diff --git a/model_training/CNN/CNN_crossVal_EarlyFusion.ipynb b/model_training/CNN/CNN_crossVal_EarlyFusion.ipynb new file mode 100644 index 0000000..a0a2a45 --- /dev/null +++ b/model_training/CNN/CNN_crossVal_EarlyFusion.ipynb @@ -0,0 +1,1725 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "47f6de7b", + "metadata": {}, + "source": [ + "Bibliotheken importieren" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "99294260", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-24 12:01:12.987684: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2026-02-24 12:01:13.000024: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2026-02-24 12:01:13.003736: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2026-02-24 12:01:13.014509: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2026-02-24 12:01:13.703038: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], + "source": [ + "import pandas as pd \n", + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "import random \n", + "import joblib \n", + "from pathlib import Path \n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "from sklearn.model_selection import GroupKFold \n", + "from sklearn.preprocessing import StandardScaler \n", + "\n", + "import tensorflow as tf \n", + "from tensorflow.keras import Input, layers, models, regularizers" + ] + }, + { + "cell_type": "markdown", + "id": "52b4ca8c", + "metadata": {}, + "source": [ + "Seed festlegen" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6e49d281", + "metadata": {}, + "outputs": [], + "source": [ + "SEED = 42 \n", + "np.random.seed(SEED) \n", + "tf.random.set_seed(SEED) \n", + "random.seed(SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "ae1a715f", + "metadata": {}, + "source": [ + "Daten laden" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "870f01c3", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = Path(r\"~/data-paulusjafahrsimulator-gpu/new_datasets/50s_25Hz_dataset.parquet\") \n", + "\n", + "data = pd.read_parquet(path=data_path)" + ] + }, + { + "cell_type": "markdown", + "id": "bedbc23b", + "metadata": {}, + "source": [ + "Labels erstellen" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "38848515", + "metadata": {}, + "outputs": [], + "source": [ + "low_all = data[((data[\"PHASE\"] == \"baseline\") | \n", + " ((data[\"STUDY\"] == \"n-back\") & (data[\"PHASE\"] != \"baseline\") & (data[\"LEVEL\"].isin([1,4]))))].copy() \n", + "\n", + "high_all = pd.concat([ \n", + " data[(data[\"STUDY\"]==\"n-back\") & (data[\"LEVEL\"].isin([2,3,5,6])) & (data[\"PHASE\"].isin([\"train\",\"test\"]))], \n", + " data[(data[\"STUDY\"]==\"k-drive\") & (data[\"PHASE\"]!=\"baseline\")] \n", + "]).copy() \n", + "\n", + "low_all[\"label\"] = 0 \n", + "high_all[\"label\"] = 1 \n", + "data = pd.concat([low_all, high_all], ignore_index=True).drop_duplicates() " + ] + }, + { + "cell_type": "markdown", + "id": "0b282acf", + "metadata": {}, + "source": [ + "Features und Labels" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5edb00a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['subjectID', 'start_time', 'STUDY', 'LEVEL', 'PHASE', 'FACE_AU01_mean', 'FACE_AU02_mean', 'FACE_AU04_mean', 'FACE_AU05_mean', 'FACE_AU06_mean', 'FACE_AU07_mean', 'FACE_AU09_mean', 'FACE_AU10_mean', 'FACE_AU11_mean', 'FACE_AU12_mean', 'FACE_AU14_mean', 'FACE_AU15_mean', 'FACE_AU17_mean', 'FACE_AU20_mean', 'FACE_AU23_mean', 'FACE_AU24_mean', 'FACE_AU25_mean', 'FACE_AU26_mean', 'FACE_AU28_mean', 'FACE_AU43_mean', 'Fix_count_short_66_150', 'Fix_count_medium_300_500', 'Fix_count_long_gt_1000', 'Fix_count_100', 'Fix_mean_duration', 'Fix_median_duration', 'Sac_count', 'Sac_mean_amp', 'Sac_mean_dur', 'Sac_median_dur', 'Blink_count', 'Blink_mean_dur', 'Blink_median_dur', 'Pupil_mean', 'Pupil_IPA', 'label']\n", + "Gefundene FACE_AU-Spalten: ['FACE_AU01_mean', 'FACE_AU02_mean', 'FACE_AU04_mean', 'FACE_AU05_mean', 'FACE_AU06_mean', 'FACE_AU07_mean', 'FACE_AU09_mean', 'FACE_AU10_mean', 'FACE_AU11_mean', 'FACE_AU12_mean', 'FACE_AU14_mean', 'FACE_AU15_mean', 'FACE_AU17_mean', 'FACE_AU20_mean', 'FACE_AU23_mean', 'FACE_AU24_mean', 'FACE_AU25_mean', 'FACE_AU26_mean', 'FACE_AU28_mean', 'FACE_AU43_mean']\n", + "Gefundene Eye Features: ['Fix_count_short_66_150', 'Fix_count_medium_300_500', 'Fix_count_long_gt_1000', 'Fix_count_100', 'Fix_mean_duration', 'Fix_median_duration', 'Sac_count', 'Sac_mean_amp', 'Sac_mean_dur', 'Sac_median_dur', 'Blink_count', 'Blink_mean_dur', 'Blink_median_dur', 'Pupil_mean', 'Pupil_IPA']\n", + "Anzahl FACE_AUs: 20\n", + "Anzahl EYE Features: 15\n", + "Gesamtzahl Features: 35\n" + ] + } + ], + "source": [ + "#Face AUs\n", + "au_columns = [col for col in data.columns if \"face\" in col.lower()] \n", + "\n", + "#Eye Features\n", + "eye_columns = [ \n", + " 'Fix_count_short_66_150', \n", + " 'Fix_count_medium_300_500', \n", + " 'Fix_count_long_gt_1000', \n", + " 'Fix_count_100', \n", + " 'Fix_mean_duration', \n", + " 'Fix_median_duration', \n", + " 'Sac_count', \n", + " 'Sac_mean_amp', \n", + " 'Sac_mean_dur', \n", + " 'Sac_median_dur', \n", + " 'Blink_count', \n", + " 'Blink_mean_dur', \n", + " 'Blink_median_dur', \n", + " 'Pupil_mean', \n", + " 'Pupil_IPA' \n", + "]\n", + "\n", + "#Early Fusion\n", + "feature_columns = au_columns + eye_columns\n", + "\n", + "#NaNs entfernen \n", + "data = data.dropna(subset=feature_columns + [\"label\"])\n", + "\n", + "X = data[feature_columns].values[..., np.newaxis] \n", + "y = data[\"label\"].values \n", + "\n", + "groups = data[\"subjectID\"].values\n", + "print(data.columns.tolist())\n", + "\n", + "print(\"Gefundene FACE_AU-Spalten:\", au_columns)\n", + "print(\"Gefundene Eye Features:\" , eye_columns)\n", + "\n", + "print(\"Anzahl FACE_AUs:\", len(au_columns)) \n", + "print(\"Anzahl EYE Features:\", len(eye_columns)) \n", + "print(\"Gesamtzahl Features:\", len(feature_columns))" + ] + }, + { + "cell_type": "markdown", + "id": "a539b83b", + "metadata": {}, + "source": [ + "CNN-Modell" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e4a7f496", + "metadata": {}, + "outputs": [], + "source": [ + "def build_model(input_shape, lr=1e-4): \n", + " model = models.Sequential([ \n", + " Input(shape=input_shape), \n", + " layers.Conv1D(32, kernel_size=3, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n", + " layers.BatchNormalization(), \n", + " layers.MaxPooling1D(pool_size=2),\n", + "\n", + " layers.Conv1D(64, kernel_size=3, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n", + " layers.BatchNormalization(), \n", + " layers.GlobalAveragePooling1D(), \n", + " \n", + " layers.Dense(32, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n", + " layers.Dropout(0.5), \n", + " layers.Dense(1, activation=\"sigmoid\") \n", + " ]) \n", + " \n", + " model.compile( \n", + " optimizer=tf.keras.optimizers.Adam(learning_rate=lr), \n", + " loss=\"binary_crossentropy\", \n", + " metrics=[\"accuracy\", tf.keras.metrics.AUC(name=\"auc\")] \n", + " ) \n", + " return model" + ] + }, + { + "cell_type": "markdown", + "id": "5905871b", + "metadata": {}, + "source": [ + "Cross-Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "90658000", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Fold 1 ---\n", + "Train-Subjects: [ 0 3 4 7 8 9 14 16 17 18 20 21 22 26 27 28]\n", + "Val-Subjects: [ 2 5 12 13]\n", + "Train Mittelwerte (erste 5 Features): [[-4.87113830e-16]\n", + " [ 2.23720113e-14]\n", + " [ 8.21018535e-15]\n", + " [-1.60279754e-14]\n", + " [-2.83393818e-15]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[ 0.07981309]\n", + " [ 0.07448289]\n", + " [-0.06465745]\n", + " [-0.00999935]\n", + " [-0.02067646]]\n", + "Val Std (erste 5 Features): [[0.91498655]\n", + " [0.82950138]\n", + " [1.01223926]\n", + " [0.92122892]\n", + " [1.02545725]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1771930876.837739 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1771930876.884670 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1771930876.886791 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1771930876.891715 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1771930876.893866 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1771930876.895139 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1771930877.060280 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "I0000 00:00:1771930877.061719 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2026-02-24 12:01:17.062947: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n", + "I0000 00:00:1771930877.063045 85923 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n", + "2026-02-24 12:01:17.064283: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 37027 MB memory: -> device: 0, name: NVIDIA A100 80GB PCIe MIG 3g.40gb, pci bus id: 0000:1e:00.0, compute capability: 8.0\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1771930879.034650 86116 service.cc:146] XLA service 0x7f26100054a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1771930879.034725 86116 service.cc:154] StreamExecutor device (0): NVIDIA A100 80GB PCIe MIG 3g.40gb, Compute Capability 8.0\n", + "2026-02-24 12:01:19.077635: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2026-02-24 12:01:19.257754: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n", + "I0000 00:00:1771930881.497193 86116 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 1 - Val Loss: 0.1972, Val Acc: 0.9458, Val AUC: 0.9896\n", + "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n", + "Konfusionsmatrix Fold 1:\n", + "[[574 64]\n", + " [ 22 928]]\n", + "\n", + "\n", + "--- Fold 2 ---\n", + "Train-Subjects: [ 2 3 4 5 7 8 9 12 13 14 18 20 22 26 27 28]\n", + "Val-Subjects: [ 0 16 17 21]\n", + "Train Mittelwerte (erste 5 Features): [[-1.21896167e-14]\n", + " [ 3.99953317e-14]\n", + " [ 1.58858053e-14]\n", + " [ 1.39544029e-15]\n", + " [-1.05876899e-15]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[-0.1614286 ]\n", + " [-0.08187427]\n", + " [-0.05609159]\n", + " [-0.08758144]\n", + " [-0.24994359]]\n", + "Val Std (erste 5 Features): [[1.06628815]\n", + " [1.05958783]\n", + " [0.96502939]\n", + " [1.01240686]\n", + " [0.73864545]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_1\"\n",
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\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_2 (Conv1D)               │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_2           │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_1 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_3 (Conv1D)               │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_3           │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_1      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_1 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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 Total params: 8,833 (34.50 KB)\n",
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 Trainable params: 8,641 (33.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 192 (768.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 2 - Val Loss: 0.2459, Val Acc: 0.9229, Val AUC: 0.9750\n", + "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n", + "Konfusionsmatrix Fold 2:\n", + "[[571 97]\n", + " [ 26 902]]\n", + "\n", + "\n", + "--- Fold 3 ---\n", + "Train-Subjects: [ 0 2 3 4 5 7 12 13 16 17 20 21 22 26 27 28]\n", + "Val-Subjects: [ 8 9 14 18]\n", + "Train Mittelwerte (erste 5 Features): [[-7.15327970e-16]\n", + " [ 3.87293138e-14]\n", + " [ 2.45055027e-15]\n", + " [-1.17281793e-14]\n", + " [ 3.58282615e-17]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[-0.0572603 ]\n", + " [-0.05167969]\n", + " [ 0.06734314]\n", + " [ 0.04175176]\n", + " [ 0.14159594]]\n", + "Val Std (erste 5 Features): [[1.04792308]\n", + " [1.02732376]\n", + " [1.06024534]\n", + " [1.01980244]\n", + " [1.11306852]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_2\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_4 (Conv1D)               │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_4           │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_2 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_5 (Conv1D)               │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_5           │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_2      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_4 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_5 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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 Total params: 8,833 (34.50 KB)\n",
