422 lines
17 KiB
Plaintext
422 lines
17 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Laden der Rohdaten"
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle\n",
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"\n",
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"# Laden der 'kirp' Liste aus der Pickle-Datei\n",
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"with open('rick.pickle', 'rb') as f:\n",
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" data_frame = pickle.load(f)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# PCA Klasse zu Reduktion der Dimensionen"
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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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"from torch.utils.data import Dataset\n",
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"import torch\n",
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"import pandas as pd\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.model_selection import train_test_split\n",
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"from typing import List, Tuple\n",
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"\n",
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"\n",
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"class GenomeDataset(Dataset):\n",
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" \"\"\"\n",
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" Eine benutzerdefinierte Dataset-Klasse, die für die Handhabung von Genomdaten konzipiert ist.\n",
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" Diese Klasse wendet eine Principal Component Analysis (PCA) auf die Frequenzen der Genome an\n",
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" und teilt den Datensatz in Trainings- und Validierungsteile auf.\n",
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"\n",
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" Attributes:\n",
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" dataframe (pd.DataFrame): Ein Pandas DataFrame, der die initialen Daten enthält.\n",
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" train_df (pd.DataFrame): Ein DataFrame, der den Trainingsdatensatz nach der Anwendung von PCA und der Aufteilung enthält.\n",
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" val_df (pd.DataFrame): Ein DataFrame, der den Validierungsdatensatz nach der Anwendung von PCA und der Aufteilung enthält.\n",
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"\n",
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" Methods:\n",
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" __init__(self, dataframe, n_pca_components=1034, train_size=0.8, split_random_state=42):\n",
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" Konstruktor für die GenomeDataset Klasse.\n",
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" _do_PCA(self, frequencies, n_components=1034):\n",
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" Wendet PCA auf die gegebenen Frequenzen an.\n",
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" _split_dataset(self, train_size=0.8, random_state=42):\n",
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" Teilt den DataFrame in Trainings- und Validierungsdatensätze auf.\n",
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" __getitem__(self, index):\n",
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" Gibt ein Tupel aus transformierten Frequenzen und dem zugehörigen Krebstyp für einen gegebenen Index zurück.\n",
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" __len__(self):\n",
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" Gibt die Gesamtlänge der kombinierten Trainings- und Validierungsdatensätze zurück.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, dataframe: pd.DataFrame, n_pca_components: int = 1034, train_size: float = 0.8, split_random_state: int = 42):\n",
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" \"\"\"\n",
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" Konstruktor für die GenomeDataset Klasse.\n",
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"\n",
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" Parameters:\n",
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" dataframe (pd.DataFrame): Der DataFrame, der die Genome Frequenzen und Krebsarten enthält.\n",
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" n_pca_components (int): Die Anzahl der PCA-Komponenten, auf die reduziert werden soll. Standardwert ist 1034.\n",
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" train_size (float): Der Anteil der Daten, der als Trainingsdaten verwendet werden soll. Standardwert ist 0.8.\n",
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" split_random_state (int): Der Zufalls-Saatwert, der für die Aufteilung des Datensatzes verwendet wird. Standardwert ist 42.\n",
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" \"\"\"\n",
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" self.dataframe = dataframe\n",
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"\n",
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" # Umwandlung der Krebsarten in numerische Werte\n",
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" self.label_encoder = LabelEncoder()\n",
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" self.dataframe['encoded_cancer_type'] = self.label_encoder.fit_transform(dataframe['cancer_type'])\n",
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"\n",
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" # Anwenden der PCA auf die Frequenzen\n",
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" self.dataframe['pca_frequencies'] = self._do_PCA(self.dataframe['genome_frequencies'].tolist(), n_pca_components)\n",
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"\n",
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" # Teilen des DataFrame in Trainings- und Validierungsdatensatz\n",
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" self._split_dataset(train_size=train_size, random_state=split_random_state)\n",
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"\n",
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" def transform_datapoint(self, datapoint: List[float]) -> List[float]:\n",
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" \"\"\"\n",
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" Transformiert einen einzelnen Datenpunkt durch Standardisierung und Anwendung der PCA.\n",
