pickel-cancer-rick/project-cancer-classification-pca-nn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Laden der Rohdaten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Laden der 'kirp' Liste aus der Pickle-Datei\n",
    "with open('rick.pickle', 'rb') as f:\n",
    "    data_frame = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aktiviere Cuda Support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "device = \"cpu\"\n",
    "if torch.cuda.is_available():\n",
    "    print(\"CUDA is available on your system.\")\n",
    "    device = \"cuda\"\n",
    "else:\n",
    "    print(\"CUDA is not available on your system.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA Klasse zu Reduktion der Dimensionen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "import torch\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from typing import List, Tuple, Dict\n",
    "\n",
    "\n",
    "class GenomeDataset(Dataset):\n",
    "    \"\"\"\n",
    "    Eine benutzerdefinierte Dataset-Klasse, die für die Handhabung von Genomdaten konzipiert ist.\n",
    "    Diese Klasse wendet eine Principal Component Analysis (PCA) auf die Frequenzen der Genome an\n",
    "    und teilt den Datensatz in Trainings- und Validierungsteile auf.\n",
    "\n",
    "    Attributes:\n",
    "        dataframe (pd.DataFrame): Ein Pandas DataFrame, der die initialen Daten enthält.\n",
    "        train_df (pd.DataFrame): Ein DataFrame, der den Trainingsdatensatz nach der Anwendung von PCA und der Aufteilung enthält.\n",
    "        val_df (pd.DataFrame): Ein DataFrame, der den Validierungsdatensatz nach der Anwendung von PCA und der Aufteilung enthält.\n",
    "\n",
    "    Methods:\n",
    "        __init__(self, dataframe, n_pca_components=1034, train_size=0.8, split_random_state=42):\n",
    "            Konstruktor für die GenomeDataset Klasse.\n",
    "        _do_PCA(self, frequencies, n_components=1034):\n",
    "            Wendet PCA auf die gegebenen Frequenzen an.\n",
    "        _split_dataset(self, train_size=0.8, random_state=42):\n",
    "            Teilt den DataFrame in Trainings- und Validierungsdatensätze auf.\n",
    "        __getitem__(self, index):\n",
    "            Gibt ein Tupel aus transformierten Frequenzen und dem zugehörigen Krebstyp für einen gegebenen Index zurück.\n",
    "        __len__(self):\n",
    "            Gibt die Gesamtlänge der kombinierten Trainings- und Validierungsdatensätze zurück.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, dataframe: pd.DataFrame, n_pca_components: int = 1034, train_size: float = 0.8, split_random_state: int = 42):\n",
    "        \"\"\"\n",
    "        Konstruktor für die GenomeDataset Klasse.\n",
    "\n",
    "        Parameters:\n",
    "            dataframe (pd.DataFrame): Der DataFrame, der die Genome Frequenzen und Krebsarten enthält.\n",
    "            n_pca_components (int): Die Anzahl der PCA-Komponenten, auf die reduziert werden soll. Standardwert ist 1034.\n",
    "            train_size (float): Der Anteil der Daten, der als Trainingsdaten verwendet werden soll. Standardwert ist 0.8.\n",
    "            split_random_state (int): Der Zufalls-Saatwert, der für die Aufteilung des Datensatzes verwendet wird. Standardwert ist 42.\n",
    "        \"\"\"\n",
    "        self.dataframe = dataframe\n",
    "\n",
    "        # Umwandlung der Krebsarten in numerische Werte\n",
    "        self.label_encoder = LabelEncoder()\n",
    "        self.dataframe['encoded_cancer_type'] = self.label_encoder.fit_transform(dataframe['cancer_type'])\n",
    "\n",
    "        # Anwenden der PCA auf die Frequenzen\n",
    "        self.dataframe['pca_frequencies'] = self._do_PCA(self.dataframe['genome_frequencies'].tolist(), n_pca_components)\n",
    "\n",
    "        # Teilen des DataFrame in Trainings- und Validierungsdatensatz\n",
    "        self._split_dataset(train_size=train_size, random_state=split_random_state)\n",
    "\n",
    "    def transform_datapoint(self, datapoint: List[float]) -> List[float]:\n",
    "        \"\"\"\n",
    "        Transformiert einen einzelnen Datenpunkt durch Standardisierung und Anwendung der PCA.\n",
    "\n",
    "        Diese Methode nimmt einen rohen Datenpunkt (eine Liste von Frequenzen), standardisiert ihn mit dem \n",
    "        zuvor angepassten Scaler und wendet dann die PCA-Transformation an, um ihn in den reduzierten \n",
    "        Feature-Raum zu überführen, der für das Training des Modells verwendet wurde.\n",
    "\n",
    "        Parameters:\n",
    "            datapoint (List[float]): Ein roher Datenpunkt, bestehend aus einer Liste von Frequenzen.\n",
    "\n",
    "        Returns:\n",
    "            List[float]: Der transformierte Datenpunkt, nach Anwendung der Standardisierung und der PCA.\n",
    "        \"\"\"\n",
    "        # Standardisierung des Datenpunkts\n",
    "        scaled_data_point = self.scaler.transform([datapoint])\n",
    "\n",
    "        # PCA-Transformation des standardisierten Datenpunkts\n",
    "        pca_transformed_point = self.pca.transform(scaled_data_point)\n",
    "\n",
    "        return pca_transformed_point.tolist()\n",
    "\n",
    "    def _do_PCA(self, frequencies: List[List[float]], n_components: int = 1034) -> List[List[float]]:\n",
    "        \"\"\"\n",
    "        Wendet PCA auf die gegebenen Frequenzen an.\n",
    "\n",
    "        Parameters:\n",
    "            frequencies (List[List[float]]): Die Liste der Frequenzen, auf die die PCA angewendet werden soll.\n",