+       "
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 Trainable params: 8,641 (33.75 KB)\n",
+       "
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 Non-trainable params: 192 (768.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 3 - Val Loss: 0.2017, Val Acc: 0.9482, Val AUC: 0.9879\n", + "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n", + "Konfusionsmatrix Fold 3:\n", + "[[600 56]\n", + " [ 27 918]]\n", + "\n", + "\n", + "--- Fold 4 ---\n", + "Train-Subjects: [ 0 2 3 5 7 8 9 12 13 14 16 17 18 20 21 27]\n", + "Val-Subjects: [ 4 22 26 28]\n", + "Train Mittelwerte (erste 5 Features): [[ 1.23445813e-14]\n", + " [ 1.70513636e-14]\n", + " [ 5.98385175e-15]\n", + " [-8.32006628e-15]\n", + " [-9.28112373e-16]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[ 0.03560763]\n", + " [-0.04014632]\n", + " [-0.02303064]\n", + " [ 0.02336224]\n", + " [ 0.13980282]]\n", + "Val Std (erste 5 Features): [[1.01651673]\n", + " [1.17441107]\n", + " [0.92628917]\n", + " [1.04731846]\n", + " [1.05993683]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_3\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_3\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_6 (Conv1D)               │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_6           │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_3 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_7 (Conv1D)               │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_7           │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_3      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ conv1d_6 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling1d_3 (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv1d_7 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,208\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ global_average_pooling1d_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 8,833 (34.50 KB)\n",
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 Trainable params: 8,641 (33.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 192 (768.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 4 - Val Loss: 0.1392, Val Acc: 0.9634, Val AUC: 0.9955\n", + "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n", + "Konfusionsmatrix Fold 4:\n", + "[[620 42]\n", + " [ 16 908]]\n", + "\n", + "\n", + "--- Fold 5 ---\n", + "Train-Subjects: [ 0 2 4 5 8 9 12 13 14 16 17 18 21 22 26 28]\n", + "Val-Subjects: [ 3 7 20 27]\n", + "Train Mittelwerte (erste 5 Features): [[-5.44187901e-15]\n", + " [ 4.35398678e-14]\n", + " [ 1.23994364e-15]\n", + " [ 7.79327421e-15]\n", + " [ 2.17670629e-15]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[0.09829466]\n", + " [0.09221573]\n", + " [0.07761458]\n", + " [0.03261047]\n", + " [0.00553267]]\n", + "Val Std (erste 5 Features): [[0.93842973]\n", + " [0.89386191]\n", + " [1.0290861 ]\n", + " [0.9948385 ]\n", + " [1.0161051 ]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_4\"\n",
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\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_4\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_8 (Conv1D)               │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_8           │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_4 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_9 (Conv1D)               │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_9           │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_4      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_4 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ conv1d_8 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling1d_4 (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv1d_9 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,208\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ global_average_pooling1d_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 8,833 (34.50 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m8,833\u001b[0m (34.50 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 8,641 (33.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 192 (768.00 B)\n",
+       "
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Val Loss: {scores[0]:.4f}, Val Acc: {scores[1]:.4f}, Val AUC: {scores[2]:.4f}\")\n", + "\n", + "\n", + " #Konfusionsmatrix \n", + " y_pred = (model.predict(X_val) > 0.5).astype(int) \n", + " cm = confusion_matrix(y_val, y_pred) \n", + " all_conf_matrices.append(cm) \n", + " \n", + " print(f\"Konfusionsmatrix Fold {fold+1}:\\n{cm}\\n\") \n", + " \n", + "# Aggregierte Matrix \n", + "agg_cm = sum(all_conf_matrices) \n", + "print(\"Aggregierte Konfusionsmatrix über alle Folds:\") \n", + "print(agg_cm)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d10b7e78", + "metadata": {}, + "source": [ + "Results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9aeba7f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Cross-Validation Ergebnisse ===\n", + "Durchschnittlicher Val-Loss: 0.2075\n", + "Durchschnittliche Val-Accuracy: 0.9432\n", + "Durchschnittliche Val-AUC: 0.9849\n", + "\n", + "=== Ergebnis-Tabelle ===\n", + " Fold Val Loss Val Accuracy Val AUC\n", + "0 1 0.197249 0.945844 0.989578\n", + "1 2 0.245916 0.922932 0.974963\n", + "2 3 0.201694 0.948157 0.987855\n", + "3 4 0.139161 0.963430 0.995495\n", + "4 5 0.253552 0.935665 0.976830\n", + "5 Ø 0.207514 0.943206 0.984944\n", + "Ergebnisse gespeichert als 'cnn_crossVal_results.csv'\n" + ] + } + ], + "source": [ + "#results\n", + "cv_results = np.array(cv_results) \n", + "print(\"\\n=== Cross-Validation Ergebnisse ===\") \n", + "print(f\"Durchschnittlicher Val-Loss: {cv_results[:,0].mean():.4f}\") \n", + "print(f\"Durchschnittliche Val-Accuracy: {cv_results[:,1].mean():.4f}\") \n", + "print(f\"Durchschnittliche Val-AUC: {cv_results[:,2].mean():.4f}\")\n", + "\n", + "#Ergebnis-Tabelle erstellen\n", + "results_table = pd.DataFrame({ \n", + " \"Fold\": np.arange(1, len(cv_results)+1), \n", + " \"Val Loss\": cv_results[:,0], \n", + " \"Val Accuracy\": cv_results[:,1], \n", + " \"Val AUC\": cv_results[:,2] }) \n", + "\n", + "# Durchschnittszeile hinzufügen \n", + "avg_row = pd.DataFrame({ \n", + " \"Fold\": [\"Ø\"], \n", + " \"Val Loss\": [cv_results[:,0].mean()], \n", + " \"Val Accuracy\": [cv_results[:,1].mean()], \n", + " \"Val AUC\": [cv_results[:,2].mean()] \n", + "}) \n", + "\n", + "results_table = pd.concat([results_table, avg_row], ignore_index=True) \n", + "\n", + "print(\"\\n=== Ergebnis-Tabelle ===\") \n", + "print(results_table) \n", + "\n", + "#Tabelle speichern \n", + "results_table.to_csv(\"cnn_crossVal_results.csv\", index=False) \n", + "print(\"Ergebnisse gespeichert als 'cnn_crossVal_results.csv'\")" + ] + }, + { + "cell_type": "markdown", + "id": "d2f6de79", + "metadata": {}, + "source": [ + "Finales Modell auf alle Daten trainieren" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6e403e5f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_5\"\n",
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\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_5\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_10 (Conv1D)              │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_10          │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_5 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_11 (Conv1D)              │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_11          │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_5      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (Dense)                │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_5 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (Dense)                │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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+       "
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+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.6010 - auc: 0.6312 - loss: 0.7564\n", + "Epoch 2/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7894 - auc: 0.8570 - loss: 0.6054\n", + "Epoch 3/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8426 - auc: 0.9051 - loss: 0.5079\n", + "Epoch 4/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8652 - auc: 0.9243 - loss: 0.4488\n", + "Epoch 5/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8768 - auc: 0.9364 - loss: 0.4108\n", + "Epoch 6/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8946 - auc: 0.9476 - loss: 0.3733\n", + "Epoch 7/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8983 - auc: 0.9525 - loss: 0.3567\n", + "Epoch 8/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9084 - auc: 0.9590 - loss: 0.3337\n", + "Epoch 9/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9159 - auc: 0.9665 - loss: 0.3109\n", + "Epoch 10/150\n", + "\u001b[1m499/499\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9885 - auc: 0.9995 - loss: 0.0675\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaler_final = StandardScaler() \n", + "X_scaled = scaler_final.fit_transform(X.reshape(len(X), -1)).reshape(X.shape) \n", + "\n", + "final_model = build_model(input_shape=(len(feature_columns),1), lr=1e-4) \n", + "final_model.summary() \n", + "\n", + "final_model.fit( \n", + " X_scaled, y, \n", + " epochs=150, \n", + " batch_size=16, \n", + " verbose=1 \n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7c7f9cc4", + "metadata": {}, + "source": [ + "Speichern des Modells" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2d3af5be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finales Modell und Scaler gespeichert als 'cnn_crossVal_EarlyFusion_V2.keras' und 'scaler_crossVal_EarlyFusion_V2.joblib'\n" + ] + } + ], + "source": [ + "final_model.save(\"cnn_crossVal_EarlyFusion_V2.keras\") \n", + "joblib.dump(scaler_final, \"scaler_crossVal_EarlyFusion_V2.joblib\") \n", + "\n", + "print(\"Finales Modell und Scaler gespeichert als 'cnn_crossVal_EarlyFusion_V2.keras' und 'scaler_crossVal_EarlyFusion_V2.joblib'\")" + ] + }, + { + "cell_type": "markdown", + "id": "c11891e0", + "metadata": {}, + "source": [ + "Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9f6a8584", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plots\n", + "def plot_cv_histories(cv_histories, metric): \n", + " plt.figure(figsize=(10,6)) \n", + " \n", + " for i, hist in enumerate(cv_histories): \n", + " plt.plot(hist[metric], label=f\"Fold {i+1} Train\", alpha=0.7) \n", + " plt.plot(hist[f\"val_{metric}\"], label=f\"Fold {i+1} Val\", linestyle=\"--\", alpha=0.7) \n", + " plt.xlabel(\"Epochs\") \n", + " plt.ylabel(metric.capitalize()) \n", + " plt.title(f\"Cross-Validation {metric.capitalize()} Verläufe\") \n", + " plt.legend() \n", + " plt.grid(True) \n", + " plt.show()\n", + " \n", + "plot_cv_histories(cv_histories, \"loss\") \n", + "plot_cv_histories(cv_histories, \"accuracy\") \n", + "plot_cv_histories(cv_histories, \"auc\")" + ] + } + ], + "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 +} diff --git a/model_training/CNN/CNN_crossVal_EarlyFusion_Filter.ipynb b/model_training/CNN/CNN_crossVal_EarlyFusion_Filter.ipynb new file mode 100644 index 0000000..f82bac2 --- /dev/null +++ b/model_training/CNN/CNN_crossVal_EarlyFusion_Filter.ipynb @@ -0,0 +1,1940 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "47f6de7b", + "metadata": {}, + "source": [ + "Bibliotheken importieren" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "99294260", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "import numpy as np \n", + "import matplotlib.pyplot as plt\n", + "import random \n", + "import joblib \n", + "from pathlib import Path \n", + "import seaborn as sns\n", + "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, balanced_accuracy_score\n", + "\n", + "from sklearn.model_selection import GroupKFold \n", + "from sklearn.preprocessing import StandardScaler \n", + "\n", + "import tensorflow as tf \n", + "from tensorflow.keras import Input, layers, models, regularizers\n" + ] + }, + { + "cell_type": "markdown", + "id": "52b4ca8c", + "metadata": {}, + "source": [ + "Seed festlegen" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6e49d281", + "metadata": {}, + "outputs": [], + "source": [ + "SEED = 42 \n", + "np.random.seed(SEED) \n", + "tf.random.set_seed(SEED) \n", + "random.seed(SEED)" + ] + }, + { + "cell_type": "markdown", + "id": "ae1a715f", + "metadata": {}, + "source": [ + "Daten laden" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "870f01c3", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = Path(r\"~/data-paulusjafahrsimulator-gpu/new_datasets/50s_25Hz_dataset.parquet\") \n", + "\n", + "data = pd.read_parquet(path=data_path)" + ] + }, + { + "cell_type": "markdown", + "id": "bedbc23b", + "metadata": {}, + "source": [ + "Labels erstellen" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "38848515", + "metadata": {}, + "outputs": [], + "source": [ + "low_all = data[((data[\"PHASE\"] == \"baseline\") | \n", + " ((data[\"STUDY\"] == \"n-back\") & (data[\"PHASE\"] != \"baseline\") & (data[\"LEVEL\"].isin([1,4]))))].copy() \n", + "\n", + "high_all = pd.concat([ \n", + " data[(data[\"STUDY\"]==\"n-back\") & (data[\"LEVEL\"].isin([2,3,5,6])) & (data[\"PHASE\"].isin([\"train\",\"test\"]))], \n", + " data[(data[\"STUDY\"]==\"k-drive\") & (data[\"PHASE\"]!