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"\n",
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" Diese Methode nimmt einen rohen Datenpunkt (eine Liste von Frequenzen), standardisiert ihn mit dem \n",
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" zuvor angepassten Scaler und wendet dann die PCA-Transformation an, um ihn in den reduzierten \n",
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" Feature-Raum zu überführen, der für das Training des Modells verwendet wurde.\n",
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"\n",
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" Parameters:\n",
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" datapoint (List[float]): Ein roher Datenpunkt, bestehend aus einer Liste von Frequenzen.\n",
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"\n",
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" Returns:\n",
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" List[float]: Der transformierte Datenpunkt, nach Anwendung der Standardisierung und der PCA.\n",
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" \"\"\"\n",
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" # Standardisierung des Datenpunkts\n",
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" scaled_data_point = self.scaler.transform([datapoint])\n",
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"\n",
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" # PCA-Transformation des standardisierten Datenpunkts\n",
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" pca_transformed_point = self.pca.transform(scaled_data_point)\n",
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"\n",
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" return pca_transformed_point.tolist()\n",
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"\n",
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" def _do_PCA(self, frequencies: List[List[float]], n_components: int = 1034) -> List[List[float]]:\n",
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" \"\"\"\n",
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" Wendet PCA auf die gegebenen Frequenzen an.\n",
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"\n",
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" Parameters:\n",
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" frequencies (List[List[float]]): Die Liste der Frequenzen, auf die die PCA angewendet werden soll.\n",
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" n_components (int): Die Anzahl der Komponenten für die PCA. Standardwert ist 1034.\n",
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"\n",
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" Returns:\n",
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" List[List[float]]: Eine Liste von Listen, die die transformierten Frequenzen nach der PCA darstellt.\n",
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" \"\"\"\n",
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"\n",
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" # Standardisieren der Frequenzen\n",
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" self.scaler = StandardScaler()\n",
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" scaled_frequencies = self.scaler.fit_transform(frequencies)\n",
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"\n",
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" # PCA-Instanz erstellen und auf die gewünschte Anzahl von Komponenten reduzieren\n",
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" self.pca = PCA(n_components=n_components)\n",
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"\n",
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" # PCA auf die Frequenzen anwenden\n",
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" pca_result = self.pca.fit_transform(scaled_frequencies)\n",
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"\n",
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" return pca_result.tolist()\n",
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"\n",
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" def _split_dataset(self, train_size: float = 0.8, random_state: int = 42):\n",
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" \"\"\"\n",
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" Teilt den DataFrame in Trainings- und Validierungsdatensätze auf.\n",
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"\n",
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" Parameters:\n",
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" train_size (float): Der Anteil der Daten, der als Trainingsdaten verwendet werden soll.\n",
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" random_state (int): Der Zufalls-Saatwert, der für die Aufteilung des Datensatzes verwendet wird.\n",
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" \"\"\"\n",
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"\n",
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" class SplittedDataset(Dataset):\n",
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" def __init__(self, dataframe):\n",
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" self.dataframe = dataframe\n",
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"\n",
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" # Umwandlung der Genome Frequenzen in Tensoren\n",
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" self.genome_frequencies = torch.tensor(dataframe['pca_frequencies'].tolist(), dtype=torch.float32)\n",
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"\n",
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" # Umwandlung der Krebsarten in numerische Werte\n",
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" self.label_encoder = LabelEncoder()\n",
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" self.cancer_types = torch.tensor(dataframe['encoded_cancer_type'].tolist(), dtype=torch.long)\n",
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"\n",
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" def __getitem__(self, index):\n",
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" # Rückgabe eines Tupels aus Genome Frequenzen und dem entsprechenden Krebstyp\n",
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" return self.genome_frequencies[index], self.cancer_types[index]\n",
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"\n",
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" def __len__(self):\n",
