = new_diff\n", "\n", " count += 1\n", "\n", "# Finalgruppen\n", "group_small = merged_sorted.loc[small_idx].reset_index(drop=True)\n", "group_large = merged_sorted.loc[large_idx].reset_index(drop=True)\n", "\n", "print(\"Finale Score-Differenz:\", diff)\n", "print(\"Größe Gruppe 1:\", len(group_small))\n", "print(\"Größe Gruppe 2:\", len(group_large))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9d1b414e", "metadata": {}, "outputs": [], "source": [ "group_large['overall_score'].mean()" ] }, { "cell_type": "code", "execution_count": null, "id": "fa71f9a5", "metadata": {}, "outputs": [], "source": [ "group_small['overall_score'].mean()" ] }, { "cell_type": "code", "execution_count": null, "id": "79ecb4a2", "metadata": {}, "outputs": [], "source": [ "training_subjects = group_large['subjectID'].values\n", "test_subjects = group_small['subjectID'].values\n", "print(training_subjects)\n", "print(test_subjects)" ] }, { "cell_type": "code", "execution_count": null, "id": "87f9fe7d", "metadata": {}, "outputs": [], "source": [ "au_columns = [col for col in df.columns if col.lower().startswith(\"au\")]" ] }, { "cell_type": "markdown", "id": "009d268b", "metadata": {}, "source": [ "Labeling" ] }, { "cell_type": "code", "execution_count": null, "id": "4fa79163", "metadata": {}, "outputs": [], "source": [ "low_all = df[\n", " ((df[\"PHASE\"] == \"baseline\") |\n", " ((df[\"STUDY\"] == \"n-back\") & (df[\"PHASE\"] != \"baseline\") & (df[\"LEVEL\"].isin([1, 4]))))\n", "]\n", "print(f\"low all: {low_all.shape}\")\n", "\n", "high_nback = df[\n", " (df[\"STUDY\"]==\"n-back\") &\n", " (df[\"LEVEL\"].isin([2, 3, 5, 6])) &\n", " (df[\"PHASE\"].isin([\"train\", \"test\"]))\n", "]\n", "print(f\"high n-back: {high_nback.shape}\")\n", "\n", "high_kdrive = df[\n", " (df[\"STUDY\"] == \"k-drive\") & (df[\"PHASE\"] != \"baseline\")\n", "]\n", "print(f\"high k-drive: {high_kdrive.shape}\")\n", "\n", "high_all = pd.concat([high_nback, high_kdrive])\n", "print(f\"high all: {high_all.shape}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "82b17d0b", "metadata": {}, "outputs": [], "source": [ "low = low_all.copy()\n", "high = high_all.copy()\n", "\n", "low[\"label\"] = 0\n", "high[\"label\"] = 1\n", "\n", "data = pd.concat([low, high], ignore_index=True)\n", "df = data.drop_duplicates()\n", "\n", "print(\"Label distribution:\")\n", "print(df[\"label\"].value_counts())" ] }, { "cell_type": "markdown", "id": "4353f87c", "metadata": {}, "source": [ "### Data cleaning with mad" ] }, { "cell_type": "code", "execution_count": null, "id": "c9afaf61", "metadata": {}, "outputs": [], "source": [ "# methode CT\n", "def calculate_mad_params(df, columns):\n", " \"\"\"\n", " Calculate median and MAD parameters for each column.\n", " This should be run ONLY on the training data.\n", " \n", " Returns a dictionary: {col: (median, mad)}\n", " \"\"\"\n", " params = {}\n", " for col in columns:\n", " median = df[col].median()\n", " mad = np.median(np.abs(df[col] - median))\n", " params[col] = (median, mad)\n", " return params\n", "\n", "def apply_mad_filter(df, params, threshold=3.5):\n", " \"\"\"\n", " Apply MAD-based outlier removal using precomputed parameters.\n", " Works on training, validation, and test data.\n", " \n", " df: DataFrame to filter\n", " params: dictionary {col: (median, mad)} from training data\n", " threshold: cutoff for robust Z-score\n", " \"\"\"\n", " df_clean = df.copy()\n", "\n", " for col, (median, mad) in params.items():\n", " if mad == 0:\n", " continue # no spread; nothing to remove for this column\n", "\n", " robust_z = 0.6745 * (df_clean[col] - median) / mad\n", " outlier_mask = np.abs(robust_z) > threshold\n", "\n", " # Remove values only in this specific column\n", " df_clean.loc[outlier_mask, col] = np.nan\n", " \n", " return