=\"baseline\")] \n", + "]).copy() \n", + "\n", + "low_all[\"label\"] = 0 \n", + "high_all[\"label\"] = 1 \n", + "data = pd.concat([low_all, high_all], ignore_index=True).drop_duplicates() " + ] + }, + { + "cell_type": "markdown", + "id": "8bdb8cab", + "metadata": {}, + "source": [ + "Filter" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3e542dfd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered dataframe shape: (8234, 41)\n", + "Remaining subsets: STUDY LEVEL PHASE \n", + "k-drive 1 baseline 1003\n", + " test 1010\n", + " train 180\n", + " 2 baseline 300\n", + " test 1020\n", + " train 180\n", + " 3 baseline 295\n", + " test 1020\n", + " train 180\n", + "n-back 1 baseline 1015\n", + " test 292\n", + " 2 baseline 285\n", + " test 286\n", + " 3 test 294\n", + " 4 test 294\n", + " 5 test 290\n", + " 6 test 290\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "FILTER_SUBSETS = False\n", + "\n", + "if(FILTER_SUBSETS):\n", + " # Special filter: Keep only specific subsets\n", + "# - k-drive L1 baseline\n", + "# - n-back L1 baseline \n", + "# - k-drive test with levels 1, 2, 3\n", + "\n", + " df = data[\n", + " (\n", + " # k-drive L1 baseline\n", + " ((data['STUDY'] == 'k-drive') & \n", + " (data['LEVEL'] == 1) & \n", + " (data['PHASE'] == 'baseline'))\n", + " ) | \n", + " (\n", + " # n-back L1 baseline\n", + " ((data['STUDY'] == 'n-back') & \n", + " (data['LEVEL'] == 1) & \n", + " (data['PHASE'] == 'baseline'))\n", + " ) | \n", + " (\n", + " # k-drive test with levels 1, 2, 3\n", + " ((data['STUDY'] == 'k-drive') & \n", + " (data['LEVEL'].isin([1, 2, 3])) & \n", + " (data['PHASE'] == 'test'))\n", + " )].copy()\n", + "\n", + "print(f\"Filtered dataframe shape: {data.shape}\")\n", + "print(f\"Remaining subsets: {data.groupby(['STUDY', 'LEVEL', 'PHASE']).size()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "0b282acf", + "metadata": {}, + "source": [ + "Features und Labels" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "5edb00a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['subjectID', 'start_time', 'STUDY', 'LEVEL', 'PHASE', 'FACE_AU01_mean', 'FACE_AU02_mean', 'FACE_AU04_mean', 'FACE_AU05_mean', 'FACE_AU06_mean', 'FACE_AU07_mean', 'FACE_AU09_mean', 'FACE_AU10_mean', 'FACE_AU11_mean', 'FACE_AU12_mean', 'FACE_AU14_mean', 'FACE_AU15_mean', 'FACE_AU17_mean', 'FACE_AU20_mean', 'FACE_AU23_mean', 'FACE_AU24_mean', 'FACE_AU25_mean', 'FACE_AU26_mean', 'FACE_AU28_mean', 'FACE_AU43_mean', 'Fix_count_short_66_150', 'Fix_count_medium_300_500', 'Fix_count_long_gt_1000', 'Fix_count_100', 'Fix_mean_duration', 'Fix_median_duration', 'Sac_count', 'Sac_mean_amp', 'Sac_mean_dur', 'Sac_median_dur', 'Blink_count', 'Blink_mean_dur', 'Blink_median_dur', 'Pupil_mean', 'Pupil_IPA', 'label']\n", + "Gefundene FACE_AU-Spalten: ['FACE_AU01_mean', 'FACE_AU02_mean', 'FACE_AU04_mean', 'FACE_AU05_mean', 'FACE_AU06_mean', 'FACE_AU07_mean', 'FACE_AU09_mean', 'FACE_AU10_mean', 'FACE_AU11_mean', 'FACE_AU12_mean', 'FACE_AU14_mean', 'FACE_AU15_mean', 'FACE_AU17_mean', 'FACE_AU20_mean', 'FACE_AU23_mean', 'FACE_AU24_mean', 'FACE_AU25_mean', 'FACE_AU26_mean', 'FACE_AU28_mean', 'FACE_AU43_mean']\n", + "Gefundene Eye Features: ['Fix_count_short_66_150', 'Fix_count_medium_300_500', 'Fix_count_long_gt_1000', 'Fix_count_100', 'Fix_mean_duration', 'Fix_median_duration', 'Sac_count', 'Sac_mean_amp', 'Sac_mean_dur', 'Sac_median_dur', 'Blink_count', 'Blink_mean_dur', 'Blink_median_dur', 'Pupil_mean', 'Pupil_IPA']\n", + "Anzahl FACE_AUs: 20\n", + "Anzahl EYE Features: 15\n", + "Gesamtzahl Features: 35\n" + ] + } + ], + "source": [ + "#Face AUs\n", + "au_columns = [col for col in data.columns if \"face\" in col.lower()] \n", + "\n", + "#Eye Features\n", + "eye_columns = [ \n", + " 'Fix_count_short_66_150', \n", + " 'Fix_count_medium_300_500', \n", + " 'Fix_count_long_gt_1000', \n", + " 'Fix_count_100', \n", + " 'Fix_mean_duration', \n", + " 'Fix_median_duration', \n", + " 'Sac_count', \n", + " 'Sac_mean_amp', \n", + " 'Sac_mean_dur', \n", + " 'Sac_median_dur', \n", + " 'Blink_count', \n", + " 'Blink_mean_dur', \n", + " 'Blink_median_dur', \n", + " 'Pupil_mean', \n", + " 'Pupil_IPA' \n", + "]\n", + "\n", + "#Early Fusion\n", + "feature_columns = au_columns + eye_columns\n", + "\n", + "#NaNs entfernen \n", + "data = data.dropna(subset=feature_columns + [\"label\"])\n", + "\n", + "X = data[feature_columns].values[..., np.newaxis] \n", + "y = data[\"label\"].values \n", + "\n", + "groups = data[\"subjectID\"].values\n", + "print(data.columns.tolist())\n", + "\n", + "print(\"Gefundene FACE_AU-Spalten:\", au_columns)\n", + "print(\"Gefundene Eye Features:\" , eye_columns)\n", + "\n", + "print(\"Anzahl FACE_AUs:\", len(au_columns)) \n", + "print(\"Anzahl EYE Features:\", len(eye_columns)) \n", + "print(\"Gesamtzahl Features:\", len(feature_columns))" + ] + }, + { + "cell_type": "markdown", + "id": "a539b83b", + "metadata": {}, + "source": [ + "CNN-Modell" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e4a7f496", + "metadata": {}, + "outputs": [], + "source": [ + "def build_model(input_shape, lr=1e-4): \n", + " model = models.Sequential([ \n", + " Input(shape=input_shape), \n", + " layers.Conv1D(32, kernel_size=3, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n", + " layers.BatchNormalization(), \n", + " layers.MaxPooling1D(pool_size=2),\n", + "\n", + " layers.Conv1D(64, kernel_size=3, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n", + " layers.BatchNormalization(), \n", + " layers.GlobalAveragePooling1D(), \n", + " \n", + " layers.Dense(32, activation=\"relu\", kernel_regularizer=regularizers.l2(0.001)), \n", + " layers.Dropout(0.5), \n", + " layers.Dense(1, activation=\"sigmoid\") \n", + " ]) \n", + " \n", + " model.compile( \n", + " optimizer=tf.keras.optimizers.Adam(learning_rate=lr), \n", + " loss=\"binary_crossentropy\", \n", + " metrics=[\"accuracy\", tf.keras.metrics.AUC(name=\"auc\")] \n", + " ) \n", + " return model" + ] + }, + { + "cell_type": "markdown", + "id": "5905871b", + "metadata": {}, + "source": [ + "Cross-Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "90658000", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Fold 1 ---\n", + "Train-Subjects: [ 0 3 4 7 8 9 14 16 17 18 20 21 22 26 27 28]\n", + "Val-Subjects: [ 2 5 12 13]\n", + "Train Mittelwerte (erste 5 Features): [[-4.87113830e-16]\n", + " [ 2.23720113e-14]\n", + " [ 8.21018535e-15]\n", + " [-1.60279754e-14]\n", + " [-2.83393818e-15]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[ 0.07981309]\n", + " [ 0.07448289]\n", + " [-0.06465745]\n", + " [-0.00999935]\n", + " [-0.02067646]]\n", + "Val Std (erste 5 Features): [[0.91498655]\n", + " [0.82950138]\n", + " [1.01223926]\n", + " [0.92122892]\n", + " [1.02545725]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_1\"\n",
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+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_2 (Conv1D)               │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_2           │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_1 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_3 (Conv1D)               │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_3           │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_1      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (Dense)                 │ (None, 1)              │            33 │\n",
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+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 1 - Val Loss: 0.2047, Val Acc: 0.9458, Val AUC: 0.9874\n", + "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step \n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Konfusionsmatrix Fold 1:\n", + "[[578 60]\n", + " [ 26 924]]\n", + "\n", + "\n", + "--- Fold 2 ---\n", + "Train-Subjects: [ 2 3 4 5 7 8 9 12 13 14 18 20 22 26 27 28]\n", + "Val-Subjects: [ 0 16 17 21]\n", + "Train Mittelwerte (erste 5 Features): [[-1.21896167e-14]\n", + " [ 3.99953317e-14]\n", + " [ 1.58858053e-14]\n", + " [ 1.39544029e-15]\n", + " [-1.05876899e-15]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[-0.1614286 ]\n", + " [-0.08187427]\n", + " [-0.05609159]\n", + " [-0.08758144]\n", + " [-0.24994359]]\n", + "Val Std (erste 5 Features): [[1.06628815]\n", + " [1.05958783]\n", + " [0.96502939]\n", + " [1.01240686]\n", + " [0.73864545]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_2\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_2\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_4 (Conv1D)               │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_4           │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_2 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_5 (Conv1D)               │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_5           │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_2      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_4 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_2 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_5 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ conv1d_4 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling1d_2 (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv1d_5 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,208\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ global_average_pooling1d_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 8,833 (34.50 KB)\n",
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 Trainable params: 8,641 (33.75 KB)\n",
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 Non-trainable params: 192 (768.00 B)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 2 - Val Loss: 0.2554, Val Acc: 0.9185, Val AUC: 0.9724\n", + "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Konfusionsmatrix Fold 2:\n", + "[[566 102]\n", + " [ 28 900]]\n", + "\n", + "\n", + "--- Fold 3 ---\n", + "Train-Subjects: [ 0 2 3 4 5 7 12 13 16 17 20 21 22 26 27 28]\n", + "Val-Subjects: [ 8 9 14 18]\n", + "Train Mittelwerte (erste 5 Features): [[-7.15327970e-16]\n", + " [ 3.87293138e-14]\n", + " [ 2.45055027e-15]\n", + " [-1.17281793e-14]\n", + " [ 3.58282615e-17]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[-0.0572603 ]\n", + " [-0.05167969]\n", + " [ 0.06734314]\n", + " [ 0.04175176]\n", + " [ 0.14159594]]\n", + "Val Std (erste 5 Features): [[1.04792308]\n", + " [1.02732376]\n", + " [1.06024534]\n", + " [1.01980244]\n", + " [1.11306852]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_3\"\n",
+       "
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_6 (Conv1D)               │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_6           │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_3 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_7 (Conv1D)               │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_7           │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_3      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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 Total params: 8,833 (34.50 KB)\n",
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 Trainable params: 8,641 (33.75 KB)\n",
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 Non-trainable params: 192 (768.00 B)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 3 - Val Loss: 0.2162, Val Acc: 0.9425, Val AUC: 0.9861\n", + "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Konfusionsmatrix Fold 3:\n", + "[[593 63]\n", + " [ 29 916]]\n", + "\n", + "\n", + "--- Fold 4 ---\n", + "Train-Subjects: [ 0 2 3 5 7 8 9 12 13 14 16 17 18 20 21 27]\n", + "Val-Subjects: [ 4 22 26 28]\n", + "Train Mittelwerte (erste 5 Features): [[ 1.23445813e-14]\n", + " [ 1.70513636e-14]\n", + " [ 5.98385175e-15]\n", + " [-8.32006628e-15]\n", + " [-9.28112373e-16]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[ 0.03560763]\n", + " [-0.04014632]\n", + " [-0.02303064]\n", + " [ 0.02336224]\n", + " [ 0.13980282]]\n", + "Val Std (erste 5 Features): [[1.01651673]\n", + " [1.17441107]\n", + " [0.92628917]\n", + " [1.04731846]\n", + " [1.05993683]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_4\"\n",
