diff --git "a/data/notebooks/B_model_training.ipynb" "b/data/notebooks/B_model_training.ipynb" new file mode 100644--- /dev/null +++ "b/data/notebooks/B_model_training.ipynb" @@ -0,0 +1,5311 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook B: Model Training\n", + "This notebook trains 6 algoritms to predict the production rates of 5 outputs of syngas fermentation based on the extracellular metabolite concentration, and gas composition." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up imports" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import sklearn.preprocessing, sklearn.neural_network, sklearn.svm, sklearn.ensemble, sklearn\n", + "import pickle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data that was generated in notebook A" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the rates data: 836 rows by 18 columns\n" + ] + } + ], + "source": [ + "rates_df = pd.read_csv(f'../data/rates_data.csv')\n", + "print(f'Shape of the rates data: {rates_df.shape[0]} rows by {rates_df.shape[1]} columns')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create train and test sets " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the training data: 657 rows by 18 columns\n", + "Shape of the testing data: 179 rows by 18 columns\n" + ] + } + ], + "source": [ + "train_data = rates_df[rates_df.composition.isin([1,2,3,4,5,6,7])]\n", + "test_data = rates_df[rates_df.composition.isin([8,9,10])]\n", + "print(f'Shape of the training data: {train_data.shape[0]} rows by {train_data.shape[1]} columns')\n", + "print(f'Shape of the testing data: {test_data.shape[0]} rows by {test_data.shape[1]} columns')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a function that generates the input and output arrays for scikit learn's API" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def get_X_y_arrays(data):\n", + "\n", + " # prevent set with copy error\n", + " data_copy = data.copy()\n", + " \n", + " # ML input\n", + " X = data_copy [[\n", + " 'biomass (g/L)', 'ethanol (mM)', 'acetate (mM)', 'butanol (mM)', \n", + " 'butyrate (mM)', 'N2', 'CO', 'CO2', 'H2', 'flow rate (mL/min)'\n", + " ]]\n", + " \n", + " # ML output\n", + " y = data_copy [[\n", + " 'biomass rate', 'ethanol rate', 'acetate rate', 'butanol rate', 'butyrate rate'\n", + " ]]\n", + " \n", + " return np.array(X), np.array(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of the train X array: 657 rows by 10 columns\n", + "Shape of the trainn y array: 657 rows by 5 columns\n", + "Shape of the test X array: 179 rows by 10 columns\n", + "Shape of the test y array: 179 rows by 5 columns\n" + ] + } + ], + "source": [ + "X_train, y_train = get_X_y_arrays(train_data)\n", + "X_test, y_test = get_X_y_arrays(test_data)\n", + "\n", + "print(f'Shape of the train X array: {X_train.shape[0]} rows by {X_train.shape[1]} columns')\n", + "print(f'Shape of the trainn y array: {y_train.shape[0]} rows by {y_train.shape[1]} columns')\n", + "print(f'Shape of the test X array: {X_test.shape[0]} rows by {X_test.shape[1]} columns')\n", + "print(f'Shape of the test y array: {y_test.shape[0]} rows by {y_test.shape[1]} columns')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train 30 different models (5 outputs each modeled with 6 algorithms)\n", + "algorithms = neural network, support vector machine, random forest, support vector, neural net, lasso
\n", + "outputs = acetate, biomass, butanol, butyrate, ethanol" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a functions to generate neural network architectures" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def gen_NN_fixed_n_layers(n_layers, n_neurons, neuron_step):\n", + " \"\"\"Generate NN hidden_layer_sizes of n_layers and up to n_neurons per layer \n", + " \"\"\"\n", + " # print (n_layers)\n", + " if n_layers == 1: \n", + " return [[i] for i in range(neuron_step, n_neurons+1, neuron_step)]\n", + " else:\n", + " pairs = [ (i, tail) for tail in gen_NN_fixed_n_layers(n_layers-1, n_neurons+1, neuron_step) for i in range(neuron_step, n_neurons+1, neuron_step) ]\n", + " return [[i]+ t for (i, t) in pairs]\n", + "\n", + "# print (gen_NN_fixed_n_layers(4, 10, 5))\n", + "\n", + "def gen_NN_uni(n_layers, n_neurons, layer_step, neuron_step):\n", + " \"\"\"Generate hidden layers of various number of layers and number of neurons \n", + " \"\"\" \n", + " various_NNs = [ gen_NN_fixed_n_layers(i , n_neurons, neuron_step) for i in range(2, n_layers+1, layer_step)]\n", + " return functools.reduce(operator.add, various_NNs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a model configuration dictionary to guide ML training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test grid is used for debugging, should be replaced with full grid" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model_cfgs = {\n", + " \"nn\":{\n", + " 'estimator': sklearn.neural_network.MLPRegressor(shuffle=True),\n", + " # Test grid\n", + " 'param_grid': {\n", + " 'activation': ['tanh', 'logistic', 'relu'], \n", + " 'max_iter': [400*i for i in range(1, 2)]\n", + " }\n", + " # Full grid\n", + " # 'param_grid': {\n", + " # 'hidden_layer_sizes': gen_NN_uni(5, 100, 1, 10), \n", + " # 'activation': ['tanh', 'logistic', 'relu'], \n", + " # 'max_iter': [400*i for i in range(1, 10, 2)]\n", + " # } \n", + " },\n", + " \"svm_rbf\":{\n", + " 'estimator': sklearn.svm.SVR(kernel='rbf'),\n", + " # Test grid\n", + " 'param_grid': {\n", + " 'C': [10**i for i in range(-1, 1)], \n", + " 'epsilon': [10**i for i in range(-1, 1)],\n", + " }\n", + " # Full grid\n", + " # 'param_grid': {\n", + " # 'C': [10**i for i in range(-5, 5)], \n", + " # 'epsilon': [10**i for i in range(-5, 5)],\n", + " # 'gamma': [10**i for i in range(-5, 5)] # gamma gave me an error\n", + " # }\n", + " },\n", + " \"rf\":{\n", + " 'estimator': sklearn.ensemble.RandomForestRegressor(),\n", + " # Test grid\n", + " 'param_grid': {\n", + " 'n_estimators': [10*i for i in range(1, 2)],\n", + " 'max_depth': [2*i for i in range(1, 1+1)],\n", + " }\n", + " # Full grid \n", + " # 'param_grid': {\n", + " # 'n_estimators': [10*i for i in range(1, 20)],\n", + " # 'max_depth': [2*i for i in range(20)], \n", + " # 'max_samples': [0.05*i for i in range(1, 10+1)] # max samples gave me an error\n", + " # }\n", + " },\n", + " 'en': {\n", + " 'estimator': sklearn.linear_model.ElasticNet(),\n", + " # Test grid\n", + " 'param_grid': {\n", + " 'alpha': [0.0001, 0.001, 0.01, 0.1],\n", + " 'l1_ratio': [0.1, 1],\n", + " }\n", + " # Full grid \n", + " # 'param_grid': {\n", + " # 'alpha': [0.0001, 0.001, 0.01, 0.1],\n", + " # 'l1_ratio': [0.1, 1],\n", + " #}\n", + " },\n", + " 'lasso': {\n", + " 'estimator': sklearn.linear_model.Lasso(),\n", + " # Test grid\n", + " 'param_grid': {\n", + " 'alpha': [0.0001, 0.001, 0.01, 0.1],\n", + " }\n", + " # Full grid \n", + " # 'param_grid': {\n", + " # 'alpha': [0.0001, 0.001, 0.01, 0.1],\n", + " # }\n", + " },\n", + " 'knn': {\n", + " 'estimator': sklearn.neighbors.KNeighborsRegressor(),\n", + " # Test grid\n", + " 'param_grid': {\n", + " 'algorithm': ['ball_tree', ],\n", + " 'leaf_size': [4,5,6],\n", + " 'n_neighbors': [2,3,4],\n", + " 'weights': ['distance'],\n", + " }\n", + " # Full grid \n", + " # 'param_grid': {\n", + " # 'algorithm': [0.0001, 0.001, 0.01, 0.1],\n", + " # 'leaf_size': [4, 5, 6],\n", + " # 'n_neighbors': [2, 3, 4],\n", + " # 'weights': ['distance'],\n", + " # }\n", + " },\n", + " \"bayesian\":{\n", + " 'estimator': sklearn.linear_model.BayesianRidge(),\n", + " 'param_grid': {\n", + " 'n_iter': [300, 500], \n", + " 'alpha_1': [10**i for i in range(-1, 1)], \n", + " 'alpha_2': [10**i for i in range(-1, 1)], \n", + " 'lambda_1': [10**i for i in range(-1, 1)], \n", + " 'lambda_2': [10**i for i in range(-1, 1)], \n", + " }\n", + " },\n", + " \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform grid search for each output and algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "biomass\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "[CV 1/10] END ...activation=tanh, max_iter=400;, score=-0.144 total time= 0.5s\n", + "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.213 total time= 0.5s\n", + "[CV 2/10] END ...activation=tanh, max_iter=400;, score=-0.938 total time= 0.5s\n", + "[CV 7/10] END ...activation=tanh, max_iter=400;, score=-0.283 total time= 0.5s\n", + "[CV 4/10] END ...activation=tanh, max_iter=400;, score=-1.635 total time= 0.6s\n", + "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.218 total time= 0.6s\n", + "[CV 10/10] END activation=logistic, max_iter=400;, score=0.330 total time= 0.3s\n", + "[CV 5/10] END ...activation=tanh, max_iter=400;, score=-0.770 total time= 0.7s\n", + "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.042 total time= 0.6s\n", + "[CV 8/10] END ...activation=tanh, max_iter=400;, score=-0.878 total time= 0.7s\n", + "[CV 10/10] END ..activation=tanh, max_iter=400;, score=-0.068 total time= 0.4s\n", + "[CV 1/10] END activation=logistic, max_iter=400;, score=0.406 total time= 0.3s\n", + "[CV 4/10] END activation=logistic, max_iter=400;, score=0.570 total time= 0.2s\n", + "[CV 2/10] END activation=logistic, max_iter=400;, score=0.346 total time= 0.2s\n", + "[CV 5/10] END activation=logistic, max_iter=400;, score=0.650 total time= 0.1s\n", + "[CV 3/10] END activation=logistic, max_iter=400;, score=0.390 total time= 0.2s\n", + "[CV 4/10] END ...activation=relu, max_iter=400;, score=-3.995 total time= 1.0s\n", + "[CV 6/10] END activation=logistic, max_iter=400;, score=0.787 total time= 0.1s\n", + "[CV 7/10] END activation=logistic, max_iter=400;, score=0.576 total time= 0.2s\n", + "[CV 9/10] END activation=logistic, max_iter=400;, score=0.458 total time= 0.1s\n", + "[CV 8/10] END activation=logistic, max_iter=400;, score=0.653 total time= 0.2s\n", + "[CV 2/10] END ..activation=relu, max_iter=400;, score=-11.889 total time= 1.4s\n", + "[CV 5/10] END ...activation=relu, max_iter=400;, score=-3.045 total time= 0.4s\n", + "[CV 3/10] END ...activation=relu, max_iter=400;, score=-5.220 total time= 0.4s\n", + "[CV 10/10] END ..activation=relu, max_iter=400;, score=-4.039 total time= 0.4s\n", + "[CV 1/10] END ...activation=relu, max_iter=400;, score=-2.688 total time= 0.5s\n", + "[CV 7/10] END ...activation=relu, max_iter=400;, score=-4.286 total time= 0.5s\n", + "[CV 8/10] END ...activation=relu, max_iter=400;, score=-7.619 total time= 0.5s\n", + "[CV 9/10] END ...activation=relu, max_iter=400;, score=-2.336 total time= 0.6s\n", + "[CV 6/10] END ...activation=relu, max_iter=400;, score=-6.897 total time= 0.7s\n", + "Best CV score: 0.517:\n", + "Best parameters: {'activation': 'logistic', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 4/10] END ...............C=0.1, epsilon=0.1;, score=0.238 total time= 0.0s\n", + "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.146 total time= 0.0s\n", + "[CV 6/10] END ...............C=0.1, epsilon=0.1;, score=0.410 total time= 0.0s\n", + "[CV 5/10] END ...............C=0.1, epsilon=0.1;, score=0.374 total time= 0.0s\n", + "[CV 8/10] END ...............C=0.1, epsilon=0.1;, score=0.296 total time= 0.0s\n", + "[CV 6/10] END ................C=0.1, epsilon=1;, score=-0.576 total time= 0.0s\n", + "[CV 7/10] END ...............C=0.1, epsilon=0.1;, score=0.318 total time= 0.0s\n", + "[CV 10/10] END ..............C=0.1, epsilon=0.1;, score=0.142 total time= 0.0s\n", + "[CV 7/10] END ................C=0.1, epsilon=1;, score=-0.583 total time= 0.0s\n", + "[CV 9/10] END ...............C=0.1, epsilon=0.1;, score=0.162 total time= 0.0s\n", + "[CV 8/10] END ................C=0.1, epsilon=1;, score=-0.642 total time= 0.0s\n", + "[CV 1/10] END ................C=0.1, epsilon=1;, score=-0.583 total time= 0.0s\n", + "[CV 9/10] END ................C=0.1, epsilon=1;, score=-0.171 total time= 0.0s\n", + "[CV 2/10] END ................C=0.1, epsilon=1;, score=-0.805 total time= 0.0s\n", + "[CV 10/10] END ...............C=0.1, epsilon=1;, score=-0.435 total time= 0.0s\n", + "[CV 4/10] END ................C=0.1, epsilon=1;, score=-0.510 total time= 0.0s\n", + "[CV 3/10] END ................C=0.1, epsilon=1;, score=-0.394 total time= 0.0s\n", + "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.183 total time= 0.0s\n", + "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.191 total time= 0.0s\n", + "[CV 5/10] END ................C=0.1, epsilon=1;, score=-0.583 total time= 0.0s\n", + "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.363 total time= 0.0s\n", + "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.478 total time= 0.0s\n", + "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.397 total time= 0.0s\n", + "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.335 total time= 0.0s\n", + "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.337 total time= 0.0s\n", + "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.259 total time= 0.0s\n", + "[CV 1/10] END ..................C=1, epsilon=1;, score=-0.583 total time= 0.0s\n", + "[CV 3/10] END ..................C=1, epsilon=1;, score=-0.394 total time= 0.0s\n", + "[CV 4/10] END ..................C=1, epsilon=1;, score=-0.510 total time= 0.0s\n", + "[CV 5/10] END ..................C=1, epsilon=1;, score=-0.583 total time= 0.0s\n", + "[CV 6/10] END ..................C=1, epsilon=1;, score=-0.576 total time= 0.0s\n", + "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.215 total time= 0.0s\n", + "[CV 7/10] END ..................C=1, epsilon=1;, score=-0.583 total time= 0.0s\n", + "[CV 8/10] END ..................C=1, epsilon=1;, score=-0.642 