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" return len(self.dataframe)\n",
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"\n",
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" # Teilen des DataFrame in Trainings- und Validierungsdatensatz\n",
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" train_df, val_df = train_test_split(self.dataframe, train_size=train_size, random_state=random_state)\n",
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" self.train_df = SplittedDataset(train_df)\n",
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" self.val_df = SplittedDataset(val_df)\n",
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"\n",
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"\n",
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" def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]:\n",
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" \"\"\"\n",
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" Gibt ein Tupel aus transformierten Frequenzen und dem entsprechenden Krebstyp für einen gegebenen Index zurück.\n",
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"\n",
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" Parameters:\n",
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" index (int): Der Index des zu abrufenden Datenelements.\n",
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"\n",
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" Returns:\n",
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" Tuple[torch.Tensor, int]: Ein Tupel, bestehend aus einem Tensor der transformierten Frequenzen und dem zugehörigen Krebstyp.\n",
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" \"\"\"\n",
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"\n",
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" print(self.train_df.shape)\n",
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" print(self.val_df.shape)\n",
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" \n",
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" if index < len(self.train_df):\n",
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" row = self.train_df.iloc[index]\n",
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" else:\n",
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" row = self.val_df.iloc[len(self.train_df) - index]\n",
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"\n",
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" pca_frequencies_tensor = torch.tensor(row['pca_frequencies'], dtype=torch.float32)\n",
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" cancer_type = row['encoded_cancer_type']\n",
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"\n",
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" return pca_frequencies_tensor, cancer_type\n",
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"\n",
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" def __len__(self) -> int:\n",
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" \"\"\"\n",
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" Gibt die Gesamtlänge der kombinierten Trainings- und Validierungsdatensätze zurück.\n",
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"\n",
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" Returns:\n",
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" int: Die Länge der kombinierten Datensätze.\n",
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" \"\"\"\n",
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" \n",
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" return len(self.train_df) + len(self.val_df)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Definition des neuronalen Netzes"
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"import torch.nn.functional as F\n",
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"\n",
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"class CancerClassifierNN(nn.Module):\n",
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" \"\"\"\n",
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" Eine benutzerdefinierte neuronale Netzwerkklassifikator-Klasse für die Krebsklassifikation.\n",
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"\n",
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" Diese Klasse definiert ein mehrschichtiges Perzeptron (MLP), das für die Klassifizierung von Krebsarten\n",
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" anhand genetischer Frequenzdaten verwendet wird.\n",
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"\n",
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" Attributes:\n",
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" fc1 (nn.Linear): Die erste lineare Schicht des Netzwerks.\n",
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" fc2 (nn.Linear): Die zweite lineare Schicht des Netzwerks.\n",
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" fc3 (nn.Linear): Die dritte lineare Schicht des Netzwerks.\n",
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" fc4 (nn.Linear): Die Ausgabeschicht des Netzwerks.\n",
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" dropout (nn.Dropout): Ein Dropout-Layer zur Vermeidung von Overfitting.\n",
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"\n",
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" Methods:\n",
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" __init__(self, input_size: int, num_classes: int):\n",
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" Konstruktor für die CancerClassifierNN Klasse.\n",
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" forward(self, x: torch.Tensor) -> torch.Tensor:\n",
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" Definiert den Vorwärtsdurchlauf des Netzwerks.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, input_size: int, num_classes: int):\n",
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" \"\"\"\n",
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" Konstruktor für die CancerClassifierNN Klasse.\n",
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"\n",
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" Parameters:\n",
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" input_size (int): Die Größe des Input-Features.\n",
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" num_classes (int): Die Anzahl der Zielklassen.\n",
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" \"\"\"\n",
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" super(CancerClassifierNN, self).__init__()\n",