+       "
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_8 (Conv1D)               │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_8           │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_4 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_9 (Conv1D)               │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_9           │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_4      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (Dense)                 │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_4 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (Dense)                 │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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 Total params: 8,833 (34.50 KB)\n",
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 Trainable params: 8,641 (33.75 KB)\n",
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 Non-trainable params: 192 (768.00 B)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 4 - Val Loss: 0.1395, Val Acc: 0.9697, Val AUC: 0.9952\n", + "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step \n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Konfusionsmatrix Fold 4:\n", + "[[633 29]\n", + " [ 19 905]]\n", + "\n", + "\n", + "--- Fold 5 ---\n", + "Train-Subjects: [ 0 2 4 5 8 9 12 13 14 16 17 18 21 22 26 28]\n", + "Val-Subjects: [ 3 7 20 27]\n", + "Train Mittelwerte (erste 5 Features): [[-5.44187901e-15]\n", + " [ 4.35398678e-14]\n", + " [ 1.23994364e-15]\n", + " [ 7.79327421e-15]\n", + " [ 2.17670629e-15]]\n", + "Train Std (erste 5 Features): [[1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n", + "Val Mittelwerte (erste 5 Features): [[0.09829466]\n", + " [0.09221573]\n", + " [0.07761458]\n", + " [0.03261047]\n", + " [0.00553267]]\n", + "Val Std (erste 5 Features): [[0.93842973]\n", + " [0.89386191]\n", + " [1.0290861 ]\n", + " [0.9948385 ]\n", + " [1.0161051 ]]\n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_5\"\n",
+       "
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_10 (Conv1D)              │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_10          │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_5 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_11 (Conv1D)              │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_11          │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_5      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (Dense)                │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_5 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (Dense)                │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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 Total params: 8,833 (34.50 KB)\n",
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 Trainable params: 8,641 (33.75 KB)\n",
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 Non-trainable params: 192 (768.00 B)\n",
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fold 5 - Val Loss: 0.2500, Val Acc: 0.9363, Val AUC: 0.9773\n", + "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n" + ] + }, + { + "data": { + "image/png": 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xJgJEREQixkSAiIhIxJgIEBERiRgTASIiIhFjIkBERCRiD+WdBZ+cf8DUIRA1ux/igkwdAlGzs2rmv1LWPq8bba3KY8uNtlZLeigTASIioiaRsDDOnwAREZGIsSJARETixUdFMxEgIiIRY2uArQEiIiIxY0WAiIjEi60BJgJERCRibA2wNUBERCRmrAgQEZF4sTXARICIiESMrQG2BoiIiMSMFQEiIhIvtgaYCBARkYixNcDWABERkZixIkBEROLF1gATASIiEjG2BtgaICIiEjNWBIiISLzYGmAiQEREIsbWAFsDREREYsaKABERiRcrAkwEiIhIxMx4jgBTISIiIhFjRYCIiMSLrQEmAkREJGK8fJCtASIiIjFjRYCIiMSLrQEmAkREJGJsDbA1QEREJGasCBARkXixNcBEgIiIRIytAbYGiIiIxIwVASIiEi+2BpgIEBGRiLE1wNYAERFRS6upqcHbb78Nd3d3WFtbw8PDA++++y7q6up0cwRBQGJiIlQqFaytrREUFISCggK9dbRaLaZOnQonJyfI5XIMHToURUVFBsXCRICIiMRLYma8zQALFy7EJ598guXLl+P06dNYtGgR3n//fSxbtkw3Z9GiRVi8eDGWL1+OnJwcKJVKhIaG4tq1a7o5arUaGRkZ2LRpE7KyslBeXo7w8HDU1tY2ORa2BoiISLyM2BrQarXQarV6YzKZDDKZrN7cQ4cOYdiwYRg8eDAAoFOnTti4cSOOHj0K4GY1ICUlBbNnz8bw4cMBAOvWrYNCocCGDRswceJElJWVYfXq1UhPT0dISAgAYP369ejQoQP27t2LsLCwJsXNigAREZERJCcnw87OTm9LTk5ucG6/fv2wb98+/PLLLwCA48ePIysrC8899xwAoLCwEBqNBgMHDtQdI5PJEBgYiIMHDwIAcnNzUV1drTdHpVLBy8tLN6cpWBEgIiLxMuJVA/Hx8Zg+fbreWEPVAACIi4tDWVkZunbtCnNzc9TW1mL+/Pl46aWXAAAajQYAoFAo9I5TKBQ4f/68bo6lpSXs7e3rzbl1fFMwESAiIvEyYiLQWBugIV988QXWr1+PDRs2oHv37sjLy4NarYZKpUJUVNT/wrutdSEIQr2x2zVlzl8xESAiImphM2fOxFtvvYVRo0YBALy9vXH+/HkkJycjKioKSqUSwM1v/a6urrrjSkpKdFUCpVKJqqoqlJaW6lUFSkpK0KdPnybHwnMEiIhIvCQS420GuH79OszM9P8Em5ub6y4fdHd3h1KpxJ49e3T7q6qqkJmZqfsj7+vrCwsLC705xcXFyM/PNygRYEWAiIjEy0R3FhwyZAjmz5+Pjh07onv37jh27BgWL16McePG3QxLIoFarUZSUhI8PT3h6emJpKQk2NjYIDIyEgBgZ2eH6OhozJgxA46OjnBwcEBsbCy8vb11VxE0BRMBIiKiFrZs2TK88847iImJQUlJCVQqFSZOnIg5c+bo5syaNQuVlZWIiYlBaWkp/P39sXv3btja2urmLFmyBFKpFCNGjEBlZSWCg4ORlpYGc3PzJsciEQRBMOqnawWenH/A1CEQNbsf4oJMHQJRs7Nq5q+r1hGfGW2tyq2vGW2tlsSKABERiRcfOsSTBYmIiMSMFQEiIhIvPn2QiQAREYmXITfeeVixNUBERCRirAgQEZFosSLARICIiMSMeQBbA0RERGLGigAREYkWWwNMBIiISMSYCLA1QEREJGqsCBARkWixIsBEgIiIRIyJAFsDREREosaKABERiRcLAkwEiIhIvNgaYGuAiIhI1FgRICIi0WJFgIkAERGJGBMBtgaIiIhEjRUBIiISLVYEmAgQEZGYMQ9ga4CIiEjMWBEgIiLRYmuAiQAREYkYEwG2BoiIiESNFQEiIhItVgSYCBARkZgxD2BrgIiISMxYESAiItFia4CJABERiRgTARMnAkVFRUhNTcXBgweh0WggkUigUCjQp08fTJo0CR06dDBleERERA89kyUCWVlZGDRoEDp06ICBAwdi4MCBEAQBJSUl2Lp1K5YtW4adO3eib9++pgqRiIgecqwImDARePPNNzF+/HgsWbKk0f1qtRo5OTktHBkREYkFEwETXjWQn5+PSZMmNbp/4sSJyM/Pb8GIiIiIxMdkiYCrqysOHjzY6P5Dhw7B1dW1BSMiIiLRkRhxM0CnTp0gkUjqbVOmTAEACIKAxMREqFQqWFtbIygoCAUFBXpraLVaTJ06FU5OTpDL5Rg6dCiKiooM/hGYrDUQGxuLSZMmITc3F6GhoVAoFJBIJNBoNNizZw9WrVqFlJQUU4VHREQiYKrWQE5ODmpra3Wv8/PzERoaihdffBEAsGjRIixevBhpaWno0qUL5s2bh9DQUPz888+wtbUFAKjVanz99dfYtGkTHB0dMWPGDISHhyM3Nxfm5uZNjkUiCIJg3I/XdF988QWWLFmC3Nxc3Q/E3Nwcvr6+mD59OkaMGHFP6z45/4ARoyRqnX6ICzJ1CETNzqqZv662n5xhtLXOpjwHrVarNyaTySCTye56rFqtxvbt23HmzBkAgEqlglqtRlxcHICb3/4VCgUWLlyIiRMnoqysDM7OzkhPT8fIkSMBAH/88Qc6dOiAHTt2ICwsrMlxm/TOgiNHjkR2djauX7+O//73v/jvf/+L69evIzs7+56TACIioqZqqDx/r1tycjLs7Oz0tuTk5LvGUFVVhfXr12PcuHGQSCQoLCyERqPBwIEDdXNkMhkCAwN1LfXc3FxUV1frzVGpVPDy8rpj270hreKGQhYWFjwfgIiIWpwxWwPx8fGYPn263lhTqgFbt27Fn3/+ibFjxwIANBoNAEChUOjNUygUOH/+vG6OpaUl7O3t6825dXxTtYpEgIiI6EHX1DbA7VavXo1BgwZBpVLpjd+epAiCcNfEpSlzbseHDhERkXiZ6KqBW86fP4+9e/di/PjxujGlUgkA9b7Zl5SU6KoESqUSVVVVKC0tbXROUzERICIi0TLmOQL3Yu3atXBxccHgwYN1Y+7u7lAqldizZ49urKqqCpmZmejTpw8AwNfXFxYWFnpziouLkZ+fr5vTVGwNEBERmUBdXR3Wrl2LqKgoSKX/+3MskUigVquRlJQET09PeHp6IikpCTY2NoiMjAQA2NnZITo6GjNmzICjoyMcHBwQGxsLb29vhISEGBRHq0gE0tPT8cknn6CwsBCHDh2Cm5sbUlJS4O7ujmHDhpk6PCIiekiZ8hbDe/fuxW+//YZx48bV2zdr1ixUVlYiJiYGpaWl8Pf3x+7du3X3EACAJUuWQCqVYsSIEaisrERwcDDS0tIMuocAYOL7CABAamoq5syZA7Vajfnz5yM/Px8eHh5IS0vDunXrsH//foPX5H0EjMPZ1hJT+z+KgEcdYGVhht+uVOK97T/hJ005AKD/Y074m48Kj7vaop2NBUavOopfLpTrrfHJmJ7wdWunN7a7oASzt55qqY/x0OJ9BJrf6pWf4qOUxRg95hXMip8NALheUYGUJR9i/3d7Ufbnn1C1b4/I0S9jxKhIE0f7cGru+wh0mrbdaGudWxputLVakskrAsuWLcPKlSsRERGBBQsW6Mb9/PwQGxtrwsjEzdZKilWv9ELu+VJM++IESiuq8Yi9Fa7dqNHNsbIwx4miMuz76SLeHvxYo2tlHPsDn2ae072+UVPb6Fyi1iL/5Al8+c8v0KWL/u/2+wuTkXPkMJIWvA9V+/Y49O9/I2neXDi7uKD/AMNKskStgckTgcLCQvj4+NQbl8lkqKioMEFEBABRAR1x4eoNvLv9Z91YcdkNvTk78y8AAFztrO641o3qOlyuqDJ+kETN5HpFBeLjZiJh7jys/DRVb9/x43kYMiwCT/b2BwC8MGIkvvznFyjIz2ci8ADi0wdbwVUD7u7uyMvLqze+c+dOdOvWreUDIgDA056OOF18DcnDu+FbdR+sj/ZFRM97u+nTs91dsOfNvvjitScxLfhR2Fga1r8iamlJ897FM88E4qmA+mdf+/Tqhcz93+HChQsQBAFHDmfj/LlC9OnbzwSR0n0z8eWDrYHJKwIzZ87ElClTcOPGjZv/T3XkCDZu3Ijk5GSsWrXqrsdrtdp693auq6mCmdSyuUIWhfb21njetz02HP4da//9G7qrbDFjYGdU1dZhx8kLTV5nV/4F/PHnDVyuqIKHsxxT+rvD00WO1zeeaMboie7dzh3f4PTpU9jwxZcN7n8r/m3MTXgHAwc8A6lUColEgoR356GXr18LR0pkHCZPBF599VXU1NRg1qxZuH79OiIjI9G+fXssXboUo0aNuuvxycnJmDt3rt6Ya/8otA8e20wRi4OZBDhdfA0rDhQCAH65UA4PZzme76UyKBHYmles+9+/XqzA71euIz3aD48p2+BnTfkdjiRqeZriYixaMB+ffLam0TvEbfhHOk6cyMPS5alQqVTIPXoUSe/NhbOzS4MVBGrd2BpoBYkAAEyYMAETJkzApUuXUFdXBxcXlyYf29C9nfsvyTZ2iKJzqbwKZy9d1xs7d+k6BnR1vq91f9KUo7q2Dh3trZkIUKtz6lQBrly+jJdGDNeN1dbWIvdoDjZt/Aeyso/io5QlWPLRcjwTGAQA6PJYV/z882msW7uaicADiIlAK0kEbnFycjL4mIbu7cy2wP07/nsZ3Bys9cY6OlhDc9sJg4Z61FkOC3MzXCrnyYPU+vg/9RS+3Pq13ljC7Hh08vDAq9ETUFdXh5qaapiZ6f/xMDMzR51pr8QmumcmTwTc3d3vmJGdPXu2BaOhWzYeKcLqKB+M7dMRe09fRHeVLf7mo0LSjv9dRdDWSgqlnRWc2txMvG4lDpfLq3C5ogrt21lhkJcC//71Cv68Xg13JxuoQx7FT5prOF5UZpLPRXQncnkbeHp20RuztrFBO7t2unG/J3tj8QfvQyazgqtKhdycHGz/aitiZ71lipDpPrEg0AoSAbVarfe6uroax44dw65duzBz5kzTBEU4VXwNM78swJT+7hj/dCf88WclFu/5D3YVlOjmPNPFCQlDuupeJw3vDgD47PtzWPnDOdTUCniykz1GPvkIbCzNceGqFv/+z2Ws/OEc6vjliR5QC99fjKUpixEfF4urZWVwVanw+htv4sWRL5k6NLoHbA20gjsLNubjjz/G0aNHsXbtWoOP5Z0FSQx4Z0ESg+a+s6DnzF1GW+vM+88aba2WZPL7CDRm0KBB2LJli6nDICKih5hEYrztQWXy1kBjvvzySzg4OJg6DCIieoixNdAKEgEfHx+9fwhBEKDRaHDx4kWsWLHChJERERE9/EyeCEREROi9NjMzg7OzM4KCgtC1a9eGDyIiIjICFgRMnAjU1NSgU6dOCAsLg1KpNGUoREQkQrffE0KMTHqyoFQqxeTJk+s9K4CIiIhahsmvGvD398exY8dMHQYREYkQrxpoBecIxMTEYMaMGSgqKoKvry/kcrne/h49epgoMiIiooefyRKBcePGISUlBSNHjgQAvPHGG7p9EokEgiBAIpGgtrbWVCESEdFDjpcPmjARWLduHRYsWIDCwkJThUBERCLHPMCEicCtOxu7ubmZKgQiIiLRM+k5AizJEBGRKfHvkIkTgS5dutz1H+HKlSstFA0REYkNEwETJwJz586FnZ2dKUMgIiISNZMmAqNGjYKLi4spQyAiIhFjQcCEiQDLMUREZGr8W2TCOwveumqAiIiITMdkFYG6ujpTvTUREREAtgaAVnCLYSIiIlNha6AVPHSIiIiITIcVASIiEi0WBJgIEBGRiLE1wNYAERGRqLEiQEREosWCABMBIiISMbYG2BogIiIyif/+978YM2YMHB0dYWNjg549eyI3N1e3XxAEJCYmQqVSwdraGkFBQSgoKNBbQ6vVYurUqXBycoJcLsfQoUNRVFRkUBxMBIiISLQkEuNthigtLUXfvn1hYWGBnTt34tSpU/jwww/Rrl073ZxFixZh8eLFWL58OXJycqBUKhEaGopr167p5qjVamRkZGDTpk3IyspCeXk5wsPDUVtb2+RY2BogIiLRMlVrYOHChejQoQPWrl2rG+vUqZPufwuCgJSUFMyePRvDhw8HAKxbtw4KhQIbNmzAxIkTUVZWhtWrVyM9PR0hISEAgPXr16NDhw7Yu3cvwsLCmhQLKwJERERGoNVqcfXqVb1Nq9U2OPerr76Cn58fXnzxRbi4uMDHxwcrV67U7S8sLIRGo8HAgQN1YzKZDIGBgTh48CAAIDc3F9XV1XpzVCoVvLy8dHOagokAERGJljFbA8nJybCzs9PbkpOTG3zfs2fPIjU1FZ6envj2228xadIkvPHGG/j8888BABqNBgCgUCj0jlMoFLp9Go0GlpaWsLe3b3ROU7A1QEREomXM1kB8fDymT5+uNyaTyRqcW1dXBz8/PyQlJQEAfHx8UFBQgNTUVLzyyiuNxicIwl1jbsqcv2JFgIiIyAhkMhnatm2rtzWWCLi6uqJbt256Y48//jh+++03AIBSqQSAet/sS0pKdFUCpVKJqqoqlJaWNjqnKZgIEBGRaJnqqoG+ffvi559/1hv75Zdf4ObmBgBwd3eHUqnEnj17dPurqqqQmZmJPn36AAB8fX1hYWGhN6e4uBj5+fm6OU3B1gAREYmWqa4aePPNN9GnTx8kJSVhxIgROHLkCD777DN89tlnurjUajWSkpLg6ekJT09PJCUlwcbGBpGRkQAAOzs7REdHY8aMGXB0dISDgwNiY2Ph7e2tu4qgKZgIEBERtbAnn3wSGRkZiI+Px7vvvgt3d3ekpKRg9OjRujmzZs1CZWUlYmJiUFpaCn9/f+zevRu2tra6OUuWLIFUKsWIESNQWVmJ4OBgpKWlwdzcvMmxSARBEIz66VqBJ+cfMHUIRM3uh7ggU4dA1Oysmvnr6jOL/220tb6f3tdoa7UkVgSIiEi0+KgBnixIREQkaqwIEBGRaPHpg0wEiIhIxJgHsDVAREQkaqwIEBGRaLE1wESAiIhEjHkAWwNERESixooAERGJlhlLAkwEiIhIvJgHsDVAREQkaqwIEBGRaPGqASYCREQkYmbMA9gaICIiEjNWBIiISLTYGmAiQEREIsY8gK0BIiIiUWNFgIiIREsClgSYCBARkWjxqgG2BoiIiESNFQEiIhItXjXARICIiESMeQBbA0RERKLGigAREYkWH0PMRICIiESMeQBbA0RERKLGigAREYkWrxpgIkBERCLGPICtASIiIlFjRYCIiESLVw0wESAiIhFjGsDWABERkaixIkBERKLFqwaYCBARkYjxMcRsDRAREYkaEwEiIhItiURitM0QiYmJ9Y5XKpW6/YIgIDExESqVCtbW1ggKCkJBQYHeGlqtFlOnToWTkxPkcjmGDh2KoqIig38GTASIiEi0JBLjbYbq3r07iouLddvJkyd1+xYtWoTFixdj+fLlyMnJgVKpRGhoKK5du6abo1arkZGRgU2bNiErKwvl5eUIDw9HbW2tQXHwHAEiIiITkEqlelWAWwRBQEpKCmbPno3hw4cDANatWweFQoENGzZg4sSJKCsrw+rVq5Geno6QkBAAwPr169GhQwfs3bsXYWFhTY6DFQEiIhItY7YGtFotrl69qrdptdpG3/vMmTNQqVRwd3fHqFGjcPbsWQBAYWEhNBoNBg4cqJsrk8kQGBiIgwcPAgByc3NRXV2tN0elUsHLy0s3p6mYCBARkWiZSYy3JScnw87OTm9LTk5u8H39/f3x+eef49tvv8XKlSuh0WjQp08fXL58GRqNBgCgUCj0jlEoFLp9Go0GlpaWsLe3b3ROU7E1QEREZATx8fGYPn263phMJmtw7qBBg3T/29vbGwEBAXj00Uexbt06PPXUUwDq3+NAEIS7npTYlDm3Y0WAiIhEy5itAZlMhrZt2+ptjSUCt5PL5fD29saZM2d05w3c/s2+pKREVyVQKpWoqqpCaWlpo3Oa6p4SgfT0dPTt2xcqlQrnz58HAKSkpGDbtm33shwREZFJSIy43Q+tVovTp0/D1dUV7u7uUCqV2LNnj25/VVUVMjMz0adPHwCAr68vLCws9OYUFxcjPz9fN6epDE4EUlNTMX36dDz33HP4888/dZcptGvXDikpKYYuR0REJDqxsbHIzMxEYWEhDh8+jBdeeAFXr15FVFQUJBIJ1Go1kpKSkJGRgfz8fIwdOxY2NjaIjIwEANjZ2SE6OhozZszAvn37cOzYMYwZMwbe3t66qwiayuBzBJYtW4aVK1ciIiICCxYs0I37+fkhNjbW0OWIiIhMxlSPIS4qKsJLL72ES5cuwdnZGU899RSys7Ph5uYGAJg1axYqKysRExOD0tJS+Pv7Y/fu3bC1tdWtsWTJEkilUowYMQKVlZUIDg5GWloazM3NDYpFIgiCYMgB1tbW+Omnn+Dm5gZbW1scP34cHh4eOHPmDHr06IHKykqDAmgOT84/YOoQiJrdD3FBpg6BqNlZNfMp7RM25xttrZUjvIy2VksyuDXg7u6OvLy8euM7d+5Et27djBETERERtRCDc62ZM2diypQpuHHjBgRBwJEjR7Bx40YkJydj1apVzREjERFRs+BjiO8hEXj11VdRU1ODWbNm4fr164iMjET79u2xdOlSjBo1qjliJCIiahbMA+7xhkITJkzAhAkTcOnSJdTV1cHFxcXYcREREVELuK/TMJycnIwVBxERUYsz1VUDrYnBiYC7u/sdeyq3HppARETU2jEPuIdEQK1W672urq7GsWPHsGvXLsycOdNYcREREVELMDgRmDZtWoPjH3/8MY4ePXrfAREREbUUXjVwDzcUaszZs2fRs2dPXL161RjL3ZfKalNHQNT8HHq/buoQiJpd5bHlzbr+1IzTRltr2d8eN9paLcloTx/88ssv4eDgYKzliIiIqAUY3Brw8fHRK6UIggCNRoOLFy9ixYoVRg2OiIioObE1cA+JQEREhN5rMzMzODs7IygoCF27djVWXERERM3OjHmAYYlATU0NOnXqhLCwMCiVyuaKiYiIiFqIQecISKVSTJ48GVqttrniISIiajFmEuNtDyqDTxb09/fHsWPHmiMWIiKiFiWRSIy2PagMPkcgJiYGM2bMQFFREXx9fSGXy/X29+jRw2jBERERUfNqciIwbtw4pKSkYOTIkQCAN954Q7dPIpFAEARIJBLU1tYaP0oiIqJm8CCX9I2lyYnAunXrsGDBAhQWFjZnPERERC3mAa7oG02TE4FbNyB0c3NrtmCIiIioZRl0jsCDfDIEERHR7fgYYgMTgS5dutw1Gbhy5cp9BURERNRSjHaf/QeYQYnA3LlzYWdn11yxEBERUQszKBEYNWoUXFxcmisWIiKiFsXOgAGJAM8PICKihw3PETCgPXLrqgEiIiJ6eDS5IlBXV9eccRAREbU4FgTu4RbDREREDwveWZBXThAREYkaKwJERCRaPFmQiQAREYkY8wC2BoiIiESNFQEiIhItnizIRICIiERMAmYCbA0QERGJGCsCREQkWmwNsCJAREQiZiYx3navkpOTIZFIoFardWOCICAxMREqlQrW1tYICgpCQUGB3nFarRZTp06Fk5MT5HI5hg4diqKiIsN/BvceOhEREd2PnJwcfPbZZ+jRo4fe+KJFi7B48WIsX74cOTk5UCqVCA0NxbVr13Rz1Go1MjIysGnTJmRlZaG8vBzh4eGora01KAYmAkREJFoSicRom6HKy8sxevRorFy5Evb29rpxQRCQkpKC2bNnY/jw4fDy8sK6detw/fp1bNiwAQBQVlaG1atX48MPP0RISAh8fHywfv16nDx5Env37jUoDiYCREQkWsZsDWi1Wly9elVv02q1jb73lClTMHjwYISEhOiNFxYWQqPRYODAgboxmUyGwMBAHDx4EACQm5uL6upqvTkqlQpeXl66OU3+GRg0m4iIiBqUnJwMOzs7vS05ObnBuZs2bcKPP/7Y4H6NRgMAUCgUeuMKhUK3T6PRwNLSUq+ScPucpuJVA0REJFrGvMVwfHw8pk+frjcmk8nqzfv9998xbdo07N69G1ZWVneITT84QRDu2oJoypzbsSJARESiZSaRGG2TyWRo27at3tZQIpCbm4uSkhL4+vpCKpVCKpUiMzMTH330EaRSqa4ScPs3+5KSEt0+pVKJqqoqlJaWNjqnyT8Dg2YTERHRfQkODsbJkyeRl5en2/z8/DB69Gjk5eXBw8MDSqUSe/bs0R1TVVWFzMxM9OnTBwDg6+sLCwsLvTnFxcXIz8/XzWkqtgaIiEi0THFDIVtbW3h5eemNyeVyODo66sbVajWSkpLg6ekJT09PJCUlwcbGBpGRkQAAOzs7REdHY8aMGXB0dISDgwNiY2Ph7e1d7+TDu2EiQEREotVaH0M8a9YsVFZWIiYmBqWlpfD398fu3btha2urm7NkyRJIpVKMGDEClZWVCA4ORlpaGszNzQ16L4kgCIKxP4CpVVabOgKi5ufQ+3VTh0DU7CqPLW/W9Zf9u9Boa03t6260tVoSKwJERCRaZnz6IBMBIiISr9baGmhJvGqAiIhIxFgRICIi0eJjiJkIEBGRiJmxN8DWABERkZixIkBERKLFggATASIiEjG2BtgaICIiEjVWBIiISLRYEGAiQEREIsayOH8GREREosaKABERiZaEvQEmAkREJF5MA9gaICIiEjVWBIiISLR4HwEmAkREJGJMA9gaICIiEjVWBIiISLTYGWAiQEREIsbLB9kaICIiEjVWBIiISLT4bZiJABERiRhbA0yGiIiIRI0VASIiEi3WA5gIEBGRiLE1wNYAERGRqLEiQEREosVvw0wEiIhIxNgaYDJEREQkaqwIEBGRaLEewESAiIhEjJ0BtgaIiIhEjRUBIiISLTM2B5gIEBGReLE1wNYAERFRi0tNTUWPHj3Qtm1btG3bFgEBAdi5c6duvyAISExMhEqlgrW1NYKCglBQUKC3hlarxdSpU+Hk5AS5XI6hQ4eiqKjI4FiYCBARkWhJjPh/hnjkkUewYMECHD16FEePHsWAAQMwbNgw3R/7RYsWYfHixVi+fDlycnKgVCoRGhqKa9eu6dZQq9XIyMjApk2bkJWVhfLycoSHh6O2ttawn4EgCIJBRzwAKqtNHQFR83Po/bqpQyBqdpXHljfr+jsKSoy21nPdXe7reAcHB7z//vsYN24cVCoV1Go14uLiANz89q9QKLBw4UJMnDgRZWVlcHZ2Rnp6OkaOHAkA+OOPP9ChQwfs2LEDYWFhTX5fVgSIiIiMQKvV4urVq3qbVqu963G1tbXYtGkTKioqEBAQgMLCQmg0GgwcOFA3RyaTITAwEAcPHgQA5Obmorq6Wm+OSqWCl5eXbk5TMREgIiLRMoPEaFtycjLs7Oz0tuTk5Ebf++TJk2jTpg1kMhkmTZqEjIwMdOvWDRqNBgCgUCj05isUCt0+jUYDS0tL2NvbNzqn6T+DVur333/HuHHjTB0GERE9xCQS423x8fEoKyvT2+Lj4xt978ceewx5eXnIzs7G5MmTERUVhVOnTv0lNv3zDgRBuOuzEZoy53atNhG4cuUK1q1bZ+owiIiImkQmk+muAri1yWSyRudbWlqic+fO8PPzQ3JyMp544gksXboUSqUSAOp9sy8pKdFVCZRKJaqqqlBaWtronKYy2X0EvvrqqzvuP3v2bAtFQkREYtWa7iMgCAK0Wi3c3d2hVCqxZ88e+Pj4AACqqqqQmZmJhQsXAgB8fX1hYWGBPXv2YMSIEQCA4uJi5OfnY9GiRQa9r8kSgYiICEgkEtzpogU+HpKIiJqToZf9Gcvf//53DBo0CB06dMC1a9ewadMmHDhwALt27YJEIoFarUZSUhI8PT3h6emJpKQk2NjYIDIyEgBgZ2eH6OhozJgxA46OjnBwcEBsbCy8vb0REhJiUCwmSwRcXV3x8ccfIyIiosH9eXl58PX1bdmgiIiIWsCFCxfw8ssvo7i4GHZ2dujRowd27dqF0NBQAMCsWbNQWVmJmJgYlJaWwt/fH7t374atra1ujSVLlkAqlWLEiBGorKxEcHAw0tLSYG5ublAsJruPwNChQ9GzZ0+8++67De4/fvw4fHx8UFdXZ/DavI8AiQHvI0Bi0Nz3Edj30yWjrRXc1cloa7Ukk1UEZs6ciYqKikb3d+7cGfv372/BiIiISGxM1RpoTUyWCDz99NN33C+XyxEYGNhC0RAREYkTnz5IRESixXPSmQgQEZGIsTXQim8oRERERM2PFQEiIhItMxYEmAgQEZF4sTXQSloD6enp6Nu3L1QqFc6fPw8ASElJwbZt20wcGd2S+vEy9PR6TG8LDuzb4Nz35s5BT6/HsD49rWWDJDKQubkZEmLCcXp7Iq4cWoxTXyci/rVn9e5qKre2xJK4F/GfXe/hyqHFOLblbUx4sV+ja25dPhmVx5ZjSFCPlvgIRPfN5BWB1NRUzJkzB2q1GvPnz0dtbS0AoF27dkhJScGwYcNMHCHd8mhnT3y6aq3utZlZ/btXfbdvL06eOA5nF5eWDI3onswYG4rxL/TDhDnpOPVrMXy7d8SniWNw9doNfLzxAABgUezzCPTrgldnf47zf1xGSMDjWBo/AsUXy7D9wEm99aaO7g/T3KKN7hWvGmgFFYFly5Zh5cqVmD17tt5tEf38/HDy5Mk7HEktzdzcHE5OzrrNwcFBb/+FCxewIOldJC38AFKphYmiJGo6/x7u2J55AruyCvBb8RVk7M3Dvuyf0KtbR70567cfxg+5Z/Bb8RWs+de/ceKX/+rNAQDvLu3xxpgBmJS4vqU/Bt0HiRG3B5XJE4HCwkLd05X+SiaT3fHOg9TyfvvtPEL798NzYQMQF/smin7/Xbevrq4Ob8fPRNTYaHTu7GnCKIma7lDer+jf+zF07nizguXdpT0Cenrg238X6OYczDuL8EBvqJztAADP+HnC080Few+e1s2xtrLAuuSxeHPhZly4fK1lPwTRfTJ5a8Dd3R15eXlwc3PTG9+5cye6det21+O1Wi20Wq3eWJ2Z7I7PgCbDeffogXlJC+Hm1gmXL1/Gyk9TETVmFLZs24527eyxdvVKmJtLETnmFVOHStRkH6zdg7ZtrHE8423U1gowN5cg4ePt2LwrVzdnxsJ/YsWcSPy6ez6qq2tRJ9Rh8rsbcDDvf49KXzTjeWQfL6zXKqDWz4y9AdMnAjNnzsSUKVNw48YNCIKAI0eOYOPGjUhOTsaqVavuenxycjLmzp2rN/b3txPw9pzEZopYnPo9/b/bPXsCeOKJnggfFIqvt22Fr9+T2LD+c2z857/46Gh6oLwY5ouXnnsSY/++Dqd+LUaPx9rj/dgXUHyxDP/4+jAAYMpLQejt3QnPT/sEvxVfQb9enbE0fiQ0l65i/+GfMTjQG0G9u+CpUQtM/GnoXvC/WCZ8+uBfrVy5EvPmzcPv/19qbt++PRITExEdHX3XY1kRMJ2J419Fx44d4ebujg8XLYCZ2f86TbW1tTAzM4NC6Yqdu78zYZQPLz598P6d2fkePli7B59u/l43Fjc+DC899yR6Dp8HK5kFLvzwPkZOX4ldWf9rF6yYE4n2Lu0w7PUVeD/2ecS8FIi6uv/9p1QqNUdtbR3+fexXhE1Y2qKf6WHT3E8fzP7Pn0Zb66nO7Yy2VksyeUUAACZMmIAJEybg0qVLqKurg4sBZ5zLZPX/6PMxxM2vqqoKhYW/opevL8KHDMNTT/XR2z95YjTChwzDsIjhJoqQ6O6srSxRJ+g/6ry2TtAltRZSc1haSFF32/el2to6mP3/nWg+WLsbazMO6u3P/XI2Zn24Bd9k5jdj9GQULAm0jkTgFienB/NZzmKw+P2FeCaoP1xdXXHlyhWs/DQVFeXlGDLsb2jXzh7t2tnrzZdKLeDo5IRO7h4mipjo7nZ8fxJx0WH4vbgUp34tRs+uj+CNMf3x+dZsAMC1ihv4/ugZJKkjUHmjGr8VX8HTvp0xOrw34hb/CwBw4fK1Bk8Q/L24FOf/uNyin4cMxxsKtYJEwN3d/Y595bNnzza6j1rOhQsaxM+ajtLSP2HvYI8ePXri8w2boVK1N3VoRPds+sJ/IiEmHEv/PhLO9m1QfLEMq7/8N5I+26mb88pba/Du1GFIS4qCfVsb/FZ8BYkfb8fKf2aZMHIi4zH5OQJLl+r3z6qrq3Hs2DHs2rULM2fOxFtvvWXwmmwNkBjwHAESg+Y+R+DI2TKjrdXbw85oa7Ukk1cEpk2b1uD4xx9/jKNHj7ZwNEREJCZsDLSCGwo1ZtCgQdiyZYupwyAiInqombwi0Jgvv/yy3i1siYiIjIolAdMnAj4+PnonCwqCAI1Gg4sXL2LFihUmjIyIiB52vGqgFSQCEREReq/NzMzg7OyMoKAgdO3a1TRBERERiYRJE4Gamhp06tQJYWFhUCqVpgyFiIhEiHdFN/HJglKpFJMnT653i2AiIiJqGSa/asDf3x/Hjh0zdRhERCRCEiNuDyqTnyMQExODGTNmoKioCL6+vpDL5Xr7e/ToYaLIiIjoofcg/wU3EpMlAuPGjUNKSgpGjhwJAHjjjTd0+yQSCQRBgEQiQW1tralCJCIieuiZLBFYt24dFixYgMLCQlOFQEREIsfLB02YCNx6xIGbm5upQiAiIpHjVQMmPlnwTk8dJCIiouZn0pMFu3Tpctdk4MqVKy0UDRERiQ2/jpo4EZg7dy7s7B7MxzYSEdFDgJmAaROBUaNGwcXFxZQhEBERiZrJEgGeH0BERKbGqwZMeLLgrasGiIiITEUiMd5miOTkZDz55JOwtbWFi4sLIiIi8PPPP+vNEQQBiYmJUKlUsLa2RlBQEAoKCvTmaLVaTJ06FU5OTpDL5Rg6dCiKiooMisVkiUBdXR3bAkREJEqZmZmYMmUKsrOzsWfPHtTU1GDgwIGoqKjQzVm0aBEWL16M5cuXIycnB0qlEqGhobh27ZpujlqtRkZGBjZt2oSsrCyUl5cjPDzcoJvxSYSH8Kt5ZbWpIyBqfg69Xzd1CETNrvLY8mZdP7+o3GhreT3S5p6PvXjxIlxcXJCZmYlnnnkGgiBApVJBrVYjLi4OwM1v/wqFAgsXLsTEiRNRVlYGZ2dnpKen6+7S+8cff6BDhw7YsWMHwsLCmvTeJn/oEBERkckY8alDWq0WV69e1dua+nTdsrIyAICDgwMAoLCwEBqNBgMHDtTNkclkCAwMxMGDBwEAubm5qK6u1pujUqng5eWlm9MUTASIiIiMIDk5GXZ2dnpbcnLyXY8TBAHTp09Hv3794OXlBQDQaDQAAIVCoTdXoVDo9mk0GlhaWsLe3r7ROU1h8qcPEhERmYoxrxqIj4/H9OnT9cZkMtldj3v99ddx4sQJZGVl1Y/vtrMQbz2Q706aMuevWBEgIiLRMuZVAzKZDG3bttXb7pYITJ06FV999RX279+PRx55RDeuVCoBoN43+5KSEl2VQKlUoqqqCqWlpY3OaQomAkRERC1MEAS8/vrr+Ne//oXvvvsO7u7uevvd3d2hVCqxZ88e3VhVVRUyMzPRp08fAICvry8sLCz05hQXFyM/P183pynYGiAiItEy1e2EpkyZgg0bNmDbtm2wtbXVffO3s7ODtbU1JBIJ1Go1kpKS4OnpCU9PTyQlJcHGxgaRkZG6udHR0ZgxYwYcHR3h4OCA2NhYeHt7IyQkpMmxMBEgIiLxMlEmkJqaCgAICgrSG1+7di3Gjh0LAJg1axYqKysRExOD0tJS+Pv7Y/fu3bC1tdXNX7JkCaRSKUaMGIHKykoEBwcjLS0N5ubmTY6F9xEgekDxPgIkBs19H4HTxRV3n9REj7vKjbZWS2JFgIiIRIvPGmAiQEREIsbn3/GqASIiIlFjRYCIiESLBQEmAkREJGbMBNgaICIiEjNWBIiISLR41QATASIiEjFeNcDWABERkaixIkBERKLFggATASIiEjNmAmwNEBERiRkrAkREJFq8aoCJABERiRivGmBrgIiISNRYESAiItFiQYCJABERiRkzAbYGiIiIxIwVASIiEi1eNcBEgIiIRIxXDbA1QEREJGqsCBARkWixIMBEgIiIRIytAbYGiIiIRI0VASIiEjGWBJgIEBGRaLE1wNYAERGRqLEiQEREosWCABMBIiISMbYG2BogIiISNVYEiIhItPisASYCREQkZswD2BogIiISM1YEiIhItFgQYCJAREQixqsG2BogIiJqcd9//z2GDBkClUoFiUSCrVu36u0XBAGJiYlQqVSwtrZGUFAQCgoK9OZotVpMnToVTk5OkMvlGDp0KIqKigyOhYkAERGJlsSI/2eIiooKPPHEE1i+fHmD+xctWoTFixdj+fLlyMnJgVKpRGhoKK5du6abo1arkZGRgU2bNiErKwvl5eUIDw9HbW2tYT8DQRAEg454AFRWmzoCoubn0Pt1U4dA1OwqjzX8h9JYLpbXGG0t5zb31m2XSCTIyMhAREQEgJvVAJVKBbVajbi4OAA3v/0rFAosXLgQEydORFlZGZydnZGeno6RI0cCAP744w906NABO3bsQFhYWJPfnxUBIiIiI9Bqtbh69areptVqDV6nsLAQGo0GAwcO1I3JZDIEBgbi4MGDAIDc3FxUV1frzVGpVPDy8tLNaSomAkREJFoSI27Jycmws7PT25KTkw2OSaPRAAAUCoXeuEKh0O3TaDSwtLSEvb19o3OailcNEBGRaBnzqoH4+HhMnz5db0wmk93zepLbghMEod7Y7Zoy53asCBARERmBTCZD27Zt9bZ7SQSUSiUA1PtmX1JSoqsSKJVKVFVVobS0tNE5TcVEgIiIRMtUVw3cibu7O5RKJfbs2aMbq6qqQmZmJvr06QMA8PX1hYWFhd6c4uJi5Ofn6+Y0FVsDREQkWqa6oVB5eTn+85//6F4XFhYiLy8PDg4O6NixI9RqNZKSkuDp6QlPT08kJSXBxsYGkZGRAAA7OztER0djxowZcHR0hIODA2JjY+Ht7Y2QkBCDYmEiQERE1MKOHj2K/v37617fOrcgKioKaWlpmDVrFiorKxETE4PS0lL4+/tj9+7dsLW11R2zZMkSSKVSjBgxApWVlQgODkZaWhrMzc0NioX3ESB6QPE+AiQGzX0fgdLrht18507sbQz7A9xasCJARESixWcN8GRBIiIiUWNFgIiIRMuYZ/s/qJgIEBGRaLE1wNYAERGRqLEiQEREosWCABMBIiISM2YCbA0QERGJGSsCREQkWrxqgIkAERGJGK8aYGuAiIhI1FgRICIi0WJBgIkAERGJGTMBtgaIiIjEjBUBIiISLV41wESAiIhEjFcNsDVAREQkahJBEARTB0EPNq1Wi+TkZMTHx0Mmk5k6HKJmwd9zelgxEaD7dvXqVdjZ2aGsrAxt27Y1dThEzYK/5/SwYmuAiIhIxJgIEBERiRgTASIiIhFjIkD3TSaTISEhgSdQ0UONv+f0sOLJgkRERCLGigAREZGIMREgIiISMSYCREREIsZEgJpVYmIievbsaeowiJoVf8/pQcZEQITGjh0LiUQCiUQCCwsLeHh4IDY2FhUVFSaJ57fffsOQIUMgl8vh5OSEN954A1VVVSaJhR4ere33fNq0afD19YVMJmPSQK0Knz4oUs8++yzWrl2L6upq/PDDDxg/fjwqKiqQmppab251dTUsLCyaJY7a2loMHjwYzs7OyMrKwuXLlxEVFQVBELBs2bJmeU8Sj9byew4AgiBg3LhxOHz4ME6cONFs70NkKFYEREomk0GpVKJDhw6IjIzE6NGjsXXrVgD/K3OuWbMGHh4ekMlkEAQBZWVleO211+Di4oK2bdtiwIABOH78uN66CxYsgEKhgK2tLaKjo3Hjxo07xrF7926cOnUK69evh4+PD0JCQvDhhx9i5cqVuHr1anN9fBKJ1vJ7DgAfffQRpkyZAg8Pj+b4qET3jIkAAQCsra1RXV2te/2f//wHmzdvxpYtW5CXlwcAGDx4MDQaDXbs2IHc3Fz06tULwcHBuHLlCgBg8+bNSEhIwPz583H06FG4urpixYoVd3zfQ4cOwcvLCyqVSjcWFhYGrVaL3Nxc439QEjVT/Z4TtWZsDRCOHDmCDRs2IDg4WDdWVVWF9PR0ODs7AwC+++47nDx5EiUlJbo7q33wwQfYunUrvvzyS7z22mtISUnBuHHjMH78eADAvHnzsHfv3jt+W9JoNFAoFHpj9vb2sLS0hEajMfZHJREz5e85UWvGioBIbd++HW3atIGVlRUCAgLwzDPP6PXk3dzcdP9xBIDc3FyUl5fD0dERbdq00W2FhYX49ddfAQCnT59GQECA3vvc/rohEomk3pggCA2OExmiNf2eE7VWrAiIVP/+/ZGamgoLCwuoVKp6J0nJ5XK913V1dXB1dcWBAwfqrdWuXbt7jkOpVOLw4cN6Y6Wlpaiurq5XKSAyVGv5PSdqzZgIiJRcLkfnzp2bPL9Xr17QaDSQSqXo1KlTg3Mef/xxZGdn45VXXtGNZWdn33HdgIAAzJ8/H8XFxXB1dQVw8wRCmUwGX1/fJsdH1JDW8ntO1JqxNUBNEhISgoCAAERERODbb7/FuXPncPDgQbz99ts4evQogJvXSa9ZswZr1qzBL7/8goSEBBQUFNxx3YEDB6Jbt254+eWXcezYMezbtw+xsbGYMGEC2rZt2xIfjUinuX7PgZsnJubl5UGj0aCyshJ5eXnIy8vjPTPI5FgRoCaRSCTYsWMHZs+ejXHjxuHixYtQKpV45plndCX8kSNH4tdff0VcXBxu3LiB559/HpMnT8a3337b6Lrm5ub45ptvEBMTg759+8La2hqRkZH44IMPWuqjEek01+85AIwfPx6ZmZm61z4+PgCAwsLCRqsPRC2BjyEmIiISMbYGiIiIRIyJABERkYgxESAiIhIxJgJEREQixkSAiIhIxJgIEBERiRgTASIiIhFjIkBERCRiTASIHgCJiYno2bOn7vXYsWMRERHR4nGcO3cOEokEeXl5Lf7eRNQ8mAgQ3YexY8dCIpFAIpHAwsICHh4eiI2NRUVFRbO+79KlS5GWltakufzjTUR3wmcNEN2nZ599FmvXrkV1dTV++OEHjB8/HhUVFUhNTdWbV11dXe8xuPfKzs7OKOsQEbEiQHSfZDIZlEolOnTogMjISIwePRpbt27VlfPXrFkDDw8PyGQyCIKAsrIyvPbaa3BxcUHbtm0xYMAAHD9+XG/NBQsWQKFQwNbWFtHR0bhx44be/ttbA3V1dVi4cCE6d+4MmUyGjh07Yv78+QAAd3d3ADcfciORSBAUFKQ7bu3atXj88cdhZWWFrl27YsWKFXrvc+TIEfj4+MDKygp+fn44duyYEX9yRNQasCJAZGTW1taorq4GcPPRs5s3b8aWLVtgbm4OABg8eDAcHBywY8cO2NnZ4dNPP0VwcDB++eUXODg4YPPmzUhISMDHH3+Mp59+Gunp6fjoo4/g4eHR6HvGx8dj5cqVWLJkCfr164fi4mL89NNPAG7+Me/duzf27t2L7t27w9LSEgCwcuVKJCQkYPny5fDx8cGxY8cwYcIEyOVyREVFoaKiAuHh4RgwYADWr1+PwsJCTJs2rZl/ekTU4gQiumdRUVHCsGHDdK8PHz4sODo6CiNGjBASEhIECwsLoaSkRLd/3759Qtu2bYUbN27orfPoo48Kn376qSAIghAQECBMmjRJb7+/v7/wxBNPNPi+V69eFWQymbBy5coGYywsLBQACMeOHdMb79Chg7Bhwwa9sffee08ICAgQBEEQPv30U8HBwUGoqKjQ7U9NTW1wLSJ6cLE1QHSftm/fjjZt2sDKygoBAQF45plnsGzZMgCAm5sbnJ2ddXNzc3NRXl4OR0dHtGnTRrcVFhbi119/BQCcPn0aAQEBeu9x++u/On36NLRaLYKDg5sc88WLF/H7778jOjpaL4558+bpxfHEE0/AxsamSXEQ0YOJrQGi+9S/f3+kpqbCwsICKpVK74RAuVyuN7eurg6urq44cOBAvXXatWt3T+9vbW1t8DF1dXUAbrYH/P399fbdamEIgnBP8RDRg4WJANF9ksvl6Ny5c5Pm9urVCxqNBlKpFJ06dWpwzuOPP47s7Gy88sorurHs7OxG1/T09IS1tTX27duH8ePH19t/65yA2tpa3ZhCoUD79u1x9uxZjB49usF1u3XrhvT0dFRWVuqSjTvFQUQPJrYGiFpQSEgIAgICEBERgW+//Rbnzp3DwYMH8fbbb+Po0aMAgGnTpmHNmjVYs2YNfvnlFyQkJKCgoKDRNa2srBAXF4dZs2bh888/x6+//ors7GysXr0aAODi4gJra2vs2rULFy5cQFlZGYCbNylKTk7G0qVL8csvv+DkyZNYu3YtFi9eDACIjIyEmZkZoqOjcerUKezYsQMffPBBM/+EiKilMREgakESiQQ7duzAM888g3HjxqFLly4YNWoUzp07B4VCAQAYOXIk5syZg7i4OPj6+uL8+fOYPHnyHdd95513MGPGDMyZMwePP/44Ro4ciZKSEgCAVCrFRx99hE8//RQqlQrDhg0DAIwfPx6rVq1CWloavL29ERgYiLS0NN3lhm3atMHXX3+NU6dOwcfHB7Nnz8bChQub8adDRKYgEdgIJCIiEi1WBIiIiESMiQAREZGIMREgIiISMSYCREREIsZEgIiISMSYCBAREYkYEwEiIiIRYyJAREQkYkwEiIiIRIyJABERkYgxESAiIhKx/wMykQrq3nye8wAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Konfusionsmatrix Fold 5:\n", + "[[615 48]\n", + " [ 54 884]]\n", + "\n", + "Aggregierte Konfusionsmatrix über alle Folds:\n", + "[[2985 302]\n", + " [ 156 4529]]\n" + ] + }, + { + "data": { + "image/png": 