    "            n_components (int): Die Anzahl der Komponenten für die PCA. Standardwert ist 1034.\n",
    "\n",
    "        Returns:\n",
    "            List[List[float]]: Eine Liste von Listen, die die transformierten Frequenzen nach der PCA darstellt.\n",
    "        \"\"\"\n",
    "\n",
    "        # Standardisieren der Frequenzen\n",
    "        self.scaler = StandardScaler()\n",
    "        scaled_frequencies = self.scaler.fit_transform(frequencies)\n",
    "\n",
    "        # PCA-Instanz erstellen und auf die gewünschte Anzahl von Komponenten reduzieren\n",
    "        self.pca = PCA(n_components=n_components)\n",
    "\n",
    "        # PCA auf die Frequenzen anwenden\n",
    "        pca_result = self.pca.fit_transform(scaled_frequencies)\n",
    "\n",
    "        return pca_result.tolist()\n",
    "\n",
    "    def _split_dataset(self, train_size: float = 0.8, random_state: int = 42):\n",
    "        \"\"\"\n",
    "        Teilt den DataFrame in Trainings- und Validierungsdatensätze auf.\n",
    "\n",
    "        Parameters:\n",
    "            train_size (float): Der Anteil der Daten, der als Trainingsdaten verwendet werden soll.\n",
    "            random_state (int): Der Zufalls-Saatwert, der für die Aufteilung des Datensatzes verwendet wird.\n",
    "        \"\"\"\n",
    "\n",
    "        class SplittedDataset(Dataset):\n",
    "            def __init__(self, dataframe):\n",
    "                self.dataframe = dataframe\n",
    "\n",
    "                # Umwandlung der Genome Frequenzen in Tensoren\n",
    "                self.genome_frequencies = torch.tensor(dataframe['pca_frequencies'].tolist(), dtype=torch.float32)\n",
    "\n",
    "                # Umwandlung der Krebsarten in numerische Werte\n",
    "                self.label_encoder = LabelEncoder()\n",
    "                self.cancer_types = torch.tensor(dataframe['encoded_cancer_type'].tolist(), dtype=torch.long)\n",
    "\n",
    "            def __getitem__(self, index):\n",
    "                # Rückgabe eines Tupels aus Genome Frequenzen und dem entsprechenden Krebstyp\n",
    "                return self.genome_frequencies[index], self.cancer_types[index]\n",
    "\n",
    "            def __len__(self):\n",
    "                return len(self.dataframe)\n",
    "\n",
    "        # Teilen des DataFrame in Trainings- und Validierungsdatensatz\n",
    "        train_df, val_df = train_test_split(self.dataframe, train_size=train_size) #, random_state=random_state)\n",
    "        self.train_df = SplittedDataset(train_df)\n",
    "        self.val_df = SplittedDataset(val_df)\n",
    "\n",
    "\n",
    "    def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]:\n",
    "        \"\"\"\n",
    "        Gibt ein Tupel aus transformierten Frequenzen und dem entsprechenden Krebstyp für einen gegebenen Index zurück.\n",
    "\n",
    "        Parameters:\n",
    "            index (int): Der Index des zu abrufenden Datenelements.\n",
    "\n",
    "        Returns:\n",
    "            Tuple[torch.Tensor, int]: Ein Tupel, bestehend aus einem Tensor der transformierten Frequenzen und dem zugehörigen Krebstyp.\n",
    "        \"\"\"\n",
    "\n",
    "        print(self.train_df.shape)\n",
    "        print(self.val_df.shape)\n",
    "        \n",
    "        if index < len(self.train_df):\n",
    "            row = self.train_df.iloc[index]\n",
    "        else:\n",
    "            row = self.val_df.iloc[len(self.train_df) - index]\n",
    "\n",
    "        pca_frequencies_tensor = torch.tensor(row['pca_frequencies'], dtype=torch.float32)\n",
    "        cancer_type = row['encoded_cancer_type']\n",
    "\n",
    "        return pca_frequencies_tensor, cancer_type\n",
    "\n",
    "    def __len__(self) -> int:\n",
    "        \"\"\"\n",
    "        Gibt die Gesamtlänge der kombinierten Trainings- und Validierungsdatensätze zurück.\n",
    "\n",
    "        Returns:\n",
    "            int: Die Länge der kombinierten Datensätze.\n",
    "        \"\"\"\n",
    "        \n",
    "        return len(self.train_df) + len(self.val_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definition des neuronalen Netzes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "class CancerClassifierNN(nn.Module):\n",
    "    \"\"\"\n",
    "    Eine benutzerdefinierte neuronale Netzwerkklassifikator-Klasse für die Krebsklassifikation.\n",
    "\n",
    "    Diese Klasse definiert ein mehrschichtiges Perzeptron (MLP), das für die Klassifizierung von Krebsarten\n",
    "    anhand genetischer Frequenzdaten verwendet wird.\n",
    "\n",
    "    Attributes:\n",
    "        fc1 (nn.Linear): Die erste lineare Schicht des Netzwerks.\n",
    "        fc2 (nn.Linear): Die zweite lineare Schicht des Netzwerks.\n",
    "        fc3 (nn.Linear): Die dritte lineare Schicht des Netzwerks.\n",
    "        fc4 (nn.Linear): Die Ausgabeschicht des Netzwerks.\n",
    "        dropout (nn.Dropout): Ein Dropout-Layer zur Vermeidung von Overfitting.\n",
    "\n",
    "    Methods:\n",
    "        __init__(self, input_size: int, num_classes: int):\n",
    "            Konstruktor für die CancerClassifierNN Klasse.\n",
    "        forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "            Definiert den Vorwärtsdurchlauf des Netzwerks.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, input_size: int, num_classes: int):\n",
    "        \"\"\"\n",
    "        Konstruktor für die CancerClassifierNN Klasse.\n",
