From 99e501075eb9debcc9386d2eeedd0fcb39c6438e Mon Sep 17 00:00:00 2001 From: Dominik Agres Date: Sun, 4 May 2025 09:31:25 +0200 Subject: [PATCH] Test code with pytorch and ROC --- src/ai_analysis/old_pytorch/knn_pytorch.ipynb | 727 +++++ src/ai_analysis/old_pytorch/rrf_pytorch.ipynb | 724 +++++ .../old_pytorch/sequential_pytorch.ipynb | 1285 +++++++++ src/ai_analysis/old_pytorch/xgb_pytorch.ipynb | 2543 +++++++++++++++++ 4 files changed, 5279 insertions(+) create mode 100644 src/ai_analysis/old_pytorch/knn_pytorch.ipynb create mode 100644 src/ai_analysis/old_pytorch/rrf_pytorch.ipynb create mode 100644 src/ai_analysis/old_pytorch/sequential_pytorch.ipynb create mode 100644 src/ai_analysis/old_pytorch/xgb_pytorch.ipynb diff --git a/src/ai_analysis/old_pytorch/knn_pytorch.ipynb b/src/ai_analysis/old_pytorch/knn_pytorch.ipynb new file mode 100644 index 0000000..5512eff --- /dev/null +++ b/src/ai_analysis/old_pytorch/knn_pytorch.ipynb @@ -0,0 +1,727 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read CSV with TrackId and Genre Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "tracks_info_df = pd.read_csv('combined_dataset.csv')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read processed song data from pkl file" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 100390 processed tracks\n" + ] + } + ], + "source": [ + "import pickle\n", + "import os\n", + "\n", + "\n", + "results_file = 'audio_features_backup.pkl'\n", + "\n", + "if os.path.exists(results_file):\n", + " with open(results_file, 'rb') as file:\n", + " saved_data = pickle.load(file)\n", + " X = saved_data.get('X', [])\n", + " y_labels = saved_data.get('y_labels', [])\n", + " \n", + " processed_files = set(saved_data.get('processed_files', []))\n", + " print(f\"Loaded {len(processed_files)} processed tracks\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Turn X into an np array for easier processing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of feature matrix X: (100390, 55)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "X = np.array(X)\n", + "print(\"shape of feature matrix X: \", X.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remove all entries where the genre is defined as None" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "X = X[np.array(y_labels) != None]\n", + "y_labels = np.array(y_labels)[np.array(y_labels) != None]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remap genres" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replace sub-genres with their parent genre based on a predefined mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "genre_mapping = {\n", + " 'lo-fi beats': 'lo-fi',\n", + " 'chillwave': 'lo-fi',\n", + "\n", + " 'nu metal': 'metal',\n", + " 'folk metal': 'metal',\n", + " 'heavy metal': 'metal',\n", + " 'metalcore': 'metal',\n", + " 'power metal': 'metal',\n", + " 'industrial metal': 'metal',\n", + " 'glam metal': 'metal',\n", + " 'melodic death metal': 'metal',\n", + " 'progressive metal': 'metal',\n", + " 'thrash metal': 'metal',\n", + " 'gothic metal': 'metal',\n", + " 'groove metal': 'metal',\n", + " 'death metal': 'metal',\n", + " 'alternative metal': 'metal',\n", + " 'black metal': 'metal',\n", + " 'speed metal': 'metal',\n", + "\n", + " 'acid house': 'acid',\n", + " 'acid techno': 'acid',\n", + "\n", + " 'afrobeat': 'afro',\n", + " 'afropop': 'afro',\n", + "\n", + " 'art rock': 'rock',\n", + "\n", + " 'britpop': 'alternative rock',\n", + " \n", + " 'art pop': 'pop',\n", + " 'acoustic pop': 'pop',\n", + " 'bedroom pop': 'pop',\n", + " 'soft pop': 'pop', \n", + " 'folk pop': 'pop',\n", + " 'norwegian pop': 'pop',\n", + " 'acoustic pop': 'pop',\n", + " 'dream pop': 'pop',\n", + " 'chamber pop': 'pop',\n", + " 'german pop': 'pop',\n", + " 'bedroom pop': 'pop',\n", + " 'city pop': 'pop',\n", + " 'dance pop': 'pop',\n", + " 'art pop': 'pop',\n", + " 'indie pop': 'pop',\n", + "\n", + " 'alt country': 'country',\n", + " 'traditional country': 'country',\n", + "\n", + " 'blues rock': 'blues',\n", + "\n", + " 'brooklyn drill': 'drill',\n", + "\n", + " 'gangster rap': 'rap',\n", + " 'memphis rap': 'rap',\n", + " 'rock rap': 'rap',\n", + " 'meme rap': 'rap',\n", + " 'punk rap': 'rap',\n", + " 'emo rap': 'rap',\n", + " 'jazz rap': 'rap',\n", + " 'melodic rap': 'rap',\n", + " 'cloud rap': 'rap',\n", + " 'k-rap': 'rap',\n", + " 'rage rap': 'rap',\n", + "\n", + " 'edm trap': 'trap',\n", + " 'italian trap': 'trap',\n", + " 'dark trap': 'trap',\n", + "\n", + " 'hip-hop': 'hip hop',\n", + " 'southern hip hop': 'hip hop',\n", + " 'west coast hip hop': 'hip hop',\n", + " 'east coast hip hop': 'hip hop',\n", + " 'finnish hip hop': 'hip hop',\n", + " 'german hip hop': 'hip hop',\n", + " 'underground hip hop': 'hip hop',\n", + " 'norwegian hip hop': 'hip hop',\n", + " 'experimental hip hop': 'hip hop',\n", + " 'alternative hip hop': 'hip hop',\n", + " 'latin hip hop': 'hip hop',\n", + "\n", + " 'jazz blues': 'jazz',\n", + " 'vocal jazz': 'jazz',\n", + " 'french jazz': 'jazz',\n", + " 'free jazz': 'jazz',\n", + " 'soul jazz': 'jazz',\n", + " 'jazz fusion': 'jazz',\n", + " 'nu jazz': 'jazz',\n", + " 'jazz funk': 'jazz',\n", + " 'cool jazz': 'jazz',\n", + " 'jazz beats': 'jazz',\n", + " 'jazz house': 'jazz',\n", + " 'indie jazz': 'jazz',\n", + " 'smooth jazz': 'jazz',\n", + "\n", + " 'melodic techno': 'techno',\n", + " 'minimal techno': 'techno',\n", + " 'hypertechno': 'techno',\n", + " 'hard techno': 'techno',\n", + " 'hardcore techno': 'techno',\n", + " 'dub techno': 'techno',\n", + "\n", + " 'future house': 'house',\n", + " 'tech house': 'house',\n", + " 'bass house': 'house',\n", + " 'electro house': 'house',\n", + " 'tropical house': 'house',\n", + " 'french house': 'house',\n", + " 'melodic house': 'house',\n", + " 'organic house': 'house',\n", + " 'progressive house': 'house',\n", + " 'latin house': 'house',\n", + " 'deep house': 'house',\n", + " 'slap house': 'house',\n", + " 'chicago house': 'house'\n", + " \n", + "} \n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Map hip-hop to rap -> Models without this are too confused due to the genres being so alike" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "genre_mapping = {\n", + " 'hip hop': 'rap'\n", + "} \n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels_normalized]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Or remove hip hop\n", + "\n", + "X = X[np.array(y_labels_normalized) != \"hip hop\"]\n", + "y_labels_normalized = np.array(y_labels_normalized)[np.array(y_labels_normalized) != \"hip hop\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run this code in case the last two cells were not executed" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "y_labels_normalized = y_labels\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extra: Keep only a popular type of genre" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered dataset size: 13254 entries (from 98347 original)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "original_labels_list = y_labels_normalized\n", + "y_labels_normalized = np.array(y_labels_normalized)\n", + "\n", + "\n", + "popular_genres = {\n", + " 'pop', 'rock', 'rap', 'hip hop', 'metal', 'country', 'jazz',\n", + " 'blues', 'classical', 'reggae', 'punk', 'funk',\n", + " 'r&b', 'soul', 'trap', 'techno', 'folk', 'indie'\n", + "}\n", + "\n", + "y_lower = np.array([genre.lower() for genre in y_labels_normalized])\n", + "\n", + "mask = np.array([genre in popular_genres for genre in y_lower])\n", + "\n", + "X = X[mask]\n", + "y_labels_normalized = y_labels_normalized[mask]\n", + "\n", + "# Optional: print some info\n", + "print(f\"Filtered dataset size: {len(y_labels_normalized)} entries (from {len(original_labels_list)} original)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chart the leftover genres" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from collections import Counter\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "y = y_labels_normalized\n", + "\n", + "freq_original = Counter(y)\n", + "\n", + "orig_genres = list(freq_original.keys())\n", + "orig_counts = list(freq_original.values())\n", + "\n", + "fig, axs = plt.subplots(figsize=(15, 6))\n", + "\n", + "sns.barplot(x=orig_genres, y=orig_counts, ax=axs)\n", + "axs.set_title(\"Original Genre Frequencies\")\n", + "axs.set_xlabel(\"Genre\")\n", + "axs.set_ylabel(\"Count\")\n", + "axs.tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove all songs with genres which do not come up more than n times" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class counts: Counter({'rap': 2100, 'metal': 1721, 'country': 1117, 'pop': 975, 'jazz': 948, 'blues': 947, 'classical': 849, 'folk': 819, 'funk': 738, 'techno': 626, 'indie': 525, 'rock': 494, 'soul': 457, 'punk': 360, 'reggae': 291, 'trap': 258, 'r&b': 29})\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_217466/1765738642.py:49: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " axs.set_xticklabels(le.classes_)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import LabelEncoder\n", + "import numpy as np\n", + "from collections import Counter\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# --- Encoding/Standardizing X ---\n", + "# Remove compex object variables\n", + "if np.iscomplexobj(X):\n", + " X = np.abs(X)\n", + "\n", + "# Standardize the features\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# --- Counting Labels ---\n", + "# Check class counts.\n", + "counter = Counter(y)\n", + "print(\"Class counts:\", counter)\n", + "\n", + "# Keep only classes with at least n samples. At least 2 or any other % 2 == 0 number\n", + "n = 600\n", + "classes_to_keep = {cls for cls, count in counter.items() if count >= n}\n", + "indices_to_keep = [i for i, label in enumerate(y) if label in classes_to_keep]\n", + "\n", + "y = np.array(y)\n", + "\n", + "# Filter the data.\n", + "X_filtered = X_scaled[indices_to_keep]\n", + "y_filtered = y[indices_to_keep]\n", + "\n", + "# Encode the Genres into numerical values\n", + "le = LabelEncoder()\n", + "y_encoded = le.fit_transform(y_filtered)\n", + "\n", + "freq_leftover = Counter(y_encoded)\n", + "leftover_genres = list(freq_leftover.keys())\n", + "leftover_counts = list(freq_leftover.values())\n", + "\n", + "fig, axs = plt.subplots(figsize=(15, 6))\n", + "\n", + "# Plot normalized label frequencies.\n", + "sns.barplot(x=leftover_genres, y=leftover_counts, ax=axs)\n", + "axs.set_title(\"Normalized Genre Frequencies\")\n", + "axs.set_xlabel(\"Genre\")\n", + "axs.set_ylabel(\"Count\")\n", + "axs.tick_params(axis='x', rotation=45)\n", + "axs.set_xticklabels(le.classes_)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove highly collerating data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropping columns: ['feature_5', 'feature_8', 'feature_9', 'feature_10', 'feature_12', 'feature_14', 'feature_16', 'feature_20', 'feature_21', 'feature_23', 'feature_25', 'feature_26', 'feature_29', 'feature_30', 'feature_31', 'feature_32', 'feature_34', 'feature_37', 'feature_38', 'feature_41', 'feature_43', 'feature_44', 'feature_46', 'feature_48', 'feature_50', 'feature_51', 'feature_53']\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "CORRELATING_VALUE = 0.9\n", + "\n", + "X = X_filtered\n", + "\n", + "def remove_highly_correlated_features(X, threshold):\n", + " # If X is a NumPy array, convert it to a DataFrame.\n", + " if isinstance(X, np.ndarray):\n", + " # Create column names if not provided\n", + " X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])\n", + " else:\n", + " X_df = X.copy()\n", + " \n", + " # Compute the absolute correlation matrix\n", + " corr_matrix = X_df.corr().abs()\n", + " \n", + " # Create an upper triangle matrix of correlations\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " \n", + " # Identify columns to drop: any feature with correlation greater than the threshold\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " print(\"Dropping columns:\", to_drop)\n", + " \n", + " # Drop these columns from the dataframe\n", + " X_df_reduced = X_df.drop(columns=to_drop)\n", + " \n", + " # If original X was a NumPy array, return as a NumPy array.\n", + " if isinstance(X, np.ndarray):\n", + " return X_df_reduced.values\n", + " else:\n", + " return X_df_reduced\n", + "\n", + "\n", + "X_reduced = remove_highly_correlated_features(X, threshold=CORRELATING_VALUE)\n", + "\n", + "# -- Create a feature correlation matrix --\n", + "df_features = pd.DataFrame(X_reduced, columns=[f'feature_{i}' for i in range(X_reduced.shape[1])])\n", + "\n", + "# Compute the correlation matrix.\n", + "corr = df_features.corr()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "sns.heatmap(corr, cmap='coolwarm', annot=False)\n", + "plt.title('Feature Correlation Matrix')\n", + "plt.show()\n", + "\n", + "X_filtered = X_reduced\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a Test-Train split" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set shape: (8672, 28) (8672,)\n", + "Test set shape: (2168, 28) (2168,)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_filtered, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded\n", + ")\n", + "\n", + "print(\"Training set shape:\", X_train.shape, y_train.shape)\n", + "print(\"Test set shape:\", X_test.shape, y_test.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperparameter tuning" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'distance'}\n", + "Best cross-validated accuracy: 0.6753\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "\n", + "# Define the parameter grid\n", + "param_grid = {\n", + " 'n_neighbors': list(range(1, 25)),\n", + " 'weights': ['uniform', 'distance'],\n", + " 'metric': ['euclidean', 'manhattan']\n", + "}\n", + "\n", + "# Set up the KNN model\n", + "knn = KNeighborsClassifier()\n", + "\n", + "# Set up GridSearchCV\n", + "grid_search = GridSearchCV(\n", + " estimator=knn,\n", + " param_grid=param_grid,\n", + " cv=5,\n", + " scoring='accuracy',\n", + " n_jobs=-1\n", + ")\n", + "\n", + "# Run the grid search\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "# Get the best model\n", + "final_model = grid_search.best_estimator_\n", + "\n", + "# Optional: print best parameters\n", + "print(\"Best parameters:\", grid_search.best_params_)\n", + "print(f\"Best cross-validated accuracy: {grid_search.best_score_:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluation Charts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Confusion Matrix - Shows what was the predicted value compared to the true value when evaluating the Model\n", + "\n", + "The more values in the diagonal line the better" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "# Predict on the test set\n", + "y_pred = final_model.predict(X_test)\n", + "\n", + "# Compute confusion matrix\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "# Display it nicely\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n", + "disp.plot(cmap='Blues')\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.title(\"Confusion Matrix\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/ai_analysis/old_pytorch/rrf_pytorch.ipynb b/src/ai_analysis/old_pytorch/rrf_pytorch.ipynb new file mode 100644 index 0000000..883e545 --- /dev/null +++ b/src/ai_analysis/old_pytorch/rrf_pytorch.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read CSV with TrackId and Genre Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "tracks_info_df = pd.read_csv('combined_dataset.csv')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read processed song data from pkl file" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 100390 processed tracks\n" + ] + } + ], + "source": [ + "import pickle\n", + "import os\n", + "\n", + "\n", + "results_file = 'audio_features_backup.pkl'\n", + "\n", + "if os.path.exists(results_file):\n", + " with open(results_file, 'rb') as file:\n", + " saved_data = pickle.load(file)\n", + " X = saved_data.get('X', [])\n", + " y_labels = saved_data.get('y_labels', [])\n", + " \n", + " processed_files = set(saved_data.get('processed_files', []))\n", + " print(f\"Loaded {len(processed_files)} processed tracks\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Turn X into an np array for easier processing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of feature matrix X: (100390, 55)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "X = np.array(X)\n", + "print(\"shape of feature matrix X: \", X.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remove all entries where the genre is defined as None" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "X = X[np.array(y_labels) != None]\n", + "y_labels = np.array(y_labels)[np.array(y_labels) != None]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remap genres" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replace sub-genres with their parent genre based on a predefined mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "genre_mapping = {\n", + " 'lo-fi beats': 'lo-fi',\n", + " 'chillwave': 'lo-fi',\n", + "\n", + " 'nu metal': 'metal',\n", + " 'folk metal': 'metal',\n", + " 'heavy metal': 'metal',\n", + " 'metalcore': 'metal',\n", + " 'power metal': 'metal',\n", + " 'industrial metal': 'metal',\n", + " 'glam metal': 'metal',\n", + " 'melodic death metal': 'metal',\n", + " 'progressive metal': 'metal',\n", + " 'thrash metal': 'metal',\n", + " 'gothic metal': 'metal',\n", + " 'groove metal': 'metal',\n", + " 'death metal': 'metal',\n", + " 'alternative metal': 'metal',\n", + " 'black metal': 'metal',\n", + " 'speed metal': 'metal',\n", + "\n", + " 'acid house': 'acid',\n", + " 'acid techno': 'acid',\n", + "\n", + " 'afrobeat': 'afro',\n", + " 'afropop': 'afro',\n", + "\n", + " 'art rock': 'rock',\n", + "\n", + " 'britpop': 'alternative rock',\n", + " \n", + " 'art pop': 'pop',\n", + " 'acoustic pop': 'pop',\n", + " 'bedroom pop': 'pop',\n", + " 'soft pop': 'pop', \n", + " 'folk pop': 'pop',\n", + " 'norwegian pop': 'pop',\n", + " 'acoustic pop': 'pop',\n", + " 'dream pop': 'pop',\n", + " 'chamber pop': 'pop',\n", + " 'german pop': 'pop',\n", + " 'bedroom pop': 'pop',\n", + " 'city pop': 'pop',\n", + " 'dance pop': 'pop',\n", + " 'art pop': 'pop',\n", + " 'indie pop': 'pop',\n", + "\n", + " 'alt country': 'country',\n", + " 'traditional country': 'country',\n", + "\n", + " 'blues rock': 'blues',\n", + "\n", + " 'brooklyn drill': 'drill',\n", + "\n", + " 'gangster rap': 'rap',\n", + " 'memphis rap': 'rap',\n", + " 'rock rap': 'rap',\n", + " 'meme rap': 'rap',\n", + " 'punk rap': 'rap',\n", + " 'emo rap': 'rap',\n", + " 'jazz rap': 'rap',\n", + " 'melodic rap': 'rap',\n", + " 'cloud rap': 'rap',\n", + " 'k-rap': 'rap',\n", + " 'rage rap': 'rap',\n", + "\n", + " 'edm trap': 'trap',\n", + " 'italian trap': 'trap',\n", + " 'dark trap': 'trap',\n", + "\n", + " 'hip-hop': 'hip hop',\n", + " 'southern hip hop': 'hip hop',\n", + " 'west coast hip hop': 'hip hop',\n", + " 'east coast hip hop': 'hip hop',\n", + " 'finnish hip hop': 'hip hop',\n", + " 'german hip hop': 'hip hop',\n", + " 'underground hip hop': 'hip hop',\n", + " 'norwegian hip hop': 'hip hop',\n", + " 'experimental hip hop': 'hip hop',\n", + " 'alternative hip hop': 'hip hop',\n", + " 'latin hip hop': 'hip hop',\n", + "\n", + " 'jazz blues': 'jazz',\n", + " 'vocal jazz': 'jazz',\n", + " 'french jazz': 'jazz',\n", + " 'free jazz': 'jazz',\n", + " 'soul jazz': 'jazz',\n", + " 'jazz fusion': 'jazz',\n", + " 'nu jazz': 'jazz',\n", + " 'jazz funk': 'jazz',\n", + " 'cool jazz': 'jazz',\n", + " 'jazz beats': 'jazz',\n", + " 'jazz house': 'jazz',\n", + " 'indie jazz': 'jazz',\n", + " 'smooth jazz': 'jazz',\n", + "\n", + " 'melodic techno': 'techno',\n", + " 'minimal techno': 'techno',\n", + " 'hypertechno': 'techno',\n", + " 'hard techno': 'techno',\n", + " 'hardcore techno': 'techno',\n", + " 'dub techno': 'techno',\n", + "\n", + " 'future house': 'house',\n", + " 'tech house': 'house',\n", + " 'bass house': 'house',\n", + " 'electro house': 'house',\n", + " 'tropical house': 'house',\n", + " 'french house': 'house',\n", + " 'melodic house': 'house',\n", + " 'organic house': 'house',\n", + " 'progressive house': 'house',\n", + " 'latin house': 'house',\n", + " 'deep house': 'house',\n", + " 'slap house': 'house',\n", + " 'chicago house': 'house'\n", + " \n", + "} \n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Map hip-hop to rap -> Models without this are too confused due to the genres being so alike" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "genre_mapping = {\n", + " 'hip hop': 'rap'\n", + "} \n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels_normalized]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Or remove hip hop\n", + "\n", + "X = X[np.array(y_labels_normalized) != \"hip hop\"]\n", + "y_labels_normalized = np.array(y_labels_normalized)[np.array(y_labels_normalized) != \"hip hop\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run this code in case the last two cells were not executed" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "y_labels_normalized = y_labels\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extra: Keep only a popular type of genre" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered dataset size: 13254 entries (from 98347 original)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "original_labels_list = y_labels_normalized\n", + "y_labels_normalized = np.array(y_labels_normalized)\n", + "\n", + "\n", + "popular_genres = {\n", + " 'pop', 'rock', 'rap', 'hip hop', 'metal', 'country', 'jazz',\n", + " 'blues', 'classical', 'reggae', 'punk', 'funk',\n", + " 'r&b', 'soul', 'trap', 'techno', 'folk', 'indie'\n", + "}\n", + "\n", + "y_lower = np.array([genre.lower() for genre in y_labels_normalized])\n", + "\n", + "mask = np.array([genre in popular_genres for genre in y_lower])\n", + "\n", + "X = X[mask]\n", + "y_labels_normalized = y_labels_normalized[mask]\n", + "\n", + "# Optional: print some info\n", + "print(f\"Filtered dataset size: {len(y_labels_normalized)} entries (from {len(original_labels_list)} original)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chart the leftover genres" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bt2526tQpa9KkiRUoUMDee+89X5d5wyW/PnzxxRf2+OOPW8+ePW3btm3O/Xf27Nl2yy232NNPP+3DKq8cIXo6l3wHvPBJM/mJdOXKlbZmzRo7cuSIlStXznr27GlmZq+88op5PB4rWrQoS45cpnnz5llgYKAVKVLEbr/9douOjnbGfOzYsZYnTx679dZbrVy5clawYEHbtGmTjyv2nd27d9uhQ4fs77//NrPz98d58+ZZgQIFrG7durZ8+XJbuXKlNWrUyKpUqcLs3iuwZs0ay5Url02dOtXMzs/m93g89tprr3m9SLVo0cIKFSqUoe+HF1q8eLF5PB779NNPnW3Lly+3rFmzWsuWLZ1tY8eOtcKFC1vt2rWtffv21qpVK8uTJw/j+A+rVq2yTz75xJYuXeo8fjds2GCVK1e2W2+91d555x2bNWuWNW7c2MqXL89j/CpcGBZ9+OGHVq5cOStWrJjzZdC5c+e8govExET75Zdf7MEHH7S6detmuA/ro0ePtixZsji/hrjzzjtt2rRpZva/4OL333+3rFmzWpkyZQjQXTzxxBOWL18+69q1q1WuXNmqVq1qL7/8shMCxcTEWHh4uFWqVMmmT5/uXI4P4hf3+eefW7Zs2ez1119P8WvPHTt2WOHCha1WrVrMqrxCzz33nLVu3dpq167tLN2SvLRL4cKFrVevXhe9HK9F/+7TTz+1//73vzZz5kznNWbx4sUWHh5urVu3tldeecVmzpxpderUsbJlyzrPoRlxWaxz587ZwoULLU+ePNaxY0evtmPHjln16tXN4/FY8+bNfVNgOnLu3DnbsGGDRUREeAXpe/bsseHDh5vH43Fe0/G/x9sPP/xgDRs2tEmTJnl9AfvFF1/Yjz/+aGbnH7+5c+e2atWqWadOnax///6WI0cO5/0Srk7y8+POnTudbcmTsB577DFr0qSJk8n17t3bcubMaQUKFLC4uLgbX6yPffbZZ5YlSxZr3ry5FS5c2PLmzWuzZs1y7rMzZsyw7NmzW9euXZ3LpPUvZwnRbwI7d+60F154wczM3n//fStfvrzXA3T27NkWHR3tzID59NNP7e6777YePXrY77//7pOa04PkB+/JkyetTZs2NmPGDDt48KAtWbLESpcubWXLlnXekK9fv94WL15s8+bNy9AfhsaMGWORkZFWunRpi4qKsi1btpjZ+SVcPvjgAwsLCzOPx2OPPPKI9e/f31nahQ82l+eNN96w9u3bm5nZL7/8YhEREc6XY8l2795tr732GjOn/6FTp06WO3du++yzz5xtK1eutPz583utSzd79mwbOnSoNWnSxJ5++mlm8v9D8oznYsWKmZ+fnzVr1syZHf3tt9/aXXfdZR6Px9q3b2+vvPIKj/GrcGEY8ddff9mWLVusWbNmFhAQYO+8847T9s8Q3ez8+4GAgABbuHDhjSvYx06ePGkvvfSSs1yG2fkQfcSIEV79YmNjbdGiRc59kQDd25QpUyw8PNyZXPHxxx+bx+OxChUq2AsvvOA8lpcvX26FChWy9u3bO6/x8JaUlGSnT5+2Dh062GOPPeZsM/N+Lty5c6flzp3bGjRowHPkJVz4nDh27FjLly+fPfroo1a/fn3z9/e3t956y8zOz46eO3eu3X777Va7dm1flZtuDR482AoXLmylS5e2SpUqWdWqVZ0QaP78+dauXTvLly+f1a5d29q2bZvhvoy88DX3wr8//vhjy5kzZ4ogvV+/fvb555+n+CVkRpaUlOQVPm7ZssV+//13Z9vGjRtTBOk7d+60MWPGeL3Gw+ynn36yXLly2ZNPPukE5mbnZ0SXKVPGnn/+eTM7/yX3J598YsWLFzc/Pz9bsWIFnxGvUfL99aOPPrJSpUrZiy++6NXWtGlT69u3r7Mt+bngr7/+uuG1+kryGP3999/Wv39/e/PNN522zp07W8GCBW3GjBlOkP7WW29Z/vz5083qGIToN4Enn3zSSpUqZe3atbMsWbKk+Kb2tddes7x58zqB+ZNPPml9+/bNkN+EXamYmBi7++67rVWrVs7B8ZKSkuzrr7+2kiVLegXpGd3QoUMtf/78NnfuXFuxYoXdddddljdvXlu3bp2ZnV+38v3337cyZcpYkyZNnMvx0+XL17t3b2vdurUdO3bMwsLCvA5++dZbb9lzzz3n4wrTngs/6PTq1csCAwNt6dKlzrYVK1akCNJxcW+//bblz5/f1q9fb3/99ZfFxsZaVFSUNWzY0NavX29m558zGzZsaCVKlHDeCPEYv3wffPCBE4APGDDAGjRoYGZmmzZtsubNm1uVKlWcYyGYpVwD08ysRo0aNnPmzBtY9Y11sRmPyTOhk0Od1q1bW79+/Zz+derUsSeeeMLpz+u29xicPn3aJk6c6EzI+PDDDy1Xrlz24osv2v3332+hoaE2adIki4+PN7PzQXqxYsWsWbNmF10TGOdVr17d9eDeyRMudu3alaHWRr0Wu3btsieeeMJWrlzpbBs+fLhlzpzZ+YB++vRpmzZtmrVo0SJDzo6+Wi+88IKFhITYN998Y2Zmr776qnk8HitXrpwT/Jw+fdoOHz7sLOFklvEC9KVLl9rAgQOtadOmNm3aNGdp1I8//tgCAwOtWbNmNnPmTOvfv78VKlTI9u/f78uy04zk144Ll7kKDw+3IkWKmL+/v3Xq1Ml5XCcH6RcebDSj3M8u19mzZ61Xr15ey1ImJiY6z3mjR4+2sLAw51dkJ0+etAULFrDcbCr6+OOPLWvWrDZ58mSLjY31ahs4cKDlypXLXn75ZevSpYsFBwdnyNf59evXW2hoqFWuXNmWLFni1dalSxcLCQmxmTNnOp8T01M2SYh+k2jZsqV5PB6vddmSPyCtWbPGatasaSVKlLCGDRta9uzZmT10GZKSkmz+/Pl2xx13WHBwsNcbgOQgvVy5cnb77bdn+DfqX3/9tVWvXt1WrVplZuePTp0rVy4rW7as5ciRwwnS4+Pjbd68ec4ByvDv9u3b57x5/Oqrr6xatWoWFBTkzEBPvu/169fP2rZt67xhwnnJ4/PVV1/ZvHnzLHPmzHbrrbd6vZgnB+kXLu2ClPr162etW7c2s/+N69atW61EiRLWvXt3Z/tXX31lNWrUsHLlytnevXt9Vm96k5SUZD179jSPx2MtWrSwnDlzer0x/+abb6x169YWHR3tdbDWC4P06dOnm8fjuWnfrF/4Wrt582bbuHGj/fHHHyn69enTx1q1amVmZg0aNLCIiAgnYIe3119/3b7//nv75ZdfbP/+/fbbb79Z6dKlnYNjbd261YKCgqxo0aJeSzx8/vnnVr58efvzzz99WX6acuFs8+PHj1vdunWtbdu2zrbkPnv27LEnn3yS2YBXYNGiRebxeCwsLMx5r5ls+PDhliVLFmdG+oWP9Yz+/vxy/PHHH9ahQwf78MMPzez88g+BgYH29NNPW+nSpa1ixYp2+PDhFJdL6z+3T23z58+3rFmzWseOHe3ee++1smXLWnR0tBP+rl+/3kqVKmWlS5e2kiVL2nfffefbgtOIHj16WNeuXZ3PMjExMXbLLbfYK6+8Yj/99JO9//77VrNmTWvUqJGzPNPGjRstd+7cVr9+fV+WnmadO3fOGjdubDVr1nSe4xISEpzXmV27dlm9evWcYxIlfzZM/mUJrs2xY8esQYMGzrEHkyXfx48ePWqdO3e2UqVKWfXq1TP0c0G9evWcpWf/OYGle/fuliVLFpszZ46Pqrt6hOjp3OnTp+3MmTPWqVMna9iwoVWtWtWeeeYZO3r0qFe/jz76yAYPHmw9evSwrVu3+qjatO1ibwaPHz9uH330kd16661Wr169FP2TQ82M/FO9MWPGWL9+/Wzs2LFmdv6Ddf78+W3y5Mm2e/duK1asmIWEhDhvMk+dOmUffvihBQYGWocOHXxZepr3008/WZYsWWzGjBlmdv6DTps2baxYsWLOQXYOHjxoTz31lOXPn9/r53z4n4ULF1r27Nnt6aeftv/7v/+z6OhoCwwMTLG0S6ZMmaxdu3Y+rDTtSkpKsi5dujgzoxMTE52g4r333rOgoCBnLUAzs3Xr1lnZsmWtWrVqlpiYmOE+bF+L5JlZL730kpl5h0Dr16+3++67z2rVqmWzZs1KcdnTp0/flD95TkpK8hqH4cOHW+HCha1w4cKWI0cOmzZtmnMcjuT2mjVrWvPmza1YsWIZbtmBS7lwHF955RXz8/OzrVu3Oh9uFi9ebKVLl3Z+vbh8+XJ76KGHbMyYMc5lk/9NXuIlo0t+fksej+SZuslL4kyYMMGr/5AhQ6xy5crp5mfLvnCx8Ltfv37m8Xic9fgvfF0ZOXKkeTwe++ijj25YjTeT+fPn259//mkbNmyw8PBwe/31183MbMKECebxeCw0NDRdzRJMbfv27bOKFSs6r8tm5w+23LZtW6tZs6bz2frMmTO2b98+r9ejjGz27NmWL18+rxDx2WeftXvvvder38qVK+2uu+5yjmdw7tw5++67727aCQHXIvm58fPPP7fg4GCrUKGCtWjRwipXrmxlypSx4sWLW+HChc3j8ViePHksIiLCGjduzOv1VXriiSfstdde89p26NAhCwsLc45R5vYZZ9++fRlyctuWLVucXzWZnZ/MUqBAAa9jaSXr3bu3bdu27UaXeM0I0dMptwfro48+apUqVUoRpB85csTM+AnzpSSP6S+//GKbNm1yDhSRkJBgH330kRUuXDjFkg9JSUleR7vOaGbPnm1hYWG2efNmO3TokJmZNWvWzB5//HEzOx9YNG7c2PLly2e1atXyWmf+o48+4s3RZejZs6flzJnTOaJ38vrIhQoVsvDwcIuKirLw8HAOfuni2LFjVrVqVRs0aJCz7ejRo9ahQwcLDAz0mpG+evXqdPlCfj39+uuv9ueff1piYqKtWrXKPB6P13IiZuePxVGpUiWv15ykpCRbv3691wF3cGnJXzbce++91rJlS8uePbstWLDAaU9+/ly/fr3VrFnTHnnkEa/L36wB8YVfzpiZjRo1ygoWLGhffPGFmZm1b9/eAgMDbcKECc5syXfffdc8Ho9FRkYSoF/gwmBy9erVNmXKFJs7d65X27x586x48eI2Z84c2717tzVt2tRrSZIL30fy5dj/xuDTTz+1Fi1aWK1atez+++93QqOXXnrJPB6PtWrVyjp27Gjt2rWzwMBAXrMv04cffugVwHXr1s1y5MiR4qfhZueXHONxfvk+//zzFMsQvPTSS9a0aVPn17czZsywLl26WO/evTP0Z8hdu3ZZaGhoivc/S5cutRIlSjiz+OFt/PjxVqJECTM7P6Fl0qRJNnbsWIuKirKEhASv15B3333XsmXLxi8YL8PChQttzpw59vfff1t0dLQ9/vjj9s0339iKFSvs008/taVLl1rhwoWta9euNmnSJFYguErHjh2zcePGpTgI69GjR+2uu+6y4cOHO685yffl1atXp/jiPKNISkqyAwcO2B133GEPPfSQbdiwwWmrU6eOhYaGXjRIT48I0dOh5AfpypUr7bHHHrPOnTvbpEmTnPYBAwZYlSpVbPTo0Xb48GEbNmyYRUZG2unTp/nA8w//+c9/vGbzffjhhxYcHGyFCxe2W265xZntcvbsWVu4cKEVKVLEa/2xjGzlypX28MMPOwfTSEpKskOHDlnhwoXt3XffNbPzLz733XefrVmzxrnvcR+8uEvN1u3Xr58FBAQ499W9e/fa2rVrbezYsfbxxx/brl27bmSp6UpcXJyVKFHC3njjDTP7X1B0+PBhq1q1qkVERNjixYt9WWKaNXjwYCtRooTlyZPHoqOj7ZVXXrHnn3/esmbNatOnT7e9e/fagQMHrGHDhtawYUMe41fhwlAzeV3vZL169bJs2bJ5Belm578U37t3b4ZYpuD//u//vL4A27p1q9WtW9c+/vhjMzv/QTJ37tzWvHlzZ8bviRMn7I8//rABAwawnuoFLnxcrl+/3jwej3k8nhTH0YmLi7N69erZ7bffbqGhoVapUiXniwge2xf30Ucfmb+/vz355JPWr18/a9SokWXNmtW5ny5btszat29vTZs2tYcffphfhF6GpKQk27dvn2XKlMlatmzpFQJ17tzZNUg34/F+OTZt2mTZs2dPMQvw0Ucftdtuu83Mzv+yonnz5jZq1Cin/WYIPy5H8nPdd999Z7t377bDhw9bxYoV7dVXXzUz79fuqlWrWpcuXXxSZ1r3zTffWPHixa127drm8Xhs4cKFNnfuXMucObPXsQ3MzNauXWslS5Z0jheBi9uwYYMFBwfblClT7NixY3b33Xc7v1hOlpSUZK1bt/Za+g9XJ/k57/PPP/fK27p162aFChWyL774wut5cejQoVatWrWLLoF1s0n+svWfZs+ebWXLlrWuXbvat99+62yvU6eOhYeH26effpruX0sI0dOp+fPnW1BQkLVr186GDRtmHo/H2rZt6/yE9PHHH7cyZcpYsWLFrECBAs6a1PDWvXt38/Pzsw8++MD++OMPK168uL3xxhu2fv16e/rpp83j8TgH2jp79qwtWrTIcufOneHX8963b58VKVLEcubMaf/5z3+82lq3bm0FCxa0l156ye6++26rWrWq80SZEUKfK/XPA7wsW7bMVq9enaJfv379LGvWrDZ79mw+IF6hZs2aWc2aNVPMFujYsaNlzpzZ6+A7OG/27NkWEhJiCxcutOnTp9sTTzxhWbNmtX79+tnrr79u/v7+FhYWZnfccYdXyMZj/PJdGEi+9dZb1rdvXxs3bpzX8mC9evWyHDly2Ny5c23v3r3WokULa9OmjdN+s4/3woULnfvW0aNH7cyZM/b2229bQkKCxcTEWGhoqL3yyitmZvbAAw9Yrly5bNiwYV7XwfPl+SVZkg82+/DDD1vXrl1t2rRpFhwcbD169HD6JY9VfHy8LVu2zD755BPn9ZtxvLgzZ85Yw4YN7cknn3S2nTx50vr3728BAQHOgVeTvyRjHN1d7Euab775xkJDQ+2+++5zDuJodv6gZLly5XIOxIzLN3r0aBs+fLjly5fP/P39rXv37vbzzz+bmdmPP/5o4eHhlj9/fitdurSVKlUqw91nk++HCxYssNDQUOc15eGHH7Z8+fLZ2rVrvfo2btzYxowZ45Na04P/+7//M4/HY9WqVXO2tW3b1vLkyWPLli1zfsWYnF1khPDxam3fvt1GjBhhQ4YMcbb16dPH8ubN63z5EB8fb08++aSFh4czyeoaXPh6dObMGScXSp48aGZ2zz332B133GH9+/e3559/3rp06WKBgYEpZq7fjHr06GFdunRx3tskHxw02QcffGAlS5a0bt26ec1Ir1y5spUsWTLdLy9EiJ4O7dy504oXL+58cDx27JjlypXLBgwY4PWBesmSJTZz5kwOXPQvHnvsMcuWLZu9/vrr1qdPH6+2559/3itIP3PmjH366acsQ2Jm33//vRUtWtSqV6/u9XPQH374wdq0aWNVqlSxli1bEq5dwuTJk61Ro0Ze39K2aNHC/P39bc2aNSn6N2/e3G677TabOXNmhvtQczmS3/AcPnzYWV7I7PwswcjISOvXr5/X/bBv37722WefsS7tP6xYscK6d+/uHFjQ7Pzs1FdffdVy5Mhhixcvtm3bttlHH31EyHaV/rmW7y233GL33XefZcuWzerXr+/164i+ffuax+NxAo2McIDMf4Zp7777rjVo0MBrhlryG/jk8ejTp49VrFjRqlevzozpC8THx9u9995r99xzjzVt2tSCgoJs69atlpSUZO+8845lyZLF64uHiz2O0/uMoetl4cKFNmHCBCtZsqSzhnTy+v3JBxbt3r27nTlzJsWXuHCX/KV28lh9++23VqBAAWvdurXXjPQWLVpY3bp1fVJjevWf//zHgoKCbPny5fb111/blClTLDAw0Hr27Ol8Xty2bZuNGTPGXnzxRed+m9GeAxYvXmzZsmWzt956y2tJsfvvv9/y589vzz33nL3zzjs2cOBACwwMvCmPRZIaTp48abVr17bu3btbqVKl7KGHHjKz8/enDh06WEBAgJUpU8aioqIsODiYZa4uIS4uzipXrmz58uXzWmJt+/btVqtWLcuRI4dVqVLFatasabfddhtjeY2SX3+SD8Z66NAhe+aZZyxnzpxey7UMHDjQGjVqZKVKlbJWrVo5X5zfzP55rIM1a9bYsGHDUhwjcN68eXbbbbdZ+/btve6PN8OXO4To6dDPP/9sVapUMTOzHTt2WGhoqPXs2dNpv3Ahf1zcPz/EJB+sqFy5cikOyvr888+bv78/swwu4vvvv7cKFSpY9+7dvWYImZ0PMpPHmXDt4pYvX25hYWHWrl0751vapKQku//++y1v3rwpZqQPGjTIgoKCrGDBghn6AE+XMn/+fKtWrZqFh4fbY489Zlu3brXExER74YUXrFKlShYVFWXjxo1z1qXlCzFvF/7K5J/PeYcPH7bmzZun+LLRLON9wE4tW7Zssfvuu8+Z2bZr1y6766677N5777VFixY5/T799FNbuHBhhv3C4rXXXrOoqCh76KGHnKUHoqOjrXfv3k6fli1bWmxsLMsKXcThw4etePHi5vF4bNy4cc72U6dO2dtvv22ZM2e24cOH+7DC9Cf5J/Xvv/++NW/e3Jo0aeLMrEq+77Vv395atGjhyzLTnbFjx1q7du2cX+klj+WGDRssZ86cdt9993mFFEzQuHznzp2zBg0aeAVwZucPDp48I/1ix4XJaK/vp06dsvvvv9+GDh1qZueXtfnll1/s+eeftyVLlljz5s2tVq1aVrRoUbvnnnu81uxHSsnPi++8844VL17c2rdv77TNmzfPXn75ZXvxxReZ9HcZNm3aZMWKFbMKFSp4TWD7448/7Pnnn7c+ffrY5MmTbceOHb4r8iaQ/LrzySefWKdOnZzP6Pv377dRo0alCNLPnj1r8fHxzooQN7sLj3WwZMkSCw0NtTx58tjQoUNTHAvrlVdesZw5c1qbNm28ZqSnd4To6dCWLVusUKFCtnDhQitcuLD17NnT+UD93XffWe3atTPEt2DXIvnJ8eDBg862ESNGmJ+fn/OT5wuNGTPGgoOD+YnZRWzatMkqVapkPXr0uOiBSwgyLi75g9+aNWuscOHC9uCDDzoz0pOSkqxVq1aWL18+W716tfNTqSeeeMJiYmK87rcZ3YX3r2+//dby5ctnw4cPt//X3r3H5Xz//wN/XB0kJdlyGqOcQ1FS2zKGoSE5xFjKYVEOmSbTwb5ZDsPYmJackmVoUzKnMefDqNAKM5HDkBzKoZRU1/P3x369P12zhjFXh8f9L9f7/b4uz67bdb0Pj+v5fr1mzZoljRo1EhcXF0lISBC1Wi07d+6UQYMGyRtvvCE9evR4bEIt+lNycrI0adJEbG1tH+tk+fDDD8XJyUlLlVUsYWFh4ujoKG+//bbG3RCpqani6OgoPXr0kE2bNj32vMoWaBSLjIyUzp07y+DBg+X27dsSGhoqOjo6MnToULGxsdEYdoDHHU137tyRXr16SadOnaR79+4SFRWlrMvNzZWVK1cqwzXRkxXfUj916lQREQkPDxcHBwcJCQnRmGx+xIgRyt0S/Ez+vb+G4HFxcaJSqcTb21sJ0ou3Wbx4sejr60vv3r01AjcG6U8nLy9PunTponzP8/PzlffO19dXjI2NZdKkSZU+gMvNzRU7Ozvx8fGRzMxMmTBhgnTq1Enq1asnjRo1kgULFkhWVpbcvHmTDS3PIDs7WyIiIqRFixZKRzo9u+TkZLG2tv7bBjZ6cWJiYqR69eoybdo0jR8Xb9y4IcHBwWJsbKwxtEtlUjzXQZcuXURHR0cOHDggS5culfr164u/v7/GMSQ6Olqsra2lY8eOFWrSYIboZVzxSfdvv/0mBw8eVG6TGDZsmBgbG0v//v01tg8ICJC33npLMjIyXnqt5UXxe7p582bp37+/rF27Vln38ccfi4GBgXz//fePPY8BeulOnDghHTp0EFdX10p/8v201Gq1Eobt379fGjduLIMGDVKC9KKiInF1dRUjIyMZOnSouLq6snO6hPXr12vcPnv+/Hn54osvZMaMGcqyxMREad++vTg7O2sMj/PgwYPHJnEkTcnJydK2bVvx8PBQuqzu378vb731lsYYyvT0/hr0HDx4UJo0aSKmpqaPTZB37tw56dSpk9ja2sqhQ4deZpllTsnwcdWqVdKxY0d5//335fr167J06VIZPHiweHl5KcO6VNYfGZ7G9evXpVevXtKlSxeNhoFHjx7JvHnz5J133mHY+wQlb6mfNGmSiPzZhebn5ycdOnSQrl27yuzZs2X48OFibGz8t80F9KeS+8Rz584pwzUlJCSIrq6ujB49WuOie9myZTJw4EBxdnZmcP4MSo7PGxISItWqVZPU1FQR+d/+cubMmdK1a1epWbOm0mFZmfcFq1evFkNDQzExMZH+/fvL6tWrReTP4dW6du1a6e4Ge1FycnIkIiJC2rRpI87Oztoup9wqbmDz9PR8bKLqyvy9fVFOnz4tr732mqxYsUJj+aVLlyQ3N1cKCgrks88+E5VKpQzlVtkUz3Xg4OCgLFu4cKESpBc39AYFBUlYWNhjIz2UdwzRy4GNGzeKsbGxNG3aVAwMDCQqKkqioqKkQ4cO0rdvX9myZYvs3r1bfH19pUaNGpViMoPnFRcXJwYGBrJgwYLHfsX19fUVAwMD2bBhg5aqK5/i4+Nl5MiRvLB5gpLh+a1bt5QulpSUFGncuLG4urpqjJEeHBwsrq6u0r9/f95h8v9duXJFOnbsqFxwZ2VlSf369cXQ0FB8fHw0to2PjxdbW1sZOHCgbNu2TRvlllsnTpyQVq1aSd26daVPnz4yYMAAsbGxUX6A4In6v7Nr1y6l87z41lwXFxeJj4/X2O7MmTPi7e3NfapoftYiIiKUIL24YaD4PWKw8WQXLlyQ3r17S/fu3SUiIkIKCwulW7duMnnyZA6F85RK3lJ//PhxEfkzjFy9erV88MEHYm9vLwMHDuQx+x+U/IxNnTpVWrZsKa+++qp07NhRtm7dKsnJyaKrqytjxoyRI0eOSHZ2tri4uMj69euV53Hf+GSxsbHSoUMHWbVqlYiIPHz4UN577z2pW7euJCcnK00FLi4u8vPPP8vs2bOlevXqcvv2be0WXgacPn1adu7cKSL/+6yNHz9ePDw8Ks2wDf+FnJwcCQsLE3t7e7l27Zq2yym3Tpw4Ifb29jJkyBCOyf+CHT58WBwcHCQ9PV3u378vS5YskS5duoi5ubm4urrKtWvX5Pbt2zJ37lxlUubK5K9zHQwZMkRZt3jxYmndurUyb56xsXGFvGOCIXoZVlRUJJmZmeLo6ChLly6Vc+fOyYwZM0RPT0+++eYbCQsLk/fff18MDQ3FyspKOnbsyOEJnkJ6errY2dlpTJononkyPnnyZFGpVBIXF/eyyyvXii+KeGHzuK1bt2p8P2NiYsTBwUEaN24szs7Osn37dklLS1OC9JKBWmFhYaWYTPBZFM8CnpKSIllZWXLkyBFp2LChdOzY8bHxKRMTE8XCwkLc3NzK/WzgL9vJkyfFwsJC3n77bY1uC34e/50DBw5I8+bNZdKkScrkt0ePHpWmTZvKgAEDHgvSi3GfWnqQfuHCBRHhe/QsLly4IAMGDBBLS0uxsLCQNm3a8MexZ1Tylvq/Nq/k5uZyH/kPSn5X161bJ3Xr1pW4uDiJjIwUPz8/0dHRke+++05OnjwpDRs2lAYNGkijRo3E2tpaeV/5OX2ymJgY8fX1FRMTE7GyslLuvL148aIMGDBADAwMpF27dtK0aVNp3ry5FBQUSExMjLRs2VKZTI/+dObMGQkMDJQaNWpUyEDoZXvw4EGF60zVhoSEBOncuXOFGiZDG4qPJ8XDscXHx4uurq54e3tLs2bNpG/fvuLv7y/h4eFiYWGhDLVYme98/OtcByWD9J07d8rChQslKCiowv7IwBC9DCr5Rc7NzZXAwEDJyspS1n/55Zeip6cnCxculBs3bsjly5clMzOTB6OnlJaWJvXr15cdO3Yoy/7uZNzf35+/7P4LvLB5XEZGhlhYWMjIkSMlLS1NTp8+rUzcOGfOHPH29hY9PT2JjIxUgvShQ4cqkw3S37t3755YWVnJ0KFDJTMzU44cOSKvv/66jBgx4rEOwOPHjythGz2bpKQkcXBwkNGjR3M4oRdg2rRp8tZbb8nHH3+szG9w9OhRad68uQwaNEgOHDig5QrLrpLHl8jISOnUqZNMnTpVHj58yGPPM0pPT5fNmzfLihUrlA5+dvI/m5K31HPYlme3d+9e8fT01GhquX//vixatEiqVq0qv/zyi1y6dEliY2Nl9erVlXZi5X/D399fzMzMZNGiRfLVV19Jy5Ytxd7eXmMYp/Xr18tXX30lixcvVt7TCRMmSOfOneX+/fvaKr3MOXbsmAwdOlQsLS3ZrEZlTsl5OOjZFZ87btu2TUaOHKmMf7527Vpxd3eXoKAgjWsfe3t7iY6O1kqtZVHxXActW7asVHMdMEQvo+Li4qRnz57SqlUradmy5WNdLl999ZVUqVJFAgMDOanJU1Cr1cpOMikpSRo0aKCE6CV/RTx69OjfTixK9LyOHz8udnZ2Mn78eAkKChI/Pz9l3b1795QJs3bt2iUpKSliamoqH374IW8ZfYLExESxs7OTUaNGSVZWlhw6dEgJ0tkt9OLwttFnVzLU/Wu3yv/93/+Jg4ODfPzxx0pHenx8vJiYmEhgYOBLrbO8Kfm++vn5SceOHTnHwQtQmTuqngf3jf/O9evXpUmTJkpDQUlZWVnSt29fmTBhwmPP4+f0yVJTU8Xc3Fw2btyoLLt8+bI4OTmJtbW1xlxQxS5duiTe3t5Ss2ZNDkP0F7m5uXLgwAFlCEEiqlhiYmLExMREPvnkE40fxP/6A0VQUJA0bNhQLl269LJLLNMq41wHOqAy59ixY/Dw8ICFhQXs7e2RlpaGiIgIXL58Wdlm0qRJCAkJwZIlS1BQUKDFass2EQEAqFQqqFQqAEC7du3QpEkTTJkyBffv34eurq6yfUxMDHbs2IEHDx5opV6quGxtbbF06VIkJiZizZo1yMvLU9aZmJjA3d0dbm5uWLFiBaysrLB161b4+/vDwMBAi1WXfXZ2dli2bBlOnDgBPz8/tGrVCuvWrcOBAwcwffp0/Pbbb9ousUKwsbFBaGgorl+/jho1ami7nHKh+JgTGRmJzz//XOM7/9lnn6Fnz57YsmUL5s+fj8zMTNjb2+PIkSMICQnRVsnlgkqlUo7txsbGSE9P13hv6d8peS5ET4/7xn+nbt26iI2NRe3atREbG4ukpCRlXc2aNVGrVi2kpaU99jx+Tp/M2NgYAJCfnw8AKCoqQsOGDREREYGMjAx8+eWX+Pbbb5XtMzMzceDAAVy8eBF79+6FlZWVVuouqwwNDfH222/j9ddf13YpRPSCnTp1CuPGjcOCBQswd+5ctG7dGgCQkZGhZGwrVqyAu7s7li9fjri4ODRq1EibJZc5RkZGGDx4MMaNG4cbN24gPT1d2yX951RSfCVCZUJaWhq+/fZbGBoawt/fHwCwZMkSzJ49G8OGDYO3t7fGF/fOnTuoWbOmtsot00QEKpUKe/bsQUxMDDIzM9GiRQsEBwfj/PnzGDBgAABg+vTpAIBDhw4hIiIChw8f5gkk/WdSUlLg4uKCqlWrYt26dWjXrp2yLigoCFu2bEF8fDyqVq2qvSLLoaSkJIwaNQq2trZYsGABfv31V/j4+GDHjh147bXXtF1ehfHw4UN+Np+BWq2Gm5sbUlNTlWO4oaGhsr5379749ddf0atXL8ybN085nhcVFTEsegIRwYYNG9C8eXO0bdtW2+VQJcd947+TkpICDw8PtG3bFr6+vmjXrh2ys7Ph5OSE1q1bY9myZdousdy5ceMGOnfuDCcnJyxcuBBqtRoiAl1dXfTq1QtXrlxB/fr1ERISAnt7ewDA/fv3oVKpUL16dS1XT0T08uzZsweBgYHYt28f8vLyEBMTg+joaKSmpqJ3794IDg5GSkoKvv32WwQFBaFly5baLrnMys3NRUFBQaVoKGAnehly//59DBkyBGFhYcjOzlaWjx07Fv7+/oiKisLy5ctx8eJFZZ2pqakWKi0fVCoV4uLi0L9/f+Tn58POzg7z5s1D//79UaNGDezevRsNGzbEp59+iqlTp+LEiRM4ePAgA3T6T1lbW+PHH3+Evr4+Fi1ahOTkZGXd7du3Ubt2bRQVFWmxwvLJxsYGERERSElJgZeXF2xsbJCQkMAA/QVjSPTP1Gq1xmMdHR2sWrUKdnZ2WLduHcLCwpCbm6ust7KyQp06dWBsbKxx0skA/clUKhUGDRrEAJ3KBO4b/x1ra2usWrUKx44dw3vvvQdnZ2eMGDECeXl5CA0NBfC/u0qpdGlpabh48SJu3LiBOnXqYN68eQgNDcUXX3wBHR0d6OrqorCwEK+88go+++wz/P7771i/fr3yfBMTEwboRFQplDymGBkZISEhAf7+/ujUqRM2b96Mdu3aYdKkSYiJicGpU6fQvXt3LF++nAH6E1SrVq1SBOgAO9HLnKSkJLz//vuoXbs2wsPD0aZNG2VdeHg4fH19ERAQgMDAQOjp6Wmx0rLv2rVr6NmzJ0aPHo2PPvoI2dnZaNKkCYYMGYKvv/5a2e7y5cswMDCAoaFhpfnik/YlJSXBw8MDubm56NSpEwwMDLBhwwbs2rVLozudnk1iYiL8/Pywfv161KtXT9vlUCWiVquho/Nnb8Lp06ehr6+PoqIiWFpaIj8/HxMnTkRSUhJcXV0xZswY1KhRA8OGDUO/fv3g6uoKlUql8RpERJXFqVOn0LdvXzRo0AAffPABvL29AQAFBQXQ19fXcnVl2/Tp0xEbG4uCggLcu3cPISEhGDRoENatW4dx48ahb9++MDMzw9mzZ5GVlYXTp0/Dy8sLly5dwo4dO7RdPhHRS1E8SkHx3Z7Fj6OiorB+/Xq0adMGI0aMgKWlJQDA3t4egYGB6Nevn7ItEcAQvUxKSUnB8OHDYW9vj4kTJypjMwHAypUr0alTJzRr1kyLFZYtfxc6iAiuXLmCfv36ITExEdeuXcObb76JPn36YOnSpQCA/fv3o3PnztoomQgAcPLkSQwYMAD5+fkYN24chg4dynHWXgDeVk8vW8mT68DAQGzYsAEPHjxAYWEhRo8ejZkzZ+LRo0eYPHkyjhw5gqysLJiZmSE7OxunTp2Crq4uA3QiqtR+/fVXeHt7w9raGp988gmaNm2q7ZLKvBkzZmDx4sVYs2YNHB0d4e7ujn379uHo0aNo3rw5Dh48iPDwcDx48AC1atVCWFgY9PX10adPH5ibmyvd/kREFVnxefrevXvx448/IisrCx07dsTgwYNRo0YNZGdna9yNExgYiLVr1+LgwYOcD4EewxC9jEpKSoKnpydsbW3h6+uLVq1aabukMu3KlSuIj4+Hq6sr1q9fj927d+PTTz9Ft27dEBwcjODgYLz77rv45ptvoKenh99//x1jx47F3LlzlfEAibTh+PHjCAgIwHfffYdatWppuxwieg7z58/HnDlz8MMPP0ClUuHixYvw9vaGu7s7VqxYgUePHmH79u1ITk6GSqVCQEAA9PT0OAY6ERH+vP7x9vZG48aNERwczNvn/4FarYaLiwuGDBkCNzc3xMXFYdSoUZg5cybGjRundPHn5+crk9TfvXsXc+bMQUREBPbv3690XBIRVXQbN26Eu7s73NzckJWVhYyMDDRo0ABhYWGoWbMmRATR0dHYtm0bduzYgZ9++gk2NjbaLpvKIIboZRhPJJ9OQUEBPDw88Mcff8DBwQELFy5EeHg4xowZA09PT3z//ffo0qULNm3apDwnMDAQe/bswcaNGznkA2kdO6eJyqeSHehqtRoDBw5E69atMXPmTGWbvXv3olu3bvj6668xYcKEx16DAToR0f8kJiZiypQpWLduHc/RSxEcHIxq1aph8eLF2LNnD9LT0+Hs7IwvvvgC3t7eyMvLw6xZs+Dl5aV0UV6+fBnLly/Hd999h40bN3LoQCKqNI4dO4YhQ4bA398fnp6euHz5MmxtbWFoaAgbGxtERUXB1NQUsbGxiI2NRVBQEH9kpFIxRC/jeCL5dO7evQsnJyckJCTA29sbYWFhAICff/4Zn376KUxMTDB8+HCYmppi586diIyMxMGDB2Ftba3lyomIqDwqOfzK7du3YWZmhtatW6N3796YN28eRASFhYXQ19eHr68vUlJSsHnzZlSpUoVzmhAR/QM2F5QuOjoaU6ZMwfbt2zFnzhzcunULBw8exOLFizFq1CgAQHp6OoYMGYIxY8Zg2LBhAP5sOrp06RKMjIw46ToRVViff/45cnJyMGPGDOU8fdOmTfjhhx+wZs0aXLp0Ce+++y46d+6MDh06IDg4GF27dlU60vPy8mBoaKjlv4LKMl7FlXEdOnTATz/9xBPJJzAyMoKRkRHatm2L8+fPY82aNRg2bBi6d++O3NxcxMbGYvz48TA3N8crr7zCAJ2IiP61kgH6l19+ifPnzyMoKAhubm5YsWIFBg8eDDs7OyUsNzY2ho6ODqpVq6bNsomIygVe9/y9/fv3Y9++fZg8eTJat26NN954A/Pnz0e3bt2UAD07Oxuenp7Q1dXF0KFDlefq6+tzTi0iqvCMjY0RFBSE6tWr45NPPoGOjg5cXFzQvHlzqNVq+Pj4wNHREStXroRarUZYWBji4uJQWFiI6OhoHn/oiRiilwP8Ij+Zvr4+tm3bhjt37sDT0xMREREQEbi7u8PFxQUuLi64evWqMt6VsbGxtksmIqJyqjhAnzp1KlatWoVFixahqKgITk5OOHr0KD799FPMmDEDdnZ2ePDgARISEtCgQQMtV01EROVVRkYGPvzwQ9y8eROBgYEAAG9vb6SlpWHPnj2wsbFBs2bN8Mcff+Dhw4dITEyErq4uhwwjokpDRODj4wNDQ0N4eXmhsLAQ/v7+0NPTg6WlJa5evYq0tDR4e3sDAO7du4c2bdpgzJgx6N+/v3J+T/RPOJwLVTgXLlzAxIkT8fDhQ3h4eMDDwwMBAQHIzMzEsmXLtF0eERFVALt378bo0aMRFRUFR0dHZfmPP/6IlStXYvfu3bC0tER+fj5EBCdOnIC+vr7GOOpERERPKyUlBQMHDkTt2rXx9ddfo3379igqKsLWrVuxf/9+FBQUwMLCAj4+PtDT00NhYSGHDyOiSqE41lSpVBARrF27Fh4eHggJCUFAQAB0dHRw69Yt9OjRAw4ODggICMCyZcvw888/Y8uWLahdu7aW/wIqLxiiU4V08eJFTJ48GefOnYOhoSHOnj2LnTt3wsHBQdulERFRBbBq1SosWLAAhw4dgqmpqcYQLxcuXEBqaioSExNRq1YteHp6MtAgIqLnlpKSguHDh8POzg4+Pj6lDk/JDnQiqkyKm1R27dqFbdu2YcyYMYiPj8eoUaMwY8YMBAQEoKioCAsXLsTKlStx9+5d6OnpYdOmTbC1tdV2+VSO8EqOKiQLCwssXrwYO3bswNWrVxEVFYUWLVpouywiIirnik/S8/LyUFRUpCxXqVRKaHH8+HHY2trCyclJWV9UVMQAnYiInou1tTUiIiLg6emJ0NBQfPTRR2jduvVj2zFAJ6LKRKVSITY2FsOGDUNgYCBycnIwfPhwPHr0CN7e3lCr1Zg2bRomTpyIXr16IT09HS1btuRwi/TM2IlORERE9IzOnDkDKysrTJs2DdOnT1eW5+TkwM3NDT169MD48eO1VyAREVVYSUlJ8PLyQqNGjTBv3jxYWFhouyQiIq1JTU2Fk5MTpkyZgrFjx2qsW7ZsGcaOHYuQkBAEBQVpqUKqKNgSRURERPSMLC0tERYWhgkTJuDOnTvo06cPqlSpgtmzZyMjIwNeXl7aLpGIiCooGxsbhIaGIjw8HI0aNdJ2OUREWvXHH39AX18fvXr1UpYVD7U4ZswYGBkZwd3dHQYGBvDz89NipVTeMUQnIiIi+hdGjx6NOnXqYOLEiYiJiYGpqSnq16+PY8eOQU9Pj2PSEhHRf8be3h4dOnSASqXSmJeDiKiyycnJQV5envJYrVZDpVIBAPbt24f27dsjOjoabdq00VaJVEFwOBciIiKi53D79m3cu3cParUaTZo0gY6ODicRJSKil6J4rg4iosrq4sWLaN26NXx9fTFr1iyNdb6+vqhevTqCg4PZ3ELPjVd3RERERM/BzMwMZmZmymO1Ws0AnYiIXgoG6ERU2VlYWCA0NBTe3t4oKCiAh4cHdHV1ERkZicjISBw5coQBOr0Q7EQnIiIiIiIiIiKickmtViMmJgZeXl4wMjJC1apVoauri3Xr1sHGxkbb5VEFwRCdiIiIiIiIiIiIyrX09HRcvnwZKpUKFhYWqFOnjrZLogqEIToRERERERERERERUSk4hTcRERERERERERERUSkYohMRERERERERERERlYIhOhERERERERERERFRKRiiExERERERERERERGVgiE6EREREREREREREVEpGKITEREREREREREREZWCIToRERERERERERERUSkYohMRERERERERERERlYIhOhERERERERERERFRKRiiExERERFVEBkZGfjoo4/QtGlTVK1aFXXq1IGjoyOWLFmC3NxcbZdHRERERFQu6Wm7ACIiIiIien4XLlyAo6MjTE1NMXv2bFhZWcHAwAAnT57EsmXLUL9+ffTt2/c/+b8fPXqEKlWq/CevTURERESkbexEJyIiIiKqAMaNGwc9PT0cO3YMgwcPhqWlJRo3bgwXFxds3boVzs7OAIC7d+/C09MTtWrVgomJCbp27Yrk5GTldaZPn4527dohKioK5ubmqFGjBoYMGYLs7Gxlm3feeQcTJkzApEmTYGZmhp49ewIATp06hffeew/GxsaoU6cO3N3dcfv27Zf7RhARERERvWAM0YmIiIiIyrnMzEzs3LkT48ePh5GR0d9uo1KpAACDBg3CzZs3sX37dhw/fhy2trbo1q0bsrKylG3T0tIQFxeHLVu2YMuWLdi/fz/mzJmj8XqrV69GlSpVcPjwYYSHh+Pu3bvo2rUrbGxscOzYMfz000+4ceMGBg8e/N/94URERERELwGHcyEiIiIiKufOnz8PEUGLFi00lpuZmeHhw4cAgPHjx8PZ2RkJCQm4efMmDAwMAADz589HXFwcNmzYgDFjxgAA1Go1IiMjUb16dQCAu7s7du/ejVmzZimv3axZM8ybN095PHPmTNjY2GD27NnKsoiICLz++utITU1F8+bN/5s/noiIiIjoP8YQnYiIiIiogkpISIBarYabmxvy8/ORnJyMnJwcvPrqqxrb5eXlIS0tTXlsbm6uBOgAUK9ePdy8eVPjOe3bt9d4nJycjL1798LY2PixOtLS0hiiExEREVG5xRCdiIiIiKica9q0KVQqFc6ePauxvHHjxgAAQ0NDAEBOTg7q1auHffv2PfYapqamyr/19fU11qlUKqjVao1lfx02JicnB87Ozpg7d+5jr12vXr2n/luIiIiIiMoahuhEREREROXcq6++iu7duyM0NBQ+Pj6ljotua2uLjIwM6Onpwdzc/IXWYGtri5iYGJibm0NPj5cZRERERFRxcGJRIiIiIqIKICwsDIWFhbCzs0N0dDTOnDmDs2fPYs2aNfj999+hq6uLd999F2+++Sb69euHnTt34tKlS/jll18QFBSEY8eOPdf/P378eGRlZWHo0KFITExEWloaduzYgZEjR6KoqOgF/ZVERERERC8fW0SIiIiIiCqAJk2aICkpCbNnz0ZAQACuXr0KAwMDtGrVCn5+fhg3bhxUKhW2bduGoKAgjBw5Erdu3ULdunXRqVMn1KlT57n+/9deew2HDx/G1KlT0aNHD+Tn56NRo0ZwcnKCjg57d4iIiIio/FKJiGi7CCIiIiIiIiIiIiKisogtIUREREREREREREREpWCITkRERERERERERERUCoboRERERERERERERESlYIhORERERERERERERFQKhuhERERERERERERERKVgiE5EREREREREREREVAqG6EREREREREREREREpWCITkRERERERERERERUCoboRERERERERERERESlYIhORERERERERERERFQKhuhERERERERERERERKVgiE5EREREREREREREVIr/B1lOOCYRCqbPAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from