diff --git "a/part2-model-training-hyperparameters-LT.ipynb" "b/part2-model-training-hyperparameters-LT.ipynb" new file mode 100644--- /dev/null +++ "b/part2-model-training-hyperparameters-LT.ipynb" @@ -0,0 +1,7048 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MODELIŲ MOKYMAS IR GERIAUSIŲ PARAMETRŲ PAIEŠKA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateTitleRating
64752017-05-22Very bad.I can't register any of my …1.0
64762017-05-19Functional platform but seriously lacking in c...3.0
64772017-05-18No reservations.5.0
64782017-05-18I HOPE YOU SEE THIS BEFORE You send …1.0
64792017-05-16Coinbase - the rock star crypto app4.0
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" + ], + "text/plain": [ + " Date Title Rating\n", + "6475 2017-05-22 Very bad.I can't register any of my … 1.0\n", + "6476 2017-05-19 Functional platform but seriously lacking in c... 3.0\n", + "6477 2017-05-18 No reservations. 5.0\n", + "6478 2017-05-18 I HOPE YOU SEE THIS BEFORE You send … 1.0\n", + "6479 2017-05-16 Coinbase - the rock star crypto app 4.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "reviews = pd.read_csv('trustpilot_coinbase_reviews-strip.csv', sep='|')\n", + "reviews.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear Regression Model:\n", + "Mean Squared (MSE): 1309806.40\n", + "Mean Absolute Error (MAE): 700.22\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.72%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "\n", + "\n", + "def mean_absolute_percentage_error(y_true, y_pred):\n", + " y_true, y_pred = np.array(y_true), np.array(y_pred)\n", + " return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n", + "\n", + "def eval_regression(X_train, X_test, y_train, y_test, model, description):\n", + " model.fit(X_train, y_train)\n", + " y_pred = model.predict(X_test)\n", + " \n", + " mse = mean_squared_error(y_test, y_pred)\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " r2 = r2_score(y_test, y_pred)\n", + " mape = mean_absolute_percentage_error(y_test, y_pred)\n", + " \n", + " print(description)\n", + " print(f\"Mean Squared (MSE): {mse:.2f}\")\n", + " print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n", + " print(f\"R^2: {r2:.2f}\")\n", + " print(f\"Mean Absolute % (MAPE): {mape:.2f}%\")\n", + " print('='*50 + '\\n')\n", + "btc_all = pd.read_csv('combined_btc_data.csv')\n", + "\n", + "btc_all['Date'] = pd.to_datetime(btc_all['Date'])\n", + "btc_all['Day'] = btc_all['Date'].dt.day\n", + "btc_all['Month'] = btc_all['Date'].dt.month\n", + "btc_all['Year'] = btc_all['Date'].dt.year\n", + "\n", + "# Create lag\n", + "btc_all['Lag_Close_1'] = btc_all['Close'].shift(1)\n", + "btc_all['Lag_Close_2'] = btc_all['Close'].shift(2)\n", + "btc_all['Lag_Volume_1'] = btc_all['Volume'].shift(1)\n", + "btc_all['Lag_Volume_2'] = btc_all['Volume'].shift(2)\n", + "\n", + "btc_all.dropna(inplace=True)\n", + "\n", + "X = btc_all[['Lag_Close_1', 'Lag_Close_2', 'Lag_Volume_1', 'Lag_Volume_2', 'Day', 'Month', 'Year']]\n", + "y = btc_all['Close']\n", + "\n", + "btc_all.sort_values('Date', inplace=True)\n", + "\n", + "split_point = int(len(btc_all) * 0.85) # only newest data for test to prevent leakage\n", + "\n", + "X_train = X.iloc[:split_point]\n", + "X_test = X.iloc[split_point:]\n", + "y_train = y.iloc[:split_point]\n", + "y_test = y.iloc[split_point:]\n", + "\n", + "# Normalize\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "model = LinearRegression()\n", + "\n", + "eval_regression(X_train, X_test, y_train, y_test, model, \"Linear Regression Model:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PRIDEDAME TRUSTPILOT REITINGUS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear Regression Model (with added Coinmarketcap rating feature):\n", + "Mean Squared (MSE): 1601648.70\n", + "Mean Absolute Error (MAE): 779.05\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.67%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "btc_all = pd.read_csv('combined_btc_data.csv')\n", + "reviews = pd.read_csv('trustpilot_coinbase_reviews-strip.csv', sep='|')\n", + "\n", + "reviews = reviews[reviews['Date'].apply(is_date)]\n", + "reviews['Date'] = pd.to_datetime(reviews['Date'])\n", + "\n", + "reviews['YearMonth'] = reviews['Date'].dt.to_period('M')\n", + "\n", + "monthly_mean_rating = reviews.groupby('YearMonth')['Rating'].mean().reset_index()\n", + "monthly_mean_rating['Date'] = monthly_mean_rating['YearMonth'].dt.to_timestamp()\n", + "\n", + "btc_all['Date'] = pd.to_datetime(btc_all['Date'])\n", + "\n", + "# date for monthly_mean_rating\n", + "btc_all['YearMonth'] = btc_all['Date'].dt.to_period('M')\n", + "\n", + "btc_all = pd.merge(btc_all, monthly_mean_rating, on='YearMonth', how='left')\n", + "btc_all['Date'] = btc_all['Date_x']\n", + "btc_all = btc_all.drop(columns=['Date_x', 'Date_y'])\n", + "\n", + "btc_all['Rating'].fillna(method='ffill', inplace=True)\n", + "\n", + "btc_all['Lag_Close_1'] = btc_all['Close'].shift(1)\n", + "btc_all['Lag_Close_2'] = btc_all['Close'].shift(2)\n", + "btc_all['Lag_Volume_1'] = btc_all['Volume'].shift(1)\n", + "btc_all['Lag_Volume_2'] = btc_all['Volume'].shift(2)\n", + "\n", + "btc_all.dropna(inplace=True)\n", + "\n", + "btc_all['Day'] = btc_all['Date'].dt.day\n", + "btc_all['Month'] = btc_all['Date'].dt.month\n", + "btc_all['Year'] = btc_all['Date'].dt.year\n", + "\n", + "X = btc_all[['Lag_Close_1', 'Lag_Close_2', 'Lag_Volume_1', 'Lag_Volume_2', 'Day', 'Month', 'Year', 'Rating']]\n", + "y = btc_all['Close']\n", + "\n", + "btc_all.sort_values('Date', inplace=True)\n", + "\n", + "split_point = int(len(btc_all) * 0.85) # 15% of most recent data for testing\n", + "X_train = X.iloc[:split_point]\n", + "X_test = X.iloc[split_point:]\n", + "y_train = y.iloc[:split_point]\n", + "y_test = y.iloc[split_point:]\n", + "\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "model = LinearRegression()\n", + "model.fit(X_train, y_train)\n", + "\n", + "eval_regression(X_train, X_test, y_train, y_test, model, \"Linear Regression Model (with added Coinmarketcap rating feature):\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GridSearch paieška / POLYNOMIAL " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Model with Grid Search and Polynomial Features\n", + "Mean Squared (MSE): 1311689.86\n", + "Mean Absolute Error (MAE): 700.54\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.72%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "btc_all = pd.read_csv('combined_btc_data.csv')\n", + "reviews = pd.read_csv('trustpilot_coinbase_reviews-strip.csv', sep='|')\n", + "\n", + "reviews = reviews[reviews['Date'].apply(is_date)]\n", + "reviews['Date'] = pd.to_datetime(reviews['Date'])\n", + "\n", + "reviews['YearMonth'] = reviews['Date'].dt.to_period('M')\n", + "\n", + "monthly_mean_rating = reviews.groupby('YearMonth')['Rating'].mean().reset_index()\n", + "monthly_mean_rating['Date'] = monthly_mean_rating['YearMonth'].dt.to_timestamp()\n", + "\n", + "btc_all['Date'] = pd.to_datetime(btc_all['Date'])\n", + "\n", + "btc_all['YearMonth'] = btc_all['Date'].dt.to_period('M')\n", + "\n", + "btc_all = pd.merge(btc_all, monthly_mean_rating, on='YearMonth', how='left')\n", + "btc_all['Date'] = btc_all['Date_x']\n", + "btc_all = btc_all.drop(columns=['Date_x', 'Date_y'])\n", + "\n", + "btc_all['Rating'].fillna(method='ffill', inplace=True)\n", + "\n", + "btc_all['Lag_Close_1'] = btc_all['Close'].shift(1)\n", + "btc_all['Lag_Close_2'] = btc_all['Close'].shift(2)\n", + "btc_all['Lag_Volume_1'] = btc_all['Volume'].shift(1)\n", + "btc_all['Lag_Volume_2'] = btc_all['Volume'].shift(2)\n", + "\n", + "btc_all.dropna(inplace=True)\n", + "\n", + "btc_all['Day'] = btc_all['Date'].dt.day\n", + "btc_all['Month'] = btc_all['Date'].dt.month\n", + "btc_all['Year'] = btc_all['Date'].dt.year\n", + "\n", + "X = btc_all[['Lag_Close_1', 'Lag_Close_2', 'Lag_Volume_1', 'Lag_Volume_2', 'Day', 'Month', 'Year', 'Rating']]\n", + "y = btc_all['Close']\n", + "\n", + "btc_all.sort_values('Date', inplace=True)\n", + "\n", + "split_point = int(len(btc_all) * 0.80)\n", + "X_train = X.iloc[:split_point]\n", + "X_test = X.iloc[split_point:]\n", + "y_train = y.iloc[:split_point]\n", + "y_test = y.iloc[split_point:]\n", + "\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "pipeline = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('poly', PolynomialFeatures()),\n", + " ('model', LinearRegression())\n", + "])\n", + "\n", + "param_grid = {\n", + " 'poly__degree': [1, 2, 3],\n", + " 'model': [LinearRegression()]\n", + "}\n", + "\n", + "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_squared_error')\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "best_model = grid_search.best_estimator_\n", + "\n", + "eval_regression(X_train, X_test, y_train, y_test, best_model, \"Best Model with Grid Search and Polynomial Features\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Model with Grid Search and Polynomial Features\n", + "Mean Squared (MSE): 1601648.70\n", + "Mean Absolute Error (MAE): 779.05\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.67%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "btc_all = pd.read_csv('combined_btc_data.csv')\n", + "reviews = pd.read_csv('trustpilot_coinbase_reviews-strip.csv', sep='|')\n", + "\n", + "reviews = reviews[reviews['Date'].apply(is_date)]\n", + "reviews['Date'] = pd.to_datetime(reviews['Date'])\n", + "\n", + "reviews['YearMonth'] = reviews['Date'].dt.to_period('M')\n", + "\n", + "monthly_mean_rating = reviews.groupby('YearMonth')['Rating'].mean().reset_index()\n", + "monthly_mean_rating['Date'] = monthly_mean_rating['YearMonth'].dt.to_timestamp()\n", + "\n", + "btc_all['Date'] = pd.to_datetime(btc_all['Date'])\n", + "\n", + "btc_all['YearMonth'] = btc_all['Date'].dt.to_period('M')\n", + "\n", + "btc_all = pd.merge(btc_all, monthly_mean_rating, on='YearMonth', how='left')\n", + "btc_all['Date'] = btc_all['Date_x']\n", + "btc_all = btc_all.drop(columns=['Date_x', 'Date_y'])\n", + "\n", + "btc_all['Rating'].fillna(method='ffill', inplace=True)\n", + "\n", + "btc_all['Lag_Close_1'] = btc_all['Close'].shift(1)\n", + "btc_all['Lag_Close_2'] = btc_all['Close'].shift(2)\n", + "btc_all['Lag_Volume_1'] = btc_all['Volume'].shift(1)\n", + "btc_all['Lag_Volume_2'] = btc_all['Volume'].shift(2)\n", + "\n", + "btc_all.dropna(inplace=True)\n", + "\n", + "btc_all['Day'] = btc_all['Date'].dt.day\n", + "btc_all['Month'] = btc_all['Date'].dt.month\n", + "btc_all['Year'] = btc_all['Date'].dt.year\n", + "\n", + "X = btc_all[['Lag_Close_1', 'Lag_Close_2', 'Lag_Volume_1', 'Lag_Volume_2', 'Day', 'Month', 'Year', 'Rating']]\n", + "y = btc_all['Close']\n", + "\n", + "btc_all.sort_values('Date', inplace=True)\n", + "\n", + "split_point = int(len(btc_all) * 0.85)\n", + "X_train = X.iloc[:split_point]\n", + "X_test = X.iloc[split_point:]\n", + "y_train = y.iloc[:split_point]\n", + "y_test = y.iloc[split_point:]\n", + "\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "pipeline = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('poly', PolynomialFeatures()),\n", + " ('model', LinearRegression())\n", + "])\n", + "\n", + "param_grid = {\n", + " 'poly__degree': [1, 2, 3],\n", + " 'model': [LinearRegression()]\n", + "}\n", + "\n", + "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_squared_error')\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "best_model = grid_search.best_estimator_\n", + "\n", + "eval_regression(X_train, X_test, y_train, y_test, best_model, \"Best Model with Grid Search and Polynomial Features\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear Regression Model:\n", + "Mean Squared (MSE): 1601648.70\n", + "Mean Absolute Error (MAE): 779.05\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.67%\n", + "==================================================\n", + "\n", + "Polynomial Regression Model:\n", + "Mean Squared (MSE): 1980812.82\n", + "Mean Absolute Error (MAE): 893.67\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.92%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "btc_all = pd.read_csv('combined_btc_data.csv')\n", + "reviews = pd.read_csv('trustpilot_coinbase_reviews-strip.csv', sep='|')\n", + "\n", + "reviews = reviews[reviews['Date'].apply(is_date)]\n", + "reviews['Date'] = pd.to_datetime(reviews['Date'])\n", + "\n", + "reviews['YearMonth'] = reviews['Date'].dt.to_period('M')\n", + "\n", + "monthly_mean_rating = reviews.groupby('YearMonth')['Rating'].mean().reset_index()\n", + "monthly_mean_rating['Date'] = monthly_mean_rating['YearMonth'].dt.to_timestamp()\n", + "\n", + "btc_all['Date'] = pd.to_datetime(btc_all['Date'])\n", + "\n", + "btc_all['YearMonth'] = btc_all['Date'].dt.to_period('M')\n", + "\n", + "btc_all = pd.merge(btc_all, monthly_mean_rating, on='YearMonth', how='left')\n", + "btc_all['Date'] = btc_all['Date_x']\n", + "btc_all = btc_all.drop(columns=['Date_x', 'Date_y'])\n", + "\n", + "btc_all['Rating'].fillna(method='ffill', inplace=True)\n", + "\n", + "btc_all['Lag_Close_1'] = btc_all['Close'].shift(1)\n", + "btc_all['Lag_Close_2'] = btc_all['Close'].shift(2)\n", + "btc_all['Lag_Volume_1'] = btc_all['Volume'].shift(1)\n", + "btc_all['Lag_Volume_2'] = btc_all['Volume'].shift(2)\n", + "\n", + "btc_all.dropna(inplace=True)\n", + "\n", + "btc_all['Day'] = btc_all['Date'].dt.day\n", + "btc_all['Month'] = btc_all['Date'].dt.month\n", + "btc_all['Year'] = btc_all['Date'].dt.year\n", + "\n", + "X = btc_all[['Lag_Close_1', 'Lag_Close_2', 'Lag_Volume_1', 'Lag_Volume_2', 'Day', 'Month', 'Year', 'Rating']]\n", + "y = btc_all['Close']\n", + "\n", + "btc_all.sort_values('Date', inplace=True)\n", + "\n", + "split_point = int(len(btc_all) * 0.85)\n", + "X_train = X.iloc[:split_point]\n", + "X_test = X.iloc[split_point:]\n", + "y_train = y.iloc[:split_point]\n", + "y_test = y.iloc[split_point:]\n", + "\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "# Linear model\n", + "linear_model = LinearRegression()\n", + "linear_model.fit(X_train, y_train)\n", + "eval_regression(X_train, X_test, y_train, y_test, linear_model, \"Linear Regression Model:\")\n", + "\n", + "# Polynomial model\n", + "poly = PolynomialFeatures(degree=2)\n", + "X_train_poly = poly.fit_transform(X_train)\n", + "X_test_poly = poly.transform(X_test)\n", + "\n", + "poly_model = LinearRegression()\n", + "poly_model.fit(X_train_poly, y_train)\n", + "eval_regression(X_train_poly, X_test_poly, y_train, y_test, poly_model, \"Polynomial Regression Model:\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model': LinearRegression(), 'poly__degree': 1}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grid_search.best_params_ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Degree 1 = NoPoly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pridedame daugiau parametrų:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=2; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=2; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=2; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=2; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=2; total time= 0.0s\n", + "[CV] END 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+ "[CV] END ....model=Ridge(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "[CV] END ....model=Ridge(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "[CV] END ....model=Ridge(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "[CV] END ....model=Ridge(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=1; total time= 0.1s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=2; total time= 0.1s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=2; total time= 0.1s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=2; total time= 0.1s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=2; total time= 0.0s\n", + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=2; total time= 0.1s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Documents\\PythonMokymai repo\\PythonMokymai0\\mokymai\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.100e+08, tolerance: 5.000e+07\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=3; total time= 0.4s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Documents\\PythonMokymai repo\\PythonMokymai0\\mokymai\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.623e+08, tolerance: 5.001e+07\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=3; total time= 0.5s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Documents\\PythonMokymai repo\\PythonMokymai0\\mokymai\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.845e+08, tolerance: 5.368e+07\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=3; total time= 0.4s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Documents\\PythonMokymai repo\\PythonMokymai0\\mokymai\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.839e+07, tolerance: 1.476e+07\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=3; total time= 0.4s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Documents\\PythonMokymai repo\\PythonMokymai0\\mokymai\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.514e+08, tolerance: 5.331e+07\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] END ......model=Lasso(), model__alpha=1, poly__degree=3; total time= 0.5s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=2; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=2; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=2; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=2; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=2; total time= 0.0s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=3; total time= 0.3s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=3; total time= 0.1s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=3; total time= 0.3s\n", + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=3; total time= 0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Documents\\PythonMokymai repo\\PythonMokymai0\\mokymai\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.084e+08, tolerance: 5.331e+07\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV] END .....model=Lasso(), model__alpha=10, poly__degree=3; total time= 