    "\n",
    "        Parameters:\n",
    "            input_size (int): Die Größe des Input-Features.\n",
    "            num_classes (int): Die Anzahl der Zielklassen.\n",
    "        \"\"\"\n",
    "        super(CancerClassifierNN, self).__init__()\n",
    "        # Definieren der Schichten\n",
    "        self.fc1 = nn.Linear(input_size, input_size)  # Eingabeschicht\n",
    "        self.fc2 = nn.Linear(input_size, input_size//2)        # Versteckte Schicht\n",
    "        self.fc3 = nn.Linear(input_size//2, input_size//4)         # Weitere versteckte Schicht\n",
    "        self.fc4 = nn.Linear(input_size//4, num_classes) # Ausgabeschicht\n",
    "        self.dropout = nn.Dropout(p=0.5)       # Dropout\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        \"\"\"\n",
    "        Definiert den Vorwärtsdurchlauf des Netzwerks.\n",
    "\n",
    "        Parameters:\n",
    "            x (torch.Tensor): Der Input-Tensor für das Netzwerk.\n",
    "\n",
    "        Returns:\n",
    "            torch.Tensor: Der Output-Tensor nach dem Durchlauf durch das Netzwerk.\n",
    "        \"\"\"\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(self.fc3(x))\n",
    "        x = self.dropout(x)\n",
    "        x = torch.softmax(self.fc4(x), dim=1)  # Oder F.log_softmax(x, dim=1) für Mehrklassenklassifikation\n",
    "        return x\n",
    "    \n",
    "    def calculate_confusion_matrix(self, model, dataset_loader):\n",
    "        \"\"\"\n",
    "        Berechnet die Konfusionsmatrix für das gegebene Modell und den Datensatz.\n",
    "\n",
    "        Parameters:\n",
    "            model (torch.nn.Module): Das PyTorch-Modell.\n",
    "            dataset_loader (torch.utils.data.DataLoader): Der DataLoader für den Datensatz.\n",
    "\n",
    "        Returns:\n",
    "            np.array: Die Konfusionsmatrix.\n",
    "        \"\"\"\n",
    "        model.eval()\n",
    "        all_preds = []\n",
    "        all_targets = []\n",
    "\n",
    "        with torch.no_grad():\n",
    "            for data, target in dataset_loader:\n",
    "                outputs = model(data)\n",
    "                _, preds = torch.max(outputs, 1)\n",
    "                all_preds.extend(preds.cpu().numpy())\n",
    "                all_targets.extend(target.cpu().numpy())\n",
    "\n",
    "        return confusion_matrix(all_targets, all_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "import torch.optim as optim\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "class ExperimentationalExperiments():\n",
    "    \"\"\"\n",
    "    Diese Klasse dient zur Durchführung und Verwaltung von Experimenten im Rahmen\n",
    "    des maschinellen Lernens, insbesondere für die Krebsklassifikation.\n",
    "\n",
    "    Attribute:\n",
    "    results : Dict\n",
    "        Speichert die Ergebnisse der durchgeführten Experimente.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        \"\"\" Konstruktor der Klasse. Initialisiert 'results' als None. \"\"\"\n",
    "        self.results = None\n",
    "\n",
    "    def run_single_experiment(self, train_loader: DataLoader, valid_loader: DataLoader, n_pca_components: int, n_epochs: int = 200, learning_rate: int = 0.0005, verbose: bool = True, experiment_num: int = None) -> Tuple:\n",
    "        \"\"\"\n",
    "        Führt ein einzelnes Experiment mit dem spezifizierten DataLoader, PCA-Komponenten und weiteren Parametern durch.\n",
    "\n",
    "        Parameter:\n",
    "        train_loader : DataLoader\n",
    "            Der DataLoader für den Trainingsdatensatz.\n",
    "        valid_loader : DataLoader\n",
    "            Der DataLoader für den Validierungsdatensatz.\n",
    "        n_pca_components : int\n",
    "            Anzahl der PCA-Komponenten, die im Modell verwendet werden.\n",
    "        n_epochs : int, optional\n",
    "            Anzahl der Epochen für das Training (Standardwert ist 200).\n",
    "        learning_rate : float, optional\n",
    "            Lernrate für den Optimierer (Standardwert ist 0.0005).\n",
    "        verbose : bool, optional\n",
    "            Gibt an, ob der Trainingsfortschritt angezeigt werden soll (Standardwert ist True).\n",
    "        experiment_num : int, optional\n",
    "            Nummer des Experiments.\n",
    "\n",
    "        Rückgabewerte:\n",
    "        Tuple\n",
    "            Ein Tupel bestehend aus Listen der Trainings- und Validierungsverluste sowie der Genauigkeiten.\n",
    "        \"\"\"\n",
    "        if not isinstance(n_pca_components, int):\n",
    "            raise TypeError(\"n_pca_components must be an integers!\")\n",
    "\n",
    "        model = CancerClassifierNN(input_size=n_pca_components, num_classes=3)\n",
    "        model.to(device=device)\n",
    "\n",
    "        # Verlustfunktion\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        # Optimierer\n",
    "        optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "        # Listen, um Verluste zu speichern\n",
    "        train_losses = []\n",
    "        valid_losses = []\n",
    "        train_accuracies = []\n",
    "        valid_accuracies = []\n",
    "\n",
    "        for epoch in range(n_epochs):\n",
    "            model.train()\n",
    "            train_loss = 0.0\n",
    "            correct_predictions = 0\n",
    "            total_predictions = 0\n",
    "\n",
    "            for i, (inputs, labels) in enumerate(train_loader):\n",