total time= 0.0s\n", + "[CV 9/10] END ..................C=1, epsilon=1;, score=-0.171 total time= 0.0s\n", + "[CV 10/10] END .................C=1, epsilon=1;, score=-0.435 total time= 0.0s\n", + "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.493 total time= 0.0s\n", + "[CV 2/10] END ..................C=1, epsilon=1;, score=-0.805 total time= 0.0s\n", + "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.129 total time= 0.0s\n", + "[CV 1/10] END ...............C=0.1, epsilon=0.1;, score=0.147 total time= 0.0s\n", + "Best CV score: 0.325:\n", + "Best parameters: {'C': 1, 'epsilon': 0.1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.403 total time= 0.0s\n", + "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.081 total time= 0.0s\n", + "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.214 total time= 0.0s\n", + "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.314 total time= 0.0s\n", + "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.451 total time= 0.0s\n", + "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.282 total time= 0.0s\n", + "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.271 total time= 0.0s\n", + "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.451 total time= 0.0s\n", + "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.378 total time= 0.0s\n", + "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.323 total time= 0.0s\n", + "Best CV score: 0.317:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.196 total time= 0.0s\n", + "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.273 total time= 0.0s\n", + "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.229 total time= 0.0s\n", + "[CV 5/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.398 total time= 0.0s\n", + "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.408 total time= 0.0s\n", + "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.199 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.278 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.219 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.0001, l1_ratio=1;, score=0.399 total time= 0.0s\n", + "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.405 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.355 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.282 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.188 total time= 0.0s\n", + "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.201 total time= 0.0s\n", + "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.282 total time= 0.0s\n", + "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.204 total time= 0.0s\n", + "[CV 5/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.400 total time= 0.0s\n", + "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.399 total time= 0.0s\n", + "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.349 total time= 0.0s\n", + "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.276 total time= 0.0s\n", + "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.185 total time= 0.0s\n", + "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.189 total time= 0.0s\n", + "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.258 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.115 total time= 0.0s\n", + "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.139 total time= 0.0s\n", + "[CV 5/10] END ..........alpha=0.001, l1_ratio=1;, score=0.385 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.238 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.160 total time= 0.0s\n", + "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.123 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.189 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.258 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.115 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.139 total time= 0.0s\n", + "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.359 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.306 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.238 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.160 total time= 0.0s\n", + "[CV 10/10] END ........alpha=0.01, l1_ratio=0.1;, score=0.123 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.306 total time= 0.0s\n", + "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.175 total time= 0.0s\n", + "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.250 total time= 0.0s\n", + "[CV 3/10] END ...........alpha=0.01, l1_ratio=1;, score=0.089 total time= 0.0s\n", + "[CV 4/10] END ...........alpha=0.01, l1_ratio=1;, score=0.141 total time= 0.0s\n", + "[CV 5/10] END ...........alpha=0.01, l1_ratio=1;, score=0.354 total time= 0.0s\n", + "[CV 6/10] END ...........alpha=0.01, l1_ratio=1;, score=0.306 total time= 0.0s\n", + "[CV 7/10] END ...........alpha=0.01, l1_ratio=1;, score=0.282 total time= 0.0s\n", + "[CV 8/10] END ...........alpha=0.01, l1_ratio=1;, score=0.211 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.385 total time= 0.0s\n", + "[CV 9/10] END ...........alpha=0.01, l1_ratio=1;, score=0.142 total time= 0.0s\n", + "[CV 10/10] END ..........alpha=0.01, l1_ratio=1;, score=0.117 total time= 0.0s\n", + "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.175 total time= 0.0s\n", + "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.249 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.089 total time= 0.0s\n", + "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.141 total time= 0.0s\n", + "[CV 5/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.353 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.305 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.282 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.211 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.142 total time= 0.0s\n", + "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.117 total time= 0.0s\n", + "[CV 1/10] END ...........alpha=0.1, l1_ratio=1;, score=-0.001 total time= 0.0s\n", + "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.040 total time= 0.0s\n", + "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.018 total time= 0.0s\n", + "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.053 total time= 0.0s\n", + "[CV 5/10] END ............alpha=0.1, l1_ratio=1;, score=0.024 total time= 0.0s\n", + "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.044 total time= 0.0s\n", + "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.047 total time= 0.0s\n", + "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.041 total time= 0.0s\n", + "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.026 total time= 0.0s\n", + "[CV 10/10] END ..........alpha=0.1, l1_ratio=1;, score=-0.013 total time= 0.0s\n", + "Best CV score: 0.261:\n", + "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 1/10] END .....................alpha=0.0001;, score=0.199 total time= 0.0s\n", + "[CV 2/10] END .....................alpha=0.0001;, score=0.278 total time= 0.0s\n", + "[CV 3/10] END .....................alpha=0.0001;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END .....................alpha=0.0001;, score=0.219 total time= 0.0s\n", + "[CV 5/10] END .....................alpha=0.0001;, score=0.399 total time= 0.0s\n", + "[CV 6/10] END .....................alpha=0.0001;, score=0.405 total time= 0.0s\n", + "[CV 7/10] END .....................alpha=0.0001;, score=0.355 total time= 0.0s\n", + "[CV 8/10] END .....................alpha=0.0001;, score=0.282 total time= 0.0s\n", + "[CV 9/10] END .....................alpha=0.0001;, score=0.188 total time= 0.0s\n", + "[CV 10/10] END ....................alpha=0.0001;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END ......................alpha=0.001;, score=0.189 total time= 0.0s\n", + "[CV 2/10] END ......................alpha=0.001;, score=0.258 total time= 0.0s\n", + "[CV 3/10] END ......................alpha=0.001;, score=0.115 total time= 0.0s\n", + "[CV 4/10] END ......................alpha=0.001;, score=0.139 total time= 0.0s\n", + "[CV 5/10] END ......................alpha=0.001;, score=0.385 total time= 0.0s\n", + "[CV 6/10] END ......................alpha=0.001;, score=0.359 total time= 0.0s\n", + "[CV 7/10] END ......................alpha=0.001;, score=0.306 total time= 0.0s\n", + "[CV 8/10] END ......................alpha=0.001;, score=0.238 total time= 0.0s\n", + "[CV 9/10] END ......................alpha=0.001;, score=0.160 total time= 0.0s\n", + "[CV 10/10] END .....................alpha=0.001;, score=0.123 total time= 0.0s\n", + "[CV 1/10] END .......................alpha=0.01;, score=0.175 total time= 0.0s\n", + "[CV 2/10] END .......................alpha=0.01;, score=0.250 total time= 0.0s\n", + "[CV 3/10] END .......................alpha=0.01;, score=0.089 total time= 0.0s\n", + "[CV 4/10] END .......................alpha=0.01;, score=0.141 total time= 0.0s\n", + "[CV 6/10] END .......................alpha=0.01;, score=0.306 total time= 0.0s\n", + "[CV 7/10] END .......................alpha=0.01;, score=0.282 total time= 0.0s\n", + "[CV 8/10] END .......................alpha=0.01;, score=0.211 total time= 0.0s\n", + "[CV 9/10] END .......................alpha=0.01;, score=0.142 total time= 0.0s\n", + "[CV 10/10] END ......................alpha=0.01;, score=0.117 total time= 0.0s\n", + "[CV 1/10] END .......................alpha=0.1;, score=-0.001 total time= 0.0s\n", + "[CV 2/10] END ........................alpha=0.1;, score=0.040 total time= 0.0s\n", + "[CV 3/10] END ........................alpha=0.1;, score=0.018 total time= 0.0s\n", + "[CV 4/10] END ........................alpha=0.1;, score=0.053 total time= 0.0s\n", + "[CV 5/10] END ........................alpha=0.1;, score=0.024 total time= 0.0s\n", + "[CV 6/10] END ........................alpha=0.1;, score=0.044 total time= 0.0s\n", + "[CV 7/10] END ........................alpha=0.1;, score=0.047 total time= 0.0s\n", + "[CV 8/10] END ........................alpha=0.1;, score=0.041 total time= 0.0s\n", + "[CV 9/10] END ........................alpha=0.1;, score=0.026 total time= 0.0s\n", + "[CV 10/10] END ......................alpha=0.1;, score=-0.013 total time= 0.0s\n", + "[CV 5/10] END .......................alpha=0.01;, score=0.354 total time= 0.0s\n", + "Best CV score: 0.260:\n", + "Best parameters: {'alpha': 0.0001} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.742 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.816 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.867 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.857 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.712 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.828 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.882 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.760 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.830 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.721 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.796 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.803 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.877 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.868 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.767 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.852 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.865 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.821 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.848 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.716 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.810 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.826 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.918 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.883 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.788 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.860 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.854 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.816 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.712 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.742 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.882 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.867 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.803 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.857 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.830 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.828 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.864 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.760 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.830 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.796 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.830 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.877 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.868 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.767 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.852 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.865 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.821 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.848 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.716 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.810 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.826 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.918 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.883 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.788 