0.3s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=1; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=1; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=1; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=1; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=1; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=2; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=2; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=2; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=2; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=2; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "[CV] END ....model=Lasso(), model__alpha=100, poly__degree=3; total time= 0.0s\n", + "Best Model with Grid Search and Poly, ext parameters:\n", + "Mean Squared (MSE): 1616735.66\n", + "Mean Absolute Error (MAE): 784.50\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.68%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "pipeline = Pipeline([\n", + " ('scaler', StandardScaler()),\n", + " ('poly', PolynomialFeatures()),\n", + " ('model', LinearRegression())\n", + "])\n", + "\n", + "param_grid = [\n", + " {\n", + " 'poly__degree': [i for i in range(1, 11)],\n", + " 'model': [LinearRegression(random_state=seed) for seed in range(5)] \n", + " },\n", + " {\n", + " 'poly__degree': [i for i in range(1, 4)],\n", + " 'model': [Ridge()],\n", + " 'model__alpha': [1, 10, 100]\n", + " },\n", + " {\n", + " 'poly__degree': [i for i in range(1, 4)],\n", + " 'model': [Lasso()],\n", + " 'model__alpha': [1, 10, 100]\n", + " }\n", + "]\n", + "\n", + "\n", + "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2)\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "best_model = grid_search.best_estimator_\n", + "\n", + "eval_regression(X_train, X_test, y_train, y_test, best_model, \"Best Model with Grid Search and Poly, ext parameters + seed search:\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model 0: || Rank: 3 || Parameters: {'model': LinearRegression(), 'poly__degree': 1} || Mean Test Score(best=0): -922988.670489825\n", + "================================================================================================================================================================\n", + "Model 1: || Rank: 13 || Parameters: {'model': LinearRegression(), 'poly__degree': 2} || Mean Test Score(best=0): -3040558.790308443\n", + "================================================================================================================================================================\n", + "Model 2: || Rank: 20 || Parameters: {'model': LinearRegression(), 'poly__degree': 3} || Mean Test Score(best=0): -257448251.76484418\n", + "================================================================================================================================================================\n", + "Model 3: || Rank: 22 || Parameters: {'model': LinearRegression(), 'poly__degree': 4} || Mean Test Score(best=0): -46139966668.20374\n", + "================================================================================================================================================================\n", + "Model 4: || Rank: 23 || Parameters: {'model': LinearRegression(), 'poly__degree': 5} || Mean Test Score(best=0): -8.761141605548803e+16\n", + "================================================================================================================================================================\n", + "Model 5: || Rank: 24 || Parameters: {'model': LinearRegression(), 'poly__degree': 6} || Mean Test Score(best=0): -4.7987076821791664e+20\n", + "================================================================================================================================================================\n", + "Model 6: || Rank: 25 || Parameters: {'model': LinearRegression(), 'poly__degree': 7} || Mean Test Score(best=0): -3.97895441926736e+21\n", + "================================================================================================================================================================\n", + "Model 7: || Rank: 26 || Parameters: {'model': LinearRegression(), 'poly__degree': 8} || Mean Test Score(best=0): -2.239047370961113e+23\n", + "================================================================================================================================================================\n", + "Model 8: || Rank: 27 || Parameters: {'model': LinearRegression(), 'poly__degree': 9} || Mean Test Score(best=0): -1.1102195736332362e+25\n", + "================================================================================================================================================================\n", + "Model 9: || Rank: 28 || Parameters: {'model': LinearRegression(), 'poly__degree': 10} || Mean Test Score(best=0): -7.49236651207744e+25\n", + "================================================================================================================================================================\n", + "Model 10: || Rank: 5 || Parameters: {'model': Ridge(), 'model__alpha': 1, 'poly__degree': 1} || Mean Test Score(best=0): -939723.896123863\n", + "================================================================================================================================================================\n", + "Model 11: || Rank: 10 || Parameters: {'model': Ridge(), 'model__alpha': 1, 'poly__degree': 2} || Mean Test Score(best=0): -2555589.732520643\n", + "================================================================================================================================================================\n", + "Model 12: || Rank: 18 || Parameters: {'model': Ridge(), 'model__alpha': 1, 'poly__degree': 3} || Mean Test Score(best=0): -165117579.6495946\n", + "================================================================================================================================================================\n", + "Model 13: || Rank: 7 || Parameters: {'model': Ridge(), 'model__alpha': 10, 'poly__degree': 1} || Mean Test Score(best=0): -1102657.792390321\n", + "================================================================================================================================================================\n", + "Model 14: || Rank: 12 || Parameters: {'model': Ridge(), 'model__alpha': 10, 'poly__degree': 2} || Mean Test Score(best=0): -2870451.2741696965\n", + "================================================================================================================================================================\n", + "Model 15: || Rank: 19 || Parameters: {'model': Ridge(), 'model__alpha': 10, 'poly__degree': 3} || Mean Test Score(best=0): -213217639.25159657\n", + "================================================================================================================================================================\n", + "Model 16: || Rank: 11 || Parameters: {'model': Ridge(), 'model__alpha': 100, 'poly__degree': 1} || Mean Test Score(best=0): -2791623.1898638974\n", + "================================================================================================================================================================\n", + "Model 17: || Rank: 16 || Parameters: {'model': Ridge(), 'model__alpha': 100, 'poly__degree': 2} || Mean Test Score(best=0): -11153033.191843534\n", + "================================================================================================================================================================\n", + "Model 18: || Rank: 21 || Parameters: {'model': Ridge(), 'model__alpha': 100, 'poly__degree': 3} || Mean Test Score(best=0): -750937892.704158\n", + "================================================================================================================================================================\n", + "Model 19: || Rank: 2 || Parameters: {'model': Lasso(), 'model__alpha': 1, 'poly__degree': 1} || Mean Test Score(best=0): -920337.2255042918\n", + "================================================================================================================================================================\n", + "Model 20: || Rank: 9 || Parameters: {'model': Lasso(), 'model__alpha': 1, 'poly__degree': 2} || Mean Test Score(best=0): -2043422.844029424\n", + "================================================================================================================================================================\n", + "Model 21: || Rank: 17 || Parameters: {'model': Lasso(), 'model__alpha': 1, 'poly__degree': 3} || Mean Test Score(best=0): -112619768.06876549\n", + "================================================================================================================================================================\n", + "Model 22: || Rank: 1 || Parameters: {'model': Lasso(), 'model__alpha': 10, 'poly__degree': 1} || Mean Test Score(best=0): -909051.9193474293\n", + "================================================================================================================================================================\n", + "Model 23: || Rank: 8 || Parameters: {'model': Lasso(), 'model__alpha': 