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252 changes: 252 additions & 0 deletions notebooks/ANNModel.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
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"Building wheels for collected packages: jax\n",
" Building wheel for jax (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for jax: filename=jax-0.4.13-py3-none-any.whl size=1518705 sha256=f1d02552a5e357681bcaab262d063c12bf8dcbbfa16daa794a4e97376772e555\n",
" Stored in directory: /home/vscode/.cache/pip/wheels/46/d9/15/d2800d4089dc4c77299ac7513c6aa1036f5491edbd2bf6ba16\n",
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"Installing collected packages: wrapt, tensorflow-estimator, ml-dtypes, keras, jax, scikeras, tensorboard, tensorflow\n",
" Attempting uninstall: wrapt\n",
" Found existing installation: wrapt 1.16.0\n",
" Uninstalling wrapt-1.16.0:\n",
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" Attempting uninstall: tensorflow-estimator\n",
" Found existing installation: tensorflow-estimator 2.13.0\n",
" Uninstalling tensorflow-estimator-2.13.0:\n",
" Successfully uninstalled tensorflow-estimator-2.13.0\n",
" Attempting uninstall: keras\n",
" Found existing installation: keras 2.13.1\n",
" Uninstalling keras-2.13.1:\n",
" Successfully uninstalled keras-2.13.1\n",
" Attempting uninstall: tensorboard\n",
" Found existing installation: tensorboard 2.13.0\n",
" Uninstalling tensorboard-2.13.0:\n",
" Successfully uninstalled tensorboard-2.13.0\n",
" Attempting uninstall: tensorflow\n",
" Found existing installation: tensorflow 2.13.1\n",
" Uninstalling tensorflow-2.13.1:\n",
" Successfully uninstalled tensorflow-2.13.1\n",
"Successfully installed jax-0.4.13 keras-2.12.0 ml-dtypes-0.2.0 scikeras-0.12.0 tensorboard-2.12.3 tensorflow-2.12.1 tensorflow-estimator-2.12.0 wrapt-1.14.1\n",
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-07-29 19:06:35.916957: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2024-07-29 19:06:35.955385: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2024-07-29 19:06:35.957278: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-07-29 19:06:37.345996: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
"Best: 0.8452076237345957 using {'model__optimizer': 'adam', 'model__learning_rate': 0.001, 'model__dropout_rate': 0.3, 'epochs': 50, 'batch_size': 64}\n",
"Mean Absolute Error on scaled data: 0.9159975888259128\n",
"Rescaled Mean Absolute Error: 14853.925471006318\n"
]
}
],
"source": [
"!pip install scikeras[tensorflow]\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import mean_absolute_error\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout\n",
"from tensorflow.keras.optimizers import Adam\n",
"from scikeras.wrappers import KerasRegressor\n",
"\n",
"# Loading the dataset\n",
"data = pd.read_csv('../data/AmesData.csv')\n",
"\n",
"# Filling NaN values in numeric columns with the median\n",
"for col in data.select_dtypes(include=[np.number]).columns:\n",
" data[col].fillna(data[col].median(), inplace=True)\n",
"\n",
"# Filling NaN values in categorical columns with the most frequent value\n",
"for col in data.select_dtypes(include=[object]).columns:\n",
" data[col].fillna(data[col].mode()[0], inplace=True)\n",
"\n",
"# Encoding categorical variables using one-hot encoding\n",
"data_encoded = pd.get_dummies(data, drop_first=True)\n",
"\n",
"# Splitting the data into features and target\n",
"X = data_encoded.drop('SalePrice', axis=1)\n",
"y = data['SalePrice']\n",
"\n",
"# Spliting the data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Initializing the scaler for the features\n",
"scaler = StandardScaler()\n",
"\n",
"# Scaling the features\n",
"X_train_scaled = scaler.fit_transform(X_train)\n",
"X_test_scaled = scaler.transform(X_test)\n",
"\n",
"# Scaling the target variable\n",
"y_train_mean = y_train.mean()\n",
"y_train_std = y_train.std()\n",
"y_train_scaled = (y_train - y_train_mean) / y_train_std\n",
"y_test_scaled = (y_test - y_train_mean) / y_train_std\n",
"\n",
"# Defining a function to create the model (for KerasRegressor)\n",
"def create_model(optimizer='adam', learning_rate=0.001, dropout_rate=0.0):\n",
" model = Sequential()\n",
" model.add(Dense(128, input_dim=X_train_scaled.shape[1], activation='relu', kernel_regularizer='l2'))\n",
" model.add(Dropout(dropout_rate))\n",
" model.add(Dense(64, activation='relu', kernel_regularizer='l2'))\n",
" model.add(Dropout(dropout_rate))\n",
" model.add(Dense(32, activation='relu', kernel_regularizer='l2'))\n",
" model.add(Dense(1))\n",
" \n",
" optimizer = Adam(learning_rate=learning_rate)\n",
" model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
" return model\n",
"\n",
"# Creating the KerasRegressor\n",
"model = KerasRegressor(model=create_model, epochs=100, batch_size=32, verbose=0)\n",
"\n",
"# Defining the hyperparameters grid\n",
"param_grid = {\n",
" 'model__optimizer': ['adam'],\n",
" 'model__learning_rate': [0.01, 0.001, 0.0001],\n",
" 'model__dropout_rate': [0.0, 0.3, 0.5],\n",
" 'batch_size': [16, 32, 64],\n",
" 'epochs': [50, 100]\n",
"}\n",
"\n",
"# Performing RandomizedSearchCV\n",
"random_search = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=10, cv=3, verbose=1)\n",
"random_search_result = random_search.fit(X_train_scaled, y_train_scaled)\n",
"\n",
"print(f\"Best: {random_search_result.best_score_} using {random_search_result.best_params_}\")\n",
"\n",
"# Evaluating the best model on the test set\n",
"best_model = random_search_result.best_estimator_\n",
"mae_scaled = best_model.score(X_test_scaled, y_test_scaled)\n",
"print(f'Mean Absolute Error on scaled data: {mae_scaled}')\n",
"\n",
"# Predicting on the test data\n",
"predictions_scaled = best_model.predict(X_test_scaled)\n",
"\n",
"# Rescaling the predictions to the original scale\n",
"predictions_rescaled = predictions_scaled * y_train_std + y_train_mean\n",
"y_test_rescaled = y_test\n",
"\n",
"# Calculating the rescaled mean absolute error\n",
"mae_rescaled = mean_absolute_error(y_test_rescaled, predictions_rescaled)\n",
"print(f'Rescaled Mean Absolute Error: {mae_rescaled}')"
]
}
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