collections import Counter\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "y = y_labels_normalized\n", + "\n", + "freq_original = Counter(y)\n", + "\n", + "orig_genres = list(freq_original.keys())\n", + "orig_counts = list(freq_original.values())\n", + "\n", + "fig, axs = plt.subplots(figsize=(15, 6))\n", + "\n", + "sns.barplot(x=orig_genres, y=orig_counts, ax=axs)\n", + "axs.set_title(\"Original Genre Frequencies\")\n", + "axs.set_xlabel(\"Genre\")\n", + "axs.set_ylabel(\"Count\")\n", + "axs.tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove all songs with genres which do not come up more than n times" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class counts: Counter({'rap': 2100, 'metal': 1721, 'country': 1117, 'pop': 975, 'jazz': 948, 'blues': 947, 'classical': 849, 'folk': 819, 'funk': 738, 'techno': 626, 'indie': 525, 'rock': 494, 'soul': 457, 'punk': 360, 'reggae': 291, 'trap': 258, 'r&b': 29})\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_216855/1765738642.py:49: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " axs.set_xticklabels(le.classes_)\n" + ] + }, + { + "data": { + "image/png": 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uXGtmOt98Sn7u/uZTnKioKGOMsWaaN27c2PTo0cMaX7VqVdO/f39rPLP/MHDgQFOoUCHTsmVLkypVqnjfiPrkk09M5syZzZ9//mmN7969O485ydCMGTOMr6+v2bNnjzl//rwJCQkxQUFBpnbt2mbPnj3GGGO2b99uateubQoUKGDCw8ONMcZ6nkLy8fbbbxtfX1+zYMECs2XLFvPCCy+YzJkzm+DgYGOMMVevXjULFy40RYoUMS+//LJ1O84V3Mv27dvNiy++aBo1amSOHz9ujLnzPuv77783BQsWNEWLFuX1zH0QpOORhIaGWqHEzp07Tfny5Y23t7f19cS4NyE9evQwLVq0MFevXnVZrUjcrly5YmrVqmWGDBni1B53fl2+fNm89tprplChQub55583P/30kwuqhCvFxsaaTp06GYfDYRo0aGDSp0/v9IHL3r17TePGjU3FihXN0qVLnW4XZ/bs2cbhcPAhTBLVsWNH8/rrr1uPC9u3bzdp06Y1kydPNr/99ptZuHChqVy5sqlTp47Zvn27MeZOmJ4hQwZTs2ZNV5aORKhGjRrWEnT/fIPQoUMHkypVKvPNN9+4qDq40t0h+oEDB8y+ffvM33//HW9ct27dTKNGjYwxxtSqVcsEBgZaITsQp2HDhsbhcJgmTZpYbXGPObt27TKVK1c2BQoUMLVr1zaenp7m4MGDrioVLtSjRw/TuHFjY8z/HoMOHTpkChQoYDp06GC179y501SoUMEUK1bMnDlzxmX1wjW+//578/zzz5tt27YZY4xZuXKl8fHxMUWLFjXp0qWzwvTIyEizaNEikytXLtO0aVNXloxELDY21ixdutTky5fPZMyY0WnCUlyYXqxYMfPMM8/cc4JBckeQjgT77bffTKpUqcycOXOMMcb8/fffplmzZubZZ581X331lTHGmLNnz5p33nnH+Pr6ml9//dWV5SIR6d+/v/nkk0+c2s6dO2dy5MhhrbF/vzVoQ0ND+UAmmYubeTxp0iRjjHPgsWfPHtOkSRNTpUoVM2/evHi3vXnzpvntt9+eWq14cr7++muTJUsWpw/R3nvvPVO9enWncVu3bjUvvPCC6dy5szHmTljx008/8eEJjDHGHDx40Ozdu9e6XqtWLePn5+e0Fm2crl27msOHDz/tEuFCsbGxTs8pQ4YMMblz5za5c+c26dKlM7NmzbLWoo3rr1y5sqlfv7559tlnWRMdTm7evGlu3bpl2rZta2rXrm3KlStnRo0aZS5fvuw0bsWKFWbAgAGmY8eO5tChQy6qFq4UGxtr2rVrZ33TMiYmxno8mT9/vvH29rZ+N8oYY4KDg03RokVN+fLlTUxMDL/dkUy8++67pkePHmb06NHGGGPWrVtnfH19zdSpU83JkyfNs88+a/z9/a1vZt64ccMsWbLEeHl5mdatW7uydCQS93qsuHr1qlmxYoXJli2bqVGjRrzxcRNm436LDP9DkI5H0qlTJ5M+fXozf/58Y8ydN6j16tUzuXLlMjlz5jRBQUEmZ86c/BABLFeuXDFjxoyxfqgrzuXLl80LL7xghgwZYr0BjXug37Fjhxk3btxTrxWJS9wbherVq5uGDRsaT09Ps2zZMqs/7nzZs2ePqVy5snnjjTecbk+wkbSNHTvWFChQwBhjzPLly81HH31kRo8ebYKCgkxUVJTTC8Mvv/zSpEmThplasMTGxprw8HCTL18+07x5c/Pjjz9afVWrVjUBAQH3DNORfNwdUhljzIgRI0zWrFnN+vXrjTHGtGrVynh5eZlx48aZCxcuGGPuPNY4HA5TunRpQnRY7hdqvvXWW6ZUqVLxwvSLFy8aY1gKKDk6evSoOX36tImJiTHbtm0zDofDaYlCY4xZuHChKVWqlNM5Exsba/bs2WP++uuvp10yXOTrr782OXLkMAcOHDDnzp0zxhhTr14907dvX2PMneeeunXrmixZspgqVapYj0PXr183K1asYEIJjDH/e376448/zP79+63HkKioKLNixQqTO3duU7du3Xi3uXHjxlOvNSkgSIctu0+6e/ToYTw8PKzZn2fOnDG7d+82o0ePNt9++605ceLE0ywVSUDcG4V169aZjz76yGpv3769yZUrl1m/fr3Tm4m3337blC9f3nrjiuTj7pmBcWvTxuncubNJkyaNU5huzJ03pGfOnOHrZ/8xe/fuNfnz5zcvvfSScTgcZvny5WbBggUmZcqU1sybOLt37zYFCxY0J0+edFG1cLW4r6b+09dff22KFi1qXn/9dfPDDz9Y7VWrVjU5c+Y0a9asIcxKht58803Tr18/6/qhQ4dMtWrVzLfffmuMufPhXYYMGUz9+vWNw+Ew48aNM9euXTN///236dWrF7/BAEvc+6WtW7eaPn36mNdee83ptW6vXr1M2bJlzciRI82FCxfM4MGDTenSpc3NmzeZVZzMDBgwwBQoUMBkypTJVKxY0UyePNmMHz/epE6d2syePducOXPGhIeHm9q1a5vatWtb5wfnSfKzdetW06VLFzNx4kRjzJ1z4Ny5cyZ37tzmyy+/NMbcmazWpEkTs2vXLs4VOPnggw+cvqm9ZMkSkzFjRpM7d26TNm1aM3v2bGPMndcwy5cvN3ny5DH16tVzVblJCkE67ik0NNTp+qZNm8yOHTvijevRo4dJnTq1+frrr3kTgfu6+8n81q1bZtiwYcbhcFgvCowxplKlSiZfvnymZ8+eZvz48aZdu3bGy8sr3gx2/Pfdfb58/vnnpnv37mbMmDFOXyvr3LmzSZcunVmwYIE5c+aMadCggWnWrJnVT5j+3/Lmm28ah8Nhypcvb7W1aNHCZMqUyWzatMmardW3b19TpEgRPnxLpjp27GjatWtnffj2zx/XWrx4sSlYsKBp376908z0MmXKmIIFC5pr16491XrhesuXL7dmlF++fNncunXLzJgxw0RFRZnt27ebgIAAM3nyZGOMMU2bNjU+Pj5m8ODBTtvg9S/iLF261Hh7e5uWLVuawYMHG4fDYVq0aGFu3rxpjPnfc9Szzz5r/Pz8rDWNkXx8/fXXxt/f3yxfvtzMnj3b9O/f36ROndr06NHDTJs2zbi7u5scOXKYfPnymVKlSlmPT7yuTX5CQ0NNnjx5TPr06c0HH3zg1Ne4cWOTNWtWM2nSJPPiiy+acuXKWZMBOFcQp0OHDsbNzc0sXrzY/P333yZ//vzms88+M3v27LHymA8//NAYc+e1zMqVK02GDBlYW/8hEKQjnqlTp5o6deo4zdhq0KCBcXd3N7t27Yo3vn79+iZ79uxm7ty5vJnAPcUFo1euXDHG3FkXfdSoUSZ9+vROS7f07t3b1KlTxxQqVMg0atTIHDhwwCX1wnXuDtGHDx9u0qZNa5o0aWLSpEljatasaVatWmX1d+/e3TgcDlO4cGFTqFAhfujtP+r69evmpZdeMh06dDCFChUyzZs3N8bc+YZL69atjYeHhylSpIgJCgoyGTNmZEmxZOqfa+nv2rXLDB48ON66josWLTLZs2c3rVq1cjpX+BZd8vLP2XpffvmlqVWrltO3WeI+mIl7bunWrZspWbKkef7555nth3j++usvkz9/fuuDlytXrhgfHx/Tq1cvp2Br7dq1Zu7cuebo0aOuKhUusmXLFtOhQwczYcIEqy0iIsJMmTLFpEuXzqxatcocPnzYrFixwqxevdoKRnl/nXz9/PPPJm/evOb55583ISEhVvsvv/ximjVrZsqWLWsaNmzIBy64rz59+pg0adKYadOmmW7dujn1jR8/3ilMv3XrllmzZg3LAT0EgnTEs3nzZpMjRw7TsmVLa8ZWbGys+b//+z+TOXPmeDPT+/XrZ7y9vU3WrFlNRESEK0pGIhb3ZnP16tWmbdu21jkVFhZmRowYES9Mv337tomMjLRm7yB5OnjwoGnSpInZvXu3MeZOyPXCCy+Y6tWrm5UrV1rj1qxZY5YvX86bjf+4uJnCX3zxhcmfP79p1aqV1bdo0SLz8ccfm4kTJxJMJGN3r6W/du1aExAQYDJlymTefvvteGvJTp482aRPn940a9bMaWY6kq9PPvnEBAUFmebNm1s/NFuxYkXTtWtXa0zDhg1NSEgIX53HPf3++++mbNmyxhhjjh8/bgICAkynTp2s/rt/7BjJz92zi999912nvgsXLpj69evHC7mMYf183AnTS5QoYTp06GB++eUXp74LFy5Yz0W8B0Kcf74+6dGjh3E4HKZYsWLxfvR6/Pjxxt3dPd7jEuwRpMNJ3KeYu3btMrlz5zavvvqqNTM9NjbWNGrUyGTJksXs2LHD+up0//79zfbt283Zs2ddVjcStyVLlpj06dObwYMHW29QjTEmPDzcDBs2zKRLl85pmRckb5988ol54YUXTIUKFUx4eLjV/scff5gXXnjB1KhRw6xYsSLe7Xiz8d935coVM3PmTJM/f35rZjpgzP/W0q9SpYpxc3Mz27dvN5999pnJli2bGThwoDl+/Lg1dsGCBaZYsWLmxRdf5IdpYZk9e7apVKmSadq0qTl//ryZMmWKcXNzM82bNzclS5Y0hQoVivej6ECcgwcPmly5cpnly5eb3Llzm06dOlnny08//WReeuklvmmZzP38888mT548plSpUvG+Pde+fXtTq1YtF1WGxG7//v2mVKlSpmPHjubgwYPx+nlOwt3izoe787mhQ4caNzc3M3fu3Hjj3333XZMxY0aWxkwAgnQ4iY2NtcKobdu2mdy5c5v/+7//s8L0mJgY06RJE5M2bVrTvHlz06RJE+Pl5cXXP3Bfhw4dMgEBAWbGjBlO7X/99Ze5fv26uX37thkxYoRxOBxm2rRpLqoSrvTPryHu2LHD5MmTx/j4+Ji1a9c69R05csRUrFjRlCpVyuzcufNplolE4urVq2bmzJmmSJEi5pVXXnF1OUhE4tbSL1eunNU2ceJEK0yPC7Heeecd88knn8SblYPk6e4AYtasWebFF180r776qgkNDTWfffaZadq0qencubP11Xk+tEXcOfPrr7+aHTt2WEtItWrVyqRLl840bNjQafygQYPM888/b8LCwp56rUhcfv75Z1O8eHHTpk0baymyyMhI8/zzz5uOHTu6tjgkavv37zdly5Y1TZo0cZocANwt7vnp22+/NQ0bNjTz58+3+nr37m08PDzMwoUL492OED1hHMYYIyR7xhjFxsYqRYoUOn/+vNzd3eXl5aVffvlFDRo0UKlSpTRgwACVKVNGkjR8+HAdOnRIMTExGjFihIoWLeriI0BitXv3bvXu3VvLli1TunTpNG/ePC1cuFDHjx9XmTJlNGnSJHl4eOiLL75Q/fr1lT9/fleXDBfZtGmTihYtKl9fX/3000969dVXVahQIb399tt67rnnrHG///67Jk2apKlTp8rNzc2FFcNVrl27pjlz5mj27NlatmyZAgICXF0SXOzGjRt6+eWXlTt3bu3evVvFihXT119/LUmaMmWKPv30U0VFRcnX11cHDhxQcHCwihQp4uKqkVgYY+RwOCRJs2bN0syZM5UtWzZNmjRJfn5+io2NlZubm6Kjo5UyZUoXV4vEYPny5WrdurX8/f116tQpzZgxQ5L08ccfK2vWrOrUqZPSpEmjVatWaebMmdq+fbuKFSvm4qqRGPz0009q1aqVLl68qDJlysjd3V3Hjx/X999/L3d3d6fHI+Bue/fu1aeffqoZM2bwHgj3tWLFCr366qsaPXq0atSo4fR6t3fv3vrkk080b948NW7c2IVVJm0E6cncmjVrlC1bNhUvXlyStHTpUo0dO1bnzp1T4cKF9eabbypfvnyqXr26SpUqpX79+lmBVkxMjGJjY5UqVSpXHgISmbgXfzdv3lTq1Km1d+9ePf/88+rYsaM2bdqkggULqlChQsqVK5c++OADTZw4UfXq1VNMTIxSpEjh6vLhIjt27FCHDh1Up04dvfPOO8qcObP27NmjVq1aqVixYhowYIBTmB4nLtxA8nP9+nXdvn1b3t7eri4FicT169fl6empmTNnauzYsSpZsqQVpm/YsEG//vqrzp07p9atW/OhLeK5X5g+ZswYBQYG8nwDSXded1y+fFn16tVTmzZt9NJLL+mbb77RiBEjNGnSJDkcDm3btk0rV65U3rx55e3trSlTpljvtQBJOnjwoOrVq6fs2bOrRYsW6tKliyTp9u3bvLeGrbjnKp6TcC+hoaGqV6+eWrRooV69elntd58vffv21YQJE7Rs2TLVr1/fVaUmaQTpyVh4eLiCgoJUuXJlDR48WDdv3lT58uU1YMAApUyZUn/99ZdmzJihGTNmqEKFCqpevbrKlSun7t27KygoyNXlIxGKe2L/7rvvtGjRIg0cOFD58uXT119/re+++07PPPOMXnvtNeXNm1eSVK5cOfXp00dNmzZ1ceVIDIYMGaLNmzerfPnyGjhwoLJkyaI9e/aoTZs2Kl68uLp3764KFSq4ukwAidzVq1e1aNEiK0yfP3++q0tCEnF3mP7ll19q5syZCgoK0ogRI+Tu7s4s0WTs7okixhi9++676tu3rzJkyCBJ+uijj9S/f3+NHz9ezZs3182bN5UuXTqlSJGCD3xxTyEhIerSpYuKFSum/v37W++PgAfhWwu4nz///FMVK1bUzJkzVaNGDUn3Pl8GDRqktm3bqkCBAq4oM8nju4nJmJ+fnxYvXqzOnTtrwoQJ8vHxUefOnfXOO+9IkiIjI1W4cGF17NhR3333nZYvX66KFSvK09NTpUqVkoeHh4uPAImNw+HQ0qVL1a5dO3Xp0kW3b9+WJDVv3lwNGzZU6tSprbGDBw9WWFiYypUr56py4SJ3P5nf/U2EUaNGyc3NTevWrdP777+vQYMGqVy5cvrqq69UvXp1PfvsswTpAB4oXbp01ge0EyZMUL169bRy5UoXV4WkwOFwWM9Rbdu21cGDB7Vr1y45HA5Ci2TO4XBoxYoVmjZtmk6dOqXY2Fi9+uqrVpDeq1cvORwO9e/fX2fPntWAAQPk5eXl4qqRmJUoUULTpk1Tly5dNGTIEA0bNoxQCw+F5yPcLW5utMPhUGRkpNP5cfd77T179ujo0aNq2bKlxowZ45Ja/yv4LkgyV6pUKX322Wf64YcfNHfuXN24ccPq8/LyUuvWrdWyZUvNmDFDRYsW1erVqzVw4EBCdNzTwYMH9eabb+rDDz/UBx98oMKFC0uSwsLCrFB9xowZat26tT7//HMtX75cOXPmdGXJcIG4J/fZs2drzJgxTo87I0aMUM2aNbVq1SqNHz9eFy5c0HPPPafg4GCNHDnSVSUDSGLSpk2rpk2b6s0331R4eLjOnDnj6pKQRMSF6dKdD2XOnDnj9DyF5OnHH39UmzZtFBgYqOeee07Hjh3TzJkzdeLECWtMz549NXLkSE2bNs163QvYKVmypKZMmaLQ0FC+uQAgQe4O0OPeX5coUUJ58uRRv379FBkZ6bR07pIlS7Ru3Tpdu3bNJfX+l7C0CyRJBw4cUP369ZU6dWp9/fXXKlGihNX3zjvvaNWqVdqzZ4/TjGLgnzZv3qy3335bW7du1Y0bN7RkyRItWLBAf/zxh+rWrathw4bpwIEDmjNnjt555x1mXSRjsbGxatmypf744w+1atVKXbp0UZo0aaz+unXrKiQkRHXq1NHYsWOtGV+spQ8gIVhLH4/KGKPFixcrX758rG+dzB07dkxz5sxRmjRpNHDgQEnStGnTNHr0aOs1zN0TQy5dumS9bgEeRtxvSwHAw4j79tzmzZu1ZMkSXbhwQfnz59ewYcN09OhRNWrUSJI0fPhwSdLOnTs1c+ZM7dq1S0WLFnVh5f8NzEiHJKlYsWJauXKlUqVKpUmTJunnn3+2+s6fPy9fX1/FxMS4sEIkVnd/Fpc2bVrt3fv/2rv3oCjrt4/jn4XFFcEflCiSh/AciiVImmNjniVHE00RR8EsRBBFGS056OjkIXWsMVMkPA+akoqaioeR1DEzEWNIGw+BeI5MUQMxEHafP55xJ550f/WUrsr79Rf397733mvhH/jcX64rS3FxcerSpYu2b9+udu3aaeLEidq8ebNOnjypXr16admyZYTo1YzZbK5y7ODgoFWrVikgIEDr169XUlKSSktLrefbtm0rT09Pubq6VgnACNEB/B21atUiRMf/i8Fg0JAhQwjRq7nffvtNISEhSkpKUnFxsXU9KipKcXFxSk1N1bJly1RQUGA95+7ubodK8TQjRAfwdxgMBm3dulUDBw5UWVmZAgICNH/+fA0cOFBubm7KzMxU48aNNW3aNE2ZMkXff/+9Dh06RIj+L2FHOqrIyclRWFiYSktL1aVLF5lMJm3atEn79u2rsksduP8U9P4O4fvHqamp2rBhg3x9ffXOO+/Ix8dHktShQwclJCQoKCiIASnVzB+nhP/4449ycnJSZWWlfHx8VFZWppiYGOXk5Gjw4MGKiIiQm5ubRowYoaCgIA0ePJjJ9AAAwG5ycnI0dOhQ1atXT8nJyfL19bWeS05OVmxsrOLj45WQkCCjkRFkAIBH68qVK+rTp49Gjx6tCRMmqLi4WM2aNVNISIgWLVpkve7ChQsymUxydnZmY8m/iCAdf3LixAkNGjRIZWVlGjt2rIYNG0Yfa1RxPwjfv3+/vvrqKxUVFen1119XcHCw3NzcVFxcrNq1a1uvT0hI0BdffKFDhw6pUaNGdqwcj9sfH5okJCRo06ZNunPnjioqKjR69GjNmjVL5eXlmjRpko4cOaKioiJ5eHiouLhYJ0+elKOjIyE6AACwqx9++EEjR45Uhw4dFBMTY50DJEkrVqxQly5d1KJFCztWCAB41jzo72CLxaJLly4pKChIx44d05UrV9SpUyf169dPn3/+uSTp4MGDeuONN+xRcrVAkI4HOn78uOLj47Vu3TrVrVvX3uXgCbRlyxbrMNqioiIVFhaqYcOGSkpK0nPPPSeLxaK0tDRlZGRoz5492r17t/z8/OxdNuxkwYIFmjt3rjZu3CiDwaCCggJFRkYqNDRUy5cvV3l5uXbt2qXc3FwZDAbFx8fLaDTSEx0AADwRcnJyFB4eLn9/f8XGxqp169b2LgkA8Iy7dOmSjh49qsGDB2vDhg3KzMzUtGnT1KNHD02fPl3Tp09Xz549tWTJEhmNRp0+fVpRUVGaN2+eOnToYO/yn0kE6Xgohp7gYbKzsxUSEqK4uDiFh4frwoUL8vf3l7Ozs/z8/JSamip3d3elp6crPT1diYmJ1hYvqB7+uBPdbDbr7bffVps2bTRr1izrNfv371ePHj20aNEijRs37k/3IEQHAABPkpycHEVGRqpp06aaPn06M38AAI/MvXv3FBYWposXL6pjx45auHChkpOTFRERofDwcH355Zfq1q2btm3bZn1NQkKCvv76a23ZskVeXl52rP7Zxf/K46EI0SFJH330kRITE6sMi7xy5Ypee+01hYeH6/z58+rRo4eCgoI0depUZWVlKSoqSjdv3tSgQYO0bNkyQvRqxmw2W0P069evy8HBQWfPnlV5ebmk/w3Z7927p27dumnChAnasmWLSktLVVFRUeU+hOgAAOBJ4ufnp8WLF+vnn3+m3ywA4JFycnLS0qVLVVlZqYULFyoyMlIRERGSpKFDh6p169a6e/eu1q1bp507d2rChAlasmSJUlJSCNEfIaahALDJ1dVViYmJql27tj744AM5ODhowIABatmypcxms8aPH6/OnTtrxYoVMpvNSkpK0tatW1VRUaG0tDQeyFQzf+zj9sknnygvL0+JiYkaPny4li9fruDgYAUEBFiHcbm6usrBwUG1atWyZ9kAAAB/yauvvqrdu3fzOy4A4JFzcXGRi4uLXnnlFeXl5Wnt2rUaMWKEevXqpdLSUqWnpys6Olre3t56/vnndejQIb388sv2LvuZRpAO4KEsFovGjx8vZ2dnjRkzRhUVFYqLi5PRaJSPj48uX76s/Px8RUZGSpJu374tX19fRUREaODAgQyIrIb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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import LabelEncoder\n", + "import numpy as np\n", + "from collections import Counter\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# --- Encoding/Standardizing X ---\n", + "# Remove compex object variables\n", + "if np.iscomplexobj(X):\n", + " X = np.abs(X)\n", + "\n", + "# Standardize the features\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# --- Counting Labels ---\n", + "# Check class counts.\n", + "counter = Counter(y)\n", + "print(\"Class counts:\", counter)\n", + "\n", + "# Keep only classes with at least n samples. At least 2 or any other % 2 == 0 number\n", + "n = 600\n", + "classes_to_keep = {cls for cls, count in counter.items() if count >= n}\n", + "indices_to_keep = [i for i, label in enumerate(y) if label in classes_to_keep]\n", + "\n", + "y = np.array(y)\n", + "\n", + "# Filter the data.\n", + "X_filtered = X_scaled[indices_to_keep]\n", + "y_filtered = y[indices_to_keep]\n", + "\n", + "# Encode the Genres into numerical values\n", + "le = LabelEncoder()\n", + "y_encoded = le.fit_transform(y_filtered)\n", + "\n", + "freq_leftover = Counter(y_encoded)\n", + "leftover_genres = list(freq_leftover.keys())\n", + "leftover_counts = list(freq_leftover.values())\n", + "\n", + "fig, axs = plt.subplots(figsize=(15, 6))\n", + "\n", + "# Plot normalized label frequencies.\n", + "sns.barplot(x=leftover_genres, y=leftover_counts, ax=axs)\n", + "axs.set_title(\"Normalized Genre Frequencies\")\n", + "axs.set_xlabel(\"Genre\")\n", + "axs.set_ylabel(\"Count\")\n", + "axs.tick_params(axis='x', rotation=45)\n", + "axs.set_xticklabels(le.classes_)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove highly collerating data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropping columns: ['feature_5', 'feature_8', 'feature_9', 'feature_10', 'feature_12', 'feature_14', 'feature_16', 'feature_20', 'feature_21', 'feature_23', 'feature_25', 'feature_26', 'feature_29', 'feature_30', 'feature_31', 'feature_32', 'feature_34', 'feature_37', 'feature_38', 'feature_41', 'feature_43', 'feature_44', 'feature_46', 'feature_48', 'feature_50', 'feature_51', 'feature_53']\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "CORRELATING_VALUE = 0.9\n", + "\n", + "X = X_filtered\n", + "\n", + "def remove_highly_correlated_features(X, threshold):\n", + " # If X is a NumPy array, convert it to a DataFrame.\n", + " if isinstance(X, np.ndarray):\n", + " # Create column names if not provided\n", + " X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])\n", + " else:\n", + " X_df = X.copy()\n", + " \n", + " # Compute the absolute correlation matrix\n", + " corr_matrix = X_df.corr().abs()\n", + " \n", + " # Create an upper triangle matrix of correlations\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " \n", + " # Identify columns to drop: any feature with correlation greater than the threshold\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " print(\"Dropping columns:\", to_drop)\n", + " \n", + " # Drop these columns from the dataframe\n", + " X_df_reduced = X_df.drop(columns=to_drop)\n", + " \n", + " # If original X was a NumPy array, return as a NumPy array.\n", + " if isinstance(X, np.ndarray):\n", + " return X_df_reduced.values\n", + " else:\n", + " return X_df_reduced\n", + "\n", + "\n", + "X_reduced = remove_highly_correlated_features(X, threshold=CORRELATING_VALUE)\n", + "\n", + "# -- Create a feature correlation matrix --\n", + "df_features = pd.DataFrame(X_reduced, columns=[f'feature_{i}' for i in range(X_reduced.shape[1])])\n", + "\n", + "# Compute the correlation matrix.\n", + "corr = df_features.corr()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "sns.heatmap(corr, cmap='coolwarm', annot=False)\n", + "plt.title('Feature Correlation Matrix')\n", + "plt.show()\n", + "\n", + "X_filtered = X_reduced\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a Test-Train split" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set shape: (8672, 28) (8672,)\n", + "Test set shape: (2168, 28) (2168,)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_filtered, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded\n", + ")\n", + "\n", + "print(\"Training set shape:\", X_train.shape, y_train.shape)\n", + "print(\"Test set shape:\", X_test.shape, y_test.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperparameter tuning" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best RF parameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 300}\n", + "Best RF accuracy: 0.7062\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import GridSearchCV\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# Parameter grid\n", + "param_grid_rf = {\n", + " 'n_estimators': [50, 100, 200, 250, 300, 350],\n", + " 'max_depth': [None, 10, 20],\n", + " 'min_samples_split': [2, 5],\n", + " 'min_samples_leaf': [1, 2]\n", + "}\n", + "\n", + "# Grid search\n", + "rf = RandomForestClassifier(random_state=42)\n", + "grid_search_rf = GridSearchCV(rf,\n", + " param_grid_rf,\n", + " cv=5,\n", + " n_jobs=-1,\n", + " scoring='accuracy'\n", + " )\n", + "grid_search_rf.fit(X_train, y_train)\n", + "\n", + "# Best model\n", + "final_model = grid_search_rf.best_estimator_\n", + "print(\"Best RF parameters:\", grid_search_rf.best_params_)\n", + "print(f\"Best RF accuracy: {grid_search_rf.best_score_:.4f}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluation Charts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Confusion Matrix - Shows what was the predicted value compared to the true value when evaluating the Model\n", + "\n", + "The more values in the diagonal line the better" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "# Predict on the test set\n", + "y_pred = final_model.predict(X_test)\n", + "\n", + "# Compute confusion matrix\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "# Display it nicely\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n", + "disp.plot(cmap='Blues')\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.title(\"Confusion Matrix\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/ai_analysis/old_pytorch/sequential_pytorch.ipynb b/src/ai_analysis/old_pytorch/sequential_pytorch.ipynb new file mode 100644 index 0000000..e5196a9 --- /dev/null +++ b/src/ai_analysis/old_pytorch/sequential_pytorch.ipynb @@ -0,0 +1,1285 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read CSV with TrackId and Genre Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "tracks_info_df = pd.read_csv('combined_dataset.csv')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read processed song data from pkl file" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 100390 processed tracks\n" + ] + } + ], + "source": [ + "import pickle\n", + "import os\n", + "\n", + "\n", + "results_file = 'audio_features_backup.pkl'\n", + "\n", + "if os.path.exists(results_file):\n", + " with open(results_file, 'rb') as file:\n", + " saved_data = pickle.load(file)\n", + " X = saved_data.get('X', [])\n", + " y_labels = saved_data.get('y_labels', [])\n", + " \n", + " processed_files = set(saved_data.get('processed_files', []))\n", + " print(f\"Loaded {len(processed_files)} processed tracks\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Turn X into an np array for easier processing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of feature matrix X: (100390, 55)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "X = np.array(X)\n", + "print(\"shape of feature matrix X: \", X.