10, 'poly__degree': 2} || Mean Test Score(best=0): -1187192.4756851317\n", + "================================================================================================================================================================\n", + "Model 24: || Rank: 15 || Parameters: {'model': Lasso(), 'model__alpha': 10, 'poly__degree': 3} || Mean Test Score(best=0): -6123848.786681439\n", + "================================================================================================================================================================\n", + "Model 25: || Rank: 6 || Parameters: {'model': Lasso(), 'model__alpha': 100, 'poly__degree': 1} || Mean Test Score(best=0): -947106.7373970256\n", + "================================================================================================================================================================\n", + "Model 26: || Rank: 4 || Parameters: {'model': Lasso(), 'model__alpha': 100, 'poly__degree': 2} || Mean Test Score(best=0): -927324.8669255708\n", + "================================================================================================================================================================\n", + "Model 27: || Rank: 14 || Parameters: {'model': Lasso(), 'model__alpha': 100, 'poly__degree': 3} || Mean Test Score(best=0): -5066692.257320166\n", + "================================================================================================================================================================\n" + ] + } + ], + "source": [ + "import shutil\n", + "\n", + "def print_all_models(results):\n", + " terminal_width = shutil.get_terminal_size().columns\n", + " terminal_width *= 2 if terminal_width == 80 else terminal_width\n", + " for i in range(len(results['params'])):\n", + " print(f\"Model {i}: || Rank: {results['rank_test_score'][i]} || Parameters: {results['params'][i]} || Mean Test Score(best=0): {results['mean_test_score'][i]}\")\n", + " print('='*terminal_width)\n", + "\n", + "print_all_models(grid_search.cv_results_)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                ('poly', PolynomialFeatures(degree=1)),\n",
+       "                ('model', Lasso(alpha=10))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler()),\n", + " ('poly', PolynomialFeatures(degree=1)),\n", + " ('model', Lasso(alpha=10))])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grid_search.best_estimator_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 1001 candidates, totalling 5005 fits\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END ...........model=LinearRegression(), poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=0), model__alpha=1, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Ridge(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=0), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=1), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=2), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=3), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=4), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=5), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=6), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=7), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=8), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=9), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=10), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=11), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=12), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=13), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=14), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=15), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=16), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=16), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=16), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=16), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=16), model__alpha=1, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=19), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=19), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=19), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=19), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=19), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=19), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=19), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=20), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=21), model__alpha=1, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=22), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=22), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=22), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=22), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=22), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=23), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=23), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=23), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=23), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=23), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=23), model__alpha=10, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=25), model__alpha=25, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=26), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=26), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=26), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=26), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=26), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=26), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=27), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=28), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=29), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=30), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=31), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=1, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=32), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=33), model__alpha=10, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=34), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=34), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=34), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=34), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=34), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=34), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=34), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=34), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=1, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=35), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=25, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=36), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=10, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=37), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=38), model__alpha=50, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=49), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=50), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=51), model__alpha=10, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=52), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=53), model__alpha=25, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=57), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=58), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=59), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=60), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=61), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=61), model__alpha=1, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=62), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=62), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=62), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=62), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=63), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=63), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=63), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=63), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=63), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=63), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=63), model__alpha=10, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=64), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=65), model__alpha=1, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=72), model__alpha=25, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=75), model__alpha=25, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=81), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=81), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=81), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=81), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=81), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=81), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=50, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=82), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=1, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=10, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=25, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=83), model__alpha=25, poly__degree=1; 