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zZ8La2hoff/xxhdt71NmzZ/Hbb78hJiYGgwcPltY/OglSjoqeXyNGjMDdu3fRrFkzjBs3Du3bt0etWrX0vmfZsmW4e/duhfu2fv16dOzYEdHR0Trrn6Stinx/P6mnOZccHBzK/PlR1ronOc+p6mOy8JCioiKsWbMGDRs2xIoVK0pt/+mnn/D1119jz549CAoKQocOHbB582bs2bNH53K62NhYnfd5eXlBrVZj8+bNmDBhgrT+6tWrOHr0qKxfkF5eXmjcuDF+++03zJ49W29shw4dsHv3bty8eVP6AVBcXIz//Oc/j91PUFAQYmNjUVRU9FSXipb3l3lQUBB2796Nhg0bPvaH+OM8nDAMGDAAsbGxeOONN9C5c2cAD36Y/+tf/5LiT5w4gfPnz2PKlClPtd+HlVzJcvr0aZ197dy5s9z3mJmZwc/PD02aNMGGDRvw66+/lvtDNCAgANu2bcP169d1zpO1a9eievXqT3WZnEKhQMuWLTFv3jzExMTg119/feK2Hm0XQKlLfZctW1Yq9tHKx9NYsWIF1q9fj1WrVqFDhw546aWX8N577z325mBP+ktYoVCUOsbTp08jISGh1LDR41Tk+/tJBQQEICoqCr/++iteeuklaf3atWuhUCjQqVOnct/bqVMn/Pjjj7hx44ZUoSwqKsKmTZvKfU9FznOq+pgsPGTPnj24fv065s6dW+ZdDX18fLBo0SKsXLkSQUFBGDx4MObNm4d33nkHs2bNQqNGjbBnzx78/PPPAB7MEyj5/4wZMzB8+HC8+eabGDJkCLKysjBjxgy4uLhIcY+zbNkyBAYGonv37ggLC0OdOnVw+/ZtnD9/Hr/++quUDEyZMgU7d+5EQEAApkyZAmtrayxduhS5ubk6/SrLwIEDsWHDBvTs2RPjx4/Hyy+/DAsLC6SmpuLQoUPo06cP+vXr99i+Nm/eHPHx8di5cydcXFxgZ2cHLy8vzJw5E/v27UO7du0wbtw4eHl54f79+7h8+TJ2796NpUuXVuimRLVq1ZIShoEDB+L777/HW2+9hWHDhmHhwoWoVq0aAgMDcfnyZXz22Wdwc3PDBx98ILv9x+nZsyfs7e0xdOhQzJw5E+bm5oiJicG1a9d04pYuXYqDBw+iV69eqFevHu7fvy8NaXXp0qXc9qdNmybN85g6dSrs7e2xYcMG7Nq1C1988QVUKlWF+vvTTz9hyZIl6Nu3Lxo0aAAhBLZu3YqsrCx07dq14h9AGZo0aYKGDRvi448/hhAC9vb22LlzJ/bt21cqtnnz5gCABQsWYPDgwbCwsICXl1epCtbjnDlzBuPGjcPgwYPx3nvvAXgw9+jNN9/E/PnzERER8dTH9aigoCD8+9//xrRp09ChQwdcvHgRM2fOhIeHR4UvAwbkf38/qQ8++ABr165Fr169MHPmTLi7u2PXrl1YsmQJRo4cqXduyKeffooff/wRnTt3xtSpU1G9enUsXrxY+plS4knPc3oOGHN2ZVXTt29fYWlpKdLT08uNGThwoDA3N5dmBV+9elW8/vrrwtbWVtjZ2Yk33nhD7N69u9SsbyGE+O6770SjRo2EpaWl8PT0FKtWrRJ9+vQRrVu3lmJKrob48ssvy9z/b7/9Jvr37y+cnJyEhYWFUKvVonPnztKVGyV++eUX4efnJ5RKpVCr1eLDDz8Uc+fOFQCkGfRClL4aQgghCgoKxFdffSVatmwprKyshK2trWjSpIkYPny4uHTpkhTn7u4uevXqVWY/k5OTxSuvvCKqV68uAOjsIyMjQ4wbN054eHgICwsLYW9vL3x9fcWUKVN0Zt+XpeTSyUdlZWWJl19+WZibm4tNmzaJoqIiMXfuXOHp6SksLCyEo6OjeOedd8S1a9dktTd48GCdq1SEKPtqCCGEOH78uGjXrp2wsbERderUEdOmTZMuty2ZJZ6QkCD69esn3N3dhVKpFA4ODqJDhw7ixx9/LLWPh2ehCyHEmTNnRO/evYVKpRKWlpaiZcuWpS6lK5ld/5///Ednfcn5VBJ/4cIF8fbbb4uGDRsKa2troVKpxMsvv1zqsuCyjrW8c7Osff/++++ia9euws7OTtSqVUu89dZb4urVq2Ue3+TJk4Wrq6uoVq2azhUC+s6vh6+GyMnJEU2aNBHe3t4iNzdXJ2706NHCwsKi1BUAhqDVakVkZKSoU6eOsLKyEi+99JLYvn17uefO466GEEL+93dZyjs/H3blyhUREhIiHBwchIWFhfDy8hJffvllqSstyvo6/d///Z9o27atzs+U77777onOc3r+KIQQ4hnnJy+82bNn49NPP8XVq1f1/pWclZUFT09P9O3bF999912l96tbt264fPky/vjjj0rfFxERvTg4DPGUFi1aBOBB6bWgoAAHDx7Et99+i3feeUcnUdBoNPj888/RqVMnODg44MqVK5g3bx7u3r2L8ePHG7xfEyZMQOvWreHm5obbt29jw4YN2Ldvn3RnSiIiIrmYLDyl6tWrY968ebh8+TK0Wi3q1auHjz76CJ9++qlOnFKpxOXLlzFq1Cjcvn1bmpy2dOlS6bJBQyoqKsLUqVOh0WigUCjg7e2NdevW4Z133jH4voiI6MXGYQgiIiLSizdlIiIiIr2YLBAREZFeTBaIiIiMKCoqCgqFQud+IGFhYdJj20uWR2/CptVqMXbsWDg6OsLGxgbBwcE6j5oHgMzMTISGhkKlUkGlUiE0NBRZWVkV7iOTBSIiIiM5ceIEvvvuO+mhbw/r0aMH0tLSpGX37t062yMiIrBt2zbExsbiyJEjyMnJQVBQEIqKiqSYkJAQJCcnIy4uDnFxcUhOTkZoaGiF+/lCXg3x2aQ9xu4CUaX7bKZh7rhIVJVZWlXur6mOiqkGaytezKxQfE5ODgYNGoTly5dj1qxZpbYrlcoyH+AFANnZ2Vi5ciXWrVsn3R1z/fr1cHNzw/79+9G9e3ecP38ecXFxSExMlG7fv3z5cvj7++PixYsVutU5KwtERGSyHi31P82i1Wpx584dnUWr1Za779GjR6NXr17l3go7Pj4eTk5O8PT0RHh4ONLT06VtSUlJKCgoQLdu3aR1rq6u8PHxwdGjRwEACQkJUKlUOs/5adu2LVQqlRQjF5MFIiIiA4iKipLmBpQsUVFRZcbGxsbi119/LXd7YGAgNmzYgIMHD+Lrr7/GiRMn0LlzZyn50Gg0sLS0LPVAPmdnZ+lpoBqNBk5OTqXadnJykvUU4oe9kMMQREREslTsKeh6TZ48WefJwkDpp68CwLVr1zB+/Hjs3btXesz9owYMGCD928fHB23atJEe/lXeY8wBQAih82j3sh7z/miMHEwWiIjIZCmqGS5bUCqVZSYHj0pKSkJ6ejp8fX2ldUVFRfjvf/+LRYsWQavVwszMTOc9Li4ucHd3x6VLlwAAarUa+fn5yMzM1KkupKeno127dlLMjRs3Su0/IyNDetS4XByGICIieoYCAgJw5swZJCcnS0ubNm0waNAgJCcnl0oUAODWrVu4du0aXFxcAAC+vr6wsLDQefR7Wloazp49KyUL/v7+yM7OxvHjx6WYY8eOITs7W4qRi5UFIiIyWRWsxhuEnZ0dfHx8dNbZ2NjAwcEBPj4+yMnJwfTp0/HGG2/AxcUFly9fxieffAJHR0f069cPAKBSqTB06FBMnDgRDg4OsLe3R2RkJJo3by5NmGzatCl69OiB8PBwLFu2DAAwbNgwBAUFVehKCIDJAhERmTJjZAuPYWZmhjNnzmDt2rXIysqCi4sLOnXqhE2bNsHOzk6KmzdvHszNzdG/f3/k5eUhICAAMTExOpWJDRs2YNy4cdJVE8HBwdLTkivihXyQFO+zQKaA91kgU1DZ91kIUM4wWFsHtNMM1lZVw8oCERGZrCpYWKiSmCwQEZHJMuTVEC8yXg1BREREerGyQEREpovjELIwWSAiIpPFXEEeDkMQERGRXqwsEBGRyaroMxJMFZMFIiIyXcwVZOEwBBEREenFygIREZks3mdBHiYLRERksjhlQR4OQxAREZFerCwQEZHpYmlBFiYLRERkspgryMNhCCIiItKLlQUiIjJZvBpCHiYLRERkujgOIQuHIYiIiEgvVhaIiMhksbAgD5MFIiIyWXyQlDwchiAiIiK9WFkgIiLTxcKCLEwWiIjIZPHSSXk4DEFERER6sbJARESmi4UFWZgsEBGRyeLVEPJwGIKIiIj0YmWBiIhMFisL8jBZICIi08X6uiz8mIiIiEgvVhaIiMhkcRhCHiYLRERkspgryMNhCCIiItKLyQIREZkuhcJwyxOKioqCQqFARESEtE4IgenTp8PV1RXW1tbo2LEjzp07p/M+rVaLsWPHwtHRETY2NggODkZqaqpOTGZmJkJDQ6FSqaBSqRAaGoqsrKwK95HJAhERmSxj5wonTpzAd999hxYtWuis/+KLL/DNN99g0aJFOHHiBNRqNbp27Yq7d+9KMREREdi2bRtiY2Nx5MgR5OTkICgoCEVFRVJMSEgIkpOTERcXh7i4OCQnJyM0NLTC/WSyQEREZAQ5OTkYNGgQli9fjlq1aknrhRCYP38+pkyZgtdffx0+Pj5Ys2YN7t27h++//x4AkJ2djZUrV+Lrr79Gly5d0Lp1a6xfvx5nzpzB/v37AQDnz59HXFwcVqxYAX9/f/j7+2P58uX46aefcPHixQr1lckCERGZLEU1hcEWrVaLO3fu6CxarbbcfY8ePRq9evVCly5ddNanpKRAo9GgW7du0jqlUokOHTrg6NGjAICkpCQUFBToxLi6usLHx0eKSUhIgEqlgp+fnxTTtm1bqFQqKUYuJgtERGS6DDgOERUVJc0NKFmioqLK3G1sbCx+/fXXMrdrNBoAgLOzs856Z2dnaZtGo4GlpaVORaKsGCcnp1LtOzk5STFy8dJJIiIiA5g8eTImTJigs06pVJaKu3btGsaPH4+9e/fCysqq3PYevQeEEOKx94V4NKaseDntPIqVBSIiMlmGnOCoVCpRo0YNnaWsZCEpKQnp6enw9fWFubk5zM3NcfjwYXz77bcwNzeXKgqP/vWfnp4ubVOr1cjPz0dmZqbemBs3bpTaf0ZGRqmqxeMwWSAiIpOlUCgMtsgVEBCAM2fOIDk5WVratGmDQYMGITk5GQ0aNIBarca+ffuk9+Tn5+Pw4cNo164dAMDX1xcWFhY6MWlpaTh79qwU4+/vj+zsbBw/flyKOXbsGLKzs6UYuTgMQURE9AzZ2dnBx8dHZ52NjQ0cHByk9REREZg9ezYaN26Mxo0bY/bs2ahevTpCQkIAACqVCkOHDsXEiRPh4OAAe3t7REZGonnz5tKEyaZNm6JHjx4IDw/HsmXLAADDhg1DUFAQvLy8KtRnJgtERGS6qmh9fdKkScjLy8OoUaOQmZkJPz8/7N27F3Z2dlLMvHnzYG5ujv79+yMvLw8BAQGIiYmBmZmZFLNhwwaMGzdOumoiODgYixYtqnB/FEII8fSHVbV8NmmPsbtAVOk+m9nV2F0gqnSWVpX7N+0An28N1tams+MM1lZVU0VzKiIiIqoqOAxBREQmi4+olofJAhERmSwF6+uy8GMiIiIivVhZICIi08VhCFmYLBARkcliriAPhyGIiIhIL1YWiIjIZCmqsbQgB5MFIiIyXRyHkIXDEERERKQXKwtERGSyWFiQh8kCERGZLM5ZkIfDEERERKQXKwtERGS6OA4hC5MFIiIyWcwV5OEwBBEREenFygIREZksTnCUh8kCERGZLuYKsnAYgoiIiPRiZYGIiEyWgjMcZWGyQEREJotzFuThMAQRERHpxcoCERGZLI5CyMNkgYiITBezBVmMmiykpqYiOjoaR48ehUajgUKhgLOzM9q1a4cRI0bAzc3NmN0jIiIiGDFZOHLkCAIDA+Hm5oZu3bqhW7duEEIgPT0d27dvx8KFC7Fnzx688sorxuoiERG94DjBUR6jJQsffPAB3n//fcybN6/c7REREThx4sQz7hkREZkKjkLIY7SrIc6ePYsRI0aUu3348OE4e/bsM+wRERERlcVoyYKLiwuOHj1a7vaEhAS4uLg8wx4REZHJUSgMt7zAjDYMERkZiREjRiApKQldu3aFs7MzFAoFNBoN9u3bhxUrVmD+/PnG6h4REZkA3sFRHqMlC6NGjYKDgwPmzZuHZcuWoaioCABgZmYGX19frF27Fv379zdW94iIiOj/M+qlkwMGDMCAAQNQUFCAmzdvAgAcHR1hYWFhzG4REZGJUPA+xrJUiZsyWVhYcH4CERE9exyGkIU5FRER0TMWHR2NFi1aoEaNGqhRowb8/f2xZ88eaXtYWBgUCoXO0rZtW502tFotxo4dC0dHR9jY2CA4OBipqak6MZmZmQgNDYVKpYJKpUJoaCiysrIq3F8mC0REZLKMdTFE3bp1MWfOHJw8eRInT55E586d0adPH5w7d06K6dGjB9LS0qRl9+7dOm1ERERg27ZtiI2NxZEjR5CTk4OgoCBpDiAAhISEIDk5GXFxcYiLi0NycjJCQ0Mr/DlViWEIIiIiYzDWHRx79+6t8/rzzz9HdHQ0EhMT0axZMwCAUqmEWq0u8/3Z2dlYuXIl1q1bhy5dugAA1q9fDzc3N+zfvx/du3fH+fPnERcXh8TERPj5+QEAli9fDn9/f1y8eBFeXl6y+8vKAhERkQFotVrcuXNHZ9FqtY99X1FREWJjY5Gbmwt/f39pfXx8PJycnODp6Ynw8HCkp6dL25KSklBQUIBu3bpJ61xdXeHj4yPdwyghIQEqlUpKFACgbdu2UKlUeu9zVJYqkSysW7cOr7zyClxdXXHlyhUAwPz587Fjxw4j94yIiF5oBhyHiIqKkuYGlCxRUVHl7vrMmTOwtbWFUqnEiBEjsG3bNnh7ewMAAgMDsWHDBhw8eBBff/01Tpw4gc6dO0vJh0ajgaWlJWrVqqXTprOzMzQajRTj5ORUar9OTk5SjFxGH4aIjo7G1KlTERERgc8//1waa6lZsybmz5+PPn36GLmHpuG1Tg3Q1McZtZ1sUVBQhGuXs7B3z0XczMiVYmxsLdGtpxcaeTrCysoCV1Ju46cdv+P2zXtSjK2tJbr3aoKGno5QKs1wMyMX/z34N86d+efEnPBxB9Syr66z//8e+gv79vxR+QdK9JBNm2OxafMmXL/+PwBAw4aNMGL4SLR/tT0AQAiB6KVL8MOW/+DOnTto3rwFpkz+FI0aNQIAZGdnYfGSxUhIOArNDQ1q1qyJzp0CMGb0WNjZ2RntuEg+Q14MMXnyZEyYMEFnnVKpLDfey8sLycnJyMrKwpYtWzB48GAcPnwY3t7eGDBggBTn4+ODNm3awN3dHbt27cLrr79ebptCCJ0bTZV106lHY+QwerKwcOFCLF++HH379sWcOXOk9W3atEFkZKQRe2Za6jewx/GjV/G/1GxUq6ZAlx6eGPz+v/DtV7+goOBBAhcy+CUUFwl8H/MrtNpCtGtfH++Fv6wT88bAlrCyMseGmCTcy81Hi1au6D+oFZZ+exRp1+9I+zvw8x84eeya9Do/vwhEz5qzkzMixn+Aem71AAA/7tyBcePH4D+btqBRo0ZYtXol1q5bg1kzP4e7e318t3wZho14Hzt37IKNjQ3S0zOQkZGOiRMi0bBhQ1y/fh3/njUTGRnp+Obr+cY9OHrmlEql3uTgUZaWllLi2aZNG5w4cQILFizAsmXLSsW6uLjA3d0dly5dAgCo1Wrk5+cjMzNTp7qQnp6Odu3aSTE3btwo1VZGRgacnZ0rdGxGH4ZISUlB69atS61XKpXIzc0t4x1UGdauPIlTSf9D+o0caNLuYuvmM6hZyxqudWsAABwcq6Oeey3s3HYO/0vNxs2MXOzcdg6WlmZo0fqfe2S4uddE4tEr+N+1bGTezsPhg3/hfl4BXOrU0NmfVluEnJx8aWGyQMbQsWMnvNb+NdSvXx/169fHuLHjUb16dZw+/RuEEFi/YR3C3x+GLl26onHjxvh81mzcv38fu3bvAgA0btwY875ZgI4dO8HNrR78/Npi7NjxiD8cj8LCQiMfHcmhqKYw2PK0hBDlznG4desWrl27Jt2TyNfXFxYWFti3b58Uk5aWhrNnz0rJgr+/P7Kzs3H8+HEp5tixY8jOzpZi5DJ6ZcHDwwPJyclwd3fXWb9nzx5p7IaePSurB6dG3r0CAIC5+YO8sqCgWIoRAigqKka9+rWQdPzBtb1XL2eieUsX/HE+A/fvF8CnhQvMzKsh5a/bOu237+iBjgENkZ19H+dOa3Dk8N8oKhLP4tCIylRUVIS9e39GXl4eWrZsidT/peLmzZto5/+KFGNpaQlf3zb47bdT6P9W2bejz8m5C1tbW5ibG/3HK8lhpJsyffLJJwgMDISbmxvu3r2L2NhYxMfHIy4uDjk5OZg+fTreeOMNuLi44PLly/jkk0/g6OiIfv36AQBUKhWGDh2KiRMnwsHBAfb29oiMjETz5s2lqyOaNm2KHj16IDw8XKpWDBs2DEFBQRW6EgKoAsnChx9+iNGjR+P+/fsQQuD48ePYuHEjoqKisGLFise+X6vVlsrECgsLYG7OW0Y/jcDeTXA55TbSb+QAADLSc5F5+x66BXpix9azKMgvQrv2HrCrYQU7u3/Kbps2JGPAoFb4ZEYXFBUVoyC/CBvX/orM2//Ma0j4vytI+98d5N0rQN16KnTt4YWa9tbY8QMfSU7P3h+X/sA7oSHIz89H9erVMX/et2jYsBGSk08BABwcHHTiHRwckHb9epltZWVlYdl3S/Hmm29Ver/p+Xbjxg2EhoYiLS0NKpUKLVq0QFxcHLp27Yq8vDycOXMGa9euRVZWFlxcXNCpUyds2rRJZy7MvHnzYG5ujv79+yMvLw8BAQGIiYmBmZmZFLNhwwaMGzdOumoiODgYixYtqnB/FUIIo/85t3z5csyaNQvXrj0Yw65Tpw6mT5+OoUOHPva906dPx4wZM3TWtW8Xgg6vvFMpfTUFQX294dmkNlZEH8Od7PvSetc6NdD3reZwca2BoqJi/P3nLZScPutWJQEAevVpijpuNbE/7g/cy81H02bO8G9fHyujE3FDk1Pm/rx9nPH2uy9h9vT9UiWDHu+zmV2N3YUXQkFBPtLS0nD37l3s278PW7dtweqVMbh79y5CB7+Dg/vjUbt2bSl++oyp0Gg0WBr9nU47OTk5GD4iHHY1amDhgkV8xo2BWFpV7t+0o19fb7C2Fm99cX/vGL2yAADh4eEIDw/HzZs3UVxcXOalHuUpa/Zp1PR4A/fQdPTq0xRNvJ1KJQoAcP1/d7Bk/v9BaWUOM7NquJebj2Fj/HE9NRsAUMu+Otq+Uh8Lv/5Fqkho0u7C3aMWXm7njp1bz5XaHwBcu5oFAHBwqI7Ue9mVd3BEZbCwsES9eg+GQZs188HZc2exfsN6DBny4I+Vmzdv6iQLt27fLlVtyM3NxYhRw2FdvToWzPuWicJzxFg3ZXreGH2C48McHR0rlCgADyZCltxbu2ThEMST6dXHG94+aqz67jiyMvPKjdPeL8S93HzYO1ZHnboqnD/34EYhlpYPTqdHi1XFxULvsGDJ5Me7dx9/8xKiSicE8gvyUbdOXTg6OiIh8Z+b1xQU5CMp6SRatvxnUnZOTg6GjQiHhYUFFi5YVKHZ8ETPC6NXFjw8PPRe7/n3338/w96YrqC+3mjR2hXfr/kV+fcLYWtrCQC4f78QhYUPJjU2a65Gbm4+srPy4Ky2Q8/gpjh/7gb+uvTg8eIZ6bm4dTMXwa/7IG7XBdzLLUBTHyc0bOyI9TEPhinc6tVEXfeaSPnzFu7fL0RdNxUCez9oJzvrftmdI6okC76dj1dfbQ+1sxq593IRF7cHJ06eQPSSZVAoFHhnUChWrFwO93ruqFfPHctXfgcrKyv06tkLwIOKwvAR4ci7fx9zZs9Bbm4OcnMfVNVq1bLXGTumqqmi9xswVUZPFiIiInReFxQU4NSpU4iLi8OHH35onE6ZIL92D8qwQ0f46azfuuk0TiU9uGGNXQ0lAns3gY2tEjl3tUhO+h/iD/wpxRYXC6xddRLdAr3wTpgvLJVmuH3zHrZuPo1LFzIAAIVFxWjewgWdujSCuXk1ZGXm4eTxazgSz6SQnr1bt27hkykfIyMjA3a2dmjs6YnoJcvQzv/BZWVD3hsKrVaLWbP/Ld2UaVn0ctjY2AAAfv/9HE6fOQ0A6BkUqNN23O69qFOnzrM9IKo45gqyVIkJjmVZvHgxTp48idWrV1f4vZ9N2vP4IKLnHCc4kimo7AmOY/t/b7C2Fm4OMVhbVU2VmrPwsMDAQGzZssXY3SAiohdYVbopU1Vm9GGI8vzwww+wt7c3djeIiOgFxjkL8hg9WWjdurXOF0sIAY1Gg4yMDCxZssSIPSMiIiKgCiQLffv21XldrVo11K5dGx07dkSTJk2M0ykiIjINL/jwgaEYNVkoLCxE/fr10b17d6jVamN2hYiITBBHIeQx6gRHc3NzjBw5stynbBEREZHxGf1qCD8/P5w6dcrY3SAiIhOkUCgMtrzIjD5nYdSoUZg4cSJSU1Ph6+sr3eykRIsWLYzUMyIiIgKMmCwMGTIE8+fPx4ABAwAA48aNk7YpFAoIIaBQKFBUVGSsLhIR0YuOExxlMVqysGbNGsyZMwcpKSnG6gIREZm4F3z0wGCMliyU3GXa3d3dWF0gIiIiGYw6Z+FFnxBCRERV24t+m2ZDMWqy4Onp+diE4fbt28+oN0REZHL4R6ssRk0WZsyYAZVKZcwuEBER0WMYNVkYOHAgnJycjNkFIiIyYRwOl8doyQK/QEREZGwKo9+a8PlgtI+p5GoIIiIiqtqMVlkoLi421q6JiIgAsMotl9Fv90xERGQ0TBZk4WgNERER6cXKAhERmSxOcJSHyQIREZkszlmQhzkVERER6cXKAhERmS4+G0IWJgtERGSyOAwhD4chiIiISC9WFoiIyGSxsCAPkwUiIjJdnLMgC4chiIiInrHo6Gi0aNECNWrUQI0aNeDv7489e/ZI24UQmD59OlxdXWFtbY2OHTvi3LlzOm1otVqMHTsWjo6OsLGxQXBwMFJTU3ViMjMzERoaCpVKBZVKhdDQUGRlZVW4v0wWiIjIZCkUCoMtFVG3bl3MmTMHJ0+exMmTJ9G5c2f06dNHSgi++OILfPPNN1i0aBFOnDgBtVqNrl274u7du1IbERER2LZtG2JjY3HkyBHk5OQgKCgIRUVFUkxISAiSk5MRFxeHuLg4JCcnIzQ0tOKfk3gBH//42aQ9jw8ies59NrOrsbtAVOksrSp3tPzTibsM1tasr3s91fvt7e3x5ZdfYsiQIXB1dUVERAQ++ugjAA+qCM7Ozpg7dy6GDx+O7Oxs1K5dG+vWrcOAAQMAANevX4ebmxt2796N7t274/z58/D29kZiYiL8/PwAAImJifD398eFCxfg5eUlu2+sLBARERmAVqvFnTt3dBatVvvY9xUVFSE2Nha5ubnw9/dHSkoKNBoNunXrJsUolUp06NABR48eBQAkJSWhoKBAJ8bV1RU+Pj5STEJCAlQqlZQoAEDbtm2hUqmkGLmYLBARkemqpjDYEhUVJc0NKFmioqLK3fWZM2dga2sLpVKJESNGYNu2bfD29oZGowEAODs768Q7OztL2zQaDSwtLVGrVi29MU5OTqX26+TkJMXIxashiIjIZBnypkyTJ0/GhAkTdNYplcpy4728vJCcnIysrCxs2bIFgwcPxuHDh8vtmxDisf19NKaseDntPIqVBSIiIgNQKpXS1Q0li75kwdLSEo0aNUKbNm0QFRWFli1bYsGCBVCr1QBQ6q//9PR0qdqgVquRn5+PzMxMvTE3btwotd+MjIxSVYvHYbJAREQmS1FNYbDlaQkhoNVq4eHhAbVajX379knb8vPzcfjwYbRr1w4A4OvrCwsLC52YtLQ0nD17Vorx9/dHdnY2jh8/LsUcO3YM2dnZUoxcHIYgIiLTZaR7Mn3yyScIDAyEm5sb7t69i9jYWMTHxyMuLg4KhQIRERGYPXs2GjdujMaNG2P27NmoXr06QkJCAAAqlQpDhw7FxIkT4eDgAHt7e0RGRqJ58+bo0qULAKBp06bo0aMHwsPDsWzZMgDAsGHDEBQUVKErIQAmC0RERM/cjRs3EBoairS0NKhUKrRo0QJxcXHo2vXBJdGTJk1CXl4eRo0ahczMTPj5+WHv3r2ws7OT2pg3bx7Mzc3Rv39/5OXlISAgADExMTAzM5NiNmzYgHHjxklXTQQHB2PRokUV7i/vs0D0nOJ9FsgUVPZ9FqZ98rPB2poxu7vB2qpqWFkgIiKTZYi5BqaAExyJiIhIL1YWiIjIZBnyPgsvMiYLRERkupgryMJhCCIiItKLlQUiIjJZHIaQh8kCERGZLOYK8nAYgoiIiPRiZYGIiEwWKwvyMFkgIiKTxTkL8nAYgoiIiPRiZYGIiEwWCwvyMFkgIiKTxWEIeTgMQURERHqxskBERCaLhQV5mCwQEZHJ4jCEPByGICIiIr1YWSAiIpPFwoI8TBaIiMhkKfiMalk4DEFERER6sbJAREQmi8MQ8jBZICIik8VkQR4OQxAREZFerCwQEZHJ4n0W5GGyQEREJou5gjwchiAiIiK9WFkgIiLTxdKCLEwWiIjIZDFXkIfDEERERKQXKwtERGSyeDWEPEwWiIjIZDFXkIfDEERERKQXKwtERGSyOAwhDysLRERkshQKwy0VERUVhX/