    "                inputs, labels = inputs.to(device), labels.to(device)\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",
    "                # Berechnen der Genauigkeit\n",
    "                _, predicted = torch.max(outputs, 1)\n",
    "                correct_predictions += (predicted == labels).sum().item()\n",
    "                total_predictions += labels.size(0)\n",
    "\n",
    "            # Durchschnittlicher Trainingsverlust und Genauigkeit\n",
    "            train_loss /= len(train_loader)\n",
    "            train_accuracy = correct_predictions / total_predictions\n",
    "            train_losses.append(train_loss)\n",
    "            train_accuracies.append(train_accuracy)\n",
    "\n",
    "            # Validierungsverlust und Genauigkeit\n",
    "            model.eval()\n",
    "            valid_loss = 0.0\n",
    "            correct_predictions = 0\n",
    "            total_predictions = 0\n",
    "\n",
    "            with torch.no_grad():\n",
    "                for inputs, labels in valid_loader:\n",
    "                    inputs, labels = inputs.to(device), labels.to(device)\n",
    "                    outputs = model(inputs)\n",
    "                    loss = criterion(outputs, labels)\n",
    "                    valid_loss += loss.item()\n",
    "\n",
    "                    # Berechnen der Genauigkeit\n",
    "                    _, predicted = torch.max(outputs, 1)\n",
    "                    correct_predictions += (predicted == labels).sum().item()\n",
    "                    total_predictions += labels.size(0)\n",
    "\n",
    "            # Durchschnittlicher Validierungsverlust und Genauigkeit\n",
    "            valid_loss /= len(valid_loader)\n",
    "            valid_accuracy = correct_predictions / total_predictions\n",
    "            valid_losses.append(valid_loss)\n",
    "            valid_accuracies.append(valid_accuracy)\n",
    "\n",
    "            # Aktualisieren des Graphen\n",
    "            clear_output(wait=True)\n",
    "            fig, ax1 = plt.subplots()\n",
    "\n",
    "            # Zeichnen der Verlustkurven\n",
    "            ax1.plot(train_losses, label='Trainingsverlust', color='r')\n",
    "            ax1.plot(valid_losses, label='Validierungsverlust', color='b')\n",
    "            ax1.set_xlabel('Epochen')\n",
    "            ax1.set_ylabel('Verlust', color='g')\n",
    "            ax1.tick_params(axis='y', labelcolor='g')\n",
    "\n",
    "            # Zweite y-Achse für die Genauigkeit\n",
    "            ax2 = ax1.twinx()\n",
    "            ax2.plot(train_accuracies, label='Trainingsgenauigkeit', color='r', linestyle='dashed')\n",
    "            ax2.plot(valid_accuracies, label='Validierungsgenauigkeit', color='b', linestyle='dashed')\n",
    "            ax2.set_ylabel('Genauigkeit', color='g')\n",
    "            ax2.tick_params(axis='y', labelcolor='g')\n",
    "\n",
    "            # Titel und Legende\n",
    "            plt.title(f'Experiment #{experiment_num}: Trainings- und Validierungsverlust und -genauigkeit über die Zeit mit \\n{n_pca_components}-Hauptkomponenten, Lernrate: {learning_rate}')\n",
    "            fig.tight_layout()\n",
    "\n",
    "            # Legende außerhalb des Graphen\n",
    "            ax1.legend(loc='upper left', bbox_to_anchor=(1.15, 1))\n",
    "            ax2.legend(loc='upper left', bbox_to_anchor=(1.15, 0.85))\n",
    "\n",
    "            # Fortschritt anzeigen, falls angegeben\n",
    "            if verbose:\n",
    "                print(f'Experiment #{experiment_num} mit {n_pca_components} PCA components: Epoch [{epoch+1}/{n_epochs}], Trainingsverlust: {train_loss:.4f}, Trainingsgenauigkeit: {train_accuracies[-1]:.4f}, Validierungsverlust: {valid_loss:.4f}, Validierungsgenauigkeit: {valid_accuracies[-1]:.4f}')\n",
    "\n",
    "        # Plot speichern\n",
    "        name = str(experiment_num) + \".png\" if experiment_num is not None else \"single_experiment.png\"\n",
    "        if not os.path.exists(\"Experiments\"):\n",
    "            os.makedirs(\"Experiments\")\n",
    "        if not os.path.exists(f\"Experiments/{str(n_pca_components)}\"):\n",
    "            os.makedirs(f\"Experiments/{str(n_pca_components)}\")\n",
    "        plt.savefig(f\"Experiments/{str(n_pca_components)}/{name}\", bbox_inches='tight')\n",
    "\n",
    "        return train_losses, valid_losses, train_accuracies, valid_accuracies\n",
    "\n",
    "    def run_single_pca_experiment(self, train_loader: DataLoader, valid_loader: DataLoader, n_pca_components: int, n_experiments: int, n_epochs: int = 200, learning_rate: int = 0.0005, verbose: bool = True) -> List:\n",
    "        \"\"\"\n",
    "        Führt eine Serie von Experimenten mit verschiedenen Konfigurationen für die PCA-Komponenten durch.\n",
    "\n",
    "        Parameter:\n",
    "        train_loader : DataLoader\n",
    "            Der DataLoader für den Trainingsdatensatz.\n",
    "        valid_loader : DataLoader\n",
    "            Der DataLoader für den Validierungsdatensatz.\n",
    "        n_pca_components : int\n",
    "            Anzahl der PCA-Komponenten, die im Modell verwendet werden.\n",
    "        n_experiments : int\n",
    "            Anzahl der durchzuführenden Experimente.\n",
    "        n_epochs : int, optional\n",
    "            Anzahl der Epochen für das Training (Standardwert ist 200).\n",
    "        learning_rate : float, optional\n",