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total time= 0.0s\n", + "[CV] END model=Lasso(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "[CV] END model=Lasso(random_state=99), model__alpha=75, poly__degree=1; total time= 0.0s\n", + "Best Model with Grid Search - seed search:\n", + "Mean Squared (MSE): 1616735.66\n", + "Mean Absolute Error (MAE): 784.50\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.68%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "param_grid = [\n", + " {\n", + " 'poly__degree': [1],\n", + " 'model': [LinearRegression()]\n", + " },\n", + " {\n", + " 'poly__degree': [1],\n", + " 'model': [Ridge(random_state=seed) for seed in range(100)],\n", + " 'model__alpha': [1, 10, 25, 50, 75]\n", + " },\n", + " {\n", + " 'poly__degree': [1],\n", + " 'model': [Lasso(random_state=seed) for seed in range(100)],\n", + " 'model__alpha': [1, 10, 25, 50, 75]\n", + " }\n", + "]\n", + "\n", + "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=2)\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "best_model = grid_search.best_estimator_\n", + "\n", + "eval_regression(X_train, X_test, y_train, y_test, best_model, \"Best Model with Grid Search - seed search:\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                ('poly', PolynomialFeatures(degree=1)),\n",
+       "                ('model', Lasso(alpha=10, random_state=0))])
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" + ], + "text/plain": [ + "Pipeline(steps=[('scaler', StandardScaler()),\n", + " ('poly', PolynomialFeatures(degree=1)),\n", + " ('model', Lasso(alpha=10, random_state=0))])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grid_search.best_estimator_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear Regression Model:\n", + "Mean Squared (MSE): 1601648.70\n", + "Mean Absolute Error (MAE): 779.05\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.67%\n", + "==================================================\n", + "\n", + "Best Model with Grid Search - seed search:\n", + "Mean Squared (MSE): 1616735.66\n", + "Mean Absolute Error (MAE): 784.50\n", + "R^2: 0.99\n", + "Mean Absolute % (MAPE): 1.68%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "eval_regression(X_train, X_test, y_train, y_test, linear_model, \"Linear Regression Model:\")\n", + "eval_regression(X_train, X_test, y_train, y_test, best_model, \"Best Model with Grid Search - seed search:\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cross_val Linear Regression:\n", + "Mean Squared Error (MSE): 1018571.67\n", + "Mean Absolute Error (MAE): 560.09\n", + "R^2: 0.99\n", + "==================================================\n", + "cross_val Grid Search - seed search, best (Lasso):\n", + "Mean Squared Error (MSE): 1008962.65\n", + "Mean Absolute Error (MAE): 557.90\n", + "R^2: 0.99\n", + "==================================================\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "import numpy as np\n", + "\n", + "def eval_regression_cv(X, y, model, model_name):\n", + " mse_scores = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=5)\n", + " mae_scores = cross_val_score(model, X, y, scoring='neg_mean_absolute_error', cv=5)\n", + " r2_scores = cross_val_score(model, X, y, scoring='r2', cv=5) # cv --> no of equal parts, train on cv-1\n", + "\n", + " print(f\"{model_name}\")\n", + " print(f\"Mean Squared Error (MSE): {-np.mean(mse_scores):.2f}\")\n", + " print(f\"Mean Absolute Error (MAE): {-np.mean(mae_scores):.2f}\")\n", + " print(f\"R^2: {np.mean(r2_scores):.2f}\")\n", + " print(\"=\"*50)\n", + "\n", + "X = np.concatenate([X_train, X_test])\n", + "y = np.concatenate([y_train, y_test])\n", + "\n", + "linear_model = LinearRegression()\n", + "eval_regression_cv(X, y, linear_model, \"cross_val Linear Regression:\")\n", + "\n", + "best_model = grid_search.best_estimator_ # Lasso (linear + regularization)\n", + "eval_regression_cv(X, y, best_model, \"cross_val Grid Search - seed search, best (Lasso):\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'model': Lasso(random_state=0), 'model__alpha': 10, 'poly__degree': 1}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grid_search.best_params_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RandomForest bandymai:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n", + "Mean Squared Error: 4996052.775517814\n", + "Best Params: {'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "btc_all = pd.read_csv('combined_btc_data.csv')\n", + "reviews = pd.read_csv('trustpilot_coinbase_reviews-strip.csv', sep='|')\n", + "\n", + "reviews['Date'] = pd.to_datetime(reviews['Date'], errors='coerce')\n", + "reviews = reviews.dropna(subset=['Date'])\n", + "reviews['YearMonth'] = reviews['Date'].dt.to_period('M')\n", + "\n", + "monthly_mean_rating = reviews.groupby('YearMonth')['Rating'].mean().reset_index()\n", + "monthly_mean_rating['Date'] = monthly_mean_rating['YearMonth'].dt.to_timestamp()\n", + "\n", + "btc_all['Date'] = pd.to_datetime(btc_all['Date'])\n", + "btc_all['YearMonth'] = btc_all['Date'].dt.to_period('M')\n", + "\n", + "btc_all = pd.merge(btc_all, monthly_mean_rating, on='YearMonth', how='left')\n", + "btc_all['Date'] = btc_all['Date_x']\n", + "btc_all = btc_all.drop(columns=['Date_x', 'Date_y'])\n", + "\n", + "btc_all['Rating'].fillna(method='ffill', inplace=True)\n", + "\n", + "btc_all['Lag_Close_1'] = btc_all['Close'].shift(1)\n", + "btc_all['Lag_Close_2'] = btc_all['Close'].shift(2)\n", + "btc_all['Lag_Volume_1'] = btc_all['Volume'].shift(1)\n", + "btc_all['Lag_Volume_2'] = btc_all['Volume'].shift(2)\n", + "\n", + "btc_all.dropna(inplace=True)\n", + "\n", + "btc_all['Day'] = btc_all['Date'].dt.day\n", + "btc_all['Month'] = btc_all['Date'].dt.month\n", + "btc_all['Year'] = btc_all['Date'].dt.year\n", + "\n", + "X = btc_all[['Lag_Close_1', 'Lag_Close_2', 'Lag_Volume_1', 'Lag_Volume_2', 'Day', 'Month', 'Year', 'Rating']]\n", + "y = btc_all['Close']\n", + "\n", + "btc_all.sort_values('Date', inplace=True)\n", + "\n", + "split_point = int(len(btc_all) * 0.85)\n", + "X_train = X.iloc[:split_point]\n", + "X_test = X.iloc[split_point:]\n", + "y_train = y.iloc[:split_point]\n", + "y_test = y.iloc[split_point:]\n", + "\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "rf = RandomForestRegressor(random_state=0)\n", + "\n", + "# Define parameter grid for GridSearchCV\n", + "param_grid = {\n", + " 'n_estimators': [100, 200, 300],\n", + " 'max_depth': [None, 10, 20, 30],\n", + " 'min_samples_split': [2, 5, 10],\n", + " 'min_samples_leaf': [1, 2, 4]\n", + "}\n", + "\n", + "\n", + "grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, n_jobs=-1, scoring='neg_mean_squared_error', verbose=2)\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "best_model = grid_search.best_estimator_\n", + "\n", + "y_pred = best_model.predict(X_test)\n", + "\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "\n", + "print(f'Mean Squared Error: {mse}')\n", + "print(f'Best Params: {grid_search.best_params_}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best random forest:\n", + "Mean Squared Error (MSE): 1944100.14\n", + "Mean Absolute Error (MAE): 829.50\n", + "R^2: 0.98\n", + "==================================================\n" + ] + } + ], + "source": [ + "best_model = grid_search.best_estimator_\n", + "eval_regression_cv(X, y, best_model, \"Best random forest:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RandomForest SEED paieška" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Random Seed: 0\n", + "Mean Squared Error (MSE): 4996052.775517814\n", + "Mean Absolute Error (MAE): 1501.4247457178783\n", + "R^2: 0.9794782977603431\n", + "==================================================\n", + "Random Seed: 1\n", + "Mean Squared Error (MSE): 5125072.549682068\n", + "Mean Absolute Error (MAE): 1533.555984498857\n", + "R^2: 0.9789483383088735\n", + "==================================================\n", + "Random Seed: 2\n", + "Mean Squared Error (MSE): 5054499.405879303\n", + "Mean Absolute Error (MAE): 1557.7345543607014\n", + "R^2: 0.9792382233657996\n", + "==================================================\n", + "Random Seed: 3\n", + "Mean Squared Error (MSE): 4935535.728085683\n", + "Mean Absolute Error (MAE): 1521.3443166752927\n", + "R^2: 0.979726876565177\n", + "==================================================\n", + "Random Seed: 4\n", + "Mean Squared Error (MSE): 4973150.644626401\n", + "Mean Absolute Error (MAE): 1528.3114010362046\n", + "R^2: 0.9795723701674862\n", + "==================================================\n", + "Random Seed: 5\n", + "Mean Squared Error (MSE): 5052138.78915618\n", + "Mean Absolute Error (MAE): 1519.7293451808173\n", + "R^2: 0.9792479197952951\n", + "==================================================\n", + "Random Seed: 6\n", + "Mean Squared Error (MSE): 5020800.998398905\n", + "Mean Absolute Error (MAE): 1509.4070503462258\n", + "R^2: 