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.860 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.864 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.854 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.816 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.712 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.742 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.882 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.867 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.803 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.857 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.828 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.760 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.830 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.796 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.830 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.877 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.868 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.767 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.852 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.865 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.821 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.848 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.810 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.826 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.918 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.883 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.788 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.860 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.864 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.854 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.721 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.716 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.721 total time= 0.0s\n", + "Best CV score: 0.837:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.197 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.273 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.228 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.398 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.408 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.358 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.197 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.273 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.228 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.398 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.408 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.358 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.195 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.271 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.135 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.231 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.397 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.409 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.195 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.271 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.135 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.231 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.397 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.409 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.197 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.274 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.227 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.398 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.408 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.358 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.284 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.197 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.274 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.227 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.398 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.358 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.284 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.195 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.271 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.135 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.231 total time= 0.0s[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.397 total time= 0.0s\n", + "\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.409 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.195 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.135 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.271 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.397 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.231 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.408 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.359 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.409 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.198 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.276 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.136 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.399 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.224 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.407 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.357 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.283 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.188 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.198 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.276 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.224 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.399 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.357 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.407 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.283 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.188 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.195 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.135 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.271 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.231 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.397 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.409 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.195 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.135 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.271 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.231 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.397 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.359 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.409 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.198 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.277 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.137 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.223 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.399 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.406 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.356 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.283 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.188 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.198 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.277 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.137 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.223 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.399 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.406 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.356 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.283 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.188 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.196 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.271 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.135 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.230 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.397 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.409 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.359 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.196 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.271 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.135 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.230 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.397 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.409 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.197 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.136 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.273 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.228 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.398 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.408 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.358 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.197 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.273 total time= 0.0s[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.136 total time= 0.0s\n", + "\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.398 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.228 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.408 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.358 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.141 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.195 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.271 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.397 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.135 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.409 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.231 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.359 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.140 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.135 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.195 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.231 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.359 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.271 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.397 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.409 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.197 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.274 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.140 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.398 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.136 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.408 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.227 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.358 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.141 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.284 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.136 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.197 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.227 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.274 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.358 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.398 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.284 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.195 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.408 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.271 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.397 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.135 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.141 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.409 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.231 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.359 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.140 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.135 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.195 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.231 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.271 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.359 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.397 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.409 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.198 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.276 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.140 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.399 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.136 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.407 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.188 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.224 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.357 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.141 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.283 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.198 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.276 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.136 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.224 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.357 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.399 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.283 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.407 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.195 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.188 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.271 