df_clean" ] }, { "cell_type": "code", "execution_count": null, "id": "4a286665", "metadata": {}, "outputs": [], "source": [ "train_df = df[df.subjectID.isin(training_subjects)]\n", "test_df = df[df.subjectID.isin(test_subjects)]\n", "print(train_df.shape, test_df.shape)" ] }, { "cell_type": "code", "execution_count": null, "id": "2671e0f4", "metadata": {}, "outputs": [], "source": [ "params = calculate_mad_params(train_df, au_columns)\n", "\n", "# Step 2: Apply filter consistently\n", "train_outlier_removed = apply_mad_filter(train_df, params, threshold=3.5)\n", "test_outlier_removed = apply_mad_filter(test_df, params, threshold=3.5)\n", "print(train_outlier_removed.shape, test_outlier_removed.shape)" ] }, { "cell_type": "markdown", "id": "6c39b37f", "metadata": {}, "source": [ "Normalisierung der Daten" ] }, { "cell_type": "code", "execution_count": null, "id": "5e6c654f", "metadata": {}, "outputs": [], "source": [ "normalizer = scaler.fit_normalizer(train_df, au_columns=au_columns, method='standard', scope='global')\n", "train_df_normal = scaler.apply_normalizer(train_df, au_columns=au_columns, normalizer_dict=normalizer)\n", "test_df_normal = scaler.apply_normalizer(test_df, au_columns=au_columns, normalizer_dict=normalizer)" ] }, { "cell_type": "markdown", "id": "b6d25e7b", "metadata": {}, "source": [ "to do insert group k fold for train_df_normal" ] }, { "cell_type": "markdown", "id": "e826a998", "metadata": {}, "source": [ "### AE first" ] }, { "cell_type": "code", "execution_count": null, "id": "e6421371", "metadata": {}, "outputs": [], "source": [ "# Beide Klassen für AE und SVM Training\n", "X_train_full = train_outlier_removed[au_columns].dropna()\n", "y_train_full = train_outlier_removed.loc[X_train_full.index, 'label'].values\n", "groups_train = train_outlier_removed.loc[X_train_full.index, 'subjectID'].values\n", "\n", "print(f\"Training data shape: {X_train_full.shape}\")\n", "print(f\"Label distribution in training: {pd.Series(y_train_full).value_counts()}\")\n", "\n", "# Test data\n", "X_test = test_outlier_removed[au_columns].dropna()\n", "y_test = test_outlier_removed.loc[X_test.index, 'label'].values\n", "\n", "print(f\"Test data shape: {X_test.shape}\")\n", "print(f\"Label distribution in test: {pd.Series(y_test).value_counts()}\")" ] }, { "cell_type": "markdown", "id": "d982e47a", "metadata": {}, "source": [ "### Custom SVM Layer (differentiable approximation)" ] }, { "cell_type": "code", "execution_count": null, "id": "50fbda1a", "metadata": {}, "outputs": [], "source": [ "class DifferentiableSVM(tf.keras.layers.Layer):\n", " \"\"\"\n", " Differentiable SVM Layer using hinge loss.\n", " This allows backpropagation through the SVM to the encoder.\n", " \"\"\"\n", " def __init__(self, C=1.0, **kwargs):\n", " super(DifferentiableSVM, self).__init__(**kwargs)\n", " self.C = C\n", " \n", " def build(self, input_shape):\n", " # SVM weights: w and bias b\n", " self.w = self.add_weight(\n", " shape=(input_shape[-1],),\n", " initializer='glorot_uniform',\n", " trainable=True,\n", " name='svm_w'\n", " )\n", " self.b = self.add_weight(\n", " shape=(1,),\n", " initializer='zeros',\n", " trainable=True,\n", " name='svm_b'\n", " )\n", " \n", " def call(self, inputs):\n", " # Decision function: w^T * x + b\n", " decision = tf.reduce_sum(inputs * self.w, axis=1, keepdims=True) + self.b\n", " return decision\n", " \n", " def compute_loss(self, inputs, labels):\n", " \"\"\"\n", " Hinge loss for SVM: max(0, 1 - y * (w^T * x + b))\n", " labels should be -1 or +1\n", " \"\"\"\n", " decision = self.call(inputs)\n", " \n", " # Convert labels from 0/1 to -1/+1\n", " labels_svm = tf.where(labels == 0, -1.0, 1.0)\n", " labels_svm = tf.cast(labels_svm, tf.float32)\n", " labels_svm = tf.reshape(labels_svm, (-1, 1))\n", " \n", " # Hinge loss\n", " hinge_loss = tf.reduce_mean(\n", " 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 }