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remove all entries where the genre is defined as None" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "X = X[np.array(y_labels) != None]\n", + "y_labels = np.array(y_labels)[np.array(y_labels) != None]\n", + "\n", + "genre_mapping = {\n", + " 'hip-hop': 'hip hop'\n", + "}\n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "y_labels_normalized_df = pd.DataFrame(y_labels_normalized)\n", + "\n", + "# Count the frequency of each label in the \"label\" column\n", + "label_counts = y_labels_normalized_df.value_counts().reset_index()\n", + "\n", + "# Rename the columns for clarity\n", + "label_counts.columns = ['Label', 'Frequency']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remap genres" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replace sub-genres with their parent genre based on a predefined mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "genre_mapping = {\n", + " 'lo-fi beats': 'lo-fi',\n", + " 'chillwave': 'lo-fi',\n", + "\n", + " 'nu metal': 'metal',\n", + " 'folk metal': 'metal',\n", + " 'heavy metal': 'metal',\n", + " 'metalcore': 'metal',\n", + " 'power metal': 'metal',\n", + " 'industrial metal': 'metal',\n", + " 'glam metal': 'metal',\n", + " 'melodic death metal': 'metal',\n", + " 'progressive metal': 'metal',\n", + " 'thrash metal': 'metal',\n", + " 'gothic metal': 'metal',\n", + " 'groove metal': 'metal',\n", + " 'death metal': 'metal',\n", + " 'alternative metal': 'metal',\n", + " 'black metal': 'metal',\n", + " 'speed metal': 'metal',\n", + "\n", + " 'acid house': 'acid',\n", + " 'acid techno': 'acid',\n", + "\n", + " 'afrobeat': 'afro',\n", + " 'afropop': 'afro',\n", + "\n", + " 'art rock': 'rock',\n", + "\n", + " 'britpop': 'alternative rock',\n", + " \n", + " 'art pop': 'pop',\n", + " 'acoustic pop': 'pop',\n", + " 'bedroom pop': 'pop',\n", + " 'soft pop': 'pop', \n", + " 'folk pop': 'pop',\n", + " 'norwegian pop': 'pop',\n", + " 'acoustic pop': 'pop',\n", + " 'dream pop': 'pop',\n", + " 'chamber pop': 'pop',\n", + " 'german pop': 'pop',\n", + " 'bedroom pop': 'pop',\n", + " 'city pop': 'pop',\n", + " 'dance pop': 'pop',\n", + " 'art pop': 'pop',\n", + " 'indie pop': 'pop',\n", + "\n", + " 'alt country': 'country',\n", + " 'traditional country': 'country',\n", + "\n", + " 'blues rock': 'blues',\n", + "\n", + " 'brooklyn drill': 'drill',\n", + "\n", + " 'gangster rap': 'rap',\n", + " 'memphis rap': 'rap',\n", + " 'rock rap': 'rap',\n", + " 'meme rap': 'rap',\n", + " 'punk rap': 'rap',\n", + " 'emo rap': 'rap',\n", + " 'jazz rap': 'rap',\n", + " 'melodic rap': 'rap',\n", + " 'cloud rap': 'rap',\n", + " 'k-rap': 'rap',\n", + " 'rage rap': 'rap',\n", + "\n", + " 'edm trap': 'trap',\n", + " 'italian trap': 'trap',\n", + " 'dark trap': 'trap',\n", + "\n", + " 'hip-hop': 'hip hop',\n", + " 'southern hip hop': 'hip hop',\n", + " 'west coast hip hop': 'hip hop',\n", + " 'east coast hip hop': 'hip hop',\n", + " 'finnish hip hop': 'hip hop',\n", + " 'german hip hop': 'hip hop',\n", + " 'underground hip hop': 'hip hop',\n", + " 'norwegian hip hop': 'hip hop',\n", + " 'experimental hip hop': 'hip hop',\n", + " 'alternative hip hop': 'hip hop',\n", + " 'latin hip hop': 'hip hop',\n", + "\n", + " 'jazz blues': 'jazz',\n", + " 'vocal jazz': 'jazz',\n", + " 'french jazz': 'jazz',\n", + " 'free jazz': 'jazz',\n", + " 'soul jazz': 'jazz',\n", + " 'jazz fusion': 'jazz',\n", + " 'nu jazz': 'jazz',\n", + " 'jazz funk': 'jazz',\n", + " 'cool jazz': 'jazz',\n", + " 'jazz beats': 'jazz',\n", + " 'jazz house': 'jazz',\n", + " 'indie jazz': 'jazz',\n", + " 'smooth jazz': 'jazz',\n", + "\n", + " 'melodic techno': 'techno',\n", + " 'minimal techno': 'techno',\n", + " 'hypertechno': 'techno',\n", + " 'hard techno': 'techno',\n", + " 'hardcore techno': 'techno',\n", + " 'dub techno': 'techno',\n", + "\n", + " 'future house': 'house',\n", + " 'tech house': 'house',\n", + " 'bass house': 'house',\n", + " 'electro house': 'house',\n", + " 'tropical house': 'house',\n", + " 'french house': 'house',\n", + " 'melodic house': 'house',\n", + " 'organic house': 'house',\n", + " 'progressive house': 'house',\n", + " 'latin house': 'house',\n", + " 'deep house': 'house',\n", + " 'slap house': 'house',\n", + " 'chicago house': 'house'\n", + " \n", + "} \n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Map hip-hop to rap -> Models without this are too confused due to the genres being so alike" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "genre_mapping = {\n", + " 'hip hop': 'rap'\n", + "} \n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels_normalized]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Or remove hip hop\n", + "\n", + "X = X[np.array(y_labels_normalized) != \"hip hop\"]\n", + "y_labels_normalized = np.array(y_labels_normalized)[np.array(y_labels_normalized) != \"hip hop\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run this code in case the last two cells were not executed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_labels_normalized = y_labels\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extra: Keep only a popular type of genre" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered dataset size: 15248 entries (from 98347 original)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "original_labels_list = y_labels_normalized\n", + "y_labels_normalized = np.array(y_labels_normalized)\n", + "\n", + "popular_genres = {\n", + " 'country', 'idm', 'black-metal', 'hardstyle', 'heavy-metal',\n", + " 'blues', 'rap', 'hip hop', 'classical', 'folk', 'rock-n-roll',\n", + " 'electronic', 'jazz', 'lo-fi', 'punk-rock', 'soul', 'punk',\n", + " 'rock'\n", + "}\n", + "\n", + "#popular_genres = {\n", + "# 'pop', 'rock', 'rap', 'hip hop', 'metal', 'country', 'jazz',\n", + "# 'blues', 'classical', 'reggae', 'punk', 'funk', 'lo-fi', 'acid'\n", + "# 'r&b', 'soul', 'trap', 'techno', 'folk', 'indie', 'house'\n", + "#}\n", + "\n", + "y_lower = np.array([genre.lower() for genre in y_labels_normalized])\n", + "\n", + "mask = np.array([genre in popular_genres for genre in y_lower])\n", + "\n", + "X = X[mask]\n", + "y_labels_normalized = y_labels_normalized[mask]\n", + "\n", + "# Optional: print some info\n", + "print(f\"Filtered dataset size: {len(y_labels_normalized)} entries (from {len(original_labels_list)} original)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chart the leftover genres" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from collections import Counter\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "y = y_labels_normalized\n", + "\n", + "freq_original = Counter(y)\n", + "\n", + "orig_genres = list(freq_original.keys())\n", + "orig_counts = list(freq_original.values())\n", + "\n", + "fig, axs = plt.subplots(figsize=(15, 6))\n", + "\n", + "sns.barplot(x=orig_genres, y=orig_counts, ax=axs)\n", + "axs.set_title(\"Original Genre Frequencies\")\n", + "axs.set_xlabel(\"Genre\")\n", + "axs.set_ylabel(\"Count\")\n", + "axs.tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove all songs with genres which do not come up more than n times" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class counts: Counter({'rap': 2100, 'country': 1117, 'idm': 989, 'black-metal': 989, 'hardstyle': 987, 'heavy-metal': 985, 'jazz': 948, 'blues': 947, 'lo-fi': 945, 'classical': 849, 'folk': 819, 'rock-n-roll': 813, 'electronic': 807, 'punk-rock': 642, 'rock': 494, 'soul': 457, 'punk': 360})\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_24713/1765738642.py:49: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " axs.set_xticklabels(le.classes_)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import LabelEncoder\n", + "import numpy as np\n", + "from collections import Counter\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# --- Encoding/Standardizing X ---\n", + "# Remove compex object variables\n", + "if np.iscomplexobj(X):\n", + " X = np.abs(X)\n", + "\n", + "# Standardize the features\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# --- Counting Labels ---\n", + "# Check class counts.\n", + "counter = Counter(y)\n", + "print(\"Class counts:\", counter)\n", + "\n", + "# Keep only classes with at least n samples. At least 2 or any other % 2 == 0 number\n", + "n = 600\n", + "classes_to_keep = {cls for cls, count in counter.items() if count >= n}\n", + "indices_to_keep = [i for i, label in enumerate(y) if label in classes_to_keep]\n", + "\n", + "y = np.array(y)\n", + "\n", + "# Filter the data.\n", + "X_filtered = X_scaled[indices_to_keep]\n", + "y_filtered = y[indices_to_keep]\n", + "\n", + "# Encode the Genres into numerical values\n", + "le = LabelEncoder()\n", + "y_encoded = le.fit_transform(y_filtered)\n", + "\n", + "freq_leftover = Counter(y_encoded)\n", + "leftover_genres = list(freq_leftover.keys())\n", + "leftover_counts = list(freq_leftover.values())\n", + "\n", + "fig, axs = plt.subplots(figsize=(15, 6))\n", + "\n", + "# Plot normalized label frequencies.\n", + "sns.barplot(x=leftover_genres, y=leftover_counts, ax=axs)\n", + "axs.set_title(\"Normalized Genre Frequencies\")\n", + "axs.set_xlabel(\"Genre\")\n", + "axs.set_ylabel(\"Count\")\n", + "axs.tick_params(axis='x', rotation=45)\n", + "axs.set_xticklabels(le.classes_)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove highly collerating data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropping columns: ['feature_5', 'feature_8', 'feature_9', 'feature_10', 'feature_12', 'feature_14', 'feature_16', 'feature_20', 'feature_21', 'feature_23', 'feature_25', 'feature_26', 'feature_29', 'feature_30', 'feature_31', 'feature_32', 'feature_34', 'feature_37', 'feature_41', 'feature_43', 'feature_44', 'feature_48', 'feature_50', 'feature_51', 'feature_53']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "CORRELATING_VALUE = 0.9\n", + "\n", + "X = X_filtered\n", + "\n", + "def remove_highly_correlated_features(X, threshold):\n", + " # If X is a NumPy array, convert it to a DataFrame.\n", + " if isinstance(X, np.ndarray):\n", + " # Create column names if not provided\n", + " X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])\n", + " else:\n", + " X_df = X.copy()\n", + " \n", + " # Compute the absolute correlation matrix\n", + " corr_matrix = X_df.corr().abs()\n", + " \n", + " # Create an upper triangle matrix of correlations\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " \n", + " # Identify columns to drop: any feature with correlation greater than the threshold\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " print(\"Dropping columns:\", to_drop)\n", + " \n", + " # Drop these columns from the dataframe\n", + " X_df_reduced = X_df.drop(columns=to_drop)\n", + " \n", + " # If original X was a NumPy array, return as a NumPy array.\n", + " if isinstance(X, np.ndarray):\n", + " return X_df_reduced.values\n", + " else:\n", + " return X_df_reduced\n", + "\n", + "\n", + "X_reduced = remove_highly_correlated_features(X, threshold=CORRELATING_VALUE)\n", + "\n", + "# -- Create a feature correlation matrix --\n", + "df_features = pd.DataFrame(X_reduced, columns=[f'feature_{i}' for i in range(X_reduced.shape[1])])\n", + "\n", + "# Compute the correlation matrix.\n", + "corr = df_features.corr()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "sns.heatmap(corr, cmap='coolwarm', annot=False)\n", + "plt.title('Feature Correlation Matrix')\n", + "plt.show()\n", + "\n", + "X_filtered = X_reduced\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a Test-Train split" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set shape: (11149, 30) (11149,)\n", + "Test set shape: (2788, 30) (2788,)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_filtered, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded\n", + ")\n", + "\n", + "print(\"Training set shape:\", X_train.shape, y_train.shape)\n", + "print(\"Test set shape:\", X_test.shape, y_test.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperparameter tuning" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "[I 2025-04-25 14:57:48,684] A new study created in memory with name: no-name-fa4ae2a3-fd71-4c6e-a51d-840533da06ed\n", + "/tmp/ipykernel_24713/521000553.py:95: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\n", + "Consider using tensor.detach() first. (Triggered internally at /pytorch/aten/src/ATen/native/Scalar.cpp:22.)\n", + " train_loss += loss.item()\n", + "[I 2025-04-25 14:58:01,400] Trial 0 finished with value: 0.5778335724533716 and parameters: {'activation_1': 'tanh', 'units_1': 128, 'dropout_rate': 0.4, 'second_layer': False, 'activation_2': 'tanh', 'units_2': 32, 'dropout_rate_2': 0.4, 'learning_rate': 0.0001}. Best is trial 0 with value: 0.5778335724533716.\n", + "[I 2025-04-25 14:58:12,869] Trial 1 finished with value: 0.5871592539454806 and parameters: {'activation_1': 'sigmoid', 'units_1': 128, 'dropout_rate': 0.6000000000000001, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 128, 'dropout_rate_2': 0.6000000000000001, 'learning_rate': 0.001}. Best is trial 1 with value: 0.5871592539454806.\n", + "[I 2025-04-25 14:58:22,631] Trial 2 finished with value: 0.6018651362984218 and parameters: {'activation_1': 'tanh', 'units_1': 32, 'dropout_rate': 0.4, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 112, 'dropout_rate_2': 0.30000000000000004, 'learning_rate': 0.001}. Best is trial 2 with value: 0.6018651362984218.\n", + "[I 2025-04-25 14:58:35,532] Trial 3 finished with value: 0.6007890961262554 and parameters: {'activation_1': 'tanh', 'units_1': 64, 'dropout_rate': 0.0, 'second_layer': True, 'activation_2': 'sigmoid', 'units_2': 112, 'dropout_rate_2': 0.7, 'learning_rate': 0.001}. Best is trial 2 with value: 0.6018651362984218.\n", + "[I 2025-04-25 14:58:48,336] Trial 4 finished with value: 0.5125538020086083 and parameters: {'activation_1': 'tanh', 'units_1': 128, 'dropout_rate': 0.5, 'second_layer': True, 'activation_2': 'sigmoid', 'units_2': 80, 'dropout_rate_2': 0.7, 'learning_rate': 0.0001}. Best is trial 2 with value: 0.6018651362984218.\n", + "[I 2025-04-25 14:58:54,607] Trial 5 finished with value: 0.5875179340028694 and parameters: {'activation_1': 'sigmoid', 'units_1': 48, 'dropout_rate': 0.7, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 64, 'dropout_rate_2': 0.0, 'learning_rate': 0.01}. Best is trial 2 with value: 0.6018651362984218.\n", + "[I 2025-04-25 14:59:07,107] Trial 6 finished with value: 0.5627690100430416 and parameters: {'activation_1': 'tanh', 'units_1': 32, 'dropout_rate': 0.30000000000000004, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 112, 'dropout_rate_2': 0.30000000000000004, 'learning_rate': 0.0001}. Best is trial 2 with value: 0.6018651362984218.\n", + "[I 2025-04-25 14:59:19,540] Trial 7 finished with value: 0.5347919655667145 and parameters: {'activation_1': 'relu', 'units_1': 16, 'dropout_rate': 0.4, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 96, 'dropout_rate_2': 0.2, 'learning_rate': 0.0001}. Best is trial 2 with value: 0.6018651362984218.\n", + "[I 2025-04-25 14:59:31,951] Trial 8 finished with value: 0.5477044476327116 and parameters: {'activation_1': 'tanh', 'units_1': 48, 'dropout_rate': 0.1, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 32, 'dropout_rate_2': 0.5, 'learning_rate': 0.0001}. Best is trial 2 with value: 0.6018651362984218.\n", + "[I 2025-04-25 14:59:36,391] Trial 9 finished with value: 0.6355810616929699 and parameters: {'activation_1': 'relu', 'units_1': 96, 'dropout_rate': 0.30000000000000004, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 48, 'dropout_rate_2': 0.4, 'learning_rate': 0.01}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 14:59:49,139] Trial 10 finished with value: 0.5649210903873745 and parameters: {'activation_1': 'tanh', 'units_1': 80, 'dropout_rate': 0.4, 'second_layer': True, 'activation_2': 'relu', 'units_2': 80, 'dropout_rate_2': 0.5, 'learning_rate': 0.0001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 14:59:55,158] Trial 11 finished with value: 0.5681492109038737 and parameters: {'activation_1': 'sigmoid', 'units_1': 48, 'dropout_rate': 0.0, 'second_layer': True, 'activation_2': 'sigmoid', 'units_2': 16, 'dropout_rate_2': 0.7, 'learning_rate': 0.01}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:00:06,276] Trial 12 finished with value: 0.4171449067431851 and parameters: {'activation_1': 'sigmoid', 'units_1': 16, 'dropout_rate': 0.5, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 112, 'dropout_rate_2': 0.4, 'learning_rate': 0.0001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:00:11,744] Trial 13 finished with value: 0.5774748923959828 and parameters: {'activation_1': 'tanh', 'units_1': 16, 'dropout_rate': 0.30000000000000004, 'second_layer': True, 'activation_2': 'sigmoid', 'units_2': 64, 'dropout_rate_2': 0.5, 'learning_rate': 0.01}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:00:26,015] Trial 14 finished with value: 0.5200860832137734 and parameters: {'activation_1': 'relu', 'units_1': 32, 'dropout_rate': 0.6000000000000001, 'second_layer': True, 'activation_2': 'relu', 'units_2': 112, 'dropout_rate_2': 0.6000000000000001, 'learning_rate': 0.0001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:00:36,622] Trial 15 finished with value: 0.5211621233859397 and parameters: {'activation_1': 'sigmoid', 'units_1': 80, 'dropout_rate': 0.30000000000000004, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 64, 'dropout_rate_2': 0.0, 'learning_rate': 0.0001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:00:45,593] Trial 16 finished with value: 0.5896700143472023 and parameters: {'activation_1': 'relu', 'units_1': 64, 'dropout_rate': 0.7, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 80, 'dropout_rate_2': 0.0, 'learning_rate': 0.001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:00:51,397] Trial 17 finished with value: 0.6323529411764706 and parameters: {'activation_1': 'sigmoid', 'units_1': 32, 'dropout_rate': 0.0, 'second_layer': False, 'activation_2': 'tanh', 'units_2': 16, 'dropout_rate_2': 0.5, 'learning_rate': 0.01}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:00:58,133] Trial 18 finished with value: 0.5900286944045912 and parameters: {'activation_1': 'tanh', 'units_1': 32, 'dropout_rate': 0.4, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 48, 'dropout_rate_2': 0.4, 'learning_rate': 0.001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:01:08,675] Trial 19 finished with value: 0.6119081779053085 and parameters: {'activation_1': 'sigmoid', 'units_1': 48, 'dropout_rate': 0.0, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 128, 'dropout_rate_2': 0.5, 'learning_rate': 0.001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:01:20,700] Trial 20 finished with value: 0.5749641319942611 and parameters: {'activation_1': 'tanh', 'units_1': 96, 'dropout_rate': 0.5, 'second_layer': True, 'activation_2': 'relu', 'units_2': 112, 'dropout_rate_2': 0.2, 'learning_rate': 0.0001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:01:26,036] Trial 21 finished with value: 0.6036585365853658 and parameters: {'activation_1': 'sigmoid', 'units_1': 128, 'dropout_rate': 0.2, 'second_layer': True, 'activation_2': 'relu', 'units_2': 112, 'dropout_rate_2': 0.5, 'learning_rate': 0.01}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:01:36,134] Trial 22 finished with value: 0.5247489239598279 and parameters: {'activation_1': 'sigmoid', 'units_1': 32, 'dropout_rate': 0.7, 'second_layer': False, 'activation_2': 'tanh', 'units_2': 32, 'dropout_rate_2': 0.6000000000000001, 'learning_rate': 0.001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:01:39,917] Trial 23 finished with value: 0.6104734576757532 and parameters: {'activation_1': 'tanh', 'units_1': 128, 'dropout_rate': 0.6000000000000001, 'second_layer': False, 'activation_2': 'relu', 'units_2': 96, 'dropout_rate_2': 0.30000000000000004, 'learning_rate': 0.01}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:01:44,252] Trial 24 finished with value: 0.6172883787661406 and parameters: {'activation_1': 'tanh', 'units_1': 96, 'dropout_rate': 0.5, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 96, 'dropout_rate_2': 0.6000000000000001, 'learning_rate': 0.01}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:01:51,314] Trial 25 finished with value: 0.5462697274031564 and parameters: {'activation_1': 'relu', 'units_1': 16, 'dropout_rate': 0.5, 'second_layer': False, 'activation_2': 'relu', 'units_2': 80, 'dropout_rate_2': 0.6000000000000001, 'learning_rate': 0.001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:02:03,783] Trial 26 finished with value: 0.48708751793400284 and parameters: {'activation_1': 'sigmoid', 'units_1': 96, 'dropout_rate': 0.1, 'second_layer': True, 'activation_2': 'relu', 'units_2': 48, 'dropout_rate_2': 0.7, 'learning_rate': 0.0001}. Best is trial 9 with value: 0.6355810616929699.