0.9793766423768341\n", + "==================================================\n", + "Random Seed: 7\n", + "Mean Squared Error (MSE): 4892024.960268405\n", + "Mean Absolute Error (MAE): 1507.0097843147093\n", + "R^2: 0.9799056006622764\n", + "==================================================\n", + "Random Seed: 8\n", + "Mean Squared Error (MSE): 5231522.949505469\n", + "Mean Absolute Error (MAE): 1544.130965100389\n", + "R^2: 0.9785110844393441\n", + "==================================================\n", + "Random Seed: 9\n", + "Mean Squared Error (MSE): 5002508.923758711\n", + "Mean Absolute Error (MAE): 1529.433460764412\n", + "R^2: 0.97945177859456\n", + "==================================================\n", + "Random Seed: 10\n", + "Mean Squared Error (MSE): 5075199.248169787\n", + "Mean Absolute Error (MAE): 1525.3188965683055\n", + "R^2: 0.9791531970422238\n", + "==================================================\n", + "Random Seed: 11\n", + "Mean Squared Error (MSE): 5327002.065051401\n", + "Mean Absolute Error (MAE): 1584.2800178064278\n", + "R^2: 0.9781188960323398\n", + "==================================================\n", + "Random Seed: 12\n", + "Mean Squared Error (MSE): 5129547.761483399\n", + "Mean Absolute Error (MAE): 1550.184723365976\n", + "R^2: 0.9789299560042477\n", + "==================================================\n", + "Random Seed: 13\n", + "Mean Squared Error (MSE): 5064349.016507703\n", + "Mean Absolute Error (MAE): 1547.4626744340108\n", + "R^2: 0.9791977652710647\n", + "==================================================\n", + "Random Seed: 14\n", + "Mean Squared Error (MSE): 5204090.049394786\n", + "Mean Absolute Error (MAE): 1565.0383482289453\n", + "R^2: 0.9786237673578273\n", + "==================================================\n", + "Random Seed: 15\n", + "Mean Squared Error (MSE): 5070361.725738939\n", + "Mean Absolute Error (MAE): 1558.9033720106063\n", + "R^2: 0.9791730675678896\n", + "==================================================\n", + "Random Seed: 16\n", + "Mean Squared Error (MSE): 5032938.550580555\n", + "Mean Absolute Error (MAE): 1551.339169001925\n", + "R^2: 0.9793267863719074\n", + "==================================================\n", + "Random Seed: 17\n", + "Mean Squared Error (MSE): 5028409.259024837\n", + "Mean Absolute Error (MAE): 1512.4673470822152\n", + "R^2: 0.9793453908136216\n", + "==================================================\n", + "Random Seed: 18\n", + "Mean Squared Error (MSE): 4899769.262338367\n", + "Mean Absolute Error (MAE): 1487.371258375203\n", + "R^2: 0.9798737902975195\n", + "==================================================\n", + "Random Seed: 19\n", + "Mean Squared Error (MSE): 5160332.901042478\n", + "Mean Absolute Error (MAE): 1511.2134005778578\n", + "R^2: 0.9788035034834629\n", + "==================================================\n", + "Random Seed: 20\n", + "Mean Squared Error (MSE): 4881337.741471195\n", + "Mean Absolute Error (MAE): 1489.414236862718\n", + "R^2: 0.9799494993022189\n", + "==================================================\n", + "Random Seed: 21\n", + "Mean Squared Error (MSE): 5047450.869555085\n", + "Mean Absolute Error (MAE): 1541.590532018119\n", + "R^2: 0.979267175814897\n", + "==================================================\n", + "Random Seed: 22\n", + "Mean Squared Error (MSE): 4924503.667756411\n", + "Mean Absolute Error (MAE): 1499.8757843043902\n", + "R^2: 0.979772191670389\n", + "==================================================\n", + "Random Seed: 23\n", + "Mean Squared Error (MSE): 5165653.504908559\n", + "Mean Absolute Error (MAE): 1558.9701342654969\n", + "R^2: 0.9787816486606297\n", + "==================================================\n", + "Random Seed: 24\n", + "Mean Squared Error (MSE): 4942393.895788298\n", + "Mean Absolute Error (MAE): 1480.32878712579\n", + "R^2: 0.9796987060710236\n", + "==================================================\n", + "Random Seed: 25\n", + "Mean Squared Error (MSE): 5030513.647057608\n", + "Mean Absolute Error (MAE): 1525.475285649701\n", + "R^2: 0.9793367468647792\n", + "==================================================\n", + "Random Seed: 26\n", + "Mean Squared Error (MSE): 5245682.245754691\n", + "Mean Absolute Error (MAE): 1575.3145721546407\n", + "R^2: 0.9784529239525347\n", + "==================================================\n", + "Random Seed: 27\n", + "Mean Squared Error (MSE): 4884480.408077386\n", + "Mean Absolute Error (MAE): 1495.2336719342766\n", + "R^2: 0.9799365905377946\n", + "==================================================\n", + "Random Seed: 28\n", + "Mean Squared Error (MSE): 4894975.225370246\n", + "Mean Absolute Error (MAE): 1492.0707952449852\n", + "R^2: 0.979893482203031\n", + "==================================================\n", + "Random Seed: 29\n", + "Mean Squared Error (MSE): 5116221.532427124\n", + "Mean Absolute Error (MAE): 1538.8658206233615\n", + "R^2: 0.9789846945982854\n", + "==================================================\n", + "Random Seed: 30\n", + "Mean Squared Error (MSE): 5219911.031001044\n", + "Mean Absolute Error (MAE): 1557.9493721533024\n", + "R^2: 0.9785587813602307\n", + "==================================================\n", + "Random Seed: 31\n", + "Mean Squared Error (MSE): 5055088.953438469\n", + "Mean Absolute Error (MAE): 1521.3648639990936\n", + "R^2: 0.9792358017501744\n", + "==================================================\n", + "Random Seed: 32\n", + "Mean Squared Error (MSE): 4882899.112355755\n", + "Mean Absolute Error (MAE): 1517.8106345797023\n", + "R^2: 0.9799430858414693\n", + "==================================================\n", + "Random Seed: 33\n", + "Mean Squared Error (MSE): 4852301.474712267\n", + "Mean Absolute Error (MAE): 1490.630517560756\n", + "R^2: 0.9800687681825434\n", + "==================================================\n", + "Random Seed: 34\n", + "Mean Squared Error (MSE): 5143777.915562762\n", + "Mean Absolute Error (MAE): 1561.1644469435291\n", + "R^2: 0.9788715044630085\n", + "==================================================\n", + "Random Seed: 35\n", + "Mean Squared Error (MSE): 4936530.043809468\n", + "Mean Absolute Error (MAE): 1473.3140926559777\n", + "R^2: 0.9797227923306557\n", + "==================================================\n", + "Random Seed: 36\n", + "Mean Squared Error (MSE): 4836851.64882843\n", + "Mean Absolute Error (MAE): 1442.508769284698\n", + "R^2: 0.9801322296271454\n", + "==================================================\n", + "Random Seed: 37\n", + "Mean Squared Error (MSE): 4990596.513926916\n", + "Mean Absolute Error (MAE): 1525.390818972996\n", + "R^2: 0.9795007098085622\n", + "==================================================\n", + "Random Seed: 38\n", + "Mean Squared Error (MSE): 5354162.908508875\n", + "Mean Absolute Error (MAE): 1581.520758170296\n", + "R^2: 0.9780073306091834\n", + "==================================================\n", + "Random Seed: 39\n", + "Mean Squared Error (MSE): 5035249.002510776\n", + "Mean Absolute Error (MAE): 1529.4617207198212\n", + "R^2: 0.9793172959984704\n", + "==================================================\n", + "Random Seed: 40\n", + "Mean Squared Error (MSE): 5153041.633966585\n", + "Mean Absolute Error (MAE): 1576.8730246473606\n", + "R^2: 0.9788334529693079\n", + "==================================================\n", + "Random Seed: 41\n", + "Mean Squared Error (MSE): 5181336.171924428\n", + "Mean Absolute Error (MAE): 1578.1630915072678\n", + "R^2: 0.978717230801715\n", + "==================================================\n", + "Random Seed: 42\n", + "Mean Squared Error (MSE): 5082093.490704729\n", + "Mean Absolute Error (MAE): 1541.3602897431633\n", + "R^2: 0.9791248783676967\n", + "==================================================\n", + "Random Seed: 43\n", + "Mean Squared Error (MSE): 5036837.956558262\n", + "Mean Absolute Error (MAE): 1553.7713113980353\n", + "R^2: 0.9793107692375853\n", + "==================================================\n", + "Random Seed: 44\n", + "Mean Squared Error (MSE): 5041537.824563743\n", + "Mean Absolute Error (MAE): 1501.9150236682808\n", + "R^2: 0.9792914641389189\n", + "==================================================\n", + "Random Seed: 45\n", + "Mean Squared Error (MSE): 4832789.903015444\n", + "Mean Absolute Error (MAE): 1479.0707273600422\n", + "R^2: 0.980148913585841\n", + "==================================================\n", + "Random Seed: 46\n", + "Mean Squared Error (MSE): 5038835.595997295\n", + "Mean Absolute Error (MAE): 1526.546807294775\n", + "R^2: 0.9793025637674687\n", + "==================================================\n", + "Random Seed: 47\n", + "Mean Squared Error (MSE): 4900110.243797848\n", + "Mean Absolute Error (MAE): 1489.1477755900016\n", + "R^2: 0.9798723896878192\n", + "==================================================\n", + "Random Seed: 48\n", + "Mean Squared Error (MSE): 4878882.7936019115\n", + "Mean Absolute Error (MAE): 1512.1292774395874\n", + "R^2: 0.9799595832047417\n", + "==================================================\n", + "Random