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.141 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.397 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.135 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.409 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.231 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.359 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.140 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.135 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.195 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.231 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.271 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.359 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.397 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.409 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.198 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.277 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.399 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.140 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.137 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.406 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.188 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.223 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.356 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.141 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.283 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.137 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.198 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.277 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.223 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.356 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.196 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.399 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.283 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.271 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.406 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.188 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.135 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.141 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.230 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.397 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.409 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.140 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.196 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.271 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.135 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.230 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.397 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.409 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.359 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.285 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.189 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.140 total time= 0.0s\n", + "Best CV score: 0.261:\n", + "Best parameters: {'alpha_1': 0.1, 'alpha_2': 1, 'lambda_1': 1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n", + "ethanol\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "[CV 2/10] END ....activation=relu, max_iter=400;, score=0.847 total time= 2.8s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 3/10] END ....activation=relu, max_iter=400;, score=0.829 total time= 3.1s\n", + "[CV 5/10] END ....activation=relu, max_iter=400;, score=0.894 total time= 3.1s\n", + "[CV 1/10] END ....activation=relu, max_iter=400;, score=0.859 total time= 3.1s\n", + "[CV 4/10] END ....activation=relu, max_iter=400;, score=0.929 total time= 3.1s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 1/10] END activation=logistic, max_iter=400;, score=0.798 total time= 3.6s\n", + "[CV 3/10] END activation=logistic, max_iter=400;, score=0.856 total time= 3.6s\n", + "[CV 2/10] END activation=logistic, max_iter=400;, score=0.853 total time= 3.6s\n", + "[CV 4/10] END activation=logistic, max_iter=400;, score=0.911 total time= 3.6s\n", + "[CV 9/10] END activation=logistic, max_iter=400;, score=0.813 total time= 3.6s\n", + "[CV 5/10] END activation=logistic, max_iter=400;, score=0.864 total time= 3.6s\n", + "[CV 6/10] END activation=logistic, max_iter=400;, score=0.879 total time= 3.6s\n", + "[CV 10/10] END activation=logistic, max_iter=400;, score=0.864 total time= 3.6s\n", + "[CV 7/10] END activation=logistic, max_iter=400;, score=0.804 total time= 3.6s\n", + "[CV 8/10] END activation=logistic, max_iter=400;, score=0.898 total time= 3.6s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 2/10] END ....activation=tanh, max_iter=400;, score=0.894 total time= 4.2s\n", + "[CV 1/10] END ....activation=tanh, max_iter=400;, score=0.870 total time= 4.2s\n", + "[CV 4/10] END ....activation=tanh, max_iter=400;, score=0.944 total time= 4.2s\n", + "[CV 7/10] END ....activation=tanh, max_iter=400;, score=0.853 total time= 4.2s\n", + "[CV 5/10] END ....activation=tanh, max_iter=400;, score=0.909 total time= 4.2s\n", + "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.845 total time= 4.2s\n", + "[CV 8/10] END ....activation=tanh, max_iter=400;, score=0.919 total time= 4.2s\n", + "[CV 10/10] END ...activation=tanh, max_iter=400;, score=0.884 total time= 4.2s\n", + "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.882 total time= 4.2s\n", + "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.917 total time= 4.2s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 6/10] END ....activation=relu, max_iter=400;, score=0.892 total time= 1.8s\n", + "[CV 9/10] END ....activation=relu, max_iter=400;, score=0.838 total time= 1.8s\n", + "[CV 8/10] END ....activation=relu, max_iter=400;, score=0.899 total time= 1.8s\n", + "[CV 10/10] END ...activation=relu, max_iter=400;, score=0.871 total time= 1.8s\n", + "[CV 7/10] END ....activation=relu, max_iter=400;, score=0.795 total time= 1.9s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best CV score: 0.892:\n", + "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 1/10] END ...............C=0.1, epsilon=0.1;, score=0.462 total time= 0.0s\n", + "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.495 total time= 0.0s\n", + "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.433 total time= 0.0s\n", + "[CV 4/10] END ...............C=0.1, epsilon=0.1;, score=0.410 total time= 0.0s\n", + "[CV 5/10] END ...............C=0.1, epsilon=0.1;, score=0.339 total time= 0.1s\n", + "[CV 6/10] END ...............C=0.1, epsilon=0.1;, score=0.443 total time= 0.1s\n", + "[CV 7/10] END ...............C=0.1, epsilon=0.1;, score=0.407 total time= 0.1s\n", + "[CV 1/10] END .................C=0.1, epsilon=1;, score=0.458 total time= 0.0s\n", + "[CV 8/10] END ...............C=0.1, epsilon=0.1;, score=0.414 total time= 0.1s\n", + "[CV 9/10] END ...............C=0.1, epsilon=0.1;, score=0.348 total time= 0.1s\n", + "[CV 2/10] END .................C=0.1, epsilon=1;, score=0.492 total time= 0.0s\n", + "[CV 3/10] END .................C=0.1, epsilon=1;, score=0.434 total time= 0.0s\n", + "[CV 10/10] END ..............C=0.1, epsilon=0.1;, score=0.397 total time= 0.1s\n", + "[CV 4/10] END .................C=0.1, epsilon=1;, score=0.408 total time= 0.0s\n", + "[CV 5/10] END .................C=0.1, epsilon=1;, score=0.341 total time= 0.0s\n", + "[CV 6/10] END .................C=0.1, epsilon=1;, score=0.432 total time= 0.0s\n", + "[CV 7/10] END .................C=0.1, epsilon=1;, score=0.408 total time= 0.0s\n", + "[CV 8/10] END .................C=0.1, epsilon=1;, score=0.409 total time= 0.0s\n", + "[CV 10/10] END ................C=0.1, epsilon=1;, score=0.389 total time= 0.0s\n", + "[CV 9/10] END .................C=0.1, epsilon=1;, score=0.346 total time= 0.0s\n", + "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.714 total time= 0.1s\n", + "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.768 total time= 0.1s\n", + "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.681 total time= 0.1s\n", + "[CV 5/10] END ...................C=1, epsilon=1;, score=0.630 total time= 0.0s\n", + "[CV 2/10] END ...................C=1, epsilon=1;, score=0.773 total time= 0.1s\n", + "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.688 total time= 0.1s\n", + "[CV 6/10] END ...................C=1, epsilon=1;, score=0.677 total time= 0.0s\n", + "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.767 total time= 0.1s\n", + "[CV 4/10] END ...................C=1, epsilon=1;, score=0.760 total time= 0.1s\n", + "[CV 9/10] END ...................C=1, epsilon=1;, score=0.626 total time= 0.0s\n", + "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.666 total time= 0.1s\n", + "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.627 total time= 0.1s\n", + "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.620 total time= 0.1s\n", + "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.623 total time= 0.1s\n", + "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.712 total time= 0.1s\n", + "[CV 10/10] END ..................C=1, epsilon=1;, score=0.662 total time= 0.0s\n", + "[CV 8/10] END ...................C=1, epsilon=1;, score=0.701 total time= 0.0s\n", + "[CV 1/10] END ...................C=1, epsilon=1;, score=0.683 total time= 0.1s\n", + "[CV 3/10] END ...................C=1, epsilon=1;, score=0.718 total time= 0.1s\n", + "[CV 7/10] END ...................C=1, epsilon=1;, score=0.633 total time= 0.1s\n", + "Best CV score: 0.687:\n", + "Best parameters: {'C': 1, 'epsilon': 0.1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.631 total time= 0.0s\n", + "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.753 total time= 0.0s\n", + "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.687 total time= 0.0s\n", + "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.699 total time= 0.0s\n", + "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.705 total time= 0.0s\n", + "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.705 total time= 0.0s\n", + "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.609 total time= 0.0s\n", + "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.758 total time= 0.0s\n", + "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.624 total time= 0.0s\n", + "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.708 total time= 0.0s\n", + "Best CV score: 0.688:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.624 total time= 0.0s\n", + "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.534 total time= 0.0s\n", + "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.645 total time= 0.0s\n", + "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.547 total time= 0.0s\n", + "[CV 5/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.555 total time= 0.0s\n", + "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.531 total time= 0.0s\n", + "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.523 total time= 0.0s\n", + "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.576 total time= 0.0s\n", + "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.474 total time= 0.0s\n", + "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.509 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.534 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.623 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.645 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.547 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.0001, l1_ratio=1;, score=0.554 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.576 total time= 0.0s\n", + "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.532 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.473 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.523 total time= 0.0s\n", + "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.508 total time= 0.0s\n", + "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.534 total time= 0.0s\n", + "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.639 total time= 0.0s\n", + "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.546 total time= 0.0s\n", + "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.632 total time= 0.0s\n", + "[CV 5/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.560 total time= 0.0s\n", + "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.528 total time= 0.0s\n", + "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.516 total time= 0.0s\n", + "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.577 total time= 0.0s\n", + "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.478 total time= 0.0s\n", + "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.513 total time= 0.0s\n", + "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.534 total time= 0.0s\n", + "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.623 total time= 0.0s\n", + "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.645 total time= 0.0s\n", + "[CV 5/10] END ..........alpha=0.001, l1_ratio=1;, score=0.555 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.547 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.576 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.474 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.531 total time= 0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6319.927607690483, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6620.246040393027, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6167.447753496275, tolerance: 