\n", + "[I 2025-04-25 15:02:08,916] Trial 27 finished with value: 0.6359397417503587 and parameters: {'activation_1': 'tanh', 'units_1': 64, 'dropout_rate': 0.30000000000000004, 'second_layer': True, 'activation_2': 'sigmoid', 'units_2': 112, 'dropout_rate_2': 0.1, 'learning_rate': 0.01}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:02:20,956] Trial 28 finished with value: 0.5211621233859397 and parameters: {'activation_1': 'sigmoid', 'units_1': 48, 'dropout_rate': 0.4, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 128, 'dropout_rate_2': 0.4, 'learning_rate': 0.0001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:02:27,552] Trial 29 finished with value: 0.6219512195121951 and parameters: {'activation_1': 'tanh', 'units_1': 112, 'dropout_rate': 0.30000000000000004, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 48, 'dropout_rate_2': 0.4, 'learning_rate': 0.01}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:02:37,965] Trial 30 finished with value: 0.5175753228120517 and parameters: {'activation_1': 'relu', 'units_1': 32, 'dropout_rate': 0.6000000000000001, 'second_layer': False, 'activation_2': 'tanh', 'units_2': 96, 'dropout_rate_2': 0.5, 'learning_rate': 0.0001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:02:50,197] Trial 31 finished with value: 0.6022238163558106 and parameters: {'activation_1': 'sigmoid', 'units_1': 96, 'dropout_rate': 0.1, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 48, 'dropout_rate_2': 0.4, 'learning_rate': 0.001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:02:59,593] Trial 32 finished with value: 0.6212338593974175 and parameters: {'activation_1': 'relu', 'units_1': 64, 'dropout_rate': 0.5, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 128, 'dropout_rate_2': 0.2, 'learning_rate': 0.001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:03:10,196] Trial 33 finished with value: 0.5179340028694405 and parameters: {'activation_1': 'sigmoid', 'units_1': 96, 'dropout_rate': 0.5, 'second_layer': False, 'activation_2': 'relu', 'units_2': 112, 'dropout_rate_2': 0.6000000000000001, 'learning_rate': 0.0001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:03:16,440] Trial 34 finished with value: 0.6190817790530847 and parameters: {'activation_1': 'tanh', 'units_1': 32, 'dropout_rate': 0.2, 'second_layer': False, 'activation_2': 'relu', 'units_2': 96, 'dropout_rate_2': 0.1, 'learning_rate': 0.01}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:03:23,990] Trial 35 finished with value: 0.6093974175035868 and parameters: {'activation_1': 'sigmoid', 'units_1': 64, 'dropout_rate': 0.6000000000000001, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 112, 'dropout_rate_2': 0.1, 'learning_rate': 0.01}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:03:34,030] Trial 36 finished with value: 0.6176470588235294 and parameters: {'activation_1': 'relu', 'units_1': 112, 'dropout_rate': 0.7, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 48, 'dropout_rate_2': 0.4, 'learning_rate': 0.001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:03:44,721] Trial 37 finished with value: 0.5100430416068866 and parameters: {'activation_1': 'sigmoid', 'units_1': 64, 'dropout_rate': 0.30000000000000004, 'second_layer': False, 'activation_2': 'relu', 'units_2': 128, 'dropout_rate_2': 0.6000000000000001, 'learning_rate': 0.0001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:03:55,297] Trial 38 finished with value: 0.6262553802008608 and parameters: {'activation_1': 'relu', 'units_1': 112, 'dropout_rate': 0.6000000000000001, 'second_layer': False, 'activation_2': 'tanh', 'units_2': 112, 'dropout_rate_2': 0.1, 'learning_rate': 0.001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:04:07,443] Trial 39 finished with value: 0.5387374461979914 and parameters: {'activation_1': 'tanh', 'units_1': 80, 'dropout_rate': 0.7, 'second_layer': True, 'activation_2': 'relu', 'units_2': 48, 'dropout_rate_2': 0.4, 'learning_rate': 0.0001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:04:09,132] Trial 40 finished with value: 0.21987087517934004 and parameters: {'activation_1': 'sigmoid', 'units_1': 32, 'dropout_rate': 0.4, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 32, 'dropout_rate_2': 0.30000000000000004, 'learning_rate': 0.0001}. Best is trial 27 with value: 0.6359397417503587.\n", + "[I 2025-04-25 15:04:13,086] Trial 41 finished with value: 0.6434720229555236 and parameters: {'activation_1': 'tanh', 'units_1': 128, 'dropout_rate': 0.0, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 96, 'dropout_rate_2': 0.0, 'learning_rate': 0.01}. Best is trial 41 with value: 0.6434720229555236.\n", + "[I 2025-04-25 15:04:20,875] Trial 42 finished with value: 0.6388091822094691 and parameters: {'activation_1': 'relu', 'units_1': 80, 'dropout_rate': 0.1, 'second_layer': True, 'activation_2': 'tanh', 'units_2': 32, 'dropout_rate_2': 0.5, 'learning_rate': 0.01}. Best is trial 41 with value: 0.6434720229555236.\n", + "[I 2025-04-25 15:04:27,171] Trial 43 finished with value: 0.6215925394548063 and parameters: {'activation_1': 'sigmoid', 'units_1': 64, 'dropout_rate': 0.4, 'second_layer': False, 'activation_2': 'tanh', 'units_2': 112, 'dropout_rate_2': 0.6000000000000001, 'learning_rate': 0.01}. Best is trial 41 with value: 0.6434720229555236.\n", + "[I 2025-04-25 15:04:38,959] Trial 44 finished with value: 0.582855093256815 and parameters: {'activation_1': 'tanh', 'units_1': 96, 'dropout_rate': 0.7, 'second_layer': True, 'activation_2': 'sigmoid', 'units_2': 112, 'dropout_rate_2': 0.5, 'learning_rate': 0.001}. Best is trial 41 with value: 0.6434720229555236.\n", + "[I 2025-04-25 15:04:48,320] Trial 45 finished with value: 0.601506456241033 and parameters: {'activation_1': 'sigmoid', 'units_1': 64, 'dropout_rate': 0.4, 'second_layer': True, 'activation_2': 'relu', 'units_2': 16, 'dropout_rate_2': 0.2, 'learning_rate': 0.01}. Best is trial 41 with value: 0.6434720229555236.\n", + "[I 2025-04-25 15:04:57,108] Trial 46 finished with value: 0.5652797704447633 and parameters: {'activation_1': 'sigmoid', 'units_1': 96, 'dropout_rate': 0.7, 'second_layer': False, 'activation_2': 'tanh', 'units_2': 80, 'dropout_rate_2': 0.0, 'learning_rate': 0.001}. Best is trial 41 with value: 0.6434720229555236.\n", + "[I 2025-04-25 15:05:06,448] Trial 47 finished with value: 0.6352223816355811 and parameters: {'activation_1': 'relu', 'units_1': 80, 'dropout_rate': 0.30000000000000004, 'second_layer': False, 'activation_2': 'tanh', 'units_2': 80, 'dropout_rate_2': 0.2, 'learning_rate': 0.001}. Best is trial 41 with value: 0.6434720229555236.\n", + "[W 2025-04-25 15:05:10,469] Trial 48 failed with parameters: {'activation_1': 'tanh', 'units_1': 80, 'dropout_rate': 0.4, 'second_layer': False, 'activation_2': 'sigmoid', 'units_2': 16, 'dropout_rate_2': 0.30000000000000004, 'learning_rate': 0.0001} because of the following error: KeyboardInterrupt().\n", + "Traceback (most recent call last):\n", + " File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/optuna/study/_optimize.py\", line 197, in _run_trial\n", + " value_or_values = func(trial)\n", + " File \"/tmp/ipykernel_24713/521000553.py\", line 96, in objective\n", + " loss.backward()\n", + " File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/torch/_tensor.py\", line 648, in backward\n", + " torch.autograd.backward(\n", + " File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/torch/autograd/__init__.py\", line 353, in backward\n", + " _engine_run_backward(\n", + " File \"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/torch/autograd/graph.py\", line 824, in _engine_run_backward\n", + " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", + "KeyboardInterrupt\n", + "[W 2025-04-25 15:05:10,477] Trial 48 failed with value None.\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 140\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;66;03m# Run Optuna random search\u001b[39;00m\n\u001b[1;32m 139\u001b[0m study \u001b[38;5;241m=\u001b[39m optuna\u001b[38;5;241m.\u001b[39mcreate_study(direction\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmaximize\u001b[39m\u001b[38;5;124m'\u001b[39m, sampler\u001b[38;5;241m=\u001b[39moptuna\u001b[38;5;241m.\u001b[39msamplers\u001b[38;5;241m.\u001b[39mRandomSampler())\n\u001b[0;32m--> 140\u001b[0m \u001b[43mstudy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjective\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mTRIAL_COUNT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;66;03m# Show best trial\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBest Hyperparameters:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/optuna/study/study.py:475\u001b[0m, in \u001b[0;36mStudy.optimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m 373\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21moptimize\u001b[39m(\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 375\u001b[0m func: ObjectiveFuncType,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 382\u001b[0m show_progress_bar: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 383\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 384\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Optimize an objective function.\u001b[39;00m\n\u001b[1;32m 385\u001b[0m \n\u001b[1;32m 386\u001b[0m \u001b[38;5;124;03m Optimization is done by choosing a suitable set of hyperparameter values from a given\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[38;5;124;03m If nested invocation of this method occurs.\u001b[39;00m\n\u001b[1;32m 474\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 475\u001b[0m \u001b[43m_optimize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 476\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 477\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 478\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_trials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 479\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 480\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 481\u001b[0m \u001b[43m \u001b[49m\u001b[43mcatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIterable\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 482\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 483\u001b[0m \u001b[43m \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgc_after_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 484\u001b[0m \u001b[43m \u001b[49m\u001b[43mshow_progress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_progress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 485\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/optuna/study/_optimize.py:63\u001b[0m, in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 63\u001b[0m \u001b[43m_optimize_sequential\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[43m \u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[43m \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mreseed_sampler_rng\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mtime_start\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:\n", + "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/optuna/study/_optimize.py:160\u001b[0m, in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 160\u001b[0m frozen_trial \u001b[38;5;241m=\u001b[39m \u001b[43m_run_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstudy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 162\u001b[0m \u001b[38;5;66;03m# The following line mitigates memory problems that can be occurred in some\u001b[39;00m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;66;03m# environments (e.g., services that use computing containers such as GitHub Actions).\u001b[39;00m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;66;03m# Please refer to the following PR for further details:\u001b[39;00m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;66;03m# https://github.com/optuna/optuna/pull/325.\u001b[39;00m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m gc_after_trial:\n", + "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/optuna/study/_optimize.py:248\u001b[0m, in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShould not reach.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 244\u001b[0m frozen_trial\u001b[38;5;241m.\u001b[39mstate \u001b[38;5;241m==\u001b[39m TrialState\u001b[38;5;241m.\u001b[39mFAIL\n\u001b[1;32m 245\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m func_err \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 246\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(func_err, catch)\n\u001b[1;32m 247\u001b[0m ):\n\u001b[0;32m--> 248\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m func_err\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m frozen_trial\n", + "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/optuna/study/_optimize.py:197\u001b[0m, in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m get_heartbeat_thread(trial\u001b[38;5;241m.\u001b[39m_trial_id, study\u001b[38;5;241m.\u001b[39m_storage):\n\u001b[1;32m 196\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 197\u001b[0m value_or_values \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mTrialPruned \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 199\u001b[0m \u001b[38;5;66;03m# TODO(mamu): Handle multi-objective cases.\u001b[39;00m\n\u001b[1;32m 200\u001b[0m state \u001b[38;5;241m=\u001b[39m TrialState\u001b[38;5;241m.\u001b[39mPRUNED\n", + "Cell \u001b[0;32mIn[13], line 96\u001b[0m, in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m 94\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(output, y_batch)\n\u001b[1;32m 95\u001b[0m train_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[0;32m---> 96\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 97\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 99\u001b[0m train_loss \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(train_loader)\n", + "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/torch/_tensor.py:648\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 638\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 639\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 640\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 641\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 646\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 647\u001b[0m )\n\u001b[0;32m--> 648\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 649\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 650\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/torch/autograd/__init__.py:353\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 348\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 350\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 351\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 353\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 355\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 356\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 357\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 358\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 359\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 360\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 361\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/projects/predictify/.venv/lib/python3.10/site-packages/torch/autograd/graph.py:824\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 822\u001b[0m unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[1;32m 823\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 824\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 825\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 826\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 827\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 828\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch.optim as optim\n", + "import optuna\n", + "import numpy as np\n", + "from sklearn.metrics import classification_report\n", + "from torch.utils.data import DataLoader, TensorDataset, random_split\n", + "\n", + "# Ensure reproducibility\n", + "torch.manual_seed(42)\n", + "np.random.seed(42)\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "#device = torch.device(\"cpu\")\n", + "\n", + "BATCH_SIZE = 128\n", + "\n", + "# Convert numpy data to PyTorch tensors\n", + "X_train_tensor = torch.tensor(X_train, dtype=torch.float32)\n", + "y_train_tensor = torch.tensor(y_train, dtype=torch.long)\n", + "X_test_tensor = torch.tensor(X_test, dtype=torch.float32)\n", + "y_test_tensor = torch.tensor(y_test, dtype=torch.long)\n", + "\n", + "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n", + "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n", + "\n", + "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n", + "val_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)\n", + "\n", + "num_features = X_train.shape[1]\n", + "num_classes = len(np.unique(y_train))\n", + "\n", + "EPOCHS = 50\n", + "EARLY_STOPPING_PATIENCE = 6\n", + "\n", + "# Define model class\n", + "class CustomNet(nn.Module):\n", + " def __init__(self, input_size, output_size, hp):\n", + " super().__init__()\n", + " self.layers = nn.Sequential()\n", + " self.layers.add_module('fc1', nn.Linear(input_size, hp['units_1']))\n", + " self.layers.add_module('act1', self.get_activation(hp['activation_1']))\n", + " self.layers.add_module('drop1', nn.Dropout(hp['dropout_rate']))\n", + " \n", + " if hp['second_layer']:\n", + " self.layers.add_module('fc2', nn.Linear(hp['units_1'], hp['units_2']))\n", + " self.layers.add_module('act2', self.get_activation(hp['activation_2']))\n", + " self.layers.add_module('drop2', nn.Dropout(hp['dropout_rate_2']))\n", + " last_dim = hp['units_2']\n", + " else:\n", + " last_dim = hp['units_1']\n", + "\n", + " self.output_layer = nn.Linear(last_dim, output_size)\n", + "\n", + " def forward(self, x):\n", + " x = self.layers(x)\n", + " return self.output_layer(x)\n", + "\n", + " def get_activation(self, name):\n", + " return {\n", + " 'relu': nn.ReLU(),\n", + " 'tanh': nn.Tanh(),\n", + " 'sigmoid': nn.Sigmoid()\n", + " }[name]\n", + "\n", + "\n", + "def objective(trial):\n", + " hp = {\n", + " 'activation_1': trial.suggest_categorical('activation_1', ['relu', 'tanh', 'sigmoid']),\n", + " 'units_1': trial.suggest_int('units_1', 16, 128, step=16),\n", + " 'dropout_rate': trial.suggest_float('dropout_rate', 0.0, 0.7, step=0.1),\n", + " 'second_layer': trial.suggest_categorical('second_layer', [True, False]),\n", + " 'activation_2': trial.suggest_categorical('activation_2', ['relu', 'tanh', 'sigmoid']),\n", + " 'units_2': trial.suggest_int('units_2', 16, 128, step=16),\n", + " 'dropout_rate_2': trial.suggest_float('dropout_rate_2', 0.0, 0.7, step=0.1),\n", + " 'learning_rate': trial.suggest_categorical('learning_rate', [1e-4, 1e-3, 1e-2])\n", + " }\n", + "\n", + " model = CustomNet(num_features, num_classes, hp).to(device)\n", + " optimizer = optim.Adam(model.parameters(), lr=hp['learning_rate'])\n", + " criterion = nn.CrossEntropyLoss()\n", + "\n", + " best_val_acc = 0\n", + " patience, wait = EARLY_STOPPING_PATIENCE, 0\n", + "\n", + " for epoch in range(EPOCHS):\n", + " model.train()\n", + " train_loss = 0.0\n", + " for X_batch, y_batch in train_loader:\n", + " X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n", + " optimizer.zero_grad()\n", + " output = model(X_batch)\n", + " loss = criterion(output, y_batch)\n", + " train_loss += loss.item()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " train_loss /= len(train_loader)\n", + " val_loss = 0.0\n", + " model.eval()\n", + " with torch.no_grad():\n", + " for X_batch, y_batch in val_loader:\n", + " X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n", + " output = model(X_batch)\n", + " loss = criterion(output, y_batch)\n", + " val_loss += loss.item()\n", + " val_loss /= len(val_loader)\n", + "\n", + " val_acc = compute_accuracy(model, val_loader)\n", + "\n", + " if val_acc > best_val_acc:\n", + " best_val_acc = val_acc\n", + " wait = 0\n", + " else:\n", + " wait += 1\n", + " if wait >= patience:\n", + " break\n", + "\n", + " return best_val_acc\n", + "\n", + "\n", + "def compute_accuracy(model, dataloader):\n", + " model.eval()\n", + " correct = 0\n", + " total = 0\n", + " with torch.no_grad():\n", + " for X_batch, y_batch in dataloader:\n", + " X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n", + " outputs = model(X_batch)\n", + " preds = torch.argmax(outputs, dim=1)\n", + " correct += (preds == y_batch).sum().item()\n", + " total += y_batch.size(0)\n", + " return correct / total\n", + "\n", + "TRIAL_COUNT = 500\n", + "\n", + "# Run Optuna random search\n", + "study = optuna.create_study(direction='maximize', sampler=optuna.samplers.RandomSampler())\n", + "study.optimize(objective, n_trials=TRIAL_COUNT)\n", + "\n", + "# Show best trial\n", + "print(\"Best Hyperparameters:\")\n", + "print(study.best_params)\n", + "\n", + "# Retrain final model on full training set with early stopping\n", + "best_hp = study.best_params\n", + "final_model = CustomNet(num_features, num_classes, best_hp).to(device)\n", + "optimizer = optim.Adam(final_model.parameters(), lr=best_hp['learning_rate'])\n", + "criterion = nn.CrossEntropyLoss()\n", + "\n", + "train_losses = []\n", + "val_losses = []\n", + "train_accuracies = []\n", + "val_accuracies = []\n", + "\n", + "# Early stopping variables\n", + "best_val_loss = float('inf')\n", + "patience = EARLY_STOPPING_PATIENCE\n", + "epochs_no_improve = 0\n", + "best_model_state = None\n", + "\n", + "for epoch in range(EPOCHS):\n", + " # Training\n", + " final_model.train()\n", + " running_loss = 0\n", + " correct_train = 0\n", + " total_train = 0\n", + "\n", + " for X_batch, y_batch in train_loader:\n", + " X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n", + " optimizer.zero_grad()\n", + " outputs = final_model(X_batch)\n", + " loss = criterion(outputs, y_batch)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item()\n", + " preds = torch.argmax(outputs, dim=1)\n", + " correct_train += (preds == y_batch).sum().item()\n", + " total_train += y_batch.size(0)\n", + "\n", + " avg_train_loss = running_loss / len(train_loader)\n", + " train_acc = correct_train / total_train\n", + "\n", + " # Validation\n", + " final_model.eval()\n", + " val_loss = 0\n", + " correct_val = 0\n", + " total_val = 0\n", + " with torch.no_grad():\n", + " for X_batch, y_batch in val_loader:\n", + " X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n", + " outputs = final_model(X_batch)\n", + " loss = criterion(outputs, y_batch)\n", + " val_loss += loss.item()\n", + " preds = torch.argmax(outputs, dim=1)\n", + " correct_val += (preds == y_batch).sum().item()\n", + " total_val += y_batch.size(0)\n", + "\n", + " avg_val_loss = val_loss / len(val_loader)\n", + " val_acc = correct_val / total_val\n", + "\n", + " # Save history\n", + " train_losses.append(avg_train_loss)\n", + " val_losses.append(avg_val_loss)\n", + " train_accuracies.append(train_acc)\n", + " val_accuracies.append(val_acc)\n", + "\n", + " print(f\"Epoch {epoch+1}/{EPOCHS} | \"\n", + " f\"Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | \"\n", + " f\"Train Acc: {train_acc:.4f} | Val Acc: {val_acc:.4f}\")\n", + "\n", + " # Early stopping check\n", + " if avg_val_loss < best_val_loss:\n", + " best_val_loss = avg_val_loss\n", + " best_model_state = final_model.state_dict()\n", + " epochs_no_improve = 0\n", + " else:\n", + " epochs_no_improve += 1\n", + " if epochs_no_improve >= patience:\n", + " print(f\"Early stopping triggered after {epoch+1} epochs.