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f5e03cc8d0
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| c816315381 |
@ -7,13 +7,331 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import xgboost as xgb"
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"import pandas as pd\n",
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"from pathlib import Path\n",
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"from sklearn.preprocessing import StandardScaler, MinMaxScaler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "13ad96f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = Path(r\"~/Fahrsimulator_MSY2526_AI/model_training/xgboost/output_windowed.parquet\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "95e1a351",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_parquet(path=data_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "68afd83e",
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"metadata": {},
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"outputs": [],
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"source": [
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"subjects = df['subjectID'].unique()\n",
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"print(subjects)\n",
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"print(len(subjects))\n",
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"print(len(subjects)*0.66)\n",
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"print(len(subjects)*0.33)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52dfd885",
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"metadata": {},
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"outputs": [],
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"source": [
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"low_all = df[\n",
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" ((df[\"PHASE\"] == \"baseline\") |\n",
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" ((df[\"STUDY\"] == \"n-back\") & (df[\"PHASE\"] != \"baseline\") & (df[\"LEVEL\"].isin([1, 4]))))\n",
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"]\n",
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"print(f\"low all: {low_all.shape}\")\n",
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"\n",
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"high_nback = df[\n",
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" (df[\"STUDY\"]==\"n-back\") &\n",
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" (df[\"LEVEL\"].isin([2, 3, 5, 6])) &\n",
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" (df[\"PHASE\"].isin([\"train\", \"test\"]))\n",
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"]\n",
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"print(f\"high n-back: {high_nback.shape}\")\n",
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"\n",
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"high_kdrive = df[\n",
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" (df[\"STUDY\"] == \"k-drive\") & (df[\"PHASE\"] != \"baseline\")\n",
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"]\n",
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"print(f\"high k-drive: {high_kdrive.shape}\")\n",
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"\n",
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"high_all = pd.concat([high_nback, high_kdrive])\n",
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"print(f\"high all: {high_all.shape}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8fba6edf",
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"metadata": {},
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"outputs": [],
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"source": [
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"def fit_normalizer(train_data, au_columns, method='standard', scope='global'):\n",
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" if method == 'standard':\n",
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" Scaler = StandardScaler\n",
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" elif method == 'minmax':\n",
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" Scaler = MinMaxScaler\n",
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" else:\n",
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" raise ValueError(\"method must be 'standard' or 'minmax'\")\n",
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" \n",
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" scalers = {}\n",
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" \n",
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" if scope == 'subject':\n",
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" for subject in train_data['subjectID'].unique():\n",
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" subject_mask = train_data['subjectID'] == subject\n",
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" scaler = Scaler()\n",
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" scaler.fit(train_data.loc[subject_mask, au_columns])\n",
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" scalers[subject] = scaler\n",
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"\n",
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" elif scope == 'global':\n",
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" scaler = Scaler()\n",
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" scaler.fit(train_data[au_columns])\n",
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" scalers['global'] = scaler\n",
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"\n",
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" else:\n",
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" raise ValueError(\"scope must be 'subject' or 'global'\")\n",
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" \n",
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" return {'scalers': scalers, 'method': method, 'scope': scope}\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "24e3a77b",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install xgboost"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8e7fa0fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, classification_report, confusion_matrix\n",
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"import xgboost as xgb\n",
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"import joblib\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "325ef71c",
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"metadata": {},
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"outputs": [],
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"source": [
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"low = low_all.copy()\n",
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"high = high_all.copy()\n",
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"\n",
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"low[\"label\"] = 0\n",
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"high[\"label\"] = 1\n",
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"\n",
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"data = pd.concat([low, high], ignore_index=True)\n",
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"data = data.drop_duplicates()\n",
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"\n",
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"print(\"Label distribution:\")\n",
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"print(data[\"label\"].value_counts())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "67d70e84",
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"metadata": {},
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"outputs": [],
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"source": [
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"au_columns = [col for col in data.columns if col.lower().startswith(\"au\")]\n",
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"print(\"Gefundene AU-Spalten:\", au_columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "960bb8c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"subjects = np.random.permutation(data[\"subjectID\"].unique())\n",
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"\n",
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"n = len(subjects)\n",
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"n_train = int(n * 0.66)\n",
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"\n",
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"train_subjects = subjects[:n_train]\n",
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"test_subjects = subjects[n_train:]\n",
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"train_subs, val_subs = train_test_split(train_subjects, test_size=0.2, random_state=42)\n",
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"\n",
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"train_df = data[data.subjectID.isin(train_subs)]\n",
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"val_df = data[data.subjectID.isin(val_subs)]\n",
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"test_df = data[data.subjectID.isin(test_subjects)]\n",