5.3515561642098675\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 365.49217076390414, tolerance: 5.252753629993796\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6153.2457714700895, tolerance: 5.345940946064122\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6625.996737154269, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1068.2223959471776, tolerance: 5.339794433705047\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 890.3915175846996, tolerance: 5.282608129133931\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 644.7861336401202, tolerance: 5.326734195711993\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 779.5621121167442, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 787.7290920642754, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 800.6096246299294, tolerance: 5.3515561642098675\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 749.1061108902595, tolerance: 5.345940946064122\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 762.8281143714757, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6616.5969210578605, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6865.090131804768, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6458.217920737769, tolerance: 5.3515561642098675\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1012.0649853789528, tolerance: 5.198433624813183\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5902.451073799269, tolerance: 5.252753629993796\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6434.024907986918, tolerance: 5.345940946064122\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6861.195403478554, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2108.424741007111, tolerance: 5.339794433705047\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5994.308952761697, tolerance: 5.282608129133931\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6029.580945628171, tolerance: 5.326734195711993\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 782.3135226202394, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 790.1314963230161, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 803.3054216148848, tolerance: 5.3515561642098675\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 751.6382247656566, tolerance: 5.345940946064122\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 765.2385899693454, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061.453675739509, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186.590745316627, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6886.35552758309, tolerance: 5.3515561642098675\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6598.009479297723, tolerance: 5.198433624813183\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6523.658353985896, tolerance: 5.252753629993796\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6836.6781675206375, tolerance: 5.345940946064122\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7174.444409408589, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6840.44674355432, tolerance: 5.339794433705047\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6560.63964769235, tolerance: 5.282608129133931\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6618.967981701236, tolerance: 5.326734195711993\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 805.6010349600365, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 809.8189764234066, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 785.1627747566799, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3252.0860010641045, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3186.482690816163, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2997.84313289648, tolerance: 5.3515561642098675\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2548.272162454812, tolerance: 5.198433624813183\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2508.315985565094, tolerance: 5.252753629993796\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2858.2139371914145, tolerance: 5.345940946064122\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3250.649979226062, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2857.334069342067, tolerance: 5.339794433705047\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2756.0505355140285, tolerance: 5.282608129133931\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2655.351402000986, tolerance: 5.326734195711993\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097.125190452523, tolerance: 5.923503688869954\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 779.5621121167442, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 787.7290920642754, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 800.6096246299294, tolerance: 5.3515561642098675\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 749.1061108902595, tolerance: 5.345940946064122\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 762.8281143714757, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 782.3135226202394, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 790.1314963230161, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 803.3054216148848, tolerance: 5.3515561642098675\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 751.6382247656566, tolerance: 5.345940946064122\n", + " positive)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.523 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.530 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.620 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.538 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.646 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.518 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.495 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.575 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END ........alpha=0.01, l1_ratio=0.1;, score=0.520 total time= 0.0s\n", + "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.534 total time= 0.0s\n", + "[CV 4/10] END ...........alpha=0.01, l1_ratio=1;, score=0.629 total time= 0.0s\n", + "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.639 total time= 0.0s\n", + "[CV 3/10] END ...........alpha=0.01, l1_ratio=1;, score=0.546 total time= 0.0s\n", + "[CV 5/10] END ...........alpha=0.01, l1_ratio=1;, score=0.558 total time= 0.0s\n", + "[CV 8/10] END ...........alpha=0.01, l1_ratio=1;, score=0.577 total time= 0.0s\n", + "[CV 6/10] END ...........alpha=0.01, l1_ratio=1;, score=0.529 total time= 0.0s\n", + "[CV 9/10] END ...........alpha=0.01, l1_ratio=1;, score=0.477 total time= 0.0s\n", + "[CV 7/10] END ...........alpha=0.01, l1_ratio=1;, score=0.517 total time= 0.0s\n", + "[CV 10/10] END ..........alpha=0.01, l1_ratio=1;, score=0.512 total time= 0.0s\n", + "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.533 total time= 0.0s\n", + "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.569 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END ............alpha=0.1, l1_ratio=1;, score=0.525 total time= 0.0s\n", + "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.609 total time= 0.0s\n", + "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.651 total time= 0.0s\n", + "[CV 5/10] END ............alpha=0.1, l1_ratio=1;, score=0.567 total time= 0.0s\n", + "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.486 total time= 0.0s\n", + "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.573 total time= 0.0s\n", + "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END ...........alpha=0.1, l1_ratio=1;, score=0.521 total time= 0.0s\n", + "Best CV score: 0.552:\n", + "Best parameters: {'alpha': 0.001, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 4/10] END .....................alpha=0.0001;, score=0.623 total time= 0.0s\n", + "[CV 1/10] END .....................alpha=0.0001;, score=0.534 total time= 0.0s\n", + "[CV 2/10] END .....................alpha=0.0001;, score=0.645 total time= 0.0s\n", + "[CV 5/10] END .....................alpha=0.0001;, score=0.554 total time= 0.0s\n", + "[CV 3/10] END .....................alpha=0.0001;, score=0.547 total time= 0.0s\n", + "[CV 8/10] END .....................alpha=0.0001;, score=0.576 total time= 0.0s\n", + "[CV 6/10] END .....................alpha=0.0001;, score=0.532 total time= 0.0s\n", + "[CV 9/10] END .....................alpha=0.0001;, score=0.473 total time= 0.0s\n", + "[CV 10/10] END ....................alpha=0.0001;, score=0.508 total time= 0.0s\n", + "[CV 7/10] END .....................alpha=0.0001;, score=0.523 total time= 0.0s\n", + "[CV 4/10] END ......................alpha=0.001;, score=0.623 total time= 0.0s\n", + "[CV 1/10] END ......................alpha=0.001;, score=0.534 total time= 0.0s\n", + "[CV 5/10] END ......................alpha=0.001;, score=0.555 total time= 0.0s\n", + "[CV 2/10] END ......................alpha=0.001;, score=0.645 total time= 0.0s\n", + "[CV 3/10] END ......................alpha=0.001;, score=0.547 total time= 0.0s\n", + "[CV 8/10] END ......................alpha=0.001;, score=0.576 total time= 0.0s\n", + "[CV 6/10] END ......................alpha=0.001;, score=0.531 total time= 0.0s\n", + "[CV 9/10] END ......................alpha=0.001;, score=0.474 total time= 0.0s\n", + "[CV 10/10] END .....................alpha=0.001;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END ......................alpha=0.001;, score=0.523 total time= 0.0s\n", + "[CV 1/10] END .......................alpha=0.01;, score=0.534 total time= 0.0s\n", + "[CV 4/10] END .......................alpha=0.01;, score=0.629 total time= 0.0s\n", + "[CV 2/10] END .......................alpha=0.01;, score=0.639 total time= 0.0s\n", + "[CV 3/10] END .......................alpha=0.01;, score=0.546 total time= 0.0s\n", + "[CV 5/10] END .......................alpha=0.01;, score=0.558 total time= 0.0s\n", + "[CV 8/10] END .......................alpha=0.01;, score=0.577 total time= 0.0s\n", + "[CV 6/10] END .......................alpha=0.01;, score=0.529 total time= 0.0s\n", + "[CV 9/10] END .......................alpha=0.01;, score=0.477 total time= 0.0s\n", + "[CV 10/10] END ......................alpha=0.01;, score=0.512 total time= 0.0s\n", + "[CV 7/10] END .......................alpha=0.01;, score=0.517 total time= 0.0s\n", + "[CV 1/10] END ........................alpha=0.1;, score=0.525 total time= 0.0s\n", + "[CV 2/10] END ........................alpha=0.1;, score=0.609 total time= 0.0s\n", + "[CV 3/10] END ........................alpha=0.1;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END ........................alpha=0.1;, score=0.651 total time= 0.0s\n", + "[CV 5/10] END ........................alpha=0.1;, score=0.567 total time= 0.0s\n", + "[CV 6/10] END ........................alpha=0.1;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END ........................alpha=0.1;, score=0.486 total time= 0.0s\n", + "[CV 8/10] END ........................alpha=0.1;, score=0.573 total time= 0.0s\n", + "[CV 9/10] END ........................alpha=0.1;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END .......................alpha=0.1;, score=0.521 total time= 0.0s\n", + "Best CV score: 0.552:\n", + "Best parameters: {'alpha': 0.01} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.909 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.912 total time= 0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 765.2385899693454, tolerance: 5.479641394818488\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 805.6010349600365, tolerance: 5.3926999456597775\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 809.8189764234066, tolerance: 5.39948427204628\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 785.1627747566799, tolerance: 5.479641394818488\n", + " positive)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.948 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.908 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.955 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.893 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.894 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.902 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.911 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.923 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.932 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.933 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.938 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.944 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.898 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.925 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.931 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.961 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.937 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.940 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.943 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.906 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.924 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.928 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.909 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.912 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.948 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.908 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.955 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.893 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.894 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.902 