Seed: 49\n", + "Mean Squared Error (MSE): 4944756.725469218\n", + "Mean Absolute Error (MAE): 1516.6677066154225\n", + "R^2: 0.9796890005516199\n", + "==================================================\n", + "Random Seed: 50\n", + "Mean Squared Error (MSE): 4884124.364671988\n", + "Mean Absolute Error (MAE): 1491.0363808401864\n", + "R^2: 0.9799380530156903\n", + "==================================================\n", + "Random Seed: 51\n", + "Mean Squared Error (MSE): 5088799.974360001\n", + "Mean Absolute Error (MAE): 1540.7020477233496\n", + "R^2: 0.9790973309283816\n", + "==================================================\n", + "Random Seed: 52\n", + "Mean Squared Error (MSE): 5094732.724666868\n", + "Mean Absolute Error (MAE): 1536.280245321759\n", + "R^2: 0.9790729616631375\n", + "==================================================\n", + "Random Seed: 53\n", + "Mean Squared Error (MSE): 5072032.670115131\n", + "Mean Absolute Error (MAE): 1528.4275352612244\n", + "R^2: 0.9791662040249111\n", + "==================================================\n", + "Random Seed: 54\n", + "Mean Squared Error (MSE): 5106477.358884996\n", + "Mean Absolute Error (MAE): 1550.5073866559203\n", + "R^2: 0.9790247196014205\n", + "==================================================\n", + "Random Seed: 55\n", + "Mean Squared Error (MSE): 5155517.748625781\n", + "Mean Absolute Error (MAE): 1526.5272978415223\n", + "R^2: 0.9788232821224353\n", + "==================================================\n", + "Random Seed: 56\n", + "Mean Squared Error (MSE): 5225642.893515378\n", + "Mean Absolute Error (MAE): 1545.9093766253407\n", + "R^2: 0.9785352372582999\n", + "==================================================\n", + "Random Seed: 57\n", + "Mean Squared Error (MSE): 5155877.991429296\n", + "Mean Absolute Error (MAE): 1544.0122046450124\n", + "R^2: 0.9788218023951627\n", + "==================================================\n", + "Random Seed: 58\n", + "Mean Squared Error (MSE): 5586133.094175109\n", + "Mean Absolute Error (MAE): 1647.0111034647227\n", + "R^2: 0.9770544937812685\n", + "==================================================\n", + "Random Seed: 59\n", + "Mean Squared Error (MSE): 4986139.598035017\n", + "Mean Absolute Error (MAE): 1526.2033962971482\n", + "R^2: 0.9795190169612186\n", + "==================================================\n", + "Random Seed: 60\n", + "Mean Squared Error (MSE): 5010887.94353227\n", + "Mean Absolute Error (MAE): 1511.7316991821747\n", + "R^2: 0.9794173610740535\n", + "==================================================\n", + "Random Seed: 61\n", + "Mean Squared Error (MSE): 5089101.069076077\n", + "Mean Absolute Error (MAE): 1548.7157393467103\n", + "R^2: 0.9790960941567967\n", + "==================================================\n", + "Random Seed: 62\n", + "Mean Squared Error (MSE): 4842849.996551496\n", + "Mean Absolute Error (MAE): 1512.5262911604484\n", + "R^2: 0.9801075909150594\n", + "==================================================\n", + "Random Seed: 63\n", + "Mean Squared Error (MSE): 4886317.680594506\n", + "Mean Absolute Error (MAE): 1491.7471273630524\n", + "R^2: 0.9799290437881456\n", + "==================================================\n", + "Random Seed: 64\n", + "Mean Squared Error (MSE): 5190463.241374699\n", + "Mean Absolute Error (MAE): 1544.9776545234156\n", + "R^2: 0.9786797406049547\n", + "==================================================\n", + "Random Seed: 65\n", + "Mean Squared Error (MSE): 5336497.608364749\n", + "Mean Absolute Error (MAE): 1560.2265114883173\n", + "R^2: 0.9780798922985452\n", + "==================================================\n", + "Random Seed: 66\n", + "Mean Squared Error (MSE): 5117403.451612183\n", + "Mean Absolute Error (MAE): 1554.5079128613224\n", + "R^2: 0.9789798397669461\n", + "==================================================\n", + "Random Seed: 67\n", + "Mean Squared Error (MSE): 5003648.993552195\n", + "Mean Absolute Error (MAE): 1540.9579727858431\n", + "R^2: 0.9794470956630769\n", + "==================================================\n", + "Random Seed: 68\n", + "Mean Squared Error (MSE): 5155804.845874008\n", + "Mean Absolute Error (MAE): 1542.433922405877\n", + "R^2: 0.9788221028466136\n", + "==================================================\n", + "Random Seed: 69\n", + "Mean Squared Error (MSE): 5166891.650160409\n", + "Mean Absolute Error (MAE): 1565.7484649996768\n", + "R^2: 0.9787765628760453\n", + "==================================================\n", + "Random Seed: 70\n", + "Mean Squared Error (MSE): 4881550.699650055\n", + "Mean Absolute Error (MAE): 1513.0763327329369\n", + "R^2: 0.97994862455879\n", + "==================================================\n", + "Random Seed: 71\n", + "Mean Squared Error (MSE): 5160105.188025443\n", + "Mean Absolute Error (MAE): 1551.9087417594546\n", + "R^2: 0.9788044388336167\n", + "==================================================\n", + "Random Seed: 72\n", + "Mean Squared Error (MSE): 5137333.235666619\n", + "Mean Absolute Error (MAE): 1548.2014720468492\n", + "R^2: 0.9788979765216118\n", + "==================================================\n", + "Random Seed: 73\n", + "Mean Squared Error (MSE): 4768115.97634129\n", + "Mean Absolute Error (MAE): 1483.8083762392123\n", + "R^2: 0.9804145671178413\n", + "==================================================\n", + "Random Seed: 74\n", + "Mean Squared Error (MSE): 5023008.485818054\n", + "Mean Absolute Error (MAE): 1521.9031894655316\n", + "R^2: 0.9793675749386886\n", + "==================================================\n", + "Random Seed: 75\n", + "Mean Squared Error (MSE): 5097266.649951574\n", + "Mean Absolute Error (MAE): 1546.2731293080378\n", + "R^2: 0.9790625533543132\n", + "==================================================\n", + "Random Seed: 76\n", + "Mean Squared Error (MSE): 4993552.388476675\n", + "Mean Absolute Error (MAE): 1510.0524784409095\n", + "R^2: 0.979488568308043\n", + "==================================================\n", + "Random Seed: 77\n", + "Mean Squared Error (MSE): 4872548.231308048\n", + "Mean Absolute Error (MAE): 1476.6568609631247\n", + "R^2: 0.9799856029461365\n", + "==================================================\n", + "Random Seed: 78\n", + "Mean Squared Error (MSE): 5087506.013243095\n", + "Mean Absolute Error (MAE): 1549.365416915502\n", + "R^2: 0.9791026459812731\n", + "==================================================\n", + "Random Seed: 79\n", + "Mean Squared Error (MSE): 5059670.811228722\n", + "Mean Absolute Error (MAE): 1529.2665317477906\n", + "R^2: 0.9792169813882806\n", + "==================================================\n", + "Random Seed: 80\n", + "Mean Squared Error (MSE): 4925669.526916264\n", + "Mean Absolute Error (MAE): 1476.967871474277\n", + "R^2: 0.9797674028069389\n", + "==================================================\n", + "Random Seed: 81\n", + "Mean Squared Error (MSE): 5182126.382862732\n", + "Mean Absolute Error (MAE): 1561.1292664846378\n", + "R^2: 0.9787139849445732\n", + "==================================================\n", + "Random Seed: 82\n", + "Mean Squared Error (MSE): 5149601.716364427\n", + "Mean Absolute Error (MAE): 1523.0675330945655\n", + "R^2: 0.9788475827169172\n", + "==================================================\n", + "Random Seed: 83\n", + "Mean Squared Error (MSE): 5221542.344806699\n", + "Mean Absolute Error (MAE): 1553.2458163977553\n", + "R^2: 0.9785520806031164\n", + "==================================================\n", + "Random Seed: 84\n", + "Mean Squared Error (MSE): 5298452.933056824\n", + "Mean Absolute Error (MAE): 1610.7965068676817\n", + "R^2: 0.9782361639661104\n", + "==================================================\n", + "Random Seed: 85\n", + "Mean Squared Error (MSE): 5192756.58345476\n", + "Mean Absolute Error (MAE): 1565.1730014485624\n", + "R^2: 0.9786703205116499\n", + "==================================================\n", + "Random Seed: 86\n", + "Mean Squared Error (MSE): 4979128.413230179\n", + "Mean Absolute Error (MAE): 1547.4548892661837\n", + "R^2: 0.979547815985844\n", + "==================================================\n", + "Random Seed: 87\n", + "Mean Squared Error (MSE): 4995333.431443561\n", + "Mean Absolute Error (MAE): 1501.0912248827842\n", + "R^2: 0.9794812525259476\n", + "==================================================\n", + "Random Seed: 88\n", + "Mean Squared Error (MSE): 4954372.745944623\n", + "Mean Absolute Error (MAE): 1508.7551970190004\n", + "R^2: 0.9796495019478634\n", + "==================================================\n", + "Random Seed: 89\n", + "Mean Squared Error (MSE): 5196425.735573604\n", + "Mean Absolute Error (MAE): 1528.0508657662886\n", + "R^2: 0.9786552491642007\n", + "==================================================\n", + "Random Seed: 90\n", + "Mean Squared Error (MSE): 4915712.212919695\n", + "Mean Absolute Error (MAE): 1473.1176265213446\n", + "R^2: 0.9798083033022149\n", + "==================================================\n", + "Random