\")\n", + " break\n", + "\n", + "# Load best model state after training\n", + "if best_model_state is not None:\n", + " final_model.load_state_dict(best_model_state)\n", + " print(\"Restored best model weights based on validation loss.\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluation Charts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Confusion Matrix - Shows what was the predicted value compared to the true value when evaluating the Model\n", + "\n", + "The more values in the diagonal line the better" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Put model in eval mode\n", + "final_model.eval()\n", + "\n", + "# Get predictions on test data\n", + "all_preds = []\n", + "all_labels = []\n", + "\n", + "with torch.no_grad():\n", + " for X_batch, y_batch in val_loader: # or test_loader if you named it that\n", + " X_batch = X_batch.to(device)\n", + " outputs = final_model(X_batch)\n", + " preds = torch.argmax(outputs, dim=1).cpu().numpy()\n", + " all_preds.extend(preds)\n", + " all_labels.extend(y_batch.numpy())\n", + "\n", + "# Optionally inverse transform labels (if using LabelEncoder)\n", + "decoded_y_test = le.inverse_transform(all_labels)\n", + "decoded_y_pred = le.inverse_transform(all_preds)\n", + "\n", + "# Create confusion matrix\n", + "cm = confusion_matrix(decoded_y_test, decoded_y_pred)\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(8, 6))\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='viridis', \n", + " xticklabels=le.classes_, yticklabels=le.classes_)\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('True')\n", + "plt.title('Confusion Matrix')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loss and Accuracy Plots - Shows how the models loss and accuracy values changed during the training\n", + "\n", + "The closer the lines to each other the better" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(15, 6.5))\n", + "\n", + "# Loss Plot\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(train_losses, label='Training Loss')\n", + "plt.plot(val_losses, label='Validation Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.title('Training vs. Validation Loss')\n", + "plt.legend()\n", + "\n", + "# Accuracy Plot\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(train_accuracies, label='Training Accuracy')\n", + "plt.plot(val_accuracies, label='Validation Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.title('Training vs. Validation Accuracy')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Corrleation Matrix - How much the features correlate to each other \n", + "\n", + "Less correlating features is better" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a DataFrame for features. If X has many features, consider selecting a subset.\n", + "df_features = pd.DataFrame(X_train, columns=[f'feature_{i}' for i in range(X_train.shape[1])])\n", + "\n", + "# Compute the correlation matrix.\n", + "corr = df_features.corr()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "sns.heatmap(corr, cmap='coolwarm', annot=False)\n", + "plt.title('Feature Correlation Matrix')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature permutation importance - shows how much each feature was important in making a decision in the model\n", + "\n", + "Potential: remove the non-important features" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.base import BaseEstimator, ClassifierMixin\n", + "from sklearn.inspection import permutation_importance\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "class TorchClassifierWrapper(BaseEstimator, ClassifierMixin):\n", + " def __init__(self, model, device):\n", + " self.model = model.to(device)\n", + " self.device = device\n", + " \n", + " def fit(self, X, y):\n", + " # Model is already trained\n", + " return self\n", + "\n", + " def predict(self, X):\n", + " self.model.eval()\n", + " with torch.no_grad():\n", + " X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)\n", + " outputs = self.model(X_tensor)\n", + " preds = torch.argmax(outputs, dim=1).cpu().numpy()\n", + " return preds\n", + "\n", + " def score(self, X, y):\n", + " preds = self.predict(X)\n", + " return np.mean(preds == y)\n", + " \n", + "# Wrap the PyTorch model\n", + "wrapper = TorchClassifierWrapper(final_model, device=device)\n", + "\n", + "# Compute permutation importance\n", + "result = permutation_importance(wrapper, X_test, y_test, n_repeats=10,\n", + " random_state=42, scoring='accuracy')\n", + "\n", + "importance_means = result.importances_mean\n", + "\n", + "features = [f'feature_{i}' for i in range(X_test.shape[1])]\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.bar(features, importance_means)\n", + "plt.xticks(rotation=90)\n", + "plt.xlabel('Feature')\n", + "plt.ylabel('Mean Importance')\n", + "plt.title('Permutation Feature Importance')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove unimportant features" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Important feature indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 18 22 23 24 25 28 29]\n", + "Important features: ['feature_0', 'feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'feature_6', 'feature_7', 'feature_8', 'feature_9', 'feature_10', 'feature_11', 'feature_12', 'feature_13', 'feature_14', 'feature_15', 'feature_18', 'feature_22', 'feature_23', 'feature_24', 'feature_25', 'feature_28', 'feature_29']\n", + "Original training shape: (5012, 31)\n", + "Reduced training shape: (5012, 23)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Assume importance_means and importance_threshold are already defined\n", + "importance_threshold = 0.01 # adjust this threshold as needed\n", + "\n", + "# Check if X_train is a DataFrame, if not, convert it and assign column names.\n", + "if isinstance(X_train, np.ndarray):\n", + " X_train_df = pd.DataFrame(X_train, columns=[f'feature_{i}' for i in range(X_train.shape[1])])\n", + "else:\n", + " X_train_df = X_train.copy()\n", + "\n", + "# Do the same for X_test.\n", + "if isinstance(X_test, np.ndarray):\n", + " X_test_df = pd.DataFrame(X_test, columns=[f'feature_{i}' for i in range(X_test.shape[1])])\n", + "else:\n", + " X_test_df = X_test.copy()\n", + "\n", + "# Identify the indices of features with importance greater than or equal to the threshold.\n", + "important_feature_indices = np.where(importance_means >= importance_threshold)[0]\n", + "print(\"Important feature indices:\", important_feature_indices)\n", + "\n", + "# Retrieve the names of the important features.\n", + "important_features = X_train_df.columns[important_feature_indices]\n", + "print(\"Important features:\", important_features.tolist())\n", + "\n", + "# Filter the training and test DataFrames to keep only the important features.\n", + "X_train_reduced = X_train_df[important_features]\n", + "X_test_reduced = X_test_df[important_features]\n", + "\n", + "print(\"Original training shape:\", X_train_df.shape)\n", + "print(\"Reduced training shape:\", X_train_reduced.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Re-run Hyperparameter search" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/ai_analysis/old_pytorch/xgb_pytorch.ipynb b/src/ai_analysis/old_pytorch/xgb_pytorch.ipynb new file mode 100644 index 0000000..3308389 --- /dev/null +++ b/src/ai_analysis/old_pytorch/xgb_pytorch.ipynb @@ -0,0 +1,2543 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read CSV with TrackId and Genre Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "tracks_info_df = pd.read_csv('combined_dataset.csv')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Read processed song data from pkl file" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 100390 processed tracks\n" + ] + } + ], + "source": [ + "import pickle\n", + "import os\n", + "\n", + "\n", + "results_file = 'audio_features_backup.pkl'\n", + "\n", + "if os.path.exists(results_file):\n", + " with open(results_file, 'rb') as file:\n", + " saved_data = pickle.load(file)\n", + " X = saved_data.get('X', [])\n", + " y_labels = saved_data.get('y_labels', [])\n", + " \n", + " processed_files = set(saved_data.get('processed_files', []))\n", + " print(f\"Loaded {len(processed_files)} processed tracks\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Turn X into an np array for easier processing" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of feature matrix X: (100390, 55)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "X = np.array(X)\n", + "print(\"shape of feature matrix X: \", X.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remove all entries where the genre is defined as None" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "X = X[np.array(y_labels) != None]\n", + "y_labels = np.array(y_labels)[np.array(y_labels) != None]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Remap genres" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Replace sub-genres with their parent genre based on a predefined mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "genre_mapping = {\n", + " 'lo-fi beats': 'lo-fi',\n", + " 'chillwave': 'lo-fi',\n", + "\n", + " 'nu metal': 'metal',\n", + " 'folk metal': 'metal',\n", + " 'heavy metal': 'metal',\n", + " 'metalcore': 'metal',\n", + " 'power metal': 'metal',\n", + " 'industrial metal': 'metal',\n", + " 'glam metal': 'metal',\n", + " 'melodic death metal': 'metal',\n", + " 'progressive metal': 'metal',\n", + " 'thrash metal': 'metal',\n", + " 'gothic metal': 'metal',\n", + " 'groove metal': 'metal',\n", + " 'death metal': 'metal',\n", + " 'alternative metal': 'metal',\n", + " 'black metal': 'metal',\n", + " 'speed metal': 'metal',\n", + "\n", + " 'acid house': 'acid',\n", + " 'acid techno': 'acid',\n", + "\n", + " 'afrobeat': 'afro',\n", + " 'afropop': 'afro',\n", + "\n", + " 'art rock': 'rock',\n", + "\n", + " 'britpop': 'alternative rock',\n", + " \n", + " 'art pop': 'pop',\n", + " 'acoustic pop': 'pop',\n", + " 'bedroom pop': 'pop',\n", + " 'soft pop': 'pop', \n", + " 'folk pop': 'pop',\n", + " 'norwegian pop': 'pop',\n", + " 'acoustic pop': 'pop',\n", + " 'dream pop': 'pop',\n", + " 'chamber pop': 'pop',\n", + " 'german pop': 'pop',\n", + " 'bedroom pop': 'pop',\n", + " 'city pop': 'pop',\n", + " 'dance pop': 'pop',\n", + " 'art pop': 'pop',\n", + " 'indie pop': 'pop',\n", + "\n", + " 'alt country': 'country',\n", + " 'traditional country': 'country',\n", + "\n", + " 'blues rock': 'blues',\n", + "\n", + " 'brooklyn drill': 'drill',\n", + "\n", + " 'gangster rap': 'rap',\n", + " 'memphis rap': 'rap',\n", + " 'rock rap': 'rap',\n", + " 'meme rap': 'rap',\n", + " 'punk rap': 'rap',\n", + " 'emo rap': 'rap',\n", + " 'jazz rap': 'rap',\n", + " 'melodic rap': 'rap',\n", + " 'cloud rap': 'rap',\n", + " 'k-rap': 'rap',\n", + " 'rage rap': 'rap',\n", + "\n", + " 'edm trap': 'trap',\n", + " 'italian trap': 'trap',\n", + " 'dark trap': 'trap',\n", + "\n", + " 'hip-hop': 'hip hop',\n", + " 'southern hip hop': 'hip hop',\n", + " 'west coast hip hop': 'hip hop',\n", + " 'east coast hip hop': 'hip hop',\n", + " 'finnish hip hop': 'hip hop',\n", + " 'german hip hop': 'hip hop',\n", + " 'underground hip hop': 'hip hop',\n", + " 'norwegian hip hop': 'hip hop',\n", + " 'experimental hip hop': 'hip hop',\n", + " 'alternative hip hop': 'hip hop',\n", + " 'latin hip hop': 'hip hop',\n", + "\n", + " 'jazz blues': 'jazz',\n", + " 'vocal jazz': 'jazz',\n", + " 'french jazz': 'jazz',\n", + " 'free jazz': 'jazz',\n", + " 'soul jazz': 'jazz',\n", + " 'jazz fusion': 'jazz',\n", + " 'nu jazz': 'jazz',\n", + " 'jazz funk': 'jazz',\n", + " 'cool jazz': 'jazz',\n", + " 'jazz beats': 'jazz',\n", + " 'jazz house': 'jazz',\n", + " 'indie jazz': 'jazz',\n", + " 'smooth jazz': 'jazz',\n", + "\n", + " 'melodic techno': 'techno',\n", + " 'minimal techno': 'techno',\n", + " 'hypertechno': 'techno',\n", + " 'hard techno': 'techno',\n", + " 'hardcore techno': 'techno',\n", + " 'dub techno': 'techno',\n", + "\n", + " 'future house': 'house',\n", + " 'tech house': 'house',\n", + " 'bass house': 'house',\n", + " 'electro house': 'house',\n", + " 'tropical house': 'house',\n", + " 'french house': 'house',\n", + " 'melodic house': 'house',\n", + " 'organic house': 'house',\n", + " 'progressive house': 'house',\n", + " 'latin house': 'house',\n", + " 'deep house': 'house',\n", + " 'slap house': 'house',\n", + " 'chicago house': 'house'\n", + " \n", + "} \n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Map hip-hop to rap -> Models without this are too confused due to the genres being so alike" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "genre_mapping = {\n", + " 'hip hop': 'rap'\n", + "} \n", + "\n", + "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels_normalized]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "KeyboardInterrupt\n", + "\n" + ] + } + ], + "source": [ + "# Or remove hip hop\n", + "\n", + "X = X[np.array(y_labels_normalized) != \"hip hop\"]\n", + "y_labels_normalized = np.array(y_labels_normalized)[np.array(y_labels_normalized) != \"hip hop\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run this code in case the last two cells were not executed" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "y_labels_normalized = y_labels\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extra: Keep only a popular type of genre" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered dataset size: 13447 entries (from 100383 original)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "original_labels_list = y_labels_normalized\n", + "y_labels_normalized = np.array(y_labels_normalized)\n", + "\n", + "\n", + "popular_genres = {\n", + " 'country', 'idm', 'black-metal', 'hardstyle', 'heavy-metal',\n", + " 'blues', 'rap', 'hip hop', 'classical', 'folk', 'rock-n-roll',\n", + " 'electronic', 'jazz', 'lo-fi', 'punk-rock', 'soul', 'punk',\n", + " 'rock'\n", + "}\n", + "\n", + "\n", + "#popular_genres = {\n", + "# 'pop', 'rock', 'rap', 'hip hop', 'metal', 'country', 'jazz',\n", + "# 'classical', 'reggae', 'punk', 'funk',\n", + "# 'r&b', 'soul', 'trap', 'techno', 'indie'\n", + "#}\n", + "# blues folk\n", + "\n", + "y_lower = np.array([genre.lower() for genre in y_labels_normalized])\n", + "\n", + "mask = np.array([genre in popular_genres for genre in y_lower])\n", + "\n", + "X = X[mask]\n", + "y_labels_normalized = y_labels_normalized[mask]\n", + "\n", + "# Optional: print some info\n", + "print(f\"Filtered dataset size: {len(y_labels_normalized)} entries (from {len(original_labels_list)} original)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Chart the leftover genres" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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585JEXzwv6/vx8OHD6uXlpZUrV9b79++rquq5c+e0Z8+earFYdP369fYsM8FYvXq1pkuXTosWLaoODg765Zdf2qwPDg7mmOH/7dmzR3PkyKHlypXTyMhIVVW9fPmyfvrpp5o2bVp1cXFRb29vzZ49u2bIkEFz5sypWbJk0dy5c9sch+H5rFu3Tp2dnXX06NG6detWDQgI0HTp0unq1auNNgcOHFAXFxetVauWHStNvPr3768eHh7atm1brV27thYtWlQ///xzY/3q1au1Ro0aWrJkST1x4oQdK331oqOjbf67a9cu/f33343f69atq2XLlo3zffvBBx9ou3bt9MGDB6+34ETC+p28bt06rVSpkmbLlk2bNWtms4+8ePFirVSpkjZs2DDJfs9YtzNV1QsXLujgwYM1X758Onz4cGP5smXLtFq1alq+fHk9evSoPcpMUKKjo+Mc+7/99ts6ZMgQ/fDDD7VRo0a6bt06/f777/XHH3/Un3/+WefNm6eXL19+JfUQor8BrAejI0aMUFdXVx07dqyGhIQY669cuaLt27fXP/74w14lJkh37tzRb7/9VjNnzqw1atSwWXf48GHNli2bHjhwwE7VJWzWD7Hjx4/rr7/+qt9++62qPu7T2bNn65w5czQ0NNQ48GjRooUOHz48SYSXT+rVq5e6u7vr9u3b1dfXV7du3aqq//sC3blzp7711lvq5+enb731lhGgW/sOz7Zv3z7t0qWLbtmyJc66RYsWad++fTVLlixqsVj0008/tUOFr1/snbPIyEh99OiRqj5+fy5btkzffvttTZs2ra5YsULnz5+vU6dO1ZkzZ+rkyZPZ7p4idn/evHlTw8LCjPfpiRMntFmzZurn56fTpk3TyMhIvXz5slauXFlr1KiRJD/znmXbtm3q7u6uzZs3twnSu3fvruXKldNUqVLpvHnzjOXXr1/XChUq6KxZs+xR7itl3Tb++OMPPXXqlKo+DtLz5MmjFotFCxYsqEWKFNFChQqpr6+v5s2bV/Ply6d//vmnPctOEJ52QPn777+rl5eXVqlSxQjSz5w5o/369Uuy+7+xDzrPnj2rBQsW1KlTp+r333+vrVu31jRp0ujGjRttHnP16lWOGf7fwYMHNXfu3Fq6dGnju/HSpUsaGBioGTNm1NatW+utW7f00KFDunv3bj1w4IBeunTJzlUnPhcvXtRy5crpxIkTVfVxH+fKlUvz5s2rTk5OunLlSqNtUFCQnjlzxl6lJlqzZs3S3Llz6759+1RVdeHChZoiRQrNnj27Dho0yGi3ePFi7dWrl81+z5ssOjpao6KiNGfOnJo/f349cuSIqj4OgatUqaIlS5bU9evX6w8//KB9+vRRNzc3As1/sX79ek2dOrWOGTNG161bp+3bt9ds2bLphAkTjDZLlizRd955R1u0aGEcoyQF1u/j0NBQvX//vvG9cvXqVR00aFCcIH3BggVar169JP+9Evvz6ODBg8b/jxw5UosVK6ZZs2ZVi8WiVapU0WzZsmm2bNm0YMGCWq5cuVf2WUaInsg9GXgMHz7cCNJv3LhhLI89KjYpu3Tpkt64cUP/+ecfVX0cKq1atUo9PDy0SpUqunXrVt2+fbvWrFlT/fz86LensH4BrF69WnPmzKlFixbV3Llza6FChXT37t02bf/55x8dPHiwuru7J8kDssDAQE2ePLmxU1aiRAmdP3++qv7vvXvu3DlNmTKlFipUiAD9OcT+Mly9erUWKVJE8+bNq2fPnlXVx591sYPL6OhoPX36tDZt2lSrVKmSpPr2888/14YNG2qlSpV0x44dqqp6//59XbZsmebKlUu7dOny1Mfxufc/sbelESNGaIkSJbRw4cJatmxZY0fuxIkTGhAQoMmSJdM8efJo06ZN9b333jNGKiWVg9F/Y+3L3bt3q6urqwYEBBhXhyxZskTz5cunVatW1bCwMFV9PEK9Ro0a+u67775x22Ts71EfHx8dMmSIXrt2TVUfB+m+vr5auHBhPXv2rEZGRmpkZKQ+ePDACIeTshs3bqizs7M2atQozrrdu3eri4uLNmvWTO/evauqSfP71LotWW3ZskUnTZqkH3zwgbEsJiZG27dvr2nTptUNGzbYtH/T3m//xYEDB+IE6efPn9chQ4ZomjRp9JtvvrFzhYnfmTNndOTIkRoWFqZXr17VfPnyaYcOHfTBgwdau3Zt9fDw0KVLl9q7zEQl9n5HdHS0Dho0SIcNG6aqqmvXrlU3NzcdPXq09ujRQ9OnT69jxox55t9404WGhmq+fPn0nXfeMUbhb9myRVu2bKmpUqXS/Pnza4kSJfT333+3b6EJ3NmzZ7V48eLG1YShoaGaJUsWfeeddzRXrlz6xRdfGG1XrlypFy5csFepr13sUfqlS5fWt99+W4sUKaLffvutRkZG6s2bN40R6YGBgcbjYg84SYpiH4cNHjxYfXx8dNWqVcayCRMmaLFixbRjx47Gvo91ZgnrY1/FZxkheiJm3cm9cOGCDhw40Fg+cuRIzZAhgw4bNkxv3rxpr/ISnJEjR6qvr68WLFhQ/f399fjx46r6+I327bffqpeXl1osFv3ggw+0V69exhuQg4m4du3apW5ubsaIwT///FMtFovOmDHD+MD65ZdftF69epozZ84kebnW/fv3dcqUKcYIQ9XHIfqQIUNs2h0+fFjXrVtnbGdJ8YD/eT05Ivj48eNap04ddXJy0q+//tpY92SIrvr4c9LJyUnXrl37+gp+zWL3z+jRozVjxoz60UcfabVq1TRFihQ6Z84cVVV98OCBrlixQrNnz66VKlWyV7mJytChQzV9+vQ6d+5c/fLLL7V27drq7OxsXGp+/PhxDQgI0HfeecfmYPThw4f2KjlBeXJE/s6dO9XV1VWbNm1qjEIaPXq0FitWTPPkyaNVq1ZVPz8/LVasmHFy8U37Lv755581VapU+tVXX9lcPaj6OKTLlSuXVqxYMcmPQHpSVFSUrl27VtOnT6+tW7e2WXfnzh0tXbq0WiwWrVu3rn0KtLPp06drzZo1ba6k7NSpk1osFi1UqJDNtmYN0t3d3fX777+3R7kJXlRUlB48eFC9vb1tgvTLly/r4MGD1WKxGIMj8Hys3wexAzTrJfcff/yx1q5d25hms1u3bpo2bVr18PAwTrDi2YKDg43/t179+uDBAz1//rxevHhRCxQoYISZu3btUldXV02dOrVOnz7dLvW+brFHA0dFRRnv6bCwMM2VK5e+8847Rkag+niwU0hIiDEAD7b7dLH//86dO9q/f3+9ePGi/v333/rWW2/phx9+qOfPn9cKFSpo+vTpbQLipGbDhg2aIkUKHTFihM6aNUs7duyoLi4uOmbMGI2OjtarV6/qsGHDNFOmTDZTLUF12LBhmjFjRt20aZNeuXLFZt2oUaO0WLFi2r9/f2NQnRUj0WEjdoCeLVs27dq1q80B5qeffqo5cuQgRP9/n376qWbKlElXrFih27Zt0zJlymiGDBl0z549qvp4vuCVK1dqoUKFtHbt2sbjGPX1dLNmzdKWLVuqqurp06fV29tbO3fubNPm0qVLOmPGDP3rr7/sUeJr97QPaWs4ZA2BGjZsqD179jTaV65cWfv372+0f9NCopfp22+/NQLw3r17a/Xq1VVV9dChQ1q3bl318/MzphVStd2ps/bru+++q4sXL36NVdvHxYsXtX///rp9+3Zj2eDBg9XR0dGYl/Dhw4c6f/58rVevXpIabRQfISEhWqxYMV2wYIHN8i5dumjatGmNIODw4cPaokULLVu2rC5btswepSZ4I0eONC7N/+2339TNzU0bN25sHMRu375dP//8cx0wYIDOnj3bWP4mnVyMiYnRhw8faqtWrfTjjz82lqnafgdcuHBB06VLp9WrV0/S3w1mB+vr16/XtGnTxgnSe/bsqT///HOcA6mkYuvWrerl5aXNmzfX/fv3G8sHDhyoFotF586dawwSsWrcuLF6eXkZo/eTopiYGJtw9/jx43ru3DljWVBQUJwg/cKFCzpy5EibwRJ4Nmt/fv/99+rj46OTJ0+2Wff+++9rjx49jGXW9zPHs89n06ZNWrduXT127Jh+9NFH6uTkZHNlyrp169THx8cI2nfv3q2NGjXSxYsXJ6nvme+//14rV66svr6+OnnyZGP0eWhoqObKlUt9fX318OHD7B8/w9WrV40rMpcvX25Mu2e9CrN///7aqFEj4+RDnz59NHfu3Fq2bFm9ceNGkpruMCoqSh89eqQNGza0uSJMVXXMmDHq7Oxs3Lvl/PnzOnr06CSTnzyPa9euqZ+fX5wrv6z5iqrq2LFjNWvWrHHu9fKqEKInAmYHEP/884/myZNHu3Tp8tTLFdjheGzv3r1aunRp/fXXX1X18Q6Em5ubFi5cWJ2dnY0gPTw8XFetWqU5c+bUJk2a2LPkBK9bt27asGFDvXPnjnp5eWnnzp2NbXDOnDlJ7uxp7Pfd0aNHNSgoSP/+++847bp3764NGjRQ1cc3YfX29rb5AsDTxcTEaOfOndVisWi9evU0bdq0evjwYWP9/v37tWHDhlquXDn97rvvbB5ntWDBArVYLG/8XJrr1q1Ti8WiXl5exmee1eDBgzV58uTGiPTY2x4HCuYuXbqkGTJkMOYOjt1vvr6+NjdNPnLkiLZq1UoLFiyoK1aseO21JmQRERHaokULrVevnjHK0HpVU+PGjU1v1PWmHtiXLl3a9Ibb1tHnFy9efOM/s57F+hm+adMm7dOnj77//vs6f/5842Zv69evVxcXF61Tp44xl2/OnDltRmImJdb3yt69ezV37tzasmVLm2n2PvjgA02VKpV+8803cQaJXL169bXWmlBYL5WPPcVSjhw5NHfu3JoiRQpt06aNcULaGqTHvtnom3SC73VZv369pkyZUqdPn26zL6eqxrzTU6dO1Xbt2qm7u3uS/gx8URs3blQ/Pz/Nly+furu7G/fQsH42bNmyRbNmzaozZ87UkJAQrVmzpk2O8KZ+38a2b98+TZMmjQ4aNEibN2+ub7/9trZq1coIhENDQ/Wtt97SPHnycCNvE+Hh4Vq5cmVt1qyZjh079qlX5NSqVUubNWtm/N6jRw+dMGGC3r59+zVXaz/W95X1e+bdd981Zo+IfaVqy5Yt1d/f33j/JYX34Ys4efKkOjs7G/szsY/vY+/LLFy48LX1HSF6AmfdSDZv3qwfffSR1q9fX7/66is9e/asRkRE6LJly+JsLNYwJCmd4TMzcuRI7dmzp44ePVpVH18+nSlTJp0+fbpeunRJ8+bNq5kzZzZ2kB88eKCrV69WFxcXbdWqlT1LT3CuXbtmHCz89ttvWqpUKXV1dTVGoFu3u549e2rz5s2TxIimmJgYm/Bx8ODBmitXLs2VK5c6Ozvr/PnzbS7/Gzx4sFaoUEHr1q2refPmZQ70F2Q9qJ0yZYqq2ga/+/bt00aNGmnFihV1yZIlcR778OHDN3K02NPC7549e6rFYjFGTsf+Lhg2bJhaLBYu3Tdh9r3p7++vDRs2NNZHRkZqVFSU1qxZ07i6xCooKEg7deqk58+ff9XlJjoLFy7UnDlz2oQiu3bt0nTp0mnz5s3f6MulY4cUd+/e1SpVqmjz5s2NZdY2ly9f1gEDBjAK6f999913mjJlSm3durW+9957WrhwYS1Xrpyx37Zv3z718fHRggULaoECBZL0nLXW7SgsLEzHjh2rbm5u2qxZM5upXbp27aopU6bUJUuWJPmrLTt16qTt27c39sF27NihadKk0WnTpumpU6d05cqVWqFCBa1Zs6ZxX5GgoCBNly6dVqtWzZ6lJ1p37tzR6tWr6+DBg22WW/8NQkNDtW3bturj46OlS5dO0u/n+OrWrZs6ODjoe++9p0FBQTbr/v77b+3cubNmzJhRvby8tGjRosaxSFLIDc6dO6fDhw/XsWPHGsu++eYbLVu2rDZv3twI0v/55x8tWrSonjt3zl6lJngbNmzQt956Sy0Wi83NMK33pho2bJgWL15cBw8erD179tT06dMnyf3iRYsWaZYsWVRVtX379po/f37jSvXY0xm+++67SeI9+G+eNnj49u3b6uPjoyNHjjSOe63fGevWrYsz+vx1BOmE6AmYdcP57rvv1MnJSRs3bqyNGzdWNzc3rVu3rv722292rjBhW7ZsmXp5eenRo0eNm6zWqVNH+/btq6qP33y1atXSjBkzasWKFY3+vn//vn7//feMfIjl1KlTmjx5cl20aJGqPt4JCwgI0Lx58xqX1oSEhOhnn32mmTJl0pMnT9qz3NfCOn+j1fDhwzVLliz6yy+/qOrjs8ouLi46fvx4vXXrlqo+DpAsFov6+voSoL+A6OhojYmJ0ffee0/r16+vqVOn1jVr1hjrre/dffv2aYUKFeJcKpcU+nj16tU2B5sdOnRQZ2dnY/R0bHPnzk0SffKiYu+4WedztFq4cKEWL17cmH7D2r5MmTJGGBD7hIZ1xzipetaBQNmyZbVp06Y2y3bv3q0Wi0WHDh36iit7/ax9YZ1Cwzr6aP369WqxWHT8+PE27QcOHKjFixfX69evv95CE6Br165psWLFjBOnqo8HlTRv3lwrVKhgXIIfERGh165de6NPwjyvVatWafr06fWDDz7QihUrqqOjozZo0ED37dtntOnWrZtaLJYkfbXMsmXLNGPGjDbfm6NGjdL33nvPpt327du1TJkyxo24o6Ki9Pfff+cY4Tn0799fZ8yYYbPsxo0b6uXlZdxTyey74tq1a0liMM7LEPvkvurj0G727NlapkwZbdiwoXECyOrKlSu6f/9+Xbt2bZK6H9O5c+fUz89PPT0941wx/c0332iZMmW0VatWunfvXlVNGicV4sPaL9abAOfMmVPbtWtn8x2j+vjKzA8//FALFSqkJUuWTFInxKx9dPPmTa1fv76xn3fo0CH19fXVOnXq2FzV2rVrV61Zs6Y+ePAgSW93Tw4Miz0tbtu2bdXPz8+4F5V1ec2aNbVp06avvd8I0ROYH3/8UY8cOWL8/vfff2vhwoV16tSpxrL9+/cbX4wXL160R5kJ3vbt27Vr167GXHsxMTF648YNzZUrly5cuFBVH4+GaNSoke7atct44yXlD65/07lzZ02bNq0uXbpUVdW4qWPOnDk1R44c6u/vrzly5EgSNxH98MMPtV+/fsbvJ06c0CpVqhjzma1du1bTpUundevWNUKSe/fu6d9//629e/fmMuDn8KxAskuXLpoqVSqbIF318Znqq1evJqmpSWJiYvTatWuaLFkyrV+/vs3NkNq2bWsapKuy/VnNmjVLjx49avw+YMAALVy4sLq6umrfvn31jz/+0IiICB0zZowWLFhQixYtql27dtVSpUqpj48P/fiE2O+/KVOm6DfffGMz+mjRokXq5+dnzFltbX/s2LE3ri+t+xQ//fST1qtXTytWrKiNGzc2DianTJmiFotFGzRooK1bt9YWLVqoi4tLkvgefR4XL15UT09Pm/tdqD6e3iV//vw2B1NJ0fLly22usLpw4YJmz55dp02bZizbunWrZsqUSevXr28zIr1Pnz5v5NVZz2vcuHGaP39+VX28zzZp0iQdPXq0+vv766NHj2yOBxYuXKipUqVKslPexMedO3d0zJgxNse0qo9HmltPPls/7619vXPnzjgnFfFssb9vg4ODjWkjVB9ffV2yZElt2LChzcC7J69ETEpTR4wdO1a9vLy0WrVqcTKUJUuWaMGCBbVTp0768OFDMoF/ERMToyEhIbpu3TotXry4Nm/ePE6Q/vDhQ71//76GhobaqUr72bt3r9aqVUurV69uDMqJjIzU5cuXq6+vr+bOnVs7d+6sDRs2VGdn5ziflUlN7M+ySZMmadOmTbV48eI6ZswYvXLlioaHh2vNmjW1WLFi2qBBAx04cKD6+/trwYIF43yXvA6E6AlIcHCwent7a7t27YyRvNevX9dcuXIZ8/xaN7D9+/ers7OzEQjjf65du6a5c+fWtGnT2lyupfr4xo5ZsmTRKVOmaNmyZbVkyZLGzkNSCt6exTrq92l69uypTk5OxnQZV69e1d27d+vo0aN1/fr1Seakztq1a40zyKGhoRoREaFz587VR48e6Y4dO9TT09M4iG3SpIm6ubnpoEGDbP7GmxYWvUyxt785c+Zojx49dMyYMTY3iuvSpYs6OzvrihUr9OrVq1qvXj0NCAgw1r/J7+envT/379+vnp6e2qhRI5s5HNu1a6dubm7GTVlha/fu3ZosWTL98MMP9dy5c7p8+XL18vLSpUuX6sSJE9Xb21sbN26sR44c0aioKN2xY4e2adNGW7ZsqX369DHex0npIPRZYp/wevjwobZo0UJLliypnp6eOmPGDD1+/LjGxMRojhw5dMSIEU/9G2/aZ+P333+vKVKk0AEDBmjPnj21Zs2amjJlSuOk65YtW7Rly5b6/vvva9euXY3R1UmR9bPt999/10uXLumtW7e0WLFixqW6sT/XS5Ysqe3atbNLnQnB5cuXtWzZssb8+aqPB97kyJFDf/rpJ1X9X39t2bJFHRwctHXr1nHulZFU7d+/X/Ply6eVKlVSi8Wia9eu1RUrVqijo6PNTblVH39PFChQwKav8e+s34s///yzTpo0yVjeoUMHzZkzp/7yyy82352ffvqplipVyrh6E88We19w1KhRWrJkSfXx8dFixYoZo89/+eUXLV26tNapU0fnzp2rNWvWVC8vrzd6H9nK7Fj2iy++0MKFC2ufPn2MG8NbrVixIklOOfI8rP155MgR/fHHH3Xp0qXGPt/atWu1ePHi2rp1ayNIHzp0qM6dO9du9dpTVFSUTpkyRd966y3NnDmzzajzyMhIPXHihPbo0UMbNmyoHTt2TNL7fU8aMGCApk+fXj/++GPt2bOnZsqUSevWratHjx7Vu3fv6sSJE7V27dpau3Zt7dGjh90GJhKiJzBBQUHq5+enHTt21GPHjmlYWJhmyZJFv/rqK1V9fIBq/eKrWrWqMR81bB05ckTz5MmjpUuXtrlpzbFjxzQgIED9/Py0fv36xodaUtiZ+Dex796u+viga+fOnXHa9ezZU1OmTKnLli1748KOf/PkDtnChQu1evXqNgdWnTp10nbt2hnbVvfu3bVYsWJaunRpRjU8hyfn706TJo02atRIU6VKpdWqVdMffvjBWN+jRw+1WCxasGBB9fHxSXI3abVe6mztswMHDqiHh4c2bNjQZkR6vXr1tEqVKnapMTFYtWqVenl5af/+/XXAgAHGXPKqqtu2bdMiRYpoo0aNdP/+/U99fFL7HDSzYcMGo+86dOhgzBn8119/6fjx47Vw4cJauHBh/fjjj7VXr15asGDBN37e74iICK1Ro4YOGDDAWHb//n3t1auXOjk5GVdAWA9Ek/K2ZP0cW7NmjXp6ehonnrt27aoZM2a0uUFmTEyM1qpVS0eOHGmXWhMK65zmx44d02PHjuk///yjHh4exs2jYx8z+Pn5qcVi0S5dupjexDep+fDDD9VisWipUqWMZc2bN9f06dPrli1bjNGTffv21UKFChHuPqfY+3ERERE6dOhQtVgsxtXBqqrly5fXt956S3v16qUTJkzQdu3aqYuLS5IfjRkfQ4YM0UyZMuny5cv1/PnzWrBgQc2XL59xbLJ582atU6eOFilSRCtXrpwk5kCPfXXDoEGD4gS648aN06JFi2qvXr2SzACwl+Hbb7/V7Nmzq6+vrxYqVEizZcummzZtUtXH00qWLl1a/f39jSuxY1/9lNSEh4frV199pRkzZtTGjRubtnuT34cv6vDhw+rt7W1zInvPnj3q7++vjRo1stl3iZ3d2WPfmRA9ATp06JC+88472qFDB71y5YpOnDhRU6RIEWdOs8qVK7+R84e+LEeOHNGiRYsaJyRiu3XrVpw55JKy6dOna82aNW2+7OrVq6cpUqTQXbt2xWlft25dzZYtmy5evDhJ99+MGTPU399fmzVrpn/++aeqqpYrV067detmtKlfv74ePnyYKYNe0PHjx7VRo0ZGcHLx4kUtU6aMvvfee7pu3Tqj3U8//ZTk5nVUfXwTmhYtWhgnv6zb1cGDBzVt2rTaqFEjmylKOFEYV+z34vLlyzVbtmzq5OSk48aNs2m3bds2ffvttzUgIEA3b978ustMNOrXr6958uTRGjVqaPr06W1OYKuqnj17Vjdu3KjvvPOO5siRQy0WizFNx5u4fa5du1bHjx+vBQoUMAZCWG9Gbb2xaMeOHTUiIsIul6ImRD/88IOmSpVK58yZY3PfkcaNG2umTJn0888/16+//lr79OmjLi4uSXo6EquwsDB9++23tXnz5nr16lX94osvNEWKFHEGQXTt2lVnzpz5xp+4el7379/XSpUqaceOHdXHx0ebNWumqo9HELZq1UqdnJy0UKFC6u/vr+7u7kyx9AKsn2N37txR1cfzoI8YMULTpk1rM11Lnz59tGbNmurj46MNGjSw2WfB8wkODlZ/f3/jasOffvpJXV1dje+c2O2uXLkS56Z8b7LVq1dr6tSptVatWurv76/Ozs5ar149ow/GjBmjfn5+2qlTJ64yeQ579+7VdOnSGfczuHjxolosFpurTH7++Wf97LPPtFWrVjaDeZIa64mq+/fv68yZM7Vw4cLavn17Y31Sv2+S1ZP7/kePHtWsWbMaVzNY1+/evVtTpEhhXMEZm732mwnRE6hDhw4ZAfCWLVu0Z8+e6ujoqBMmTNB58+Zp37591cXFRf/44w97l5qgWU9IdOrU6akf5kn9gNVq69at6uXlpS1atDDuTB4TE6ONGzfWDBkyxDkY69evn7q6umqWLFk0LCzMHiUnGAsWLNDy5ctrkyZN9ObNm/rll1+qg4ODNmvWTIsVK2YzZzLb2/OZMWOGlilTRt99912bm+udPn1ay5Qpo1WrVo0zp6Pqmz2lxpM7GmvXrlWLxaJdu3Y1gnRrm2nTpmny5Mm1Vq1aNoHJmxhU/lex35M//PCDZsyYUevUqRPn0srt27dr5syZjZuI4n9i92GhQoXUYrHYjBB+8n0ZGRmpO3bs0KZNm2r+/PnfyJvHHTx4UN3d3XXlypVat25drV27tnFjUWt/tWzZUuvVq2fPMhOUBw8eaOPGjfXTTz9V1cc3Yj19+rROmDBBN27cqHXr1tWKFStqnjx5tHz58knqJmX/5sCBA8ZVrJs3b9YePXqoo6OjTpkyRZctW6Yff/yxZs6cWW/evGnvUhMU63vy66+/1nz58mnLli2NdatWrdKpU6fq5MmTOfHwAqyfbz/++KO2adPGOKYIDg7W4cOHxwnSIyMjNTw83LjhMl7MmTNn1NPTUx88eKA///yzOjs7GwH6nTt3dPz48XGu0kwK+4IXLlxQb29v46bU9+/f1507d6qnp6fWr1/faDds2DAtV66cBgcH26vURGPRokXGTeFPnz6tOXLkeOqMCDExMW/08di/sb5265S3ERER+uWXX2rRokW1U6dOdq4uYerRo4fOnDlTjxw5oi4uLsZNz2NfTVe0aFGdMGGCPcu0QYiegFnv4NulSxfdvn27fvnll5o7d24tVKiQlilThgOI53To0CH18/PTRo0aMc/ZU1g/nHbt2qW5cuXSpk2bGiPSY2JitEGDBpoxY0bduXOncea0f//+umPHDg0JCbFb3fYWOziaP3++li1bVps2barXrl3TWbNmaZMmTbRLly7GzmtS3qH4N0/u0O/cuVNz586tbm5ucW6KeebMGS1Xrpy+8847NjdKepPF7p8zZ84YI2b279+vyZIl006dOtnc8Gz27NnasGFDff/995PEwdJ/Ffu9vHr1as2aNat++OGHcUa5Hjp0iPfxE6zbV0xMjN69e1fr1q2rVatW1UKFCumCBQuMkMrab7H77+DBg1qoUKE4N6JK7M6cOaNDhgzRTz75RFVVZ86cqSVLltTAwECbS1Hbtm1rTP3FCdbHIUfx4sW1R48eeuvWLe3evbuWK1dOs2TJojly5NAvvvhCb9++rSEhIUn+5P3THDp0SIsXL65dunTRbdu26dSpUzVHjhz61ltvacGCBTUoKMjeJSZYd+7c0Xnz5mm+fPmMEemIv9WrV2vatGl10KBBxlWaqo/v8zV06FB1dna2mdoFz8fse6JKlSrasmVLdXZ2NqZyUn08lZq/v7/++OOPr6tEu4rdP2fOnNHs2bMb95iz2r59u7q4uOiyZcuMZUzT9HwGDBig1apV03/++UezZ8+unTt3NvYBFy5caDNtXVJlHTR34cIF9fLyMq5svXPnjs6YMUNz5Mih3bt3t2eJCULs9+revXs1ffr0xhQu/fr109SpU9tM4Xfnzh318fExroJICAjRE7igoCD19fXVjh076rVr1/TRo0d69+5dDiBe0L59+7Rdu3YESk8R+4zxr7/+qrly5dLGjRsbQXp0dLQ2atRI06RJo82aNdNGjRqpi4uLnjlzxp5lJwixvwTmzZtnBOnWEQ1J6bLJl2Hz5s3GyPNDhw5p3rx5tW7dunFCtlOnTmnXrl2TxPs59jb2ySefaP78+TV9+vRatmxZ/fHHH/XIkSOaLFky7dy5s+7Zs0fv3LmjdevW1eXLlxuPSwr99F/F7ueVK1caQfrTrvYiSH8s9nb17bff2hysNmnSRH18fGyCdFWNM5+hl5eXzbaa2IWFhWnx4sU1Y8aM2qtXL1V9/Pnft29f9fPz00qVKuno0aO1TZs26uzsnKQvd36ahQsXaqpUqdTFxUXr16+vCxcuVNXHo5QqVarEd+m/ePKY4f79+3rr1i1Coudw9+5dnTdvnhYqVEjff/99e5eTaJ04cUI9PT3j3FDwwoULev/+fY2MjNThw4erxWKJM+UIzMX+vg0LCzOmyomIiNAhQ4ZohgwZtEWLFkab+/fva82aNbVatWpJah9wzZo1Om3aNL1x44a6uLjY3ONGVfWff/7RggULchInHoKCgrRUqVKaNm1aY0S1ddvq1auXNmnSxNguk4LY07TGfo9dunRJ06RJE+c4NTw8XOfMmaNnz5597bUmVNOmTdMhQ4ZoYGCgsezvv//W1q1ba7JkyfSzzz7TUaNGadWqVbVw4cIJah+QED0RsI6kbtq0KXfv/Q+sH3ZJaWfiWWKH5zdu3DBOzBw9elRz5cqljRo1spkjfejQodqoUSOtX78+8xbGYhaknzt3TlXZ3p7Xjh07jJtM3bhxQ1Ufn53OkyePNmjQwHS06pvcv7Ff27JlyzRz5sy6du1aXbBggfbt21cdHBx0yZIleuzYMc2ePbtmy5ZNc+TIoUWKFEkSN476r57sm9i/r1q1SnPkyKHNmjXjplNPEbuv+vfvr3ny5NHx48fbXBLduHFjLVSokM6aNUuvXLmiFSpU0Lp16xrrV61apalTp9bTp0+/ztJfOesJwKJFixqjf6OionThwoXavHlzLVGihDZs2JDvURMnTpzQX375RVX/9xnYrVs3bd26NVM+PAeOGeLv7t27OmPGDC1RooReuXLF3uUkSrt27dKSJUvq1atXjRvrVaxYUXPmzKmNGjXSK1eu6M2bN3Xs2LFMSRoPQ4YM0XLlyqmPj48uXrxYVVVv3rypAQEBWrhwYX3//ff1o48+0rJly2rhwoWNfcE3eV/Z6vfff9eMGTPqrFmz9O7du9q2bVutWrVqnHvZlCtXTidOnGinKhM+6/7dqVOn9LfffjOufr1x44a2b99e8+TJY5wAu3r1qn722WeaMWPGOKP+32TWPtq4caN27txZAwICjKujT506pb169bJ5z8W+ahOPBQcHa5UqVYypSVX/1z+hoaE6ceJE9fPz0woVKmiLFi0S3JX9hOiJxP79+7V8+fI2l+zjxfHh9Xiewtg3fFu9erWWLFlSc+XKpe+//75u2LBBz549awTpscPLqKioOHPrwXa7WrBggZYrV04/+eQTffjwIdvcCxg0aJCWLl1a+/TpY0wVtHfvXn3rrbe0cePGcW6unFRs27ZNO3bsaLPTHx4erlOmTNGUKVPq7t279cKFC/rdd9/pwoULk9xNVp/H8x5Axn6/Llq0yOYmVIhr1KhRmiFDBt27d+9Td2xbt26tefLk0Vy5cqmvr6/NzZQ2b978xgXoVkeOHNEiRYpox44d9ciRIzbr7t+/z/foczp16pR++umn6urqGucG8TDHMUP83bt3T0NDQ+1dRqJh/c60XmW0b98+TZYsmXbt2lXz5s2rderU0QEDBujMmTPV29vbuJ9NQglCEpM5c+aop6enjhs3Tjt16qQWi0U/++wzVX0ccM6dO1dr1aqlrVq10s8++8zYB0wK+4J//vmnDhkyRPv27Wss27Fjh7733ntaqVIlnTFjhu7evVv79Omj6dKl4z4H/+K7775TZ2dnzZ07t6ZMmVJnzZqlqo9vJtqkSRPNlSuXZsqUSUuWLKne3t5J8sbLGzdu1NSpU2uDBg20bNmymjx5cv3yyy/tXVaicvDgQW3cuLHNlZmxj7nu3Lljc1yWkD7LCNETkdiXQQPxERwcrN7e3tquXTs9e/asnjhxQtOmTasjR47Uzz//XLt27aqOjo66YMECI0hv1qyZzbxUeLrYH/J9+/bVsmXLcvdtE7H76skDqSFDhmjJkiW1T58+xoj0ffv2qYuLi3HDuaTk2rVrmjt3buN9Gtvt27e1Tp06T51fjwPU/4m9vU2aNEk7deqk9evX1x9++EH//vvvOG2eduKLIF111qxZNkHm9evXtWLFirp06VJVfXxw9fPPP2vz5s11yJAhRruNGzfq2rVrjW0yqXwuWm9s3rFjR6ZtiYeDBw9qs2bNtECBAjYn/vF8OGbAq2b9rvzpp5+0Xbt2xvznS5cuNYLc2FM/lihRwrhhHP7dk/sdixYt0m+++cb4/euvv1aLxaKffvqp6fv9Td0XjL2fdvPmTfX19VV3d/c4N7r87bfftGvXrurm5qb58+fXwoULc0+5Z4iOjtZbt25p6dKldebMmfrnn39qYGCgWiwWHT16tKo+nhLnyJEjOnnyZN28ebMxUj0puX37tn7++ec2U1KNHj1aHRwc9Msvv0wy+7nxFfuz7cSJE1qlShX19PQ0rp6zhuWx2yW0QYmE6EASExQUpMWLF9du3brpZ599ZnPWPiwsTKdNm6bJkyfXzZs369GjR9XNzU07dOjAZdTPwfoBP2zYMM2VKxejmf7F/P9r784Dasr7P4C/b7slsiU19iwjoTC2LEOyJmbwZN9Sspedse9LDCJ7aGxRyr5lDU0q2dcoYzAYpIWo+/n94ek83YnfM/OMcav7fv3DPefc63Ouc+4553M+5/P195eZM2dKamqqxvQpU6ZI5cqVZdy4cfL8+XMR+XCQzasXA//NpUuXpGLFimJvb5+t2mPAgAHSpk0bLUWW82U9Afvhhx+kcOHC4ubmJg4ODmJtbS39+/fPs9XQn9O5c+dEX19fo098RkaGNGjQQLp37y5Hjx4VFxcXqV+/vri4uIixsbF4e3tn+xxd24djYmLkm2++EVdX12wD1dL/LzU1VU6fPq2TF+hEuUVQUJAUKlRIxo4dq3Gz8I9J3UmTJkmZMmUkPj7+S4eYK2VNGG3fvl18fHzEyclJ/P39NZbLTKRPmTJFeYIzr8o8n8uaoIyKipKUlBQJDQ0VW1tb+frrr7MVfqnVann+/LkkJCTIixcvvmjMuUXWJ0pSU1Nl4sSJ8vLlS2W+j4+PqFQqmTt3rk71Pf+Ya9euibGxsVSpUiXbmD6ZifSVK1cyb/IJarVao2WQyIfv1NnZWUqXLq1My+nFS0yiE+mg6Oho+eabb6Rs2bIyZMgQjXmvXr2Svn37iqurq4h86G/IQUT/PLVaLYGBgayc+y8yMjLE1dVV7O3tZfHixdkS6W3bthVLS0txc3PTOOnVtSRcpkuXLknNmjWld+/eShXN69evpWHDhsoAP/Rpjx8/lq5du8qZM2eUaatWrZKmTZvK0KFDecPrT9i5c6eULl1aBg8erCRL1q5dK7Vr1xYTExMZP368nDhxQkQ+9Env0aOHzu6vWbG1BhHlRVeuXJGSJUvK2rVrNaY/fvxYXr9+LSIfjhE9e/YUc3NznWz58L/ImkCfNGmSGBkZScOGDUWlUomLi4tS8Z/J399fVCpVtv+HvOjBgwdiY2OjJM7NzMzk/PnzIiISGhoq9vb20rNnT4mKilLek5NaQORkoaGh0qZNG7GxsZFq1apla6Hm4+MjhoaGMm3aNGX/1lUjRowQlUolPj4+IqK5z86fP19n9sf/5mNP+Wb+GRwcLFZWVsq+GhsbKy4uLmJgYJArbrYyiU6koy5duiTlypWTqlWrZnu0beLEiVKjRg0+DkyfzcfuKL9580bc3d2lbt26smjRIklJSVHmjRs3Tuzs7LINzqLLYmJipFq1amJhYSHt27eX7777Tuzs7JSqnJz2qJu2bN68WaNSZt26dVKwYEGpWrVqtv7UixcvFisrK1a7/j+y9u/etGmTlC5dWoYNG6Z8Z4mJidmq+Zs2bSqjRo36onHmZDyWElFeExYWJvXq1ZM3b97IixcvZO3ateLo6ChlypQRT09PefLkiRw5ckR69uzJJ3H+B5GRkfL9998rldU//fSTlCpVSry9vbP19N63b59OJIsTEhKkWbNmYmFhIfr6+tkqgXfu3Cl16tSRnj17KgN7038XGRkphQoVEg8PD+nVq5cYGBiIl5eXJCQkaCw3a9YsKVKkiPKUsK7KyMiQYcOGiYmJiTLOQ1aLFy/WqYFWPybr79H8+fNlxYoVyuugoCApWLCgrFq1SuM9kZGRMmbMmFxRgMMkOpEOu3z5stja2krfvn01Kqfd3d3F0dFRkpOTtRgd5RVZk+BXr16VW7duKScXb9++VRLp8+fPl5cvX4parZbu3btLYGCgkhhmIv2DK1euSPny5aVx48Yavfg4UOEHgYGBUqNGDY3t5bfffpMWLVqISqWSPXv2iMh/tie1Wi1FixZlxcgnZL0xM2PGDJk4caIUL15c9PT0pH///kprF5EPAwCFh4dLq1atpEaNGjpxQU9EpEuyHhMiIiJEpVLJiBEjpHr16tKhQwcZPXq0LF68WMzNzeXYsWMiwpuI/4tNmzZJq1atxMnJSeNJTX9/f7G0tBQvL6+PDo6pC8fdgIAAUalUUrRoUWXspKznwDt37pQGDRpIhw4d2P/8T7h7965MmTJF5s2bp0xbvny5fPXVVzJx4sRsifTff//9S4eYY/zxWsvDw0Py5cv30US6rlq8eLHy97S0NHn37p3Y2trKoUOHlOl9+vTRuIb9WBFYTk+kM4lOpONiYmKkevXqUqFCBenbt694eHhIsWLFeOJBn0XWA+OECROkUqVKYmlpKebm5jJp0iQR+XCQHTp0qNSuXVvKly8vdevWlapVqyoHUCbQNV28eFHq1asnAwcOZKulj8jcXsLDw5VWQM+ePZP69etLpUqV5PLly8qyjx8/lgoVKkhQUJBWYs0t5s+fL4ULF5awsDAJDw+XlStXiqmpqbi7uyuPlu/du1e6d+8ubdq0US40cvpJMBER/XeZ53KZv+mZrzdv3ixt27aVsWPHalRe1q1bV3bv3q2xLP15/v7+Ym1tLebm5hIREaExb+PGjVKmTBnp37+/Mjh6Xpe5DaWmpsq1a9fE399f2rRpI5aWlsrNhKy90nft2iVNmzaVX3/9VSvx5hYPHjyQOnXqSIkSJeSHH37QmLd06VKxsrKSyZMny/3795Xpuro/Z96gunv3rvTu3VuZPnjwYClUqJAEBgZqK7QcIzw8XAwMDKR79+7KtOTkZKlYsaKEhYVpMbLPj0l0IpLLly+LtbW1lC5dWubOnZsrelFR7rJw4UIpVqyYHD9+XE6cOCEbNmwQIyMjGTBggIh8OPkNCQmR6dOny4wZM5STFSbhPo4DFmaX9amFyMhIUalUMnPmTGVwpOfPn0udOnWkXLlyMmPGDNm8ebO0b99eqlevrhPVW39F5o2IjIwMycjIkLZt28rIkSM1ltm6dasYGhqKu7u7xMfHy/v37yU2NlZ5L79TIqLcL/PYevz4cRk5cqT07t1b1qxZo4wl8sf+yBMmTJCyZcuyTdrfFBISIra2ttK9e/ds/eRXrFghLi4uOlFkkrn9HTp0SIYPH66MbXP//n1xdHSUUqVKaSR59+3bJ8nJyRotIunT1q1bJ9bW1uLg4CDXrl3TmOfr6ysmJiYyc+ZMnTyni4yM1Ki8j4+Pl9KlS4urq6vGzYTevXuLpaWlzg+6mpKSItu3b5eyZcsqY+uJiFSpUkXZb9PS0jSu7XPrTRkm0YlIRD6McN6yZcs8P7o7fRlZD4oZGRnSsWNHpfI80/Hjx0WlUsny5cs/+hlMoP//OGDhf3zsJMzHx0cMDAxkzpw5SiL92bNn0qRJE1GpVDJgwACZNm0ab9j8QdbvMjIyUkREnJycZNiwYSLy4QQ488J9xIgRUrhwYenVq5f88ssvyvt04cKeiEhXBAcHS4ECBcTd3V06d+4sDg4O4urqqjztpVarZdu2bdKrVy8OIvo3qNVqjXORoKAgqV27tsag8lmXFdGN421wcLAYGxvLnDlzNJ4mTEhIkJYtW0qpUqXkwIEDMnr0aDE3N8/WgoSyy7rdbNq0SWrUqCEeHh7ZenmvXr0627g3edEfW9jevXtXrKyslLaFiYmJ0qBBA3F3d//oNcfjx4+/SJw5XWpqqmzbtk1Kly4tXbt2FRGRRo0ayenTp7Mtm5vbBqtEREBEBODt27cwMTHRdhiUy6nVaujp6QEAnj9/juLFi8PGxgbt2rXDggULICJIT0+HoaEhvLy8cPnyZezduxdGRkYwMDDQcvS5C/dZ4N27dzAyMgKQ/ftYsmQJRo0ahdmzZ8PT0xNmZmZ49uwZvv/+eyQlJWHnzp2wtrZGRkYG9PX1tbUKOUbWfdfb2xsbN27Eo0ePsGTJEsycOROxsbGoXLmy8n1Nnz4dhw4dgpWVFQIDA5X3EhFR3hAVFQVXV1eMHz8ebm5uSEhIgL29PfLlywc7OzsEBATAzMwMwcHBCA4OxqRJk/D1119rO+wcT0SgUqmU1+np6dDX14dKpcKRI0eQmpqKjh07Yvv27fDx8YGtrS0GDRqEb7755pOfkRfdvXsXbdu2hbe3NwYNGpRt/qNHjzB06FBERUXB1NQUmzdvRu3atbUQae6Snp4OAwMDvH//HoaGhvD398fy5ctRt25deHl5oWrVqtoO8Yvx8/PDypUrcfjwYVhaWgIA7t27B0dHR1y4cAFFihRBeno6Tpw4AScnpzy/z/1Vf/wdSklJwd69ezFq1ChYW1vj7t27KFKkCMzMzPDmzRvlPY6OjliwYIG2wv5beLVDRApdT8bR35c1Cbd48WJMmTIFv/76K3r06IFdu3YhKioKKpVKSZYXLFgQenp6yJ8/PxPo/wNd3mfPnDkDAEoC/ccff4Srqyvc3d1x+PBhAICXlxd8fHwwadIkrFq1Cq9evUKJEiUQHBwMAwMDdO7cGTdv3mQC/d8y991Hjx4hIyMDQUFBMDExwdChQ+Ho6IimTZsiNjYWb9++RVpaGmJiYjBmzBjs3LkTenp6UKvVWl4DIiL6X82dOxeTJk3S+C3/9ddfUb9+fbi5uSE+Ph4tWrRAx44d8cMPPyAyMhKenp54+fIlvvvuO6xdu5YJ9D8pM+m0efNmPHjwAAYGBlCpVNi9ezdat26NtLQ0AICrqytGjx6NsLAwHD169KOfkZf9/vvvSE9Ph4ODgzItaw2opaUlgoODceDAAZw6dYoJ9I/4Y81sZgI9Pj4ezs7OuH79Ovr164fBgwcjNjYWM2fOxO3bt7UU7ZfXsmVLJCUloXv37nj06BEAIDExEYaGhjAzM4NKpYKRkRFatWqlE/vcX6FWq5XvJD09He/evUOBAgXQvn17+Pj4IDExEUlJSZgyZQrc3NzQt29fuLu7o0+fPpgzZ46Wo/8btFYDT0REedbYsWOlRIkSsnXrVklISJDo6GhxdnaW1q1by4ULF0Tkw2NcTk5O0rdvXy1HS7nN7NmzxdbWVrZu3SoiH0aDL1SokHh7e0uVKlXEwcFBFi1apCy/ePFi0dfXlwkTJig9C589eybW1tbSoEEDZSBMEgkICJD8+fOLra2tMmCXiMiNGzekS5cuYmxsLLVq1ZJKlSpJlSpVlHY4ubWvIRERfbBs2TJRqVQyd+5cjXYP169fl4yMDGnfvr0yqF5GRobY2tqKiYmJdO7cWTIyMngc+Ivu3Lkj1apVk02bNomIyLlz50SlUsmqVatERLPlxtGjR3Wq7VzmthQaGiolSpRQBlHNOoBoREREnhuw8HPKuv2EhYXJ8ePHldf37t0TKysr6devn0a/8+XLl0vTpk11rj3J/fv3pWLFiuLg4CBPnjyRc+fOSdWqVXWyF/z/Yt68efL9999L8+bNldYtma1dKlSoIB4eHh99X279TWM7FyIi+qzCwsIwcOBABAQEoFGjRsr0PXv2YP369QgLC8PXX3+NtLQ0iAhiYmJgaGioE4+l0udx584deHt7IyUlBb169UJERAT+9a9/oXnz5nj16hVGjx6NGzduoFOnThg9ejQAYObMmTh06BDOnDmjVFy/ePECiYmJKF++vDZXJ0c5ceIEFi5ciNOnTyM6OhpVqlTR2DeDg4Px+PFjqNVqeHp6wsDAgO1wiIhyuczf+XXr1sHDwwPTp0/H+PHjlacEHz58CCcnJyxcuBDt2rXDy5cvMWTIEDRs2BCdOnWClZWVltcgd/r+++/x+++/4+TJk/jll19w8+ZNtGzZUpmf9QlPAHn6ePux64CUlBRUqVIFDRs2RGBgoMY8Ly8v5M+fH1OmTIGxsfGXDDVHu3DhAmrUqAFjY2Okp6cjPT0d1atXx+LFi9GhQwe8f/8eAwcOhIhg48aNUKlUGtvZq1evYGZmpt2V0IL4+Hg4OjqiYsWKGDp0KKZMmYL+/fvD0tISxYoVQ1JSEp4/f466deuievXq2g5Xq7JuL3PnzsWSJUvQvXt33Lx5EydOnMCKFSvg5uaGt2/fYs+ePRgzZgysra0RFham5cg/DybRiYjos/L394ePjw/Cw8NhZmamcaC9d+8ebt++jQsXLqBEiRJwc3ODgYGB8mgh0Z91//59DB06FG/fvsXTp08RFBSEypUrAwCePXuGCRMm4ObNm+jUqRNGjRoF4D8XaH+8KNVVH/se1Go1zp8/D29vbzx79gw///wzSpQo8cl9NC9f0BMR6YLMdIBKpYKIYOvWrejduzdmzJiBCRMmQE9PD8+ePYOTkxPq1auHCRMmYM2aNTh69Cj27dsHc3NzLa9BzvepZPjNmzfRrl07zJkzB//617+0GKF2ZZ6fnT9/HidOnMCbN29gY2MDV1dXBAcHw93dHU2aNMHs2bORmJiI0NBQ+Pn54dy5c6hWrZq2w88x9u/fr/SPHzJkCIyMjJCcnAxbW1sEBATAwcEBIoInT56gVKlSGu/VtWKmzPW9desWRARVq1ZFfHw8WrZsibi4OFSrVg36+vpQq9UwNjbG69evoaenhz179ijXG7ruwYMHWLFiBdq2bYumTZsCAKZMmYK5c+di5cqVGDhwINLS0rBt2zaEhoYiKCgoT1x/5f41ICKiHCHzIuzNmzfIyMhQpqtUKuV1dHQ0KlWqhMmTJ2PQoEFKFSsT6PRXlS9fHr6+vjA1NcW9e/dw6NAhZV6JEiUwb9482NjYYPXq1di6dasyT0TyxAnc35X1gv7atWu4c+cO7ty5Az09PTRo0ACLFy+GpaUlmjVrhqdPnyoDUP0RE+hERLmfSqXCsWPHMGrUKNSuXRsbNmzAlClTMG/ePIgIihQpgh49euDUqVOoX78+Nm/ejFWrVjGB/idlHm9DQ0ORnJysnBcXL14ctra2OHHiBIDs/at1hUqlQnBwMJydnREZGYlHjx6he/fumDx5Mpo3b44dO3bg2rVrcHR0RPfu3bF//36cOHGCCfQ/aNasGerVq4ddu3bBz88PaWlpKFiwIExNTWFhYQHgww2czL+r1WqNm2i6IjOBHhwcjO+++w7btm3DkydPUK5cORw9ehT29vbQ09PD7t27cfHiRURERODy5cu4ePEiE+j/tnfvXpQrVw7btm3T2HYyb74OGTIE69atg7GxMXr06IHdu3fnnfGTvlznGCIi0gXXr18XfX19mTp1qsb0pKQk6dChg/j6+monMMqTHjx4IM7OztKkSROlR3qmJ0+eyNy5c3Ntz71/Sta+tVOnThUbGxspX768VKlSRTZv3qwsEx4eLo0bN5bq1avrXH9MIiJdEhQUJPny5ZOZM2cqY9esWbNG9PT0ZObMmSLyoR/1tWvX5OjRo/LLL79oM9xcI2tf6jt37oiZmZnUqlVL3Nzc5MaNGyIicurUKTExMZEzZ85oK0ytyPrd3L59W8qUKaNcIzx8+FDy5csnI0aMUJZJS0uT8+fPy9WrV+Xp06dfOtwcL/NcNyUlRfr27Sv16tWTpUuXyoMHD6RWrVoSFxeX7T26PI7B4cOHJV++fOLn55dte7p//75UqFBBvv32W3nw4IGWIsxZsu6vmYYPHy4qlUo2btwoIprb07Rp00SlUkloaOgXi/FLYTsXIiL67NasWYOhQ4fC09MT7du3h5GREebMmYMnT54gOjqalef0Wd2/fx/Dhg1DamoqBg4ciG7dumVbhm1Hsps2bRpWrlyJLVu2oFy5cpg+fTq2bt2KFStWwNPTEyKC8+fPY8CAAahduzZ++uknbYdMRESf2e3bt9G6dWuMGTMGnp6eGvPWrFkDT09PzJgxA5MmTdJShLnf1q1bYW5ujoYNG8LPzw+nT59GWFgY+vXrhzp16uDMmTMoVqwY5s6dCwB5+om5HTt2KK1rMp+K+/nnnzFq1CiEh4cjPj4eDg4OcHZ2hp+fHwAgNjYWtWrV0mLUuUtKSgqGDRuGO3fuoFGjRli9ejU6duwIU1NT5M+fHwCQmJiIKlWqYMSIETpXhf7u3TsMHDgQ5ubmWLRokVKZnvVaISEhAXZ2dqhXrx727dvHa4h/Cw4ORoUKFZT90c3NDTt27MCuXbvQqlUrjWXXr1+PPn365Lnr/rz760xERFozcOBA7Ny5EyEhIejXrx+GDBkCAIiKilJauBB9LuXLl8fy5ctRoEABbNiwAevXr8+2DE9+NUVHR+PUqVPYvn07WrZsidu3b2P//v1o164dhgwZgtWrV0OlUqF+/foIDAzEpk2btB0yERH9Ax48eABDQ0O0bdtWmZb5yL27uzs2b96MyZMnY9GiRdoKMdcSESQlJWHSpEkICQlB/vz54e3tjdDQUKxatQoZGRkYO3YsNmzYgB07diA5ORl6enp5tq3Lw4cP0adPHyXZlnmzQK1W4+XLlzh58iS+/fZbtGvXDr6+vgA+DJQ5a9YsxMXFaS3unC5ze4mIiMCRI0dQoEAB+Pr6omLFiggKCkKhQoXw4sULJCYm4uHDh7h9+zYSEhLg5OSkUwl04EPbGmNjY8TFxSm/c5nfQea1wi+//IKyZcsiNjYWy5cv5zUEoPTS79q1K2bMmIFr164BANatW4fOnTujc+fOOHz4sMZ7BgwYoIx9lpcwiU5ERJ+dSqWCi4uLkqjbvXs3Dh48CENDQ6Snp/NkhD678uXLY9myZUhOTsalS5e0HU6O88cL8pIlS6J169Zo1KgRjh8/joEDB2Lu3LnYvn07HB0d4enpiUWLFkFPTw+2trbQ19fnzS8iojwoOTkZb968UV6r1WolqXTy5EnUrl0bO3bsQLt27bQVYq5mamqKRYsWISgoCGfPnlW+2549e+LHH3/E+fPnMXr0aOjp6WHOnDkA8m5/6q+++gpHjhzBzZs3NW7aWFlZwdLSEi4uLmjYsCFWr16tXCvs3LkTiYmJMDMz01LUOVtmFXVQUBA6dOiAPXv2ICEhAfnz54efnx+aNm2KsmXLokWLFti4cSN++uknBAcH48CBAzrVUz7zPDgjIwMpKSnInz8/fvvtN2Va5jIPHz7EypUrERcXhzJlysDa2lprMWtb1msHlUoFCwsLnD9/Hj///DOmTZuGq1evAgD8/f3RpUsXuLq6IjQ0NNvn5LVKdLZzISKiLybrYIZE/4THjx+jZMmS3M6yyPp4alxcHAoWLIiSJUsq+2Pfvn2RP39+LF26FIaGhhg0aBCio6NhYmKC06dP59mLeSIi+tASzcbGBl5eXpg9e7bGPC8vL5iammLq1KksgPgTMhOaf3z94MEDuLm5oVmzZpg4cSLS09NhYGCgHIffvn2L2bNnIzIyEvv3789zSaesRARnz55F165dUbNmTRw8eBDAh9ZBU6ZMQZcuXdCjRw+YmJggICAAGzZswOnTp2Fra6vlyHOu8PBwtG3bFsuWLUPnzp1RsGBBZdtLTU3FkCFDcPv2bbRr1