961+ws7ODk5MT+vbti4sXL+rEhIWFQaFQ6Cxt27bVidFqtRg7diwcHR1hY2OD4OBgpKam6sRkZmYiNDQUKpUKKpUKoaGhyMrKqlB/mSwQERE9Y4cPH8bo0aORmJiIffv2obCwEN26dUNubq5OXI8ePZCWliYtu3fv1tkeERGBbdu2ITY2FkeOHEFOTg6CgoJQVFQkxYSEhCA5ORlxcXGIi4tDcnIyQkNDK9RfDkMQEZHJMtYgRFxcnM7r1atXw8nJCUlJSXjttdek9UqlEmq1usw2srOzsXLlSqxbtw5dunQBAKxfvx5ubm7Yv38/unfvjvPnzyMuLg6JiYnw8/MDACxfvhz+/v64ePEivLy8ZPWXlQUiIjJZj5b5n2bRarW4c+eOzqLVamX1Izs7GwBgb2+vsz4+Ph5OTk7w9PREeHg40tPTpW1JSUkoKChAt27dpHWurq7w8fHB0aNHAQAJCQlQqVRSogAAbdu2hUqlkmLkYLJARERkAFFRUdK8gJIlKirqse8TQmDChAl49dVX4ePjI60PDAzEhg0bcPDgQXz99dc4ceIEOnfuLCUgGo0GlpaWqFWrlk57zs7O0Gg0UoyTk1OpfTo5OUkxcnAYgoiITJYhL4aYPHkyJkyYoLNOqVQ+9n1jxozB6dOnceTIEZ31AwYMkP7t4+ODNm3awN3dHbt27cLrr79ebntCCJ2rPMq64uPRmMd5osrCunXr8Morr8DV1RVXrlwBAMyfPx87dux4kuaIiIiMwpDDEEqlEjVq1NBZHpcsjB07Fj/++CMOHTqEunXr6o11cXGBu7s7Ll26BABQq9XIz89HZmamTlx6ejqcnZ2lmBs3bpRqKyMjQ4qRo8LJQnR0NCZMmICePXsiKytLmnFZs2ZNzJ8/v6LNERERmRwhBMaMGYOtW7fi4MGD8PDweOx7bt26hWvXrsHFxQUA4OvrCwsLC+zbt0+KSUtLw9mzZ9GuXTsAgL+/P7Kzs3H8+HEp5tixY8jOzpZi5KhwsrBw4UIsX74cU6ZMgZmZmbS+TZs2OHPmTEWbIyIiMhpj3Wdh9OjRWL9+Pb7//nvY2dlBo9FAo9EgLy8PAJCTk4PIyEgkJCTg8uXLiI+PR+/eveHo6Ih+/foBAFQqFYYOHYqJEyfiwIEDOHXqFN555x00b95cujqiadOm6NGjB8LDw5GYmIjExESEh4cjKChI9pUQwBPMWUhJSUHr1q1LrVcqlaWuDyUiIqrKjHUHx+joaABAx44dddavXr0aYWFhMDMzw5kzZ7B27VpkZWXBxcUFnTp1wqZNm2BnZyfFz5s3D+bm5ujfvz/y8vIQEBCAmJgYnT/mN2zYgHHjxklXTQQHB2PRokUV6m+FkwUPDw8kJyfD3d1dZ/2ePXvg7e1d0eaIiIhMjhBC73Zra2v8/PPPj23HysoKCxcuxMKFC8uNsbe3x/r16yvcx4dVOFn48MMPMXr0aNy/fx9CCBw/fhwbN25EVFQUVqxY8VSdISIiepb4aAh5KpwsvPfeeygsLMSkSZNw7949hISEoE6dOliwYAEGDhxYGX0kIiKqFEwW5Hmi+yyEh4cjPDwcN2/eRHFxcZk3fCAiIqIXw1PdlMnR0dFQ/SAiInrm+IhqeZ5ogqO+D/fvv/9+qg4RERE9K8wV5KlwshAREaHzuqCgAKdOnUJcXBw+/PBDQ/WLiIiIqogKJwvjx48vc/3ixYtx8uTJp+4QERHRs8JhCHkU4nEXe8r0999/o1WrVrhz544hmnsqRYXFxu4CUaULsJhu7C4QVbp4MbNS21+94pjB2nrvfb/HBz2nDPaI6h9++KHUc7iJiIjo+VfhYYjWrVvrlG2EENBoNMjIyMCSJUsM2jkiIqLKxGEIeSqcLPTt21fndbVq1VC7dm107NgRTZo0MVS/iIiIKh2TBXkqlCwUFhaifv366N69O9RqdWX1iYiIiKqQCs1ZMDc3x8iRI6HVaiurP0RERM+MsR5R/byp8ARHPz8/nDp1qjL6QkRE9EwpFAqDLS+yCs9ZGDVqFCZOnIjU1FT4+vrCxsZGZ3uLFi0M1jkiIiIyPtnJwpAhQzB//nwMGDAAADBu3Dhpm0KhgBACCoUCRUVFhu8lERFRJXjBCwIGIztZWLNmDebMmYOUlJTK7A8REdEz86IPHxiK7GSh5EaP7u7uldYZIiIiqnoqNGeBGRgREb1I+HtNngolC56eno/9YG/fvv1UHSIiInpWmCvIU6FkYcaMGVCpVJXVFyIiIqqCKpQsDBw4EE5OTpXVFyIiomeKwxDyyE4W+IESEdGLRlGNv9vkkH0Hx5KrIYiIiMi0yK4sFBcXV2Y/iIiInjkWzeWp8O2eiYiIXhQcYpenwg+SIiIiItPCygIREZksFhbkYbJAREQmi8MQ8nAYgoiIiPRiZYGIiEwWKwvyMFkgIiKTxVxBHg5DEBERkV5MFoiIyHQpFIZbKiAqKgr/+te/YGdnBycnJ/Tt2xcXL17UiRFCYPr06XB1dYW1tTU6duyIc+fO6cRotVqMHTsWjo6OsLGxQXBwMFJTU3ViMjMzERoaCpVKBZVKhdDQUGRlZVWov0wWiIjIZCkUCoMtFXH48GGMHj0aiYmJ2LdvHwoLC9GtWzfk5uZKMV988QW++eYbLFq0CCdOnIBarUbXrl1x9+5dKSYiIgLbtm1DbGwsjhw5gpycHAQFBaGoqEiKCQkJQXJyMuLi4hAXF4fk5GSEhoZW7HMSL+BDH4oKeWtqevEFWEw3dheIKl28mFmp7W/fdu7xQTL17dfsid+bkZEBJycnHD58GK+99hqEEHB1dUVERAQ++ugjAA+qCM7Ozpg7dy6GDx+O7Oxs1K5dG+vWrcOAAQMAANevX4ebmxt2796N7t274/z58/D29kZiYiL8/PwAAImJifD398eFCxfg5eUlq3+sLBARkcky5CiEVqvFnTt3dBatViurH9nZ2QAAe3t7AEBKSgo0Gg26desmxSiVSnTo0AFHjx4FACQlJaGgoEAnxtXVFT4+PlJMQkICVCqVlCgAQNu2baFSqaQYOZgsEBGRyVJUUxhsiYqKkuYFlCxRUVGP7YMQAhMmTMCrr74KHx8fAIBGowEAODs768Q6OztL2zQaDSwtLVGrVi29MU5OTqX26eTkJMXIwUsniYiIDGDy5MmYMGGCzjqlUvnY940ZMwanT5/GkSNHSm17dC6EEOKx8yMejSkrXk47D2NlgYiITJYhhyGUSiVq1KihszwuWRg7dix+/PFHHDp0CHXr1pXWq9VqACj11396erpUbVCr1cjPz0dmZqbemBs3bpTab0ZGRqmqhT5MFoiIyGQZ62oIIQTGjBmDrVu34uDBg/Dw8NDZ7uHhAbVajX379knr8vPzcfjwYbRr1w4A4OvrCwsLC52YtLQ0nD17Vorx9/dHdnY2jh8/LsUcO3YM2dnZUowcHIYgIiJ6xkaPHo3vv/8eO3bsgJ2dnVRBUKlUsLa2hkKhQEREBGbPno3GjRujcePGmD17NqpXr46QkBApdujQoZg4cSIcHBxgb2+PyMhING/eHF26dAEANG3aFD169EB4eDiWLVsGABg2bBiCgoJkXwkBMFkgIiITZqxnQ0RHRwMAOnbsqLN+9erVCAsLAwBMmjQJeXl5GDVqFDIzM+Hn54e9e/fCzs5Oip83bx7Mzc3Rv39/5OXlISAgADExMTAzM5NiNmzYgHHjxklXTQQHB2PRokUV6i/vs0D0nOJ9FsgUVPZ9FnbvvmCwtnr2bGKwtqoazlkgIiIivTgMQUREJouPqJaHyQIREZksJgvycBiCiIiI9GJlgYiITBYLC/IwWSAiIpPFYQh5OAxBREREerGyQEREJouVBXmYLBARkcliriAPhyGIiIhIL1YWiIjIZCmqsbQgB5MFIiIyWRyGkIfDEERERKQXKwtERGSyFGBpQQ4mC0REZLqYK8jCYQgiIiLSi5UFIiIyWbwpkzxMFoiIyGQxV5CHwxBERESkFysLRERksjgMIQ+TBSIiMlnMFeThMAQRERHpxcoCERGZLA5DyMNkgYiITBZzBXk4DEFERER6sbJAREQmi8MQ8jBZICIik8VcQR4OQxAREZFerCwQEZHJYmVBHiYLRERkshR8RrUsHIYgIiIivVhZICIik8VhCHmYLBARkcnipZPycBiCiIiI9GKyQEREJkuhMNxSEf/973/Ru3dvuLq6QqFQYPv27Trbw8LCoFAodJa2bdvqxGi1WowdOxaOjo6wsbFBcHAwUlNTdWIyMzMRGhoKlUoFlUqF0NBQZGVlVfhzYrJAREQm69FfyE+zVERubi5atmyJRYsWlRvTo0cPpKWlScvu3bt1tkdERGDbtm2IjY3FkSNHkJOTg6CgIBQVFUkxISEhSE5ORlxcHOLi4pCcnIzQ0NCKfUjgnAUiIiKD0Gq10Gq1OuuUSiWUSmWp2MDAQAQGBuptT6lUQq1Wl7ktOzsbK1euxLp169ClSxcAwPr16+Hm5ob9+/eje/fuOH/+POLi4pCYmAg/Pz8AwPLly+Hv74+LFy/Cy8tL9rGxskBERCbLkMMQUVFRUrm/ZImKinrivsXHx8PJyQmenp4IDw9Henq6tC0pKQkFBQXo1q2btM7V1RU+Pj44evQoACAhIQEqlUpKFACgbdu2UKlUUoxcrCwQEZHJMuTVEJMnT8aECRN01pVVVZAjMDAQb731Ftzd3ZGSkoLPPvsMnTt3RlJSEpRKJTQaDSwtLVGrVi2d9zk7O0Oj0QAANBoNnJycSrXt5OQkxcjFZIGIiMgAyhtyeBIDBgyQ/u3j44M2bdrA3d0du3btwuuvv17u+4QQOglQWcnQozFycBiCiIhMl8KASyVycXGBu7s7Ll26BABQq9XIz89HZmamTlx6ejqcnZ2lmBs3bpRqKyMjQ4qRq8omC9euXcOQIUOM3Q0iInqBGetqiIq6desWrl27BhcXFwCAr68vLCwssG/fPikmLS0NZ8+eRbt27QAA/v7+yM7OxvHjx6WYY8eOITs7W4qRq8omC7dv38aaNWuM3Q0iIiKDy8nJQXJyMpKTkwEAKSkpSE5OxtWrV5GTk4PIyEgkJCTg8uXLiI+PR+/eveHo6Ih+/foBAFQqFYYOHYqJEyfiwIEDOHXqFN555x00b95cujqiadOm6NGjB8LDw5GYmIjExESEh4cjKCioQldCAEacs/Djjz/q3f73338/o54QEZGpMtbdnk+ePIlOnTpJr0smRg4ePBjR0dE4c+YM1q5di6ysLLi4uKBTp07YtGkT7OzspPfMmzcP5ubm6N+/P/Ly8hAQEICYmBiYmZlJMRs2bMC4ceOkqyaCg4P13tuhPAohhHjSg30a1apVg0KhgL7dKxQKnZtLyFVUWPw0XSN6LgRYTDd2F4gqXbyYWantnz5TsasC9GnRvOx7IrwIjDYM4eLigi1btqC4uLjM5ddffzVW14iIiOghRksWfH199SYEj6s6EBERPa3n5GIIozPanIUPP/wQubm55W5v1KgRDh069Ax7REREpoaPqJbHaMlC+/bt9W63sbFBhw4dnlFviIiIqDy8gyMREZksFhbkYbJAREQmi8MQ8lTZmzIRERFR1cDKAhERmSwWFuRhskBERCaLyYI8VWIYYt26dXjllVfg6uqKK1euAADmz5+PHTt2GLlnpu3kyRMYNWokOnR8Dd7NmmL/gf062z/5ZDK8mzXVWQa+PaBUO8nJp/Dee2HwbfMS/Nq+jMFh7+L+/fvP6jCIyhTycXvEi5kYMy9QWvfx6n6IFzN1liUJ4dJ2u1rWGPdtT6y9MA5xuZ9i05UJGLugJ2xq6D6WuHFrF3y1dzB+ypyMHTc/xsRlwbC2sXxmx0ZkaEavLERHR2Pq1KmIiIjA559/Lt3euWbNmpg/fz769Olj5B6arnt5efDy8kK/fv0wPmJ8mTGvvtoen8/6XHptYWGhsz05+RSGDR+G8PeH4ZMpU2BhYYGLFy6iWrUqkaeSifJq44rew9rgz99K3+r32J5LmPveNul1Qf4/t5x3dLWDg6sdoiN/xpXf0+HsXhMTlvaGo6sdpr21CQDg4GKHr/cPxqFNZ7FgzE+oXkOJMfN74uOYflIMVR2c4CiP0ZOFhQsXYvny5ejbty/mzJkjrW/Tpg0iIyON2DN6rf1reK39a3pjLC0tUbt27XK3z5k7B+8Megfh4f/8dVbfvb6hukhUYdY2lvh0w5v4KnwHQj8tfS+XAm0hbt/IKfO9KefSMe3Nf37hX/87EyumHMCU9W/AzKwaioqK4R/kicKCYswfvUu6C+2C0T9hRfIo1Gloj//9dbtyDoyeCHMFeYz+511KSgpat25dar1SqdR7h0eqGk6cOI5X27+CwJ49MHXqZ7h165a07datWzh9+jTsHRwQMuhttH/tVbw7OBRJSUlG7DGZuvGLeyFx1x9IOlD2k21bdayPbTcmYd3FcYj8Lhg1a9vobc9WpcS9O1oUFT14gJ2F0hyF+UU6t6vX5hUAAJq/Ws9AR0H0bBk9WfDw8JCe5/2wPXv2wNvb+7Hv12q1uHPnjs6i1Woroaf0qPbt2+OLuV9g9arVmPThRzhz9izeGxKG/Px8AEBq6jUAwOLFi/Dmm29h2bLv4N3UG0OGvofLVy4bsedkqjoP8IHnS65YPnl/mduP7bmEWYO2YELnGCyZ+DOa/KsO5h0Mg4WlWZnxNeytEfpZR+xcdlJad+rg37BX22JA5CswtzCDbU0rvD+7CwDA3sWuzHbIeBQKhcGWF5nRhyE+/PBDjB49Gvfv34cQAsePH8fGjRsRFRWFFStWPPb9UVFRmDFjhs66zz6bimlTp1VWl+n/CwzsKf27cWNP+Pg0Q0CXLjh8OB5du3ZDcfGDv6z69x+A1/u9DgDwbuqNxGOJ2Lp1KyZ8MMEo/SbTVLtuDYxZ0BMfdluDfG1hmTGHNp+V/p1yLh0XT/4Pm65MQNtenvhl23md2Op2SszZ9Q6u/J6BmBn/PMfm8u8ZiBq8FaO/6YFhUV1QVCSw9dtE3NbcRXERH45HzyejJwvvvfceCgsLMWnSJNy7dw8hISGoU6cOFixYgIEDBz72/ZMnT8aECbq/dMzNLMqJpspUu7YTXF1dpCtaSuYyNGzYUCeuQYMGSEtLe+b9I9Pm5esKe2dbfJc0QlpnZm6GFq+5o9+Yl9FVOVNKcEvc1uTgxpVs1G3soLPe2tYSX8SFIi8nH5/124iiwmKd7Qc2nsGBjWdQy8kG93MLIITAWxPaIS0ls/IOkKgSGT1ZAIDw8HCEh4fj5s2bKC4uhpOTk+z3KpVKKJW6ly09+o1Lz0ZWViY0Go2UJNSpUwdOTk64nJKiE3f58pXHPkiMyNCSDvyN93wW6az7aHU/XL2QgY1zj5RKFIAHwwxObjVwK+2utK66nRJf/vwuCrSF+CT4+3KrFACQmf5g3lXge62Rf78QSfv+MtDRkKG86MMHhlIlkoUSjo6Oxu4CPSQ3NxdXr16VXv8vNRXnz5+HSqWCSqXC4iWL0a1rV9Su7YT//e9/mL9gHmrVqoUuXboCePBNOOS9IVi0eBG8vJqgSZMm2LFjO1JS/sb8efONdFRkqvJy8pFyLl1n3f3cfNy5lYeUc+mwtrFE2PROOLzld9xOuwt1/Zp4f3YXZN+8Jw1BWNta4qu970JZ3QKfv/MDbGoopXssZGXkSglHv9Ev4+zRa8jLyUebrg0x4stu+O7jfcjJ5v1FqhrmCvIYPVnw8PDQm9n9/XfZM5ap8p07dw5h7w2WXs/9Yi4AoG+fvpg6dRou/fEHfvxxB+7cuYvatR3h97Ifvv7qG9jY/DN7/N13B0OrzcfcL+YgOzsbXl5eWLF8JerV46xwqlqKiorh0dwZ3d5tCduaVriVloPkQymYMWAz8nIeTNr18nWFd1s3AMD3f32g8/6B9b+B5koWAKDJy3URNqMzrG0tcfXCTXw9fCf2rf/tmR4PkSEpxMPX9xjBggULdF4XFBTg1KlTiIuLw4cffoiPP/64wm1yGIJMQYDFdGN3gajSxYuZldr+X3/denyQTA0bOjw+6Dll9MrC+PFl3xlw8eLFOHnyZJnbiIiIDIHDEPIY/T4L5QkMDMSWLVuM3Q0iIiKTZ/TKQnl++OEH2NvbG7sbRET0AlOApQU5jJ4stG7dWmeCoxACGo0GGRkZWLJkiRF7RkRELzzmCrIYPVno27evzutq1aqhdu3a6NixI5o0aWKcThEREZHEqMlCYWEh6tevj+7du0OtVhuzK0REZII4wVEeo05wNDc3x8iRI/ngJyIioirM6FdD+Pn54dSpU8buBhERmSCFAf97kRl9zsKoUaMwceJEpKamwtfXV+fufwDQokULI/WMiIheeC/273iDMVqyMGTIEMyfPx8DBgwAAIwbN07aplAoIISAQqFAUVGRsbpIREREMGKysGbNGsyZMwcpjzyRkIiI6FlhYUEeoyULJY+kcHd3N1YXiIjIxPER1fIYdYIjv0hERERVn1GTBU9PT9jb2+tdiIiIKo3CgEsF/Pe//0Xv3r3h6uoKhUKB7du362wXQmD69OlwdXWFtbU1OnbsiHPnzunEaLVajB07Fo6OjrCxsUFwcDBSU1N1YjIzMxEaGgqVSgWVSoXQ0FBkZWVVrLMw8tUQM2bMgEqlMmYXiIjIhBmrvp2bm4uWLVvivffewxtvvFFq+xdffIFvvvkGMTEx8PT0xKxZs9C1a1dcvHgRdnZ2AICIiAjs3LkTsbGxcHBwwMSJExEUFISkpCSYmZkBAEJCQpCamoq4uDgAwLBhwxAaGoqdO3dWqL8KUTJ54BmrVq0aNBoNnJycDN52UWGxwdskqmoCLKYbuwtElS5ezKzU9lOvZRmsrdpO1qVuMqhUKqFUKvW+T6FQYNu2bdLjD4QQcHV1RUREBD766CMAD6oIzs7OmDt3LoYPH47s7GzUrl0b69atk64qvH79Otzc3LB79250794d58+fh7e3NxITE+Hn5wcASExMhL+/Py5cuAAvLy/Zx2a0YQjOVyAiImNTKBQGW6KioqRyf8kSFRVV4T6lpKRAo9GgW7du0jqlUokOHTrg6NGjAICkpCQUFBToxLi6usLHx0eKSUhIgEqlkhIFAGjbti1UKpUUI5fRr4YgIiJ6EUyePBkTJkzQWfe4qkJZNBoNAMDZ2VlnvbOzM65cuSLFWFpaolatWqViSt5fXvXeyclJipHLaMlCcTGHCoiI6MUhZ8ihIh6twJfcrFCfR2PKipfTzqOM/mwIIiIiY1EoDLcYSslTmB/96z89PV2qNqjVauTn5yMzM1NvzI0bN0q1n5GRUapq8ThMFoiIyGQZcs6CoXh4eECtVmPfvn3Suvz8fBw+fBjt2rUDAPj6+sLCwkInJi0tDWfPnpVi/P39kZ2djePHj0sxx44dQ3Z2thQjl9EfJEVERGRqcnJy8Oeff0qvU1JSkJycDHt7e9SrVw8RERGYPXs2GjdujMaNG2P27NmoXr06QkJCAAAqlQpDhw7FxIkT4eDgAHt7e0RGRqJ58+bo0qULAKBp06bo0aMHwsPDsWzZMgAPLp0MCgqq0JUQAJMFIiKiZ+7kyZPo1KmT9LpkYuTgwYMRExODSZMmIS8vD6NGjUJmZib8/Pywd+9e6R4LADBv3jyYm5ujf//+yMvLQ0BAAGJiYqR7LADAhg0bMG7cOOmqieDgYCxatKjC/TXafRYqE++zQKaA91kgU1DZ91nQpN0xWFtqlxoGa6uq4ZwFIiIi0ovDEEREZLIUfEi1LEwWiIjIdDFXkIXDEERERKQXKwtERGSy+JgieZgsEBGRyWKuIA+HIYiIiEgvVhaIiMh0cRxCFiYLRERkspgqyMNhCCIiItKLlQUiIjJZHIWQh8kCERGZLmYLsnAYgoiIiPRiZYGIiEwW6wryMFkgIiKTxVEIeTgMQURERHqxskBERCaMpQU5mCwQEZHJ4jCEPByGICIiIr2YLBAREZFeHIYgIiKTxWEIeVhZICIiIr1YWSAiIhPG0oIcTBaIiMhkcRhCHg5DEBERkV5MFoiIiEgvDkMQEZHp4jCELKwsEBERkV6sLBARkclSsLQgCysLREREpBeTBSIiItKLwxBERGSyeJ8FeVhZICIiIr2YLBARET1j06dPh0Kh0FnUarW0XQiB6dOnw9XVFdbW1ujYsSPOnTun04ZWq8XYsWPh6OgIGxsbBAcHIzU1tVL6y2SBiIhMl0JhuKWCmjVrhrS0NGk5c+aMtO2LL77AN998g0WLFuHEiRNQq9Xo2rUr7t69K8VERERg27ZtiI2NxZEjR5CTk4OgoCAUFRUZ5KN5GOcsEBGRyTLklAWtVgutVquzTqlUQqlUlhlvbm6uU00oIYTA/PnzMWXKFLz++usAgDVr1sDZ2Rnff/89hg8fjuzsbKxcuRLr1q1Dly5dAADr16+Hm5sb9u/fj+7duxvwyFhZICIiMoioqCioVCqdJSoqqtz4S5cuwdXVFR4eHhg4cCD+/vtvAEBKSgo0Gg26desmxSqVSnTo0AFHjx4FACQlJaGgoEAnxtXVFT4+PlKMIbGyQEREpsuApYXJkydjwoQJOuvKqyr4+flh7dq18PT0xI0bNzBr1iy0a9cO586dg0ajAQA4OzvrvMfZ2RlXrlwBAGg0GlhaWqJWrVqlYkreb0hMFoiIyGQZchhC35DDowIDA6V/N2/eHP7+/mjYsCHWrFmDtm3bPujbI/MghBCl1j1KTsyT4DAEERGRkdnY2KB58+a4dOmSNI/h0QpBenq6VG1Qq9XIz89HZmZmuTGGxGSBiIhMlxGvhniYVqvF+fPn4eLiAg8PD6jVauzbt0/anp+fj8OHD6Ndu3YAAF9fX1hYWOjEpKWl4ezZs1KMIXEYgoiI6BmLjIxE7969Ua9ePaSnp2PWrFm4c+cOBg8eDIVCgYiICMyePRuNGzdG48aNMXv2bFSvXh0hISEAAJVKhaFDh2LixIlwcHCAvb09IiMj0bx5c+nqCENiskBERPSMpaam4u2338bNmzdRu3ZttG3bFomJiXB3dwcATJo0CXl5eRg1ahQyMzPh5+eHvXv3ws7OTmpj3rx5MDc3R//+/ZGXl4eAgADExMTAzMzM4P1VCCGEwVs1sqLCYmN3gajSBVhMN3YXiCpdvJhZqe3n5eYbrC1rG0uDtVXVsLJARESmiw+SkoUTHImIiEgvVhaIiMhkKVhakIXJAhERmS7mCrJwGIKIiIj0YmWBiIhMFgsL8jBZICIi08VsQRYOQxAREZFerCwQEZEJY2lBDiYLRERkspgqyMNhCCIiItKLlQUiIjJdLC3IwmSBiIhMFnMFeTgMQURERHqxskBERKZLwdqCHKwsEBERkV5MFoiIiEgvDkMQEZHJ4iiEPKwsEBERkV5MFoiIiEgvDkMQEZHJUnAcQhZWFoiIiEgvJgtERESkl0IIIYzdCXq+abVaREVFYfLkyVAqlcbuDlGl4HlOpozJAj21O3fuQKVSITs7GzVq1DB2d4gqBc9zMmUchiAiIiK9mCwQERGRXkwWiIiISC8mC/TUlEolpk2bxklf9ELjeU6mjBMciYiISC9WFoiIiEgvJgtERESkF5MFIiIi0ovJAlWq6dOno1WrVsbuBlGl4nlOLzomCyYoLCwMCoUCCoUCFhYWaNCgASIjI5Gbm2uU/ly9ehW9e/eGjY0NHB0dMW7cOOTn5xulL/TiqGrn+fjx4+Hr6wulUsnEgp47fES1ierRowdWr16NgoIC/PLLL3j//feRm5uL6OjoUrEFBQWwsLColH4UFRWhV69eqF27No4cOYJbt25h8ODBEEJg4cKFlbJPMh1V5TwHACEEhgwZgmPHjuH06dOVth+iysDKgolSKpVQq9Vwc3NDSEgIBg0ahO3btwP4p6S6atUqNGjQAEqlEkIIZGdnY9iwYXByckKNGjXQuXNn/PbbbzrtzpkzB87OzrCzs8PQoUNx//59vf3Yu3cvfv/9d6xfvx6tW7dGly5d8PXXX2P58uW4c+dOZR0+mYiqcp4DwLfffovRo0ejQYMGlXGoRJWKyQIBAKytrVFQUCC9/vPPP7F582Zs2bIFycnJAIBevXpBo9Fg9+7dSEpKwksvvYSAgADcvn0bALB582ZMmzYNn3/+OU6ePAkXFxcsWbJE734TEhLg4+MDV1dXaV337t2h1WqRlJRk+AMlk2as85zoecdhCMLx48fx/fffIyAgQFqXn5+PdevWoXbt2gCAgwcP4sy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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gkf = GroupKFold(n_splits=5) \n", + "cv_histories = [] \n", + "cv_results = [] \n", + "fold_subjects = []\n", + "all_conf_matrices = []\n", + "\n", + "for fold, (train_idx, val_idx) in enumerate(gkf.split(X, y, groups)):\n", + " train_subjects = np.unique(groups[train_idx]) \n", + " val_subjects = np.unique(groups[val_idx]) \n", + " fold_subjects.append({\"Fold\": fold+1, \n", + " \"Train_Subjects\": train_subjects, \n", + " \"Val_Subjects\": val_subjects}) \n", + " \n", + " print(f\"\\n--- Fold {fold+1} ---\") \n", + " print(\"Train-Subjects:\", train_subjects) \n", + " print(\"Val-Subjects:\", val_subjects) \n", + "\n", + " #Split\n", + " X_train, X_val = X[train_idx], X[val_idx] \n", + " y_train, y_val = y[train_idx], y[val_idx] # Normalisierung pro Fold \n", + "\n", + " #Normalisierung pro Fold\n", + " scaler = StandardScaler() \n", + " X_train = scaler.fit_transform(X_train.reshape(len(X_train), -1)).reshape(X_train.shape) \n", + " X_val = scaler.transform(X_val.reshape(len(X_val), -1)).reshape(X_val.shape) \n", + "\n", + " # Plausibilitäts-Check \n", + " print(\"Train Mittelwerte (erste 5 Features):\", X_train.mean(axis=0)[:5]) \n", + " print(\"Train Std (erste 5 Features):\", X_train.std(axis=0)[:5]) \n", + " print(\"Val Mittelwerte (erste 5 Features):\", X_val.mean(axis=0)[:5]) \n", + " print(\"Val Std (erste 5 Features):\", X_val.std(axis=0)[:5]) \n", + "\n", + " # Modell \n", + " model = build_model(input_shape=(len(feature_columns),1), lr=1e-4) \n", + " model.summary() \n", + "\n", + " callbacks = [ \n", + " tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=10, restore_best_weights=True), \n", + " tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=5, min_lr=1e-6) \n", + " ] \n", + "\n", + " history = model.fit( \n", + " X_train, y_train, \n", + " validation_data=(X_val, y_val), \n", + " epochs=100, \n", + " batch_size=16, \n", + " callbacks=callbacks, \n", + " verbose=0 \n", + " ) \n", + "\n", + " cv_histories.append(history.history) \n", + " scores = model.evaluate(X_val, y_val, verbose=0) \n", + " cv_results.append(scores) \n", + " print(f\"Fold {fold+1} - Val Loss: {scores[0]:.4f}, Val Acc: {scores[1]:.4f}, Val AUC: {scores[2]:.4f}\")\n", + "\n", + "\n", + " #Konfusionsmatrix \n", + " y_pred = (model.predict(X_val) > 0.5).astype(int) \n", + " cm = confusion_matrix(y_val, y_pred) \n", + " all_conf_matrices.append(cm) \n", + "\n", + " #Heatmap \n", + " plt.figure(figsize=(6,5)) \n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n", + " xticklabels=[\"Pred 0\", \"Pred 1\"], \n", + " yticklabels=[\"True 0\", \"True 1\"]) \n", + " plt.title(f\"Konfusionsmatrix – Fold {fold+1}\") \n", + " plt.xlabel(\"Predicted\") \n", + " plt.ylabel(\"True\") \n", + " plt.show()\n", + " \n", + " print(f\"Konfusionsmatrix Fold {fold+1}:\\n{cm}\\n\") \n", + " \n", + "# Aggregierte Matrix \n", + "agg_cm = sum(all_conf_matrices) \n", + "print(\"Aggregierte Konfusionsmatrix über alle Folds:\") \n", + "print(agg_cm)\n", + "plt.figure(figsize=(6,5))\n", + "sns.heatmap(agg_cm, annot=True, fmt=\"d\", cmap=\"Purples\",\n", + " xticklabels=[\"Pred 0\", \"Pred 1\"],\n", + " yticklabels=[\"True 0\", \"True 1\"])\n", + "plt.title(\"Aggregierte Konfusionsmatrix – alle Folds\")\n", + "plt.xlabel(\"Predicted\")\n", + "plt.ylabel(\"True\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "d10b7e78", + "metadata": {}, + "source": [ + "Results" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "9aeba7f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Cross-Validation Ergebnisse ===\n", + "Durchschnittlicher Val-Loss: 0.2132\n", + "Durchschnittliche Val-Accuracy: 0.9426\n", + "Durchschnittliche Val-AUC: 0.9837\n", + "\n", + "=== Ergebnis-Tabelle ===\n", + " Fold Val Loss Val Accuracy Val AUC\n", + "0 1 0.204724 0.945844 0.987373\n", + "1 2 0.255400 0.918546 0.972358\n", + "2 3 0.216190 0.942536 0.986086\n", + "3 4 0.139484 0.969735 0.995214\n", + "4 5 0.250006 0.936290 0.977334\n", + "5 Ø 0.213161 0.942590 0.983673\n", + "Ergebnisse gespeichert als 'cnn_crossVal_Filter.csv'\n" + ] + } + ], + "source": [ + "#results\n", + "cv_results = np.array(cv_results) \n", + "print(\"\\n=== Cross-Validation Ergebnisse ===\") \n", + "print(f\"Durchschnittlicher Val-Loss: {cv_results[:,0].mean():.4f}\") \n", + "print(f\"Durchschnittliche Val-Accuracy: {cv_results[:,1].mean():.4f}\") \n", + "print(f\"Durchschnittliche Val-AUC: {cv_results[:,2].mean():.4f}\")\n", + "\n", + "#Ergebnis-Tabelle erstellen\n", + "results_table = pd.DataFrame({ \n", + " \"Fold\": np.arange(1, len(cv_results)+1), \n", + " \"Val Loss\": cv_results[:,0], \n", + " \"Val Accuracy\": cv_results[:,1], \n", + " \"Val AUC\": cv_results[:,2] }) \n", + "\n", + "# Durchschnittszeile hinzufügen \n", + "avg_row = pd.DataFrame({ \n", + " \"Fold\": [\"Ø\"], \n", + " \"Val Loss\": [cv_results[:,0].mean()], \n", + " \"Val Accuracy\": [cv_results[:,1].mean()], \n", + " \"Val AUC\": [cv_results[:,2].mean()] \n", + "}) \n", + "\n", + "results_table = pd.concat([results_table, avg_row], ignore_index=True) \n", + "\n", + "print(\"\\n=== Ergebnis-Tabelle ===\") \n", + "print(results_table) \n", + "\n", + "#Tabelle speichern \n", + "results_table.to_csv(\"cnn_crossVal_Filter.csv\", index=False) \n", + "print(\"Ergebnisse gespeichert als 'cnn_crossVal_Filter.csv'\")" + ] + }, + { + "cell_type": "markdown", + "id": "d2f6de79", + "metadata": {}, + "source": [ + "Finales Modell auf alle Daten trainieren" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "6e403e5f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_6\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv1d_12 (Conv1D)              │ (None, 33, 32)         │           128 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_12          │ (None, 33, 32)         │           128 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling1d_6 (MaxPooling1D)  │ (None, 16, 32)         │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv1d_13 (Conv1D)              │ (None, 14, 64)         │         6,208 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_13          │ (None, 14, 64)         │           256 │\n",
+       "│ (BatchNormalization)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ global_average_pooling1d_6      │ (None, 64)             │             0 │\n",
+       "│ (GlobalAveragePooling1D)        │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_12 (Dense)                │ (None, 32)             │         2,080 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_6 (Dropout)             │ (None, 32)             │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_13 (Dense)                │ (None, 1)              │            33 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ conv1d_12 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ max_pooling1d_6 (\u001b[38;5;33mMaxPooling1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ conv1d_13 (\u001b[38;5;33mConv1D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,208\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ batch_normalization_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n", + "│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ global_average_pooling1d_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m) │ │ │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_12 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m2,080\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense_13 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m33\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 8,833 (34.50 KB)\n",
+       "
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 Trainable params: 8,641 (33.75 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m8,641\u001b[0m (33.75 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 192 (768.00 B)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.6010 - auc: 0.6312 - loss: 0.7564\n", + "Epoch 2/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7887 - auc: 0.8571 - loss: 0.6053\n", + "Epoch 3/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8441 - auc: 0.9050 - loss: 0.5080\n", + "Epoch 4/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8658 - auc: 0.9244 - loss: 0.4486\n", + "Epoch 5/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8762 - auc: 0.9365 - loss: 0.4105\n", + "Epoch 6/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8945 - auc: 0.9477 - loss: 0.3730\n", + "Epoch 7/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8976 - auc: 0.9526 - loss: 0.3565\n", + "Epoch 8/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9087 - auc: 0.9591 - loss: 0.3334\n", + "Epoch 9/150\n", + "\u001b[1m499/499\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9168 - auc: 0.9665 - loss: 0.3108\n", + "Epoch 10/150\n", + "\u001b[1m499/499\u001b[0m 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\u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9908 - auc: 0.9996 - loss: 0.0642\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaler_final = StandardScaler() \n", + "X_scaled = scaler_final.fit_transform(X.reshape(len(X), -1)).reshape(X.shape) \n", + "\n", + "final_model = build_model(input_shape=(len(feature_columns),1), lr=1e-4) \n", + "final_model.summary() \n", + "\n", + "final_model.fit( \n", + " X_scaled, y, \n", + " epochs=150, \n", + " batch_size=16, \n", + " verbose=1 \n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5679d289", + "metadata": {}, + "source": [ + "Evaluation auf alle Daten" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "5a09f80c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n", + "Final Loss: 0.0531\n", + "Final Accuracy: 0.9944\n", + "Final AUC: 0.9996\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision: 0.9959384352287303\n", + "Recall: 0.9944503735325507\n", + "F1‑Score: 0.9951938481256007\n", + "Balanced Accuracy: 0.9943350133558707\n" + ] + } + ], + "source": [ + "# Vorhersagen\n", + "y_prob_final = final_model.predict(X_scaled).flatten()\n", + "y_pred_final = (y_prob_final > 0.5).astype(int)\n", + "\n", + "# Metriken\n", + "loss, acc, auc_val = final_model.evaluate(X_scaled, y, verbose=0)\n", + "print(f\"Final Loss: {loss:.4f}\")\n", + "print(f\"Final Accuracy: {acc:.4f}\")\n", + "print(f\"Final AUC: {auc_val:.4f}\")\n", + "\n", + "# Konfusionsmatrix\n", + "cm_final = confusion_matrix(y, y_pred_final)\n", + "\n", + "plt.figure(figsize=(6,5))\n", + "sns.heatmap(cm_final, annot=True, fmt=\"d\", cmap=\"Oranges\",\n", + " xticklabels=[\"Pred 0\", \"Pred 1\"],\n", + " yticklabels=[\"True 0\", \"True 1\"])\n", + "plt.title(\"Konfusionsmatrix – Finales Modell (alle Daten)\")\n", + "plt.xlabel(\"Predicted\")\n", + "plt.ylabel(\"True\")\n", + "plt.show()\n", + "\n", + "\n", + "print(\"Precision:\", precision_score(y, y_pred_final)) \n", + "print(\"Recall:\", recall_score(y, y_pred_final)) \n", + "print(\"F1‑Score:\", f1_score(y, y_pred_final)) \n", + "print(\"Balanced Accuracy:\", balanced_accuracy_score(y, y_pred_final))" + ] + }, + { + "cell_type": "markdown", + "id": "7c7f9cc4", + "metadata": {}, + "source": [ + "Speichern des Modells" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "2d3af5be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finales Modell und Scaler gespeichert als 'cnn_crossVal_EarlyFusion_Filter.keras' und 'scaler_crossVal_EarlyFusion_V2.joblib'\n" + ] + } + ], + "source": [ + "final_model.save(\"cnn_crossVal_EarlyFusion_Filter.keras\") \n", + "joblib.dump(scaler_final, \"scaler_crossVal_EarlyFusion_Filter.joblib\") \n", + "\n", + "print(\"Finales Modell und Scaler gespeichert als 'cnn_crossVal_EarlyFusion_Filter.keras' und 'scaler_crossVal_EarlyFusion_V2.joblib'\")" + ] + }, + { + "cell_type": "markdown", + "id": "c11891e0", + "metadata": {}, + "source": [ + "Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "9f6a8584", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plots\n", + "def plot_cv_histories(cv_histories, metric): \n", + " plt.figure(figsize=(10,6)) \n", + " \n", + " for i, hist in enumerate(cv_histories): \n", + " plt.plot(hist[metric], label=f\"Fold {i+1} Train\", alpha=0.7) \n", + " plt.plot(hist[f\"val_{metric}\"], label=f\"Fold {i+1} Val\", linestyle=\"--\", alpha=0.7) \n", + " plt.xlabel(\"Epochs\") \n", + " plt.ylabel(metric.capitalize()) \n", + " plt.title(f\"Cross-Validation {metric.capitalize()} Verläufe\") \n", + " plt.legend() \n", + " plt.grid(True) \n", + " plt.show()\n", + " \n", + "plot_cv_histories(cv_histories, \"loss\") \n", + "plot_cv_histories(cv_histories, \"accuracy\") \n", + "plot_cv_histories(cv_histories, \"auc\")" + ] + } + ], + "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 +}