Seed: 91\n", + "Mean Squared Error (MSE): 4780981.655278671\n", + "Mean Absolute Error (MAE): 1497.514691352214\n", + "R^2: 0.9803617202717155\n", + "==================================================\n", + "Random Seed: 92\n", + "Mean Squared Error (MSE): 5055109.728302232\n", + "Mean Absolute Error (MAE): 1525.6744677913232\n", + "R^2: 0.979235716415694\n", + "==================================================\n", + "Random Seed: 93\n", + "Mean Squared Error (MSE): 5304119.148499988\n", + "Mean Absolute Error (MAE): 1594.9860156216166\n", + "R^2: 0.9782128895149836\n", + "==================================================\n", + "Random Seed: 94\n", + "Mean Squared Error (MSE): 5079296.914582357\n", + "Mean Absolute Error (MAE): 1524.7130759765387\n", + "R^2: 0.9791363655366784\n", + "==================================================\n", + "Random Seed: 95\n", + "Mean Squared Error (MSE): 5207832.197105448\n", + "Mean Absolute Error (MAE): 1577.7686816790904\n", + "R^2: 0.9786083961749145\n", + "==================================================\n", + "Random Seed: 96\n", + "Mean Squared Error (MSE): 5262963.205168681\n", + "Mean Absolute Error (MAE): 1574.8502435843855\n", + "R^2: 0.9783819409746832\n", + "==================================================\n", + "Random Seed: 97\n", + "Mean Squared Error (MSE): 5058405.145946968\n", + "Mean Absolute Error (MAE): 1508.7756794842323\n", + "R^2: 0.9792221802136762\n", + "==================================================\n", + "Random Seed: 98\n", + "Mean Squared Error (MSE): 4975929.326361981\n", + "Mean Absolute Error (MAE): 1510.3945385652708\n", + "R^2: 0.9795609565011864\n", + "==================================================\n", + "Random Seed: 99\n", + "Mean Squared Error (MSE): 5504875.429337279\n", + "Mean Absolute Error (MAE): 1602.0557165047603\n", + "R^2: 0.9773882663968547\n", + "==================================================\n" + ] + } + ], + "source": [ + "rf_best_param = grid_search.best_params_\n", + "\n", + "results = []\n", + "for seed in range(100):\n", + " rf = RandomForestRegressor(random_state=seed, **rf_best_param)\n", + " rf.fit(X_train, y_train)\n", + " \n", + " y_pred = rf.predict(X_test)\n", + " mse = mean_squared_error(y_test, y_pred)\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " r2 = r2_score(y_test, y_pred)\n", + " \n", + " results.append({\n", + " 'seed': seed,\n", + " 'mse': mse,\n", + " 'mae': mae,\n", + " 'r2': r2\n", + " })\n", + "for result in results:\n", + " print(f\"Random Seed: {result['seed']}\")\n", + " print(f\"Mean Squared Error (MSE): {result['mse']}\")\n", + " print(f\"Mean Absolute Error (MAE): {result['mae']}\")\n", + " print(f\"R^2: {result['r2']}\")\n", + " print(\"=\"*50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Model based on MSE:\n", + "Random Seed: 73\n", + "Mean Squared Error (MSE): 4768115.97634129\n", + "Mean Absolute Error (MAE): 1483.8083762392123\n", + "R^2: 0.9804145671178413\n", + "==================================================\n" + ] + } + ], + "source": [ + "best_rf_seed = min(results, key=lambda x: x['mse'])\n", + "\n", + "print(\"Best Model based on MSE:\")\n", + "print(f\"Random Seed: {best_rf_seed['seed']}\")\n", + "print(f\"Mean Squared Error (MSE): {best_rf_seed['mse']}\")\n", + "print(f\"Mean Absolute Error (MAE): {best_rf_seed['mae']}\")\n", + "print(f\"R^2: {best_rf_seed['r2']}\")\n", + "print(\"=\"*50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n", + "Mean Squared Error: 420941.00918314257\n", + "Best Params: {'alpha': 75, 'max_iter': 25000, 'tol': 0.001}\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "btc_all = pd.read_csv('combined_btc2_data.csv')\n", + "reviews = pd.read_csv('trustpilot_coinbase_reviews-strip.csv', sep='|')\n", + "\n", + "reviews['Date'] = pd.to_datetime(reviews['Date'], errors='coerce')\n", + "reviews = reviews.dropna(subset=['Date'])\n", + "reviews['YearMonth'] = reviews['Date'].dt.to_period('M')\n", + "monthly_mean_rating = reviews.groupby('YearMonth')['Rating'].mean().reset_index()\n", + "monthly_mean_rating['Date'] = monthly_mean_rating['YearMonth'].dt.to_timestamp()\n", + "\n", + "btc_all['Date'] = pd.to_datetime(btc_all['Date'])\n", + "btc_all['YearMonth'] = btc_all['Date'].dt.to_period('M')\n", + "\n", + "btc_all = pd.merge(btc_all, monthly_mean_rating, on='YearMonth', how='left')\n", + "btc_all['Date'] = btc_all['Date_x']\n", + "btc_all = btc_all.drop(columns=['Date_x', 'Date_y'])\n", + "btc_all['Rating'].fillna(method='ffill', inplace=True)\n", + "\n", + "btc_all['Lag_Close_1'] = btc_all['Close'].shift(1)\n", + "btc_all['Lag_Close_2'] = btc_all['Close'].shift(2)\n", + "btc_all['Lag_Volume_1'] = btc_all['Volume'].shift(1)\n", + "btc_all['Lag_Volume_2'] = btc_all['Volume'].shift(2)\n", + "btc_all['Lag_Open_1'] = btc_all['Open'].shift(1)\n", + "btc_all['Lag_Open_2'] = btc_all['Open'].shift(2)\n", + "btc_all['Lag_High_1'] = btc_all['High'].shift(1)\n", + "btc_all['Lag_High_2'] = btc_all['High'].shift(2)\n", + "btc_all['Lag_Low_1'] = btc_all['Low'].shift(1)\n", + "btc_all['Lag_Low_2'] = btc_all['Low'].shift(2)\n", + "\n", + "btc_all.dropna(inplace=True)\n", + "\n", + "btc_all['Day'] = btc_all['Date'].dt.day\n", + "btc_all['Month'] = btc_all['Date'].dt.month\n", + "btc_all['Year'] = btc_all['Date'].dt.year\n", + "\n", + "X = btc_all[['Lag_Close_1', 'Lag_Close_2', 'Lag_Volume_1', 'Lag_Volume_2', 'Lag_Open_1', 'Lag_Open_2', 'Lag_High_1', 'Lag_High_2', 'Lag_Low_1', 'Lag_Low_2', 'Open', 'High', 'Low', 'Day', 'Month', 'Year', 'Rating']]\n", + "y = btc_all['Close']\n", + "\n", + "assert len(X) == len(y), \"Feature and target arrays do not have the same length\"\n", + "\n", + "split_point = int(len(btc_all) * 0.85)\n", + "X_train = X.iloc[:split_point].copy()\n", + "X_test = X.iloc[split_point:].copy()\n", + "y_train = y.iloc[:split_point].copy()\n", + "y_test = y.iloc[split_point:].copy()\n", + "\n", + "assert len(X_train) == len(y_train), \"Training feature and target arrays do not have the same length\"\n", + "assert len(X_test) == len(y_test), \"Test feature and target arrays do not have the same length\"\n", + "\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "lasso = Lasso(random_state=0)\n", + "\n", + "param_grid = {\n", + " 'alpha': [75, 100, 130], # 75 is lowest without convergence warning\n", + " 'max_iter': [25000, 100000, 300000, 500000],\n", + " 'tol': [0.0001, 0.00001, 0.001, 0.01]\n", + "}\n", + "\n", + "grid_search = GridSearchCV(estimator=lasso, param_grid=param_grid, cv=5, n_jobs=-1, scoring='neg_mean_squared_error', verbose=2)\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "best_model = grid_search.best_estimator_\n", + "y_pred = best_model.predict(X_test)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "\n", + "print(f'Mean Squared Error: {mse}')\n", + "print(f'Best Params: {grid_search.best_params_}')\n", + "lasso_params = grid_search.best_params_\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lasso 0:\n", + "Mean Squared (MSE): 299080.84\n", + "Mean Absolute Error (MAE): 370.32\n", + "R^2: 1.00\n", + "Mean Absolute % (MAPE): 0.82%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "split_point = int(len(btc_all) * 0.85)\n", + "X_train = X.iloc[:split_point].copy()\n", + "X_test = X.iloc[split_point:].copy()\n", + "y_train = y.iloc[:split_point].copy()\n", + "y_test = y.iloc[split_point:].copy()\n", + "\n", + "lasso = Lasso(**lasso_params, random_state=0)\n", + "lasso.fit(X_train, y_train)\n", + "y_pred = lasso.predict(X_test)\n", + "eval_regression(X_train, X_test, y_train, y_test, best_model, \"Lasso 0:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lasso su Convergence perspėjimu (overfitting):
\n", + "- Mean Squared (MSE): 162928.95 \n", + "- Mean Absolute Error (MAE): 282.09 \n", + "- Mean Absolute % (MAPE): 0.64% " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'alpha': 75, 'max_iter': 25000, 'tol': 0.001}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lasso_params" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lasso 0:\n", + "Mean Squared (MSE): 299080.84\n", + "Mean Absolute Error (MAE): 370.32\n", + "R^2: 1.00\n", + "Mean Absolute % (MAPE): 0.82%\n", + "==================================================\n", + "\n" + ] + } + ], + "source": [ + "split_point = int(len(btc_all) * 0.85)\n", + "X_train = X.iloc[:split_point].copy()\n", + "X_test = X.iloc[split_point:].copy()\n", + "y_train = y.iloc[:split_point].copy()\n", + "y_test = y.iloc[split_point:].copy()\n", + "\n", + "lasso = Lasso(**lasso_params, random_state=0) \n", + "lasso.fit(X_train, y_train)\n", + "y_pred = lasso.predict(X_test)\n", + "eval_regression(X_train, X_test, y_train, y_test, best_model, \"Lasso 0:\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ^ net ir be convergence klaidos (eliminavus \"overfitting\"), Lasso (linear regression + regularization) matome kad susitvarko geriausiai ir rodo geriausius rezultatus, lenkiant RandomForest. Lasso atveju random_state su mūsų duomenimis įtakos nedaro, tad seed paieškos žingsį praleidžiu. " + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}