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"\n",
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"print(train_df.shape, val_df.shape, test_df.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "802a45c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"def apply_normalizer(df_to_transform, normalizer_dict, au_columns):\n",
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" scalers = normalizer_dict[\"scalers\"]\n",
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" scope = normalizer_dict[\"scope\"]\n",
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" df_out = df_to_transform.copy()\n",
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"\n",
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" if scope == \"global\":\n",
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" scaler = scalers[\"global\"]\n",
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" df_out[au_columns] = scaler.transform(df_out[au_columns])\n",
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"\n",
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" elif scope == \"subject\":\n",
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" for subj, subdf in df_out.groupby(\"subjectID\"):\n",
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" if subj in scalers:\n",
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" df_out.loc[subdf.index, au_columns] = scalers[subj].transform(subdf[au_columns])\n",
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" elif \"global\" in scalers:\n",
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" df_out.loc[subdf.index, au_columns] = scalers[\"global\"].transform(subdf[au_columns])\n",
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"\n",
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" return df_out"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "289f6b89",
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"metadata": {},
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"outputs": [],
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"source": [
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"normalizer = fit_normalizer(train_df, au_columns, method=\"standard\", scope=\"global\")\n",
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"\n",
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"train_scaled = apply_normalizer(train_df, normalizer, au_columns)\n",
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"val_scaled = apply_normalizer(val_df, normalizer, au_columns)\n",
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"test_scaled = apply_normalizer(test_df, normalizer, au_columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5df30e8d",
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, y_train = train_scaled[au_columns].values, train_scaled[\"label\"].values\n",
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"X_val, y_val = val_scaled[au_columns].values, val_scaled[\"label\"].values\n",
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"X_test, y_test = test_scaled[au_columns].values, test_scaled[\"label\"].values"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6fb7c86a",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = xgb.XGBClassifier(\n",
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" objective=\"binary:logistic\",\n",
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" eval_metric=\"auc\",\n",
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" learning_rate=0.05,\n",
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" max_depth=6,\n",
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" n_estimators=500,\n",
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" subsample=0.8,\n",
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" colsample_bytree=0.8,\n",
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" random_state=42\n",
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")\n",
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"\n",
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"model.fit(\n",
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" X_train, y_train,\n",
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" eval_set=[(X_val, y_val)],\n",
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" #early_stopping_rounds=30,\n",
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" verbose=True\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "09a8cd21",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_auc_score, classification_report, ConfusionMatrixDisplay\n",
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"\n",
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"def evaluate(model, X, y, title=\"Evaluation\"):\n",
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" # Vorhersagen\n",
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" preds_proba = model.predict_proba(X)[:, 1]\n",
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" preds = (preds_proba > 0.5).astype(int)\n",
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"\n",
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" # Metriken ausgeben\n",
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" print(\"Accuracy:\", accuracy_score(y, preds))\n",
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" print(\"F1:\", f1_score(y, preds))\n",
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" print(\"AUC:\", roc_auc_score(y, preds))\n",
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" print(\"Confusion:\\n\", confusion_matrix(y, preds))\n",
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" print(classification_report(y, preds))\n",
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"\n",
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" # Confusion Matrix plotten\n",
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" def plot_confusion_matrix(true_labels, predictions, label_names):\n",
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" for normalize in [None, 'true']:\n",
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" cm = confusion_matrix(true_labels, predictions, normalize=normalize)\n",
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" cm_disp = ConfusionMatrixDisplay(cm, display_labels=label_names)\n",
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" cm_disp.plot(cmap=\"Blues\")\n",
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" #cm = confusion_matrix(y, preds)\n",
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" plot_confusion_matrix(y,preds, label_names=['Low','High'])\n",
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" # plt.figure(figsize=(5,4))\n",
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" # sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False,\n",
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" # xticklabels=[\"Predicted low\", \"Predicted high\"],\n",
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" # yticklabels=[\"Actual low\", \"Actual high\"])\n",
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" # plt.title(f\"Confusion Matrix - {title}\")\n",
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" # plt.ylabel(\"True label\")\n",
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" # plt.xlabel(\"Predicted label\")\n",
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" # plt.show()\n",
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"\n",
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"# Aufrufen für Train/Val/Test\n",
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"print(\"TRAIN:\")\n",
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"evaluate(model, X_train, y_train, title=\"Train\")\n",
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"\n",
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"print(\"VAL:\")\n",
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"evaluate(model, X_val, y_val, title=\"Validation\")\n",
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"\n",
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"print(\"TEST:\")\n",
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"evaluate(model, X_test, y_test, title=\"Test\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c43b0c80",
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"metadata": {},
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"outputs": [],
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"source": [
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"joblib.dump(model, \"xgb_model.joblib\")\n",
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"joblib.dump(normalizer, \"normalizer.joblib\")\n",
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"print(\"Model gespeichert.\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "project_fahrsimulator",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -27,7 +345,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.19"
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"version": "3.12.10"
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}
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},
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"nbformat": 4,
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