    "            Lernrate für den Optimierer (Standardwert ist 0.0005).\n",
    "        verbose : bool, optional\n",
    "            Gibt an, ob der Trainingsfortschritt angezeigt werden soll (Standardwert ist True).\n",
    "\n",
    "        Rückgabewerte:\n",
    "        List\n",
    "            Eine Liste von Ergebnissen der einzelnen Experimente.\n",
    "        \"\"\"\n",
    "        if not isinstance(n_pca_components, int):\n",
    "            raise TypeError(\"n_pca_components must be an integers!\")\n",
    "\n",
    "        results = []\n",
    "\n",
    "        for n in range(n_experiments):\n",
    "            res = self.run_single_experiment(train_loader, valid_loader, n_pca_components, n_epochs=n_epochs, learning_rate=learning_rate, verbose=verbose, experiment_num=n+1)\n",
    "            results.append(res)\n",
    "\n",
    "        return results\n",
    "            \n",
    "\n",
    "    def run(self, n_pca_components: List[int], n_experiments: int, n_epochs: int = 200, learning_rate: int = 0.0005, batch_size: int = 64, verbose: bool = True) -> Dict:\n",
    "        \"\"\"\n",
    "        Hauptmethode zum Ausführen von Experimenten mit verschiedenen Anzahlen von PCA-Komponenten.\n",
    "\n",
    "        Parameter:\n",
    "        n_pca_components : List[int]\n",
    "            Eine Liste von Anzahlen der PCA-Komponenten, die in den Experimenten verwendet werden sollen.\n",
    "        n_experiments : int\n",
    "            Anzahl der durchzuführenden Experimente pro PCA-Komponentenanzahl.\n",
    "        n_epochs : int, optional\n",
    "            Anzahl der Epochen für das Training (Standardwert ist 200).\n",
    "        learning_rate : float, optional\n",
    "            Lernrate für den Optimierer (Standardwert ist 0.0005).\n",
    "        batch_size : int, optional\n",
    "            Batch-Größe für das Laden der Daten (Standardwert ist 64).\n",
    "        verbose : bool, optional\n",
    "            Gibt an, ob der Trainingsfortschritt angezeigt werden soll (Standardwert ist True).\n",
    "\n",
    "        Rückgabewerte:\n",
    "        Dict\n",
    "            Ein Wörterbuch, das die Ergebnisse der Experimente für jede Anzahl von PCA-Komponenten enthält.\n",
    "        \"\"\"\n",
    "        if not isinstance(n_pca_components, list):\n",
    "            raise TypeError(\"n_pca_components must be a list of integers!\")\n",
    "\n",
    "        plt.ioff()\n",
    "        self.n_pca_components = n_pca_components\n",
    "\n",
    "        results = {}\n",
    "\n",
    "        for n_pca_comps in n_pca_components:\n",
    "            genome_dataset = GenomeDataset(data_frame, n_pca_components=n_pca_comps)\n",
    "            train_dataset = genome_dataset.train_df\n",
    "            valid_dataset = genome_dataset.val_df\n",
    "\n",
    "            train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
    "            valid_loader = DataLoader(dataset=valid_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "            res = self.run_single_pca_experiment(train_loader, valid_loader, n_pca_comps, n_experiments, n_epochs=n_epochs, learning_rate=learning_rate, verbose=verbose)\n",
    "            results[str(n_pca_comps)] = res\n",
    "\n",
    "            self.plot_and_save_results(res, n_pca_comps)\n",
    "\n",
    "        self.results = results\n",
    "\n",
    "        # Speichern der Daten in einer lokalen Datei\n",
    "        if len(n_pca_components) > 1:\n",
    "            with open('Experiments/results.pickle', 'wb') as f:\n",
    "                pickle.dump(self.results, f)\n",
    "        else:\n",
    "            with open(f'Experiments/{str(n_pca_components[0])}/results_{str(n_pca_components[0])}.pickle', 'wb') as f:\n",
    "                pickle.dump(self.results, f)\n",
    "\n",
    "        plt.ion()\n",
    "\n",
    "        return results\n",
    "\n",
    "    def plot_and_save_results(self, results: List[Tuple], n_pca_components: int) -> None:\n",
    "        \"\"\"\n",
    "        Erstellt und speichert Plots für die Ergebnisse der Experimente.\n",
    "\n",
    "        Parameter:\n",
    "        results : List[Tuple]\n",
    "            Eine Liste von Tupeln mit den Ergebnissen der Experimente.\n",
    "        n_pca_components : int\n",
    "            Anzahl der PCA-Komponenten, für die die Ergebnisse geplottet werden sollen.\n",
    "\n",
    "        Keine Rückgabewerte, da die Methode Plots speichert.\n",
    "        \"\"\"\n",
    "        \n",
    "        # Mittelwerte und Standardabweichungen berechnen\n",
    "        train_losses, valid_losses, train_accuracies, valid_accuracies = zip(*results)\n",
    "\n",
    "        train_losses = np.array(train_losses)\n",
    "        valid_losses = np.array(valid_losses)\n",
    "        train_accuracies = np.array(train_accuracies)\n",
    "        valid_accuracies = np.array(valid_accuracies)\n",
    "\n",
    "        avg_train_losses = np.mean(train_losses, axis=0)\n",
    "        avg_valid_losses = np.mean(valid_losses, axis=0)\n",
    "        avg_train_acc = np.mean(train_accuracies, axis=0)\n",
    "        avg_valid_acc = np.mean(valid_accuracies, axis=0)\n",
    "\n",
    "        std_train_losses = np.std(train_losses, axis=0)\n",
    "        std_valid_losses = np.std(valid_losses, axis=0)\n",
    "        std_train_acc = np.std(train_accuracies, axis=0)\n",
    "        std_valid_acc = np.std(valid_accuracies, axis=0)\n",
    "\n",
    "        # Erstellen von Plots\n",