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.911 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.923 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.932 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.933 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.938 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.944 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.898 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.925 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.931 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.961 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.937 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.940 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.943 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.906 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.924 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.928 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.909 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.912 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.948 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.908 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.955 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.893 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.894 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.902 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.911 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.923 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.932 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.933 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.938 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.944 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.898 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.925 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.931 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.961 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.937 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.940 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.943 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.906 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.924 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.928 total time= 0.0s\n", + "Best CV score: 0.933:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.649 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.566 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.649 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.566 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.533 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.533 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.611 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.648 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.565 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.490 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.482 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.611 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.648 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.565 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.490 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.482 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.532 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.532 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.531 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.566 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.649 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.531 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.649 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.566 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.533 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.568 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.533 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.568 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.611 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.648 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.565 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.490 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.482 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.531 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.611 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.648 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.565 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.490 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.482 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.532 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.532 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.649 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.566 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.531 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.566 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.649 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.533 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.568 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.650 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.533 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.611 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.648 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.565 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.490 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.482 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.611 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.531 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.565 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.648 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.490 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.482 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.532 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.532 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.649 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.566 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.649 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.566 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.533 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.533 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.611 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.531 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.648 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.565 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.490 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.482 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.531 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.611 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.648 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.565 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.490 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.482 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.521 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.532 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.521 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.610 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.532 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.650 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.568 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.572 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.483 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.521 total time= 0.0s\n", + "Best CV score: 0.546:\n", + "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n", + "acetate\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 3/10] END ....activation=relu, max_iter=400;, score=0.742 total time= 3.4s\n", + "[CV 5/10] END ....activation=relu, max_iter=400;, score=0.910 total time= 3.4s\n", + "[CV 2/10] END ....activation=relu, max_iter=400;, score=0.762 total time= 3.4s\n", + "[CV 1/10] END ....activation=relu, max_iter=400;, score=0.730 total time= 3.5s\n", + "[CV 4/10] END ....activation=relu, max_iter=400;, score=0.881 total time= 3.5s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 1/10] END activation=logistic, max_iter=400;, score=0.685 total time= 4.0s\n", + "[CV 8/10] END activation=logistic, max_iter=400;, score=0.739 total time= 4.0s\n", + "[CV 3/10] END activation=logistic, max_iter=400;, score=0.619 total time= 4.0s\n", + "[CV 10/10] END activation=logistic, max_iter=400;, score=0.733 total time= 4.0s\n", + "[CV 7/10] END activation=logistic, max_iter=400;, score=0.807 total time= 4.0s\n", + "[CV 5/10] END activation=logistic, max_iter=400;, score=0.813 total time= 4.0s\n", + "[CV 9/10] END activation=logistic, max_iter=400;, score=0.731 total time= 4.0s\n", + "[CV 2/10] END activation=logistic, max_iter=400;, score=0.744 total time= 4.1s\n", + "[CV 4/10] END activation=logistic, max_iter=400;, score=0.786 total time= 4.1s\n", + "[CV 6/10] END activation=logistic, max_iter=400;, score=0.745 total time= 4.1s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 1/10] END ....activation=tanh, max_iter=400;, score=0.811 total time= 4.6s\n", + "[CV 5/10] END ....activation=tanh, max_iter=400;, score=0.946 total time= 4.6s\n", + "[CV 4/10] END ....activation=tanh, max_iter=400;, score=0.885 total time= 4.6s\n", + "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.754 total time= 4.6s\n", + "[CV 2/10] END ....activation=tanh, max_iter=400;, score=0.784 total time= 4.6s\n", + "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.853 total time= 4.6s\n", + "[CV 7/10] END ....activation=tanh, max_iter=400;, score=0.887 total time= 4.6s\n", + "[CV 8/10] END ....activation=tanh, max_iter=400;, score=0.885 total time= 4.6s\n", + "[CV 10/10] END ...activation=tanh, max_iter=400;, score=0.844 total time= 4.6s\n", + "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.877 total time= 4.6s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 6/10] END ....activation=relu, max_iter=400;, score=0.856 total time= 1.6s\n", + "[CV 10/10] END ...activation=relu, max_iter=400;, score=0.775 total time= 1.6s\n", + "[CV 7/10] END ....activation=relu, max_iter=400;, score=0.873 total time= 1.6s\n", + "[CV 9/10] END ....activation=relu, max_iter=400;, score=0.883 total time= 1.6s\n", + "[CV 8/10] END ....activation=relu, max_iter=400;, score=0.897 total time= 1.7s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best CV score: 0.853:\n", + "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.062 total time= 0.0s\n", + "[CV 1/10] END ..............C=0.1, epsilon=0.1;, score=-0.051 total time= 0.0s\n", + "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.126 total time= 0.0s\n", + "[CV 4/10] END ..............C=0.1, epsilon=0.1;, score=-0.045 total time= 0.1s\n", + "[CV 5/10] END ..............C=0.1, epsilon=0.1;, score=-0.077 total time= 0.1s\n", + "[CV 7/10] END ..............C=0.1, epsilon=0.1;, score=-0.097 total time= 0.1s\n", + "[CV 8/10] END ..............C=0.1, epsilon=0.1;, score=-0.159 total time= 0.1s\n", + "[CV 6/10] END ..............C=0.1, epsilon=0.1;, score=-0.118 total time= 0.1s\n", + "[CV 9/10] END ..............C=0.1, epsilon=0.1;, score=-0.012 total time= 0.1s\n", + "[CV 1/10] END ................C=0.1, epsilon=1;, score=-0.049 total time= 0.1s\n", + "[CV 10/10] END .............C=0.1, epsilon=0.1;, score=-0.220 total time= 0.1s\n", + "[CV 2/10] END .................C=0.1, epsilon=1;, score=0.061 total time= 0.1s\n", + "[CV 3/10] END .................C=0.1, epsilon=1;, score=0.128 total time= 0.1s\n", + "[CV 4/10] END ................C=0.1, epsilon=1;, score=-0.043 total time= 0.1s\n", + "[CV 5/10] END ................C=0.1, epsilon=1;, score=-0.074 total time= 0.1s\n", + "[CV 6/10] END ................C=0.1, epsilon=1;, score=-0.114 total time= 0.0s\n", + "[CV 7/10] END ................C=0.1, epsilon=1;, score=-0.094 total time= 0.0s\n", + "[CV 8/10] END ................C=0.1, epsilon=1;, score=-0.148 total time= 0.0s\n", + "[CV 9/10] END ................C=0.1, epsilon=1;, score=-0.011 total time= 0.0s\n", + "[CV 10/10] END ...............C=0.1, epsilon=1;, score=-0.205 total time= 0.0s\n", + "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.214 total time= 0.1s\n", + "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.351 total time= 0.1s\n", + "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.411 total time= 0.1s\n", + "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.162 total time= 0.1s\n", + "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.261 total time= 0.1s\n", + "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.220 total time= 0.1s\n", + "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.256 total time= 0.1s\n", + "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.197 total time= 0.1s\n", + "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.193 total time= 0.1s\n", + "[CV 1/10] END ...................C=1, epsilon=1;, score=0.234 total time= 0.1s\n", + "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.166 total time= 0.1s\n", + "[CV 3/10] END ...................C=1, epsilon=1;, score=0.415 total time= 0.1s\n", + "[CV 2/10] END ...................C=1, epsilon=1;, score=0.361 total time= 0.1s\n", + "[CV 6/10] END ...................C=1, epsilon=1;, score=0.180 total time= 0.0s\n", + "[CV 4/10] END ...................C=1, epsilon=1;, score=0.278 total time= 0.0s\n", + "[CV 5/10] END ...................C=1, epsilon=1;, score=0.225 total time= 0.0s\n", + "[CV 8/10] END ...................C=1, epsilon=1;, score=0.208 total time= 0.0s\n", + "[CV 7/10] END ...................C=1, epsilon=1;, score=0.274 total time= 0.0s\n", + "[CV 10/10] END ..................C=1, epsilon=1;, score=0.178 total time= 0.0s\n", + "[CV 9/10] END ...................C=1, epsilon=1;, score=0.207 total time= 0.0s\n", + "Best CV score: 0.256:\n", + "Best parameters: {'C': 1, 'epsilon': 1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.613 total time= 0.0s\n", + "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.710 total time= 0.0s\n", + "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.651 total time= 0.0s\n", + "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.655 total time= 0.0s\n", + "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.748 total time= 0.0s\n", + "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.706 total time= 0.0s\n", + "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.366 total time= 0.0s\n", + "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.664 total time= 0.0s\n", + "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.515 total time= 0.0s\n", + "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.626 total time= 0.0s\n", + "Best CV score: 0.625:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.386 total time= 0.0s\n", + "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.508 total time= 0.0s\n", + "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.369 total time= 0.0s\n", + "[CV 5/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.557 total time= 0.0s\n", + "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.498 total time= 0.0s\n", + "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.528 total time= 0.0s\n", + "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.513 total time= 0.0s\n", + "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.507 total time= 0.0s\n", + "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.419 total time= 0.0s\n", + "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.386 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.369 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.508 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.498 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.0001, l1_ratio=1;, score=0.557 total time= 0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49493.681107514465, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50716.13278762021, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49078.79649163709, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47706.64963062391, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48509.56706162072, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47515.6191775647, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49970.10219212296, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46643.935788883355, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48054.59492315794, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48442.61203858821, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4252.348966222722, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4451.743602846676, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4360.982042964155, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.479934531468, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4118.769163615638, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4097.520171475844, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.811282062699, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.864723005841, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4240.133706657871, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4228.450441768931, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49431.830542375006, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50730.49152157803, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49222.313718523554, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47929.67901107358, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48566.544619539905, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47601.31920429702, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49968.119879619466, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46667.045188132564, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48113.12207094836, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48446.70247123788, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4253.459914357954, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4362.690555760491, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4454.331302843115, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4245.176876319107, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4120.74332484584, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4099.54275815787, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4173.424263122375, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4173.6120532843925, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.08871382341, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4230.097637977247, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48742.78646057141, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50180.90975493966, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49020.60761365638, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47842.27349859188, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48145.21143356779, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47213.17033387661, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49410.46300960829, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46187.592232230825, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47704.07441246217, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47888.83543134161, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4264.094199816609, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4375.487907155082, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4475.968665716209, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4267.880035531183, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4136.34698929415, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4115.623386722087, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4185.368672953715, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4186.865302993072, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4257.431237919605, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.37117427327, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38723.0981820787, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38876.67344477996, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38291.18492337677, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 35963.30283090114, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37313.12875906742, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 35479.965126869094, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38298.67166961717, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 35347.22554307138, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37875.64020276432, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36647.310389868246, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42000.2145192141, tolerance: 27.122111107765193\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4252.348966222722, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4360.982042964155, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4451.743602846676, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.479934531468, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4118.769163615638, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4097.520171475844, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.811282062699, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.864723005841, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4240.133706657871, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4228.450441768931, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4253.459914357954, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4362.690555760491, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4454.331302843115, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4245.176876319107, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4120.74332484584, tolerance: 23.792475772151427\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4099.54275815787, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4173.424263122375, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4173.6120532843925, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.08871382341, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4230.097637977247, tolerance: 24.079772047834943\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4264.094199816609, tolerance: 24.718924822210536\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4375.487907155082, tolerance: 25.083856589346837\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4475.968665716209, tolerance: 24.798436508448894\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4267.880035531183, tolerance: 24.448613738487428\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4136.34698929415, tolerance: 23.792475772151427\n", + " positive)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.528 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.513 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.507 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.419 total time= 0.0s\n", + "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.386 total time= 0.0s\n", + "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.508 total time= 0.0s\n", + "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.372 total time= 0.0s\n", + "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.501 total time= 0.0s\n", + "[CV 5/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.558 total 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score=0.483 total time= 0.0s\n", + "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.386 total time= 0.0s\n", + "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.511 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.382 total time= 0.0s\n", + "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.510 total time= 0.0s\n", + "[CV 5/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.558 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.529 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.514 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.506 total time= 0.0s\n", + "[CV 1/10] END ............alpha=0.1, l1_ratio=1;, score=0.386 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.419 total time= 0.0s\n", + "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.509 total time= 0.0s\n", + "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.484 total time= 0.0s\n", + "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.381 total time= 0.0s\n", + "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END ............alpha=0.1, l1_ratio=1;, score=0.559 total time= 0.0s\n", + "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.529 total time= 0.0s\n", + "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.514 total time= 0.0s\n", + "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.506 total time= 0.0s\n", + "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.420 total time= 0.0s\n", + "[CV 10/10] END ...........alpha=0.1, l1_ratio=1;, score=0.483 total time= 0.0s\n", + "Best CV score: 0.480:\n", + "Best parameters: {'alpha': 0.1, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 1/10] END .....................alpha=0.0001;, score=0.386 total time= 0.0s\n", + "[CV 2/10] END .....................alpha=0.0001;, score=0.508 total time= 0.0s\n", + "[CV 3/10] END .....................alpha=0.0001;, score=0.369 total time= 0.0s\n", + "[CV 4/10] END .....................alpha=0.0001;, score=0.498 total time= 0.0s\n", + "[CV 5/10] END .....................alpha=0.0001;, score=0.557 total time= 0.0s\n", + "[CV 6/10] END .....................alpha=0.0001;, score=0.528 total time= 0.0s\n", + "[CV 7/10] END .....................alpha=0.0001;, score=0.513 total time= 0.0s\n", + "[CV 8/10] END .....................alpha=0.0001;, score=0.507 total time= 0.0s\n", + "[CV 9/10] END .....................alpha=0.0001;, score=0.419 total time= 0.0s\n", + "[CV 10/10] END ....................alpha=0.0001;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END ......................alpha=0.001;, score=0.386 total time= 0.0s\n", + "[CV 2/10] END ......................alpha=0.001;, score=0.508 total time= 0.0s\n", + "[CV 3/10] END ......................alpha=0.001;, score=0.369 total time= 0.0s\n", + "[CV 4/10] END ......................alpha=0.001;, score=0.498 total time= 0.0s\n", + "[CV 5/10] END ......................alpha=0.001;, score=0.557 total time= 0.0s\n", + "[CV 6/10] END ......................alpha=0.001;, score=0.528 total time= 0.0s\n", + "[CV 7/10] END ......................alpha=0.001;, score=0.513 total time= 0.0s\n", + "[CV 8/10] END ......................alpha=0.001;, score=0.507 total time= 0.0s\n", + "[CV 9/10] END ......................alpha=0.001;, score=0.419 