{ "cells": [ { "cell_type": "markdown", "id": "cf894f6f", "metadata": {}, "source": [ "# Intermediate Fusion mit Deep SVDD" ] }, { "cell_type": "markdown", "id": "494626b1", "metadata": {}, "source": [ "* Input: gemeinsames Dataset aus EYE Tracking und Action Units mit selber Abtastfrequenz\n", "* Verarbeitung: Intermediate Fusion\n", "* Modell: Deep SVDD --> Erlernen einer Kugel durch ein neuronales Netz, dass die Normaldaten einschließt" ] }, { "cell_type": "markdown", "id": "bef91203", "metadata": {}, "source": [ "### Imports + GPU " ] }, { "cell_type": "code", "execution_count": null, "id": "f0b8274a", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from pathlib import Path\n", "import sys\n", "import os\n", "import time\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, performance_split\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", "from tensorflow.keras import layers, models, regularizers\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, auc, roc_curve) " ] }, { "cell_type": "code", "execution_count": null, "id": "f03c8da9", "metadata": {}, "outputs": [], "source": [ "# Check GPU availability\n", "print(\"TensorFlow version:\", tf.__version__)\n", "print(\"GPU Available:\", tf.config.list_physical_devices('GPU'))\n", "print(\"CUDA Available:\", tf.test.is_built_with_cuda())\n", "\n", "# Get detailed GPU info\n", "gpus = tf.config.list_physical_devices('GPU')\n", "if gpus:\n", " print(f\"\\nNumber of GPUs: {len(gpus)}\")\n", " for gpu in gpus:\n", " print(f\"GPU: {gpu}\")\n", " \n", " # Enable memory growth to prevent TF from allocating all GPU memory\n", " try:\n", " for gpu in gpus:\n", " tf.config.experimental.set_memory_growth(gpu, True)\n", " print(\"\\nGPU memory growth enabled\")\n", " except RuntimeError as e:\n", " print(e)\n", "else:\n", " print(\"\\nNo GPU found - running on CPU\")" ] }, { "cell_type": "markdown", "id": "f00a477c", "metadata": {}, "source": [ "### Data Preprocessing" ] }, { "cell_type": "markdown", "id": "504c1df7", "metadata": {}, "source": [ "Laden der Daten" ] }, { "cell_type": "code", "execution_count": null, "id": "6482542b", "metadata": {}, "outputs": [], "source": [ "dataset_path = Path(r\"data-paulusjafahrsimulator-gpu/new_datasets/combined_dataset_25hz.parquet\")" ] }, { "cell_type": "code", "execution_count": null, "id": "ce8ab464", "metadata": {}, "outputs": [], "source": [ "df = pd.read_parquet(path=dataset_path)" ] }, { "cell_type": "code", "execution_count": null, "id": "c2115f65", "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": "markdown", "id": "c045c46d", "metadata": {}, "source": [ "Performance based split" ] }, { "cell_type": "code", "execution_count": null, "id": "1660ec95", "metadata": {}, "outputs": [], "source": [ "train_ids, temp_ids, diff1 = performance_split.performance_based_split(\n", " subject_ids=df[\"subjectID\"].unique(),\n", " performance_df=performance_df,\n", " split_ratio=0.6, # 60% train, 40% temp\n", " random_seed=42\n", ")\n", "\n", "val_ids, test_ids, diff2 = performance_split.performance_based_split(\n", " subject_ids=temp_ids,\n", " performance_df=performance_df,\n", " split_ratio=0.5, # 50/50 split of remaining 40%\n", " random_seed=43\n", ")\n", "print(diff1, diff2)" ] }, { "cell_type": "markdown", "id": "195b7283", "metadata": {}, "source": [ "Labeling" ] }, { "cell_type": "code", "execution_count": null, "id": "05b6b73d", "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": "60148c0b", "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": "c8fefca7", "metadata": {}, "source": [ "Split" ] }, { "cell_type": "code", "execution_count": null, "id": "da6a2f87", "metadata": {}, "outputs": [], "source": [ "train_df = df[\n", " (df.subjectID.isin(train_ids)) & (df['label'] == 0)\n", "].copy()\n", "\n", "# Validation: balanced sampling of label=0 and label=1\n", "val_df_full = df[df.subjectID.isin(val_ids)].copy()\n", "\n", "# Get all label=0 samples\n", "val_df_label0 = val_df_full[val_df_full['label'] == 0]\n", "\n", "# Sample same number from label=1\n", "n_samples = len(val_df_label0)\n", "val_df_label1 = val_df_full[val_df_full['label'] == 1].sample(\n", " n=n_samples, random_state=42\n", ")\n", "\n", "# Combine\n", "val_df = pd.concat([val_df_label0, val_df_label1], ignore_index=True)\n", "test_df = df[df.subjectID.isin(test_ids)]\n", "print(train_df.shape, val_df.shape,test_df.shape)" ] }, { "cell_type": "code", "execution_count": null, "id": "e8375760", "metadata": {}, "outputs": [], "source": [ "val_df['label'].value_counts()" ] }, { "cell_type": "markdown", "id": "f0570a3c", "metadata": {}, "source": [ "Normalization" ] }, { "cell_type": "code", "execution_count": null, "id": "acec4a03", "metadata": {}, "outputs": [], "source": [ "def fit_normalizer(train_data, au_columns, method='standard', scope='global'):\n", " \"\"\"\n", " Fit normalization scalers on training data.\n", " \n", " Parameters:\n", " -----------\n", " train_data : pd.DataFrame\n", " Training dataframe with AU columns and subjectID\n", " au_columns : list\n", " List of AU column names to normalize\n", " method : str, default='standard'\n", " Normalization method: 'standard' for StandardScaler or 'minmax' for MinMaxScaler\n", " scope : str, default='global'\n", " Normalization scope: 'subject' for per-subject or 'global' for across all subjects\n", " \n", " Returns:\n", " --------\n", " dict\n", " Dictionary containing fitted scalers and statistics for new subjects\n", " \"\"\"\n", " if method == 'standard':\n", " Scaler = StandardScaler\n", " elif method == 'minmax':\n", " Scaler = MinMaxScaler\n", " else:\n", " raise ValueError(\"method must be 'standard' or 'minmax'\")\n", " \n", " scalers = {}\n", " if scope == 'subject':\n", " # Fit one scaler per subject\n", " subject_stats = []\n", " \n", " for subject in train_data['subjectID'].unique():\n", " subject_mask = train_data['subjectID'] == subject\n", " scaler = Scaler()\n", " scaler.fit(train_data.loc[subject_mask, au_columns].values)\n", " scalers[subject] = scaler\n", " \n", " # Store statistics for averaging\n", " if method == 'standard':\n", " subject_stats.append({\n", " 'mean': scaler.mean_,\n", " 'std': scaler.scale_\n", " })\n", " elif method == 'minmax':\n", " subject_stats.append({\n", " 'min': scaler.data_min_,\n", " 'max': scaler.data_max_\n", " })\n", " \n", " # Calculate average statistics for new subjects\n", " if method == 'standard':\n", " avg_mean = np.mean([s['mean'] for s in subject_stats], axis=0)\n", " avg_std = np.mean([s['std'] for s in subject_stats], axis=0)\n", " fallback_scaler = StandardScaler()\n", " fallback_scaler.mean_ = avg_mean\n", " fallback_scaler.scale_ = avg_std\n", " fallback_scaler.var_ = avg_std ** 2\n", " fallback_scaler.n_features_in_ = len(au_columns)\n", " elif method == 'minmax':\n", " avg_min = np.mean([s['min'] for s in subject_stats], axis=0)\n", " avg_max = np.mean([s['max'] for s in subject_stats], axis=0)\n", " fallback_scaler = MinMaxScaler()\n", " fallback_scaler.data_min_ = avg_min\n", " fallback_scaler.data_max_ = avg_max\n", " fallback_scaler.data_range_ = avg_max - avg_min\n", " fallback_scaler.scale_ = 1.0 / fallback_scaler.data_range_\n", " fallback_scaler.min_ = -avg_min * fallback_scaler.scale_\n", " fallback_scaler.n_features_in_ = len(au_columns)\n", " \n", " scalers['_fallback'] = fallback_scaler\n", " \n", " elif scope == 'global':\n", " # Fit one scaler for all subjects\n", " scaler = Scaler()\n", " scaler.fit(train_data[au_columns].values)\n", " scalers['global'] = scaler\n", " \n", " else:\n", " raise ValueError(\"scope must be 'subject' or 'global'\")\n", " \n", " return {'scalers': scalers, 'method': method, 'scope': scope}\n", "\n", "def apply_normalizer(data, columns, normalizer_dict):\n", " \"\"\"\n", " Apply fitted normalization scalers to data.\n", " \n", " Parameters:\n", " -----------\n", " data : pd.DataFrame\n", " Dataframe with AU columns and subjectID\n", " au_columns : list\n", " List of AU column names to normalize\n", " normalizer_dict : dict\n", " Dictionary containing fitted scalers from fit_normalizer()\n", " \n", " Returns:\n", " --------\n", " pd.DataFrame\n", " DataFrame with normalized AU columns\n", " \"\"\"\n", " normalized_data = data.copy()\n", " scalers = normalizer_dict['scalers']\n", " scope = normalizer_dict['scope']\n", " normalized_data[columns] = normalized_data[columns].astype(np.float64)\n", "\n", " if scope == 'subject':\n", " # Apply per-subject normalization\n", " for subject in data['subjectID'].unique():\n", " subject_mask = data['subjectID'] == subject\n", " \n", " # Use the subject's scaler if available, otherwise use fallback\n", " if subject in scalers:\n", " scaler = scalers[subject]\n", " else:\n", " # Use averaged scaler for new subjects\n", " scaler = scalers['_fallback']\n", " print(f\"Info: Subject {subject} not in training data. Using averaged scaler from training subjects.\")\n", " \n", " normalized_data.loc[subject_mask, columns] = scaler.transform(\n", " data.loc[subject_mask, columns].values\n", " )\n", " \n", " elif scope == 'global':\n", " # Apply global normalization\n", " scaler = scalers['global']\n", " normalized_data[columns] = scaler.transform(data[columns].values)\n", " \n", " return normalized_data\n" ] }, { "cell_type": "code", "execution_count": null, "id": "53c6ee6f", "metadata": {}, "outputs": [], "source": [ "def save_normalizer(normalizer_dict, filepath):\n", " \"\"\"\n", " Save fitted normalizer to disk.\n", "\n", " Parameters:\n", " -----------\n", " normalizer_dict : dict\n", " Dictionary containing fitted scalers from fit_normalizer()\n", " filepath : str\n", " Path to save the normalizer (e.g., 'normalizer.pkl')\n", " \"\"\"\n", " # Create directory if it does not exist\n", " dirpath = os.path.dirname(filepath)\n", " if dirpath:\n", " os.makedirs(dirpath, exist_ok=True)\n", "\n", " with open(filepath, 'wb') as f:\n", " pickle.dump(normalizer_dict, f)\n", "\n", " print(f\"Normalizer saved to {filepath}\")\n", "\n", "def load_normalizer(filepath):\n", " \"\"\"\n", " Load fitted normalizer from disk.\n", " \n", " Parameters:\n", " -----------\n", " filepath : str\n", " Path to the saved normalizer file\n", " \n", " Returns:\n", " --------\n", " dict\n", " Dictionary containing fitted scalers\n", " \"\"\"\n", " with open(filepath, 'rb') as f:\n", " normalizer_dict = pickle.load(f)\n", " print(f\"Normalizer loaded from {filepath}\")\n", " return normalizer_dict" ] }, { "cell_type": "markdown", "id": "7280f64f", "metadata": {}, "source": [ "save Normalizer" ] }, { "cell_type": "code", "execution_count": null, "id": "8420afc2", "metadata": {}, "outputs": [], "source": [ "normalizer_path=Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/normalizer.pkl')" ] }, { "cell_type": "code", "execution_count": null, "id": "cdd2ba73", "metadata": {}, "outputs": [], "source": [ "face_au_cols = [c for c in train_df.columns if c.startswith(\"FACE_AU\")]\n", "eye_cols = ['Fix_count_short_66_150', 'Fix_count_medium_300_500',\n", " 'Fix_count_long_gt_1000', 'Fix_count_100', 'Fix_mean_duration',\n", " 'Fix_median_duration', 'Sac_count', 'Sac_mean_amp', 'Sac_mean_dur',\n", " 'Sac_median_dur', 'Blink_count', 'Blink_mean_dur', 'Blink_median_dur',\n", " 'Pupil_mean', 'Pupil_IPA']\n", "print(len(eye_cols))\n", "all_signal_columns = face_au_cols+eye_cols\n", "print(len(all_signal_columns))\n", "normalizer = fit_normalizer(train_df, all_signal_columns, method='standard', scope='subject')\n", "save_normalizer(normalizer, normalizer_path )" ] }, { "cell_type": "code", "execution_count": null, "id": "76afc4d3", "metadata": {}, "outputs": [], "source": [ "normalizer = load_normalizer(normalizer_path)\n", "# 3. Apply normalization to all sets\n", "train_df_norm = apply_normalizer(train_df, all_signal_columns, normalizer)\n", "val_df_norm = apply_normalizer(val_df, all_signal_columns, normalizer)\n", "test_df_norm = apply_normalizer(test_df, all_signal_columns, normalizer)" ] }, { "cell_type": "markdown", "id": "77deead9", "metadata": {}, "source": [ "Outlier removal (later)" ] }, { "cell_type": "markdown", "id": "fd139799", "metadata": {}, "source": [ "Change of dtypes for keras pandas" ] }, { "cell_type": "code", "execution_count": null, "id": "8587343e", "metadata": {}, "outputs": [], "source": [ "X_face = train_df_norm[face_au_cols].to_numpy(dtype=np.float32)\n", "X_eye = train_df_norm[eye_cols].to_numpy(dtype=np.float32)" ] }, { "cell_type": "markdown", "id": "b736bc58", "metadata": {}, "source": [ "### Autoencoder Pre-Training" ] }, { "cell_type": "markdown", "id": "aa11faf3", "metadata": {}, "source": [ "Vor-Training der Gewichte mit Autoencoder, Loss: MSE" ] }, { "cell_type": "code", "execution_count": null, "id": "3eab9d94", "metadata": {}, "outputs": [], "source": [ "def build_intermediate_fusion_autoencoder(\n", " input_dim_mod1=15,\n", " input_dim_mod2=20,\n", " encoder_hidden_dim_mod1=12, # individuell\n", " encoder_hidden_dim_mod2=20, # individuell\n", " latent_dim=6, # Änderung: Bottleneck vergrößert für stabilere Repräsentation\n", " dropout_rate=0.4, # Dropout in Hidden Layers\n", " neg_slope=0.1,\n", " weight_decay=1e-4,\n", " decoder_hidden_dims=[16, 32] # Änderung: Decoder größer für bessere Rekonstruktion\n", "):\n", " \"\"\"\n", " Verbesserter Intermediate-Fusion Autoencoder für Deep SVDD.\n", " Änderungen:\n", " - Bottleneck vergrößert (latent_dim)\n", " - Dropout nur in Hidden Layers, nicht im Bottleneck\n", " - Decoder größer für stabileres Pretraining\n", " - Parametrisierbare Hidden-Dimensions für Encoder\n", " \"\"\"\n", "\n", " l2 = regularizers.l2(weight_decay)\n", " act = layers.LeakyReLU(negative_slope=neg_slope)\n", "\n", " # -------- Inputs --------\n", " x1_in = layers.Input(shape=(input_dim_mod1,), name=\"modality_1\")\n", " x2_in = layers.Input(shape=(input_dim_mod2,), name=\"modality_2\")\n", "\n", " # -------- Encoder 1 --------\n", " e1 = layers.Dense(\n", " encoder_hidden_dim_mod1,\n", " use_bias=False,\n", " kernel_regularizer=l2\n", " )(x1_in)\n", " e1 = act(e1)\n", " e1 = layers.Dropout(dropout_rate)(e1) # Dropout nur hier\n", "\n", " e1 = layers.Dense(\n", " 16, # Änderung: Hidden Layer größer für stabilere Fusion\n", " use_bias=False,\n", " kernel_regularizer=l2\n", " )(e1)\n", " e1 = act(e1)\n", "\n", " # -------- Encoder 2 --------\n", " e2 = layers.Dense(\n", " encoder_hidden_dim_mod2,\n", " use_bias=False,\n", " kernel_regularizer=l2\n", " )(x2_in)\n", " e2 = act(e2)\n", " e2 = layers.Dropout(dropout_rate)(e2) # Dropout nur hier\n", "\n", " e2 = layers.Dense(\n", " 16, # Änderung: Hidden Layer größer\n", " use_bias=False,\n", " kernel_regularizer=l2\n", " )(e2)\n", " e2 = act(e2)\n", "\n", " # -------- Intermediate Fusion --------\n", " fused = layers.Concatenate(name=\"fusion\")([e1, e2]) # 16+16=32 Dimensionen\n", "\n", " # -------- Joint Encoder / Bottleneck --------\n", " # sinnvoll kleiner als Fusion\n", " h = layers.Dense(\n", " latent_dim,\n", " use_bias=False,\n", " kernel_regularizer=l2\n", " )(fused)\n", " h = act(h)\n", " h = layers.Dropout(dropout_rate)(h)\n", "\n", " z = layers.Dense(\n", " latent_dim,\n", " activation=None, # linear, für Deep SVDD\n", " use_bias=False,\n", " kernel_regularizer=l2,\n", " name=\"latent\"\n", " )(h)\n", " # Dropout entfernt direkt vor Bottleneck\n", "\n", " # -------- Decoder --------\n", " d = layers.Dense(\n", " decoder_hidden_dims[0], # größerer Decoder\n", " use_bias=False,\n", " kernel_regularizer=l2\n", " )(z)\n", " d = act(d)\n", "\n", " d = layers.Dense(\n", " decoder_hidden_dims[1],\n", " use_bias=False,\n", " kernel_regularizer=l2\n", " )(d)\n", " d = act(d)\n", "\n", " x1_out = layers.Dense(\n", " input_dim_mod1,\n", " activation=None,\n", " use_bias=False,\n", " name=\"recon_modality_1\"\n", " )(d)\n", "\n", " x2_out = layers.Dense(\n", " input_dim_mod2,\n", " activation=None,\n", " use_bias=False,\n", " name=\"recon_modality_2\"\n", " )(d)\n", "\n", " model = models.Model(\n", " inputs=[x1_in, x2_in],\n", " outputs=[x1_out, x2_out],\n", " name=\"IntermediateFusionAE_Improved\"\n", " )\n", "\n", " return model\n" ] }, { "cell_type": "code", "execution_count": null, "id": "80cb8eb0", "metadata": {}, "outputs": [], "source": [ "model = build_intermediate_fusion_autoencoder(\n", " input_dim_mod1=len(face_au_cols),\n", " input_dim_mod2=len(eye_cols),\n", " encoder_hidden_dim_mod1=15, # individuell\n", " encoder_hidden_dim_mod2=10, # individuell\n", " latent_dim=8,\n", " dropout_rate=0.3, # einstellbar\n", " neg_slope=0.1,\n", " weight_decay=1e-3\n", ")\n", "\n", "model.compile(\n", " loss={\n", " \"recon_modality_1\": \"mse\",\n", " \"recon_modality_2\": \"mse\",\n", " },\n", " loss_weights={\n", " \"recon_modality_1\": 1.0,\n", " \"recon_modality_2\": 1.0,\n", " },\n", " optimizer=tf.keras.optimizers.Adam(1e-2)\n", " \n", ")\n", "\n", "batch_size_ae=64\n", "# model.summary()" ] }, { "cell_type": "code", "execution_count": null, "id": "95d36a07", "metadata": {}, "outputs": [], "source": [ "model.fit(\n", " x=[X_face, X_eye],\n", " y=[X_face, X_eye],\n", " batch_size=batch_size_ae,\n", " epochs=150,\n", " shuffle=True\n", ")\n", "model.compile(\n", " loss={\n", " \"recon_modality_1\": \"mse\",\n", " \"recon_modality_2\": \"mse\",\n", " },\n", " loss_weights={\n", " \"recon_modality_1\": 1.0,\n", " \"recon_modality_2\": 1.0,\n", " },\n", " optimizer=tf.keras.optimizers.Adam(1e-5),\n", ")\n", "model.fit(\n", " x=[X_face, X_eye],\n", " y=[X_face, X_eye],\n", " batch_size=batch_size_ae,\n", " epochs=100,\n", " shuffle=True\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9ccfbc71", "metadata": {}, "outputs": [], "source": [ "encoder = tf.keras.Model(\n", " inputs=model.inputs,\n", " outputs=model.get_layer(\"latent\").output,\n", " name=\"SVDD_Encoder\"\n", ")" ] }, { "cell_type": "markdown", "id": "e4e1b5ff", "metadata": {}, "source": [ "Speichern" ] }, { "cell_type": "code", "execution_count": null, "id": "7e591264", "metadata": {}, "outputs": [], "source": [ "encoder_save_path =Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/encoder_6_deep.keras')\n", "encoder.save(encoder_save_path)" ] }, { "cell_type": "markdown", "id": "372dc754", "metadata": {}, "source": [ "Laden Encoder / Deepsvdd" ] }, { "cell_type": "code", "execution_count": null, "id": "83199fc6", "metadata": {}, "outputs": [], "source": [ "encoder_load_path = encoder_save_path\n", "encoder = tf.keras.models.load_model(encoder_load_path)" ] }, { "cell_type": "markdown", "id": "92046112", "metadata": {}, "source": [ "Check, if encoder works" ] }, { "cell_type": "code", "execution_count": null, "id": "db2fa21c", "metadata": {}, "outputs": [], "source": [ "ans= encoder.predict([X_face, X_eye])\n", "print(ans[:6,:])" ] }, { "cell_type": "markdown", "id": "d7bcc35d", "metadata": {}, "source": [ "### Deep SVDD Training" ] }, { "cell_type": "code", "execution_count": null, "id": "806a2479", "metadata": {}, "outputs": [], "source": [ "encoder_load_path = encoder_save_path\n", "deep_svdd_net = tf.keras.models.load_model(encoder_load_path) " ] }, { "cell_type": "code", "execution_count": null, "id": "54083759", "metadata": {}, "outputs": [], "source": [ "def get_center(model, dataset):\n", " center = model.predict(dataset).mean(axis=0)\n", "\n", " eps = 0.1\n", " center[(abs(center) < eps) & (center < 0)] = -eps\n", " center[(abs(center) < eps) & (center >= 0)] = eps\n", "\n", " return center\n", "def dist_per_sample(output, center):\n", " return tf.reduce_sum(tf.square(output - center), axis=-1)\n", "\n", "def score_per_sample(output, center, radius):\n", " return dist_per_sample(output, center) - radius**2\n", "\n", "def train_loss(output, center):\n", " return tf.reduce_mean(dist_per_sample(output, center))" ] }, { "cell_type": "code", "execution_count": null, "id": "fd6f47c0", "metadata": {}, "outputs": [], "source": [ "center = get_center(deep_svdd_net, [X_face, X_eye])" ] }, { "cell_type": "code", "execution_count": null, "id": "da140072", "metadata": {}, "outputs": [], "source": [ "# def get_radius(nu, dataset):\n", "# x_face, x_eye = dataset # <-- zwingend entpacken\n", "\n", "# dataset_tuple=[x_face, x_eye]\n", "\n", "# dists = dist_per_sample(deep_svdd_net.predict(dataset_tuple), center)\n", "# return np.quantile(np.sqrt(dists), 1-nu).astype(np.float32)" ] }, { "cell_type": "code", "execution_count": null, "id": "b47b52f6", "metadata": {}, "outputs": [], "source": [ "def get_radius_from_arrays(nu, X_face, X_eye):\n", " z = deep_svdd_net.predict([X_face, X_eye])\n", " dists = dist_per_sample(z, center)\n", " return np.quantile(np.sqrt(dists), 1 - nu).astype(np.float32)" ] }, { "cell_type": "code", "execution_count": null, "id": "b062bd19", "metadata": {}, "outputs": [], "source": [ "@tf.function\n", "def train_step(batch):\n", " with tf.GradientTape() as grad_tape:\n", " output = deep_svdd_net(batch, training=True)\n", " batch_loss = train_loss(output, center)\n", "\n", " gradients = grad_tape.gradient(batch_loss, deep_svdd_net.trainable_variables)\n", " optimizer.apply_gradients(zip(gradients, deep_svdd_net.trainable_variables))\n", "\n", " return batch_loss" ] }, { "cell_type": "code", "execution_count": null, "id": "4c144130", "metadata": {}, "outputs": [], "source": [ "def train(dataset, epochs, nu):\n", " for epoch in range(epochs):\n", " start = time.time()\n", " losses = []\n", " for batch in dataset:\n", " batch_loss = train_step(batch)\n", " losses.append(batch_loss)\n", "\n", " print(f'{epoch+1}/{epochs} epoch: Loss of {np.mean(losses)} ({time.time()-start} secs)')\n", "\n", " return