    "        epochs = range(1, len(avg_train_losses) + 1)\n",
    "\n",
    "        # Plot für Verluste\n",
    "        plt.clf()\n",
    "        plt.plot(epochs, avg_train_losses, label='Mittlerer Trainingsverlust', color='r')\n",
    "        plt.fill_between(epochs, np.subtract(avg_train_losses, std_train_losses), np.add(avg_train_losses, std_train_losses), color='r', alpha=0.2)\n",
    "        plt.plot(epochs, avg_valid_losses, label='Mittlerer Validierungsverlust', color='b')\n",
    "        plt.fill_between(epochs, np.subtract(avg_valid_losses, std_valid_losses), np.add(avg_valid_losses, std_valid_losses), color='b', alpha=0.2)\n",
    "        plt.title(f'Mittelwert und Standardabweichung der Verluste für {n_pca_components} PCA-Komponenten')\n",
    "        plt.xlabel('Experiment Nummer')\n",
    "        plt.ylabel('Verlust')\n",
    "        plt.legend()\n",
    "        plt.savefig(f\"Experiments/{n_pca_components}/average_losses.png\", bbox_inches='tight')\n",
    "        plt.clf()\n",
    "\n",
    "        # Plot für Genauigkeiten\n",
    "        plt.plot(epochs, avg_train_acc, label='Mittlere Trainingsgenauigkeit', color='r')\n",
    "        plt.fill_between(epochs, np.subtract(avg_train_acc, std_train_acc), np.add(avg_train_acc, std_train_acc), color='r', alpha=0.2)\n",
    "        plt.plot(epochs, avg_valid_acc, label='Mittlere Validierungsgenauigkeit', color='b')\n",
    "        plt.fill_between(epochs, np.subtract(avg_valid_acc, std_valid_acc), np.add(avg_valid_acc, std_valid_acc), color='b', alpha=0.2)\n",
    "        plt.title(f'Mittelwert und Standardabweichung der Genauigkeiten für {n_pca_components} PCA-Komponenten')\n",
    "        plt.xlabel('Experiment Nummer')\n",
    "        plt.ylabel('Genauigkeit')\n",
    "        plt.legend()\n",
    "        plt.savefig(f\"Experiments/{n_pca_components}/average_accuracies.png\", bbox_inches='tight')\n",
    "        plt.clf()\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starten der einzelnen Experimente, da in einer Schleife RAM Probleme auftreten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e1 = ExperimentationalExperiments()\n",
    "results = e1.run([1024], 10, n_epochs=500)\n",
    "del e1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e2 = ExperimentationalExperiments()\n",
    "results = e2.run([512], 10, n_epochs=500)\n",
    "del e2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e3 = ExperimentationalExperiments()\n",
    "results = e3.run([256], 10, n_epochs=500)\n",
    "del e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e4 = ExperimentationalExperiments()\n",
    "results = e4.run([128], 10, n_epochs=500)\n",
    "del e4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e5 = ExperimentationalExperiments()\n",
    "results = e5.run([64], 10, n_epochs=500)\n",
    "del e5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e6 = ExperimentationalExperiments()\n",
    "results = e6.run([32], 10, n_epochs=500)\n",
    "del e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e7 = ExperimentationalExperiments()\n",
    "results = e7.run([16], 10, n_epochs=500)\n",
    "del e7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e8 = ExperimentationalExperiments()\n",
    "results = e7.run([1034], 10, n_epochs=500)\n",
    "del e8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesen der Daten und Erstellen der Mittelwerte und anschließender Auswertung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List, Dict, Tuple\n",
    "\n",
    "def load_results(path: str) -> Dict:\n",
    "    \"\"\"\n",
    "    Lädt und konvertiert die Ergebnisse aus Pickle-Dateien in den spezifizierten Verzeichnissen.\n",
    "\n",
    "    Argumente:\n",
    "    path (str): Der Pfad zum Basisverzeichnis, das die Ergebnis-Unterverzeichnisse enthält.\n",
    "\n",
    "    Rückgabe:\n",
    "    Dict: Ein Dictionary, in dem jeder Schlüssel einer PCA-Komponentenanzahl entspricht und\n",
    "          dessen Werte Achtertupel aus Durchschnitt und Standardabweichung von Trainingsverlust,\n",
    "          Validierungsverlust, Trainingsgenauigkeit und Validierungsgenauigkeit sind.\n",
    "    \"\"\"\n",
    "\n",
    "    results = {}\n",
    "\n",
    "    # Über alle Ordner im Basispfad iterieren\n",
    "    for directory in os.listdir(path):\n",
    "        full_path = os.path.join(path, directory)\n",
    "\n",
    "        # Überprüfen, ob es sich um einen Ordner handelt\n",
    "        if os.path.isdir(full_path):\n",
    "            pickle_file = f'results_{directory}.pickle'\n",
    "            pickle_path = os.path.join(full_path, pickle_file)\n",
    "\n",
    "            # Überprüfen, ob die Pickle-Datei existiert\n",
    "            if os.path.isfile(pickle_path):\n",
    "                # Pickle-Datei laden\n",
    "                with open(pickle_path, 'rb') as file:\n",
    "                    results[directory] = pickle.load(file)\n",
    "\n",
    "    converted_results = {}\n",
    "    for values in list(results.values()):\n",
    "        key = list(values.keys())[0]\n",
    "        value = list(values.values())[0]\n",
    "        converted_results[key] = value\n",
    "\n",
    "    return converted_results\n",
    "\n",
    "\n",
    "def calculate_means_for_n_last(results: Dict, n_last: int) -> Dict:\n",
    "    \"\"\"\n",
    "    Berechnet Durchschnittswerte und Standardabweichungen für die letzten `n_last` Ergebnisse.\n",
    "\n",
    "    Argumente:\n",