total time= 0.0s\n", + "[CV 10/10] END .....................alpha=0.001;, score=0.483 total time= 0.0s\n", + "[CV 1/10] END .......................alpha=0.01;, score=0.386 total time= 0.0s\n", + "[CV 2/10] END .......................alpha=0.01;, score=0.508 total time= 0.0s\n", + "[CV 3/10] END .......................alpha=0.01;, score=0.373 total time= 0.0s\n", + "[CV 4/10] END .......................alpha=0.01;, score=0.501 total time= 0.0s\n", + "[CV 5/10] END .......................alpha=0.01;, score=0.558 total time= 0.0s\n", + "[CV 6/10] END .......................alpha=0.01;, score=0.529 total time= 0.0s\n", + "[CV 7/10] END .......................alpha=0.01;, score=0.513 total time= 0.0s\n", + "[CV 8/10] END .......................alpha=0.01;, score=0.507 total time= 0.0s\n", + "[CV 1/10] END ........................alpha=0.1;, score=0.386 total time= 0.0s\n", + "[CV 9/10] END .......................alpha=0.01;, score=0.420 total time= 0.0s\n", + "[CV 2/10] END ........................alpha=0.1;, score=0.509 total time= 0.0s\n", + "[CV 10/10] END ......................alpha=0.01;, score=0.483 total time= 0.0s\n", + "[CV 3/10] END ........................alpha=0.1;, score=0.381 total time= 0.0s\n", + "[CV 4/10] END ........................alpha=0.1;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END ........................alpha=0.1;, score=0.559 total time= 0.0s\n", + "[CV 6/10] END ........................alpha=0.1;, score=0.529 total time= 0.0s\n", + "[CV 7/10] END ........................alpha=0.1;, score=0.514 total time= 0.0s\n", + "[CV 8/10] END ........................alpha=0.1;, score=0.506 total time= 0.0s\n", + "[CV 9/10] END ........................alpha=0.1;, score=0.420 total time= 0.0s\n", + "[CV 10/10] END .......................alpha=0.1;, score=0.483 total time= 0.0s\n", + "Best CV score: 0.480:\n", + "Best parameters: {'alpha': 0.1} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4115.623386722087, tolerance: 23.821176586325123\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4185.368672953715, tolerance: 24.524031583747142\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4186.865302993072, tolerance: 22.98759672522025\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4257.431237919605, tolerance: 24.398424889946494\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4242.37117427327, tolerance: 24.079772047834943\n", + " positive)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.557 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.653 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.799 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.885 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.926 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.934 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.753 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.766 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.609 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.718 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.711 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.891 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.945 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.862 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.861 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.911 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.898 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.771 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.643 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.691 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.861 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.879 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.853 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.910 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.915 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.744 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.557 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.653 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.753 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.738 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.799 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.885 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.926 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.934 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.766 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.609 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.718 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.711 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.891 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.945 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.862 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.861 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.911 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.898 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.771 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.643 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.691 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.738 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.861 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.879 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.853 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.910 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.915 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.744 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.557 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.653 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.753 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.936 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.799 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.885 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.926 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.934 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.766 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.609 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.718 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.711 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.891 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.945 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.862 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.861 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.911 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.898 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.771 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.643 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.691 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.738 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.861 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.879 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.853 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.910 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.915 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.744 total time= 0.0s\n", + "Best CV score: 0.819:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.503 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.503 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.385 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.514 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.382 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.504 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.416 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.385 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.514 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.382 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.504 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.416 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.517 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.556 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.502 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.517 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.556 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.502 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.516 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.503 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.516 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.503 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.503 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.503 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.385 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.514 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.382 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.416 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.504 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.385 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.382 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.514 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.504 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.416 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.517 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.556 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.502 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.517 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.556 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.502 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.516 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.503 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.516 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.503 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.503 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.503 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.385 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.514 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.382 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.416 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.504 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.382 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.385 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.514 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.504 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.416 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.517 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.556 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.502 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.517 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.556 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.502 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.516 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.515 total time= 0.0s[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.503 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.516 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.503 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.503 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.503 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.385 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.514 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.382 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.416 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.382 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.385 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.504 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.514 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.504 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.416 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.517 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.556 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.526 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.502 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.556 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.517 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.502 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.526 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.384 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.516 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.383 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.557 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.503 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.485 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.384 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.516 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.383 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.509 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.557 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.515 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.503 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.485 total time= 0.0s\n", + "Best CV score: 0.480:\n", + "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n", + "butanol\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "[CV 5/10] END ....activation=relu, max_iter=400;, score=0.912 total time= 1.5s\n", + "[CV 3/10] END ....activation=relu, max_iter=400;, score=0.945 total time= 2.2s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 2/10