w6jRo2CsbFxtm01r8r6PeTPnx9paWkwNjbGvn370KFDByxYsACjR49Wlp84cSKOHj2K/fv3c+Dkf0tJSUGBAgWU7zIqKgrt27eHg4MDpk+fDhsbGwBAp06dkJycjKNHj2o54n8Wk+hERESU5/CGDeDn54cGDRoofQsnTJiAvXv34smTJ+jfvz+6dOmCunXrws7ODo6Ojli4cCHevHmDnj17on///krVoa5caBER6aoNGzZg0KBBGDlyJHr37g19fX1s3LgRa9aswfnz51G1alVth5irbNmyBWZmZhrV+7NmzcKSJUtw8+ZNlChRQjm2Zp6vXLp0CS1btsTp06fz5Pf9x3OJ8PBw9OzZE9bW1jh27BgAYOHChdizZw8iIyNRrVo1qFQqbNiwgf3Q/4uFCxfi/PnzCAwMhEqlUp4ezLzxlZqaij59+uDVq1cIDAxEkSJFtBzxl5G5zR08eBBr1qxBYmIiihcvjokTJ6JWrVpYtmwZRo4ciU6dOqFgwYLIyMjA3r17cfLkSdjZ2Wk7/Bxh7ty5uHbtGhYtWgQLCwvlO42Ojsa3336LVq1aYcqUKcpNLl24/mISnYiIiCiPuX//Ppo0aYI2bdpg7NixuH79OgYPHgxfX19cvnwZBw4cgKWlJX744QeEh4dj9OjR6NevH2JjY/H+/XtcuHAB+vr6TKATEekAtVqNoKAgeHh4oECBAjAxMYG+vj62bdvGZNJfFBcXB29vb+zduxdubm5wcHBA7969ISJwcnJCzZo1MX/+/GyV/fPnz8fy5csRExOT5ypgM88lIiIiEBsbi5cvX6JevXowMDCAu7s7vvrqKyWR/vjxYzx+/BhFixZFoUKFULRoUS1Hn/P17NkTcXFxOH/+PADNROadO3dQqVIlpKamIjExEaVKldJmqF/cnj170KVLF3h7eyM1NRV3797F8ePHsXPnTrRv3x7Hjx+Hv78/EhMTYWVlhWHDhulUm5s/+mMSPDQ0FJ06dYKHhwemTp0KCwsLZRlfX194e3vDyckJS5cuRcWKFT/6GXkNk+hEREREeVBsbCzc3NzQuHFj6OnpoVq1ahgwYAAAYN++ffDx8UGRIkXg6uqK58+fY8+ePbCyssKqVatgaGioUcVERER536NHj5CQkACVSoXy5cujZMmS2g4px/tYwuj9+/c4e/Ysli5dips3b6Jo0aLK4KK///47Nm7cCFNTU433jB8/Hq6urnm26jooKAgDBgxAmzZtkJCQALVaDVtbW/Tu3Ruurq6wtbXFoUOHtB1mrpB5UyLzzw0bNmDJkiVYvnw5mjVrBuBDK7/ExER4eXmhX79+ynRd8v79e7i4uKBmzZqYO3cuAODNmzeYOHEi/Pz8cOHCBdja2uLdu3cwMjJS2izpqqy/ZXfv3oWxsTFKly6NCxcuoEGDBujfvz+mT5+u3IhZu3YtDh8+jHfv3iEkJCRPJ86z0o21JCIiItIxtWrVwpo1axAeHg5/f38kJSUp89q3bw9vb2+8fv0agYGBqFmzJg4dOoT169dzAGAiIh1laWmJBg0aoH79+kyg/wlZk05xcXGIjo7G+/fvAQDNmjXDunXrEBISgsKFC2PJkiU4fvw4du/ejT179iifkVnTOG/evDybQL9x4wa8vb0xf/58bNu2DevXr8fly5dhYWGBxo0bY8eOHbh79y4aNGig7VBztMxtJSUlBQCQnp4OAMrTImvXrlUq+t++fYvly5fj5MmTKFu2rBai1a7Q0FAsXboU8fHxyvqLCIyNjTFr1iw0btwYy5Ytw/v375V9WJfPe0VE+R7Gjx8PZ2dn2NnZoXHjxnj27BliYmKwYcMGTJs2DREREUhOTsb+/fvRpUsX7NmzB3p6elCr1Vpeiy+DlehEREREediVK1fQsWNHVKxYET4+PhqDc+3fvx/jx49Hu3btMG/ePADsgU5ERPTfZE2gT548GUFBQXj06BGqVauGPn36oFu3bihUqJCyfFhYGM6dO4f9+/cjPDxcpypejxw5gvHjxyMmJgb379/Ht99+CycnJ6xZswYAEBUVhdevX2P48OE4ePAgSpcureWIc57Mc7PDhw9jxYoVSElJQdGiRbFs2TKUKlUKJ0+exMSJE5GYmAi1Wg0LCwtcuXIFR48e1bmWTNHR0XBycsKqVauwZcsWZGRkYMeOHcifP7/yPfbq1QvJycnYvXu3tsPVuqy/Zdu3b4eXlxdWrVqFV69e4erVq1i8eDECAgJQo0YNtGvXDmq1Gvr6+ihcuDCioqJgaGioU9cOTKITERER5XGXLl1Cv379UKdOHYwYMQI2NjbKvHPnzqFevXo6XYFDRET0v5gxYwb8/Pywdu1aNG/eHC4uLnjw4AH69OmDoUOHaiTSs9Kl1hFHjx7Fjz/+iBUrVqBx48Zo27YtVq5cCX19fZw9exYHDhyAu7s7zM3NkS9fPm2Hm6NkTU6GhoaiZ8+eGDZsGKysrBAYGIiHDx/i4MGDqFy5Mm7cuIF79+7h2LFjqFq1Klq0aAFra2str8GXdffuXQQEBCAtLQ3z5s3D6tWr4e/vj3bt2mHMmDEwMTEBAPTr1w8qlQqrV6+GgYGBziSA/z8nT57Eli1bUK1aNXh5eQEAkpKS4O/vj3HjxuH48eOwtLRETEwMkpKS0KNHD+jr6+vUbxnAJDoRERGRTrh48SLc3NxQu3ZtjBw5MtvASeyBTkRE9OddunRJGXCvTZs2OH78OFxcXGBnZ4eHDx/Cw8MDgwcPhqmpqUa1py5VbQJAfHw8bGxs8ObNGwwbNgxLly5V5g0fPhy3bt3Cjh07YGZmpr0gc5gnT57AwsJCeX3r1i1069YNbm5uGDx4MH755Rc4ODggKSkJhoaGOHXqFKpWrarFiLXv9evXaNGiBRISEtCjRw8sWbIE6enpmDBhAk6dOgVTU1M4Ojri1q1bCAoKQkREhEZRiS578uQJHBwc8PTpU4wbNw6TJk1S5r18+RJ9+/ZFmTJlsHz5co336eK1A3uiExEREekAOzs7rFu3DrGxsZg6dSru37+vMV/XToKJiIj+DisrKwwfPhzNmjXDqVOn0K1bNyxZsgSnT5+GhYUF1q1bh9mzZyMlJUVj0D1dSqADQLly5bB161bkz58f+fLlw507d3D16lWMGTMGAQEB8PHxYQI9i5UrV2LAgAGIiopSpr1+/RrffvstPDw88PDhQ7Ro0QJOTk44e/YszMzM0LFjR9y8eVOLUWtfoUKFsGbNGpiZmeHkyZOIiYmBgYEB5s2bh6FDh8LCwgIhISFITk7GuXPnmEDPwsLCAsHBwTA3N0dwcDAuXryozCtSpAhKlCiBuLi4bO/TxWsHVqITERER6ZDIyEisWrUK69at07ioJyIioo/LWkmeSUSQkpKC/Pnzo2/fvihatCh8fHygr6+P3r17IyIiAk5OTli+fLnOJc7/KCMjAwEBARgxYgQKFSoEU1NTGBkZwd/fX+d6dv83J06cQJ8+fdCkSRN4eXmhdu3aAIDbt2+jcuXK6NevH5KTk7FlyxYYGRmhU6dOCA0NRcWKFXHt2jUYGRlpeQ206/Lly+jVqxe++eYbDBs2DDVq1FDmvXnzBgYGBjA0NNRihDnX5cuX0bt3b9SsWRNeXl6oVasWkpKS0Lp1a9jY2CjjGOgyJtGJiIiIdEzmo+QfSwoQERHRf2Q9Vh4+fBgvX76ESqWCo6MjihUrBgBo06YNypYti5UrV0JPTw/du3dHnz590LJlS+jp6elcC5dPefjwIeLj41GwYEF89dVXKF68uLZDylEyt7Vz584piWBvb2/UrVsXAJCSkgInJye4urpi2LBhAABPT0+0b98e9vb2KFWqlDbDzzEyWxja29tj5MiRrDr/Cy5evIiePXvixYsXqFOnDoyMjHD//n1ERETAyMhI53/LmEQnIiIi0kG6fhJMRET0V4wbNw5bt25FlSpVcPPmTVSpUgXDhw+Hi4sLPD09ERkZia+//hrx8fF4+fIlLl++DH19fd6wpj9NRKBWq6Gvr4/Tp08rg8KPHTtWqUhv27Yt4uPjsWLFCuzevRshISEIDw9HmTJltBx9znLx4kUMGjQIFSpUwNSpU3W+Z/xfcfXqVXTo0AFfffUVunfvjkGDBgEA3r9/r/NV/PwlJyIiItJBTKATERH9OevXr0dAQABCQkJw7NgxTJ48GadOnVJaZyxZsgQODg7IyMhAxYoVERsbywQ6/WkigoyMDKhUKrx8+RKvX79GkyZNEBISgqioKMyfPx8XLlwAAMyZMwdFihRBnz59EBYWhtDQUCbQP8LOzg6+vr54/PgxChcurO1wcpXq1asjODgY7969Q0xMDO7evQsAOp9AB1iJTkRERERERET0SWPGjEFaWhqWLVuGHTt2wMPDA3PnzoWnpyeSkpJgaGgIExMTjfekp6fDwMBASxFTbnDgwAFYWVmhZs2aAIDg4GAsWLAAz549g42NDQYPHozKlSujZcuWsLe3xw8//ICaNWtCRHDr1i2Ym5ujaNGiWl6LnO3t27fZ9k36c1jNnx1viRIRERERERER4UNVcFZqtRoPHjxA+fLlERMTAzc3N8ybNw+enp5Qq9Xw9/fHtm3boFarNT6DCXT6//z2228YOnQoli5dinv37uH69evo27cvnJ2d4e7uDisrKzg7O+PMmTM4evQoYmJiMG/ePEREREClUqFq1apMoP8JTKD/71jNnx0r0YmIiIiIiIhI52VkZEBfXx8AcO/ePRQsWBDm5uYIDAxEnz59kJaWhi1btqBbt24AgOTkZHz33Xf45ptvMGvWLG2GTrlQTEwMPDw8UK9ePZiZmSEtLQ0LFy4EALx+/RqbN2+Gt7c3Dh48CHNzczRp0gTff/89fH19mRymL4bV/P/BJDoRERERERER6Sw/Pz80aNAAtWrVAgBMmDABe/fuxZMnT9C/f380btwYZ86cwZYtWxAQEIAGDRrg0aNHGDp0KJ49e4aIiAhWntP/JCYmBp6envjtt9/Qvn17+Pr6KvMSExMxcuRIvH37Ftu2bcO5c+dgbm4Oa2trLUZMpLuYRCciIiIiIiIinXT//n00adIEbdq0wdixY3H9+nUMHjwYvr6+uHz5Mg4dOoQyZcrA3t4ev/76K1auXAlLS0sUKVIEpqamOH78OAwNDTWq2In+isuXL8PFxQUmJibYtm2bcjMHACZNmoR9+/bh559/ZjUwkZYxiU5EREREREREOis2NhZubm5o3Lgx9PT0UK1aNQwYMAAAsGfPHixfvhxFihTBwIEDYWlpievXr6NEiRJo0qQJ9PT0OIgo/W1XrlxBjx49ULt2bYwcOVIZbNTDwwP37t1DSEgIChQooOUoiXQbk+hEREREREREpNMy+1PHxcVhypQpGDlypDJv7969+PHHH1GoUCGMGzcO9evXV+axAp0+l4sXL6J3795ITU1FkyZNYGxsjF27duHYsWMa1elEpB162g6AiIiIiIiIiEib7O3tsWHDBhQpUgQHDhzAlStXlHnOzs4YNWoU7t69i9DQUABAZj0iE+j0udjZ2WHr1q3Q09NDWFgYypUrh+joaCbQiXIIVqITEREREREREQG4dOkS+vXrhzp16mDEiBGwsbFR5p07dw716tVj4pz+UdHR0ZgwYQK2bNmCEiVKaDscIvo3JtGJiIiIiIiIiP7t4sWLcHNzU/pTV6tWTWM+W7jQP+3t27ccSJQoh2ESnYiIiIiIiIgoi4sXL8LDwwNly5bFggULUL58eW2HREREWsSe6EREREREREREWdjZ2cHX1xempqYoW7astsMhIiItYyU6EREREREREdFHiAhUKhXUajX09FiHSESkq5hEJyIiIiIiIiL6hMxEOhER6S7eRiUiIiIiIiIi+gQm0ImIiEl0IiIiIiIiIiIiIqJPYBKdiIiIiIiIiIiIiOgTmEQnIiIiIiIiIiIiIvoEJtGJiIiIiIiIiIiIiD6BSXQiIiIiIiIiIiIiok9gEp2IiIiIiIiIiIiI6BOYRCciIiIiyiOePHmCESNGwNraGiYmJihZsiQaNWoEPz8/pKamajs8IiIiIqJcyUDbARARERER0d937949NGrUCGZmZpgzZw5sbW1hbGyMK1euYM2aNbCyskKHDh3+kX/73bt3MDIy+kc+m4iIiIhI21iJTkRERESUBwwePBgGBgaIiopC165d8fXXX6NChQpwcXHB/v374ezsDAB49eoV3NzcUKJECRQqVAjNmzfHpUuXlM+ZNm0aatWqhYCAAJQrVw6FCxeGq6srkpKSlGWaNWuGoUOHYuTIkShevDhatWoFALh69SratGmDggULomTJkujVqxeeP3/+Zb8IIiIiIqLPjEl0IiIiIqJc7vfff8eRI0cwZMgQFChQ4KPLqFQqAECXLl3w9OlTHDx4ENHR0bC3t0eLFi3w4sULZdm4uDiEhIRg37592LdvH06dOoV58+ZpfN6mTZtgZGSEs2fPYtWqVXj16hWaN28OOzs7REVF4dChQ/jtt9/QtWvXf27FiYiIiIi+ALZzISIiIiLK5e7evQsRQZUqVTSmFy9eHG/fvgUADBkyBM7OzoiMjMTTp09hbGwMAFi0aBFCQkKwa9cuuLu7AwDUajU2btwIU1NTAECvXr0QFhaG2bNnK59dqVIlLFiwQHk9a9Ys2NnZYc6cOcq0DRs2oHTp0rh9+zYqV678z6w8EREREdE/jEl0IiIiIqI8KjIyEmq1Gj169EBaWhouXbqE5ORkFCtWTGO5N2/eIC4uTnldrlw5JYEOAKVKlcLTp0813lO7dm2N15cuXcKJEydQsGDBbHHExcUxiU5EREREuRaT6EREREREuZy1tTVUKhVu3bqlMb1ChQoAgHz58gEAkpOTUapUKZw8eTLbZ5iZmSl/NzQ01JinUqmgVqs1pv2xbUxycjKcnZ0xf/78bJ9dqlSpP70uREREREQ5DZPoRERERES5XLFixdCyZUv4+vpi2LBhn+yLbm9vjydPnsDAwADlypX7rDHY29sjKCgI5cqVg4EBLzOIiIiIKO/gwKJERERERHnAypUrkZ6ejjp16mDHjh24ceMGbt26hZ9++gk3b96Evr4+HB0d0aBBA3Ts2BFHjhxBfHw8zp07h0mTJiEqKupv/ftDhgzBixcv0K1bN1y4cAFxcXE4fPgw+vXrh4yMjM+0lkREREREXx5LRIiIiIiI8oCKFSvi4sWLmDNnDiZMmICHDx/C2NgY1apVw+jRozF48GCoVCocOHAAkyZNQr9+/fDs2TNYWFigSZMmKFmy5N/69y0tLXH27FmMGzcOTk5OSEtLQ9myZdG6dWvo6bF2h4iIiIhyL5WIiLaDICIiIiIiIiIiIiLKiVgSQkRERERERERERET0CUyiExERERERERERERF9ApPoRERERERERERERESfwCQ6EREREREREREREdEnMIlORERERERERERERPQJTKITEREREREREREREX0Ck+hERERERERERERERJ/AJDoRERERERERERER0ScwiU5ERERERERERERE9AlMohMRERERERERERERfQKT6EREREREREREREREn8AkOhERERERERERERHRJ/wfjJvwDNlIhrwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from collections import Counter\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "y = y_labels_normalized\n", + "\n", + "freq_original = Counter(y)\n", + "\n", + "orig_genres = list(freq_original.keys())\n", + "orig_counts = list(freq_original.values())\n", + "\n", + "fig, axs = plt.subplots(figsize=(15, 6))\n", + "\n", + "sns.barplot(x=orig_genres, y=orig_counts, ax=axs)\n", + "axs.set_title(\"Original Genre Frequencies\")\n", + "axs.set_xlabel(\"Genre\")\n", + "axs.set_ylabel(\"Count\")\n", + "axs.tick_params(axis='x', rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove all songs with genres which do not come up more than n times" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class counts: Counter({'country': 1012, 'idm': 989, 'black-metal': 989, 'hardstyle': 987, 'heavy-metal': 985, 'blues': 931, 'rap': 925, 'classical': 849, 'folk': 819, 'rock-n-roll': 813, 'electronic': 807, 'jazz': 738, 'lo-fi': 727, 'punk-rock': 642, 'soul': 457, 'punk': 360, 'rock': 351, 'hip hop': 66})\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_49934/2892467199.py:49: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " axs.set_xticklabels(le.classes_)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import LabelEncoder\n", + "import numpy as np\n", + "from collections import Counter\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# --- Encoding/Standardizing X ---\n", + "# Remove compex object variables\n", + "if np.iscomplexobj(X):\n", + " X = np.abs(X)\n", + "\n", + "# Standardize the features\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "# --- Counting Labels ---\n", + "# Check class counts.\n", + "counter = Counter(y)\n", + "print(\"Class counts:\", counter)\n", + "\n", + "# Keep only classes with at least n samples. At least 2 or any other % 2 == 0 number\n", + "n = 100\n", + "classes_to_keep = {cls for cls, count in counter.items() if count >= n}\n", + "indices_to_keep = [i for i, label in enumerate(y) if label in classes_to_keep]\n", + "\n", + "y = np.array(y)\n", + "\n", + "# Filter the data.\n", + "X_filtered = X_scaled[indices_to_keep]\n", + "y_filtered = y[indices_to_keep]\n", + "\n", + "# Encode the Genres into numerical values\n", + "le = LabelEncoder()\n", + "y_encoded = le.fit_transform(y_filtered)\n", + "\n", + "freq_leftover = Counter(y_encoded)\n", + "leftover_genres = list(freq_leftover.keys())\n", + "leftover_counts = list(freq_leftover.values())\n", + "\n", + "fig, axs = plt.subplots(figsize=(15, 6))\n", + "\n", + "# Plot normalized label frequencies.\n", + "sns.barplot(x=leftover_genres, y=leftover_counts, ax=axs)\n", + "axs.set_title(\"Normalized Genre Frequencies\")\n", + "axs.set_xlabel(\"Genre\")\n", + "axs.set_ylabel(\"Count\")\n", + "axs.tick_params(axis='x', rotation=45)\n", + "axs.set_xticklabels(le.classes_)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Remove highly collerating data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropping columns: ['feature_5', 'feature_8', 'feature_9', 'feature_10', 'feature_12', 'feature_14', 'feature_16', 'feature_20', 'feature_21', 'feature_23', 'feature_25', 'feature_26', 'feature_30', 'feature_31', 'feature_32', 'feature_34', 'feature_37', 'feature_38', 'feature_41', 'feature_43', 'feature_44', 'feature_48', 'feature_50', 'feature_51', 'feature_53']\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "CORRELATING_VALUE = 0.9\n", + "\n", + "X = X_filtered\n", + "\n", + "def remove_highly_correlated_features(X, threshold):\n", + " # If X is a NumPy array, convert it to a DataFrame.\n", + " if isinstance(X, np.ndarray):\n", + " # Create column names if not provided\n", + " X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])\n", + " else:\n", + " X_df = X.copy()\n", + " \n", + " # Compute the absolute correlation matrix\n", + " corr_matrix = X_df.corr().abs()\n", + " \n", + " # Create an upper triangle matrix of correlations\n", + " upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n", + " \n", + " # Identify columns to drop: any feature with correlation greater than the threshold\n", + " to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n", + " print(\"Dropping columns:\", to_drop)\n", + " \n", + " # Drop these columns from the dataframe\n", + " X_df_reduced = X_df.drop(columns=to_drop)\n", + " \n", + " # If original X was a NumPy array, return as a NumPy array.\n", + " if isinstance(X, np.ndarray):\n", + " return X_df_reduced.values\n", + " else:\n", + " return X_df_reduced\n", + "\n", + "\n", + "X_reduced = remove_highly_correlated_features(X, threshold=CORRELATING_VALUE)\n", + "\n", + "# -- Create a feature correlation matrix --\n", + "df_features = pd.DataFrame(X_reduced, columns=[f'feature_{i}' for i in range(X_reduced.shape[1])])\n", + "\n", + "# Compute the correlation matrix.\n", + "corr = df_features.corr()\n", + "\n", + "plt.figure(figsize=(12,10))\n", + "sns.heatmap(corr, cmap='coolwarm', annot=False)\n", + "plt.title('Feature Correlation Matrix')\n", + "plt.show()\n", + "\n", + "X_filtered = X_reduced\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create a Test-Train split" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set shape: (10704, 30) (10704,)\n", + "Test set shape: (2677, 30) (2677,)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_filtered, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded\n", + ")\n", + "\n", + "print(\"Training set shape:\", X_train.shape, y_train.shape)\n", + "print(\"Test set shape:\", X_test.shape, y_test.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyperparameter tuning" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:38] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:41] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:41] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:13] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:29] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:29] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:30] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:39] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:44] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:15] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:28] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:31] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:37] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:43] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:44] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:46] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:40] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:40] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:44] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:45] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:56] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:57] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:59] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:02:07] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:00] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:00] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:01] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:11] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:20] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:27] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:48] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:02] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:00] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:02] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:05] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:05] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:12:07] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:12:16] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:12:35] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:12:55] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:06] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:07] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:08] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:14] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:47] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:58] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:01] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:11] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:26] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:27] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:31] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:32] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:41] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:43] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:48] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:51] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:03] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:03] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:04] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:09] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:24] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:26] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:31] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:34] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:40] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:45] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:50] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:52] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:13] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:14] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:16] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:17] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:42] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:43] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:43] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:44] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:54] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:57] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:10] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:11] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:24] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:28] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:32] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:33] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:53] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:53] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:02] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:08] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:19] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:22] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:23] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:33] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:40] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:40] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:54] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:56] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:56] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:59] WARNING: /workspace/src/learner.cc:738: 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"/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:29] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:30] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:35] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:37] WARNING: /workspace/src/learner.cc:738: 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sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Parameter grid\n", + "param_grid_xgb = {\n", + " 'n_estimators': [300, 400, 500, 500, 600],\n", + " 'max_depth': [9, 11, 13],\n", + " 'learning_rate': [0.1, 0.3],\n", + " 'subsample': [0.4, 0.8, 1],\n", + "}\n", + "\n", + "# Grid search\n", + "xgb = XGBClassifier(objective='multi:softmax', num_class=len(set(y)), use_label_encoder=False, eval_metric='mlogloss', random_state=42)\n", + "grid_search_xgb = GridSearchCV(xgb, param_grid_xgb, cv=5, n_jobs=-1, scoring='accuracy')\n", + "grid_search_xgb.fit(X_train, y_train)\n", + "\n", + "# Best model\n", + "final_model = grid_search_xgb.best_estimator_\n", + "print(\"Best XGBoost parameters:\", grid_search_xgb.best_params_)\n", + "print(f\"Best XGBoost accuracy: {grid_search_xgb.best_score_:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluation Charts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Confusion Matrix - Shows what was the predicted value compared to the true value when evaluating the Model\n", + "\n", + "The more values in the diagonal line the better" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "# Predict on the test set\n", + "y_pred = final_model.predict(X_test)\n", + "\n", + "# Confusion matrix\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n", + "disp.plot(cmap='Blues')\n", + "plt.title(\"XGBoost Confusion Matrix\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}