get_radius_from_arrays(nu, X_face, X_eye)\n", "\n", "\n", "nu = 0.05\n", "\n", "train_dataset = tf.data.Dataset.from_tensor_slices((X_face, X_eye)).shuffle(64).batch(64)\n", "# train_dataset = tf.data.Dataset.from_tensor_slices((X_face, X_eye))\n", "\n", "optimizer = tf.keras.optimizers.Adam(1e-3)\n", "train(train_dataset, epochs=150, nu=nu)\n", "\n", "optimizer.learning_rate = 1e-4\n", "radius = train(train_dataset, 100, nu=nu)" ] }, { "cell_type": "markdown", "id": "24f0cef0", "metadata": {}, "source": [ "prepare valid & test set" ] }, { "cell_type": "code", "execution_count": null, "id": "acb9c8f1", "metadata": {}, "outputs": [], "source": [ "# Test set\n", "X_face_test = test_df_norm[face_au_cols].to_numpy(dtype=np.float32)\n", "X_eye_test = test_df_norm[eye_cols].to_numpy(dtype=np.float32)\n", "y_test = test_df_norm[\"label\"].to_numpy(dtype=np.float32)\n", "\n", "# Validation set\n", "X_face_val = val_df_norm[face_au_cols].to_numpy(dtype=np.float32)\n", "X_eye_val = val_df_norm[eye_cols].to_numpy(dtype=np.float32)\n", "y_val = val_df_norm[\"label\"].to_numpy(dtype=np.float32)" ] }, { "cell_type": "code", "execution_count": null, "id": "49737d5d", "metadata": {}, "outputs": [], "source": [ "valid_scores = (score_per_sample(deep_svdd_net.predict([X_face_val, X_eye_val]), center, radius)).numpy()\n", "\n", "valid_fpr, valid_tpr, _ = roc_curve(y_val, valid_scores, pos_label=1)\n", "valid_auc = auc(valid_fpr, valid_tpr)\n", "\n", "plt.figure()\n", "plt.title('Deep SVDD')\n", "plt.plot(valid_fpr, valid_tpr, 'b-')\n", "plt.text(0.5, 0.5, f'AUC: {valid_auc:.4f}')\n", "plt.xlabel('False positive rate')\n", "plt.ylabel('True positive rate')\n", "plt.show()\n", "\n", "valid_predictions = (valid_scores > 0).astype(int)\n", "\n", "normal_acc = np.mean(valid_predictions[y_val == 0] == 0)\n", "anomaly_acc = np.mean(valid_predictions[y_val == 1] == 1)\n", "print(f'Accuracy on Validation set: {accuracy_score(y_val, valid_predictions)}')\n", "print(f'Accuracy for normals: {normal_acc:.4f}')\n", "print(f'Accuracy for anomalies: {anomaly_acc:.4f}')\n", "print(f'F1 on Validation set: {f1_score(y_val, valid_predictions)}')" ] }, { "cell_type": "code", "execution_count": null, "id": "475381db", "metadata": {}, "outputs": [], "source": [ "deep_svdd_save_path =Path('data-paulusjafahrsimulator-gpu/saved_models/deepsvdd_save/deep_svdd_05.keras')\n", "deep_svdd_net.save(deep_svdd_save_path)" ] }, { "cell_type": "markdown", "id": "6ede1b15", "metadata": {}, "source": [ "### Results" ] }, { "cell_type": "markdown", "id": "c8481d07", "metadata": {}, "source": [ "Validation set" ] }, { "cell_type": "code", "execution_count": null, "id": "719b41b2", "metadata": {}, "outputs": [], "source": [ "valid_predictions = (valid_scores > 0).astype(int)\n", "evaluation_tools.plot_confusion_matrix(true_labels=y_val, predictions=valid_predictions, label_names=[\"low\",\"high\"])\n" ] }, { "cell_type": "markdown", "id": "f33230b1", "metadata": {}, "source": [ "Test set" ] }, { "cell_type": "code", "execution_count": null, "id": "f1189a28", "metadata": {}, "outputs": [], "source": [ "test_scores = (\n", " score_per_sample(\n", " deep_svdd_net.predict([X_face_test, X_eye_test]),\n", " center,\n", " radius\n", " )\n", ").numpy()\n", "\n", "test_predictions = (test_scores > 0).astype(int)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5acade06", "metadata": {}, "outputs": [], "source": [ "evaluation_tools.plot_confusion_matrix(true_labels=y_test, predictions=test_predictions, label_names=[\"low\",\"high\"])\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 5 }