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" # Definieren der Schichten\n",
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" self.fc1 = nn.Linear(input_size, 1024) # Eingabeschicht\n",
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" self.fc2 = nn.Linear(1024, 512) # Versteckte Schicht\n",
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" self.fc3 = nn.Linear(512, 256) # Weitere versteckte Schicht\n",
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" self.fc4 = nn.Linear(256, num_classes) # Ausgabeschicht\n",
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" self.dropout = nn.Dropout(p=0.5) # Dropout\n",
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"\n",
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" def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
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" \"\"\"\n",
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" Definiert den Vorwärtsdurchlauf des Netzwerks.\n",
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"\n",
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" Parameters:\n",
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" x (torch.Tensor): Der Input-Tensor für das Netzwerk.\n",
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"\n",
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" Returns:\n",
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" torch.Tensor: Der Output-Tensor nach dem Durchlauf durch das Netzwerk.\n",
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" \"\"\"\n",
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" x = F.relu(self.fc1(x))\n",
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" x = self.dropout(x)\n",
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" x = F.relu(self.fc2(x))\n",
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" x = self.dropout(x)\n",
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" x = F.relu(self.fc3(x))\n",
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" x = torch.sigmoid(self.fc4(x)) # Oder F.log_softmax(x, dim=1) für Mehrklassenklassifikation\n",
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" return x"
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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": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"from torch.utils.data import DataLoader\n",
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"\n",
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"# Erstellen der Dataframe Klasse\n",
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"genome_dataset = GenomeDataset(data_frame)\n",
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"train_dataset = genome_dataset.train_df\n",
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"valid_dataset = genome_dataset.val_df\n",
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"\n",
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"# Annahme: input_size ist die Länge Ihrer Genome-Frequenzen und num_classes ist die Anzahl der Krebsarten\n",
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"model = CancerClassifierNN(input_size=1034, num_classes=3)\n",
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"\n",
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"# Daten-Loader\n",
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"train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n",
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"valid_loader = DataLoader(dataset=valid_dataset, batch_size=64, shuffle=False)"
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]
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},
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||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import torch.optim as optim\n",
|
||
|
|
"\n",
|
||
|
|
"# Verlustfunktion\n",
|
||
|
|
"criterion = nn.CrossEntropyLoss()\n",
|
||
|
|
"\n",
|
||
|
|
"# Optimierer\n",
|
||
|
|
"optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
|
||
|
|
"\n",
|
||
|
|
"# Anzahl der Epochen\n",
|
||
|
|
"num_epochs = 10\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Epoch [1/10], Trainingsverlust: 0.8623, Validierungsverlust: 0.7387\n",
|
||
|
|
"Epoch [2/10], Trainingsverlust: 0.7593, Validierungsverlust: 0.7306\n",
|
||
|
|
"Epoch [3/10], Trainingsverlust: 0.7496, Validierungsverlust: 0.7388\n",
|
||
|
|
"Epoch [4/10], Trainingsverlust: 0.7466, Validierungsverlust: 0.7342\n",
|
||
|
|
"Epoch [5/10], Trainingsverlust: 0.7456, Validierungsverlust: 0.7340\n",
|
||
|
|
"Epoch [6/10], Trainingsverlust: 0.7454, Validierungsverlust: 0.7265\n",
|
||
|
|
"Epoch [7/10], Trainingsverlust: 0.7448, Validierungsverlust: 0.7252\n",
|
||
|
|
"Epoch [8/10], Trainingsverlust: 0.7469, Validierungsverlust: 0.7239\n",
|
||
|
|
"Epoch [9/10], Trainingsverlust: 0.7453, Validierungsverlust: 0.7242\n",
|
||
|
|
"Epoch [10/10], Trainingsverlust: 0.7449, Validierungsverlust: 0.7252\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Listen, um Verluste zu speichern\n",
|
||
|
|
"train_losses = []\n",
|
||
|
|
"valid_losses = []\n",
|
||
|
|
"\n",
|
||
|
|
"for epoch in range(num_epochs):\n",
|
||
|
|
" model.train()\n",
|
||
|
|
" train_loss = 0.0\n",
|
||
|
|
" for i, (inputs, labels) in enumerate(train_loader):\n",
|
||
|
|
" optimizer.zero_grad()\n",
|
||
|
|
" outputs = model(inputs)\n",
|
||
|
|
" loss = criterion(outputs, labels)\n",
|
||
|
|
" loss.backward()\n",
|
||
|
|
" optimizer.step()\n",
|
||
|
|
" train_loss += loss.item()\n",
|
||
|
|
"\n",
|
||
|
|
" # Durchschnittlicher Trainingsverlust\n",
|
||
|
|
" train_loss /= len(train_loader)\n",
|
||
|
|
" train_losses.append(train_loss)\n",
|
||
|
|
"\n",
|
||
|
|
" # Validierungsverlust\n",
|
||
|
|
" model.eval()\n",
|
||
|
|
" valid_loss = 0.0\n",
|
||
|
|
" with torch.no_grad():\n",
|
||
|
|
" for inputs, labels in valid_loader:\n",
|
||
|
|
" outputs = model(inputs)\n",
|
||
|
|
" loss = criterion(outputs, labels)\n",
|
||
|
|
" valid_loss += loss.item()\n",
|
||
|
|
"\n",
|
||
|
|
" # Durchschnittlicher Validierungsverlust\n",
|
||
|
|
" valid_loss /= len(valid_loader)\n",
|
||
|
|
" valid_losses.append(valid_loss)\n",
|
||
|
|
"\n",
|
||
|
|
" print(f'Epoch [{epoch+1}/{num_epochs}], Trainingsverlust: {train_loss:.4f}, Validierungsverlust: {valid_loss:.4f}')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "rl",
|
||
|
|
"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": "3.8.18"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|