    "    results (Dict): Ein Dictionary von Ergebnissen, wie von `load_results` zurückgegeben.\n",
    "    n_last (int): Anzahl der letzten Ergebnisse, die zur Berechnung verwendet werden sollen.\n",
    "\n",
    "    Rückgabe:\n",
    "    Dict: Ein Dictionary mit Schlüsseln als PCA-Komponentenanzahlen und Werten als Achtertupel,\n",
    "          bestehend aus Durchschnitt und Standardabweichung von Trainingsverlust, \n",
    "          Validierungsverlust, Trainingsgenauigkeit und Validierungsgenauigkeit.\n",
    "    \"\"\"\n",
    "\n",
    "    assert results is not None\n",
    "    assert n_last <= len(list(results.values())[0][0][0])\n",
    "\n",
    "    means_and_stds = {}\n",
    "\n",
    "    for key, value in results.items():\n",
    "        train_losses, valid_losses, train_accuracies, valid_accuracies = zip(*value)\n",
    "        \n",
    "        train_losses = train_losses[:n_last]      \n",
    "        valid_losses = valid_losses[:n_last]\n",
    "        train_accuracies = train_accuracies[:n_last]\n",
    "        valid_accuracies = valid_accuracies[:n_last]\n",
    "\n",
    "        avg_train_loss = np.mean(train_losses)#, axis=0)\n",
    "        avg_valid_loss = np.mean(valid_losses)#, axis=0)\n",
    "        avg_train_acc = np.mean(train_accuracies)#, axis=0)\n",
    "        avg_valid_acc = np.mean(valid_accuracies)#, axis=0)\n",
    "\n",
    "        std_train_loss = np.std(train_losses)#, axis=0)\n",
    "        std_valid_loss = np.std(valid_losses)#, axis=0)\n",
    "        std_train_acc = np.std(train_accuracies)#, axis=0)\n",
    "        std_valid_acc = np.std(valid_accuracies)#, axis=0)\n",
    "\n",
    "        print(f\"### {key} PCA Komponenten ###\")\n",
    "        print(f\"Trainingsverlust:        {avg_train_loss:.3f} \\u00B1 {std_train_loss:.3f}\")\n",
    "        print(f\"Validierungsverlust:     {avg_valid_loss:.3f} \\u00B1 {std_valid_loss:.3f}\")\n",
    "        print(f\"Trainingsgenauigkeit:    {avg_train_acc:.3f} \\u00B1 {std_train_acc:.3f}\")\n",
    "        print(f\"Validierungsgenauigkeit: {avg_valid_acc:.3f} \\u00B1 {std_valid_acc:.3f}\\n\")\n",
    "\n",
    "        means_and_stds[key] = (avg_train_loss, std_train_loss, avg_valid_loss, std_valid_loss, avg_train_acc, std_train_acc, avg_valid_acc, std_valid_acc)\n",
    "\n",
    "    # Initialisierung der Variablen für die Minima und Maxima\n",
    "    min_train_loss = float('inf')\n",
    "    min_valid_loss = float('inf')\n",
    "    max_train_acc = 0\n",
    "    max_valid_acc = 0\n",
    "\n",
    "    # Durchlaufen aller berechneten Mittelwerte und Standardabweichungen\n",
    "    for key, (avg_train_loss, std_train_loss, avg_valid_loss, std_valid_loss, avg_train_acc, std_train_acc, avg_valid_acc, std_valid_acc) in means_and_stds.items():\n",
    "        if avg_train_loss < min_train_loss:\n",
    "            min_train_loss = avg_train_loss\n",
    "            min_train_loss_key = key\n",
    "\n",
    "        if avg_valid_loss < min_valid_loss:\n",
    "            min_valid_loss = avg_valid_loss\n",
    "            min_valid_loss_key = key\n",
    "\n",
    "        if avg_train_acc > max_train_acc:\n",
    "            max_train_acc = avg_train_acc\n",
    "            max_train_acc_key = key\n",
    "\n",
    "        if avg_valid_acc > max_valid_acc:\n",
    "            max_valid_acc = avg_valid_acc\n",
    "            max_valid_acc_key = key\n",
    "\n",
    "    # Drucken der Endresultate\n",
    "    print(f\"### Auswertung ###\")\n",
    "    print(f\"Niedrigster Trainingsverlust:    {min_train_loss:.3f} bei {min_train_loss_key} PCA-Komponenten\")\n",
    "    print(f\"Niedrigster Validierungsverlust: {min_valid_loss:.3f} bei {min_valid_loss_key} PCA-Komponenten\")\n",
    "    print(f\"Höchste Trainingsgenauigkeit:    {max_train_acc:.3f} bei {max_train_acc_key} PCA-Komponenten\")\n",
    "    print(f\"Höchste Validierungsgenauigkeit: {max_valid_acc:.3f} bei {max_valid_acc_key} PCA-Komponenten\")\n",
    "\n",
    "    return means_and_stds\n",
    "\n",
    "\n",
    "def plot_results(results: Dict, show_lines: bool = True) -> None:\n",
    "    \"\"\"\n",
    "    Stellt die Ergebnisse als Fehlerbalkendiagramme dar. Jedes Diagramm zeigt Mittelwert und\n",
    "    Standardabweichung von Trainings- und Validierungsverlust sowie -genauigkeit. \n",
    "    Fügt zusätzlich eine rote Linie für den höchsten Genauigkeitswert und den geringsten Verlustwert hinzu,\n",
    "    mit einer Beschriftung, die den Schlüssel des entsprechenden höchsten bzw. niedrigsten Werts anzeigt.\n",
    "\n",
    "    Argumente:\n",
    "    results (Dict): Ein Dictionary von berechneten Mittelwerten und Standardabweichungen,\n",
    "                    wie von `calculate_means_for_n_last` zurückgegeben.\n",
    "    show_lines (bool): Ein flag, das angibt, ob die Maximal- / Minimallinie gezeichnet werden soll.\n",
    "    \"\"\"\n",
    "    # Schlüssel sortieren\n",
    "    sorted_keys = sorted(results.keys(), key=lambda x: int(x))\n",
    "\n",
    "    # Listen für das Plotten vorbereiten\n",
    "    mean_train_loss = [results[k][0] for k in sorted_keys]\n",
    "    std_train_loss = [results[k][1] for k in sorted_keys]\n",
    "    mean_validation_loss = [results[k][2] for k in sorted_keys]\n",
    "    std_validation_loss = [results[k][3] for k in sorted_keys]\n",
    "    mean_train_accuracy = [results[k][4] for k in sorted_keys]\n",
    "    std_train_accuracy = [results[k][5] for k in sorted_keys]\n",
    "    mean_validation_accuracy = [results[k][6] for k in sorted_keys]\n",
    "    std_validation_accuracy = [results[k][7] for k in sorted_keys]\n",
    "\n",
    "    # Plotten\n",
    "    plt.figure(figsize=(12, 8))\n",
    "\n",
    "    # Verluste\n",
    "    plt.errorbar(sorted_keys, mean_train_loss, yerr=std_train_loss, label='Trainingverlust', fmt='o', linestyle='--', alpha=0.5)\n",
    "    plt.errorbar(sorted_keys, mean_validation_loss, yerr=std_validation_loss, label='Validierungsverlust', fmt='o', linestyle='--', alpha=0.5)\n",
    "\n",
    "    # Genauigkeiten\n",
    "    plt.errorbar(sorted_keys, mean_train_accuracy, yerr=std_train_accuracy, label='Trainingsgenauigkeit', fmt='x', linestyle='--', alpha=0.5)\n",
    "    plt.errorbar(sorted_keys, mean_validation_accuracy, yerr=std_validation_accuracy, label='Validierungsgenauigkeit', fmt='x', linestyle='--', alpha=0.5)\n",
    "\n",
    "    # Gestaltung\n",
    "    plt.xlabel('Anzahl der PCA Komponenten')\n",
    "    plt.ylabel('Werte')\n",
    "    plt.title('Trainings- und Validierungsverlust und -genauigkeit')\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Höchste Genauigkeit und geringster Verlust\n",
    "    highest_accuracy = max(max(mean_train_accuracy), max(mean_validation_accuracy))\n",
    "    lowest_loss = min(min(mean_train_loss), min(mean_validation_loss))\n",
    "\n",
    "    # Schlüssel für höchste Genauigkeit und geringsten Verlust finden\n",
    "    highest_acc_key = sorted_keys[mean_train_accuracy.index(max(mean_train_accuracy))] if max(mean_train_accuracy) > max(mean_validation_accuracy) else sorted_keys[mean_validation_accuracy.index(max(mean_validation_accuracy))]\n",
    "    lowest_loss_key = sorted_keys[mean_train_loss.index(min(mean_train_loss))] if min(mean_train_loss) < min(mean_validation_loss) else sorted_keys[mean_validation_loss.index(min(mean_validation_loss))]\n",
    "\n",
    "    plt.legend()\n",
    "\n",
    "    # Linien und Text für höchste Genauigkeit und geringsten Verlust\n",
    "    if show_lines:\n",
    "        plt.axhline(y=highest_accuracy, color='r', linestyle='-', alpha=0.8)\n",
    "        plt.text(0.95, highest_accuracy, f'Höchste Genauigkeit (PCA: {highest_acc_key})', verticalalignment='bottom', horizontalalignment='right', color='red', fontsize=10, transform=plt.gca().get_yaxis_transform())\n",
    "\n",
    "        plt.axhline(y=lowest_loss, color='r', linestyle='-', alpha=0.8)\n",
    "        plt.text(0.95, lowest_loss, f'Geringster Verlust (PCA: {lowest_loss_key})', verticalalignment='top', horizontalalignment='right', color='red', fontsize=10, transform=plt.gca().get_yaxis_transform())\n",
    "        \n",
    "        plt.savefig('Experiments/Endergebnisse_mit_Linien.png')\n",
    "    else:\n",
    "        plt.savefig('Experiments/Endergebnisse_ohne_Linien.png')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### 128 PCA Komponenten ###\n",
      "Trainingsverlust:        0.575 ± 0.051\n",
      "Validierungsverlust:     0.591 ± 0.029\n",
      "Trainingsgenauigkeit:    0.977 ± 0.052\n",
      "Validierungsgenauigkeit: 0.952 ± 0.028\n",
      "\n",
      "### 64 PCA Komponenten ###\n",
      "Trainingsverlust:        0.613 ± 0.064\n",
      "Validierungsverlust:     0.628 ± 0.047\n",
      "Trainingsgenauigkeit:    0.940 ± 0.066\n",
      "Validierungsgenauigkeit: 0.933 ± 0.051\n",
      "\n",
      "### 512 PCA Komponenten ###\n",
      "Trainingsverlust:        0.558 ± 0.026\n",
      "Validierungsverlust:     0.606 ± 0.018\n",
      "Trainingsgenauigkeit:    0.994 ± 0.026\n",
      "Validierungsgenauigkeit: 0.950 ± 0.016\n",
      "\n",
      "### 16 PCA Komponenten ###\n",
      "Trainingsverlust:        0.792 ± 0.082\n",
      "Validierungsverlust:     0.704 ± 0.084\n",
      "Trainingsgenauigkeit:    0.773 ± 0.089\n",
      "Validierungsgenauigkeit: 0.842 ± 0.083\n",
      "\n",
      "### 32 PCA Komponenten ###\n",
      "Trainingsverlust:        0.698 ± 0.076\n",
      "Validierungsverlust:     0.642 ± 0.054\n",
      "Trainingsgenauigkeit:    0.855 ± 0.082\n",
      "Validierungsgenauigkeit: 0.919 ± 0.049\n",
      "\n",
      "### 1024 PCA Komponenten ###\n",
      "Trainingsverlust:        0.558 ± 0.021\n",
      "Validierungsverlust:     0.606 ± 0.018\n",
      "Trainingsgenauigkeit:    0.994 ± 0.021\n",
      "Validierungsgenauigkeit: 0.946 ± 0.017\n",
      "\n",
      "### 256 PCA Komponenten ###\n",
      "Trainingsverlust:        0.563 ± 0.035\n",
      "Validierungsverlust:     0.595 ± 0.020\n",
      "Trainingsgenauigkeit:    0.989 ± 0.035\n",
      "Validierungsgenauigkeit: 0.956 ± 0.016\n",
      "\n",
      "### Auswertung ###\n",
      "Niedrigster Trainingsverlust:    0.558 bei 512 PCA-Komponenten\n",
      "Niedrigster Validierungsverlust: 0.591 bei 128 PCA-Komponenten\n",
      "Höchste Trainingsgenauigkeit:    0.994 bei 512 PCA-Komponenten\n",
      "Höchste Validierungsgenauigkeit: 0.956 bei 256 PCA-Komponenten\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Ergebnisse laden\n",
    "base_path = 'Experiments'\n",
    "loaded_results = load_results(base_path)\n",
    "\n",
    "# Ergebnisse verarbeiten und plotten\n",
    "m_a_s = calculate_means_for_n_last(loaded_results, 50)\n",
    "plot_results(m_a_s, show_lines=False)\n",
    "plot_results(m_a_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO MNIST datenstaz mit wget ohne tensorflow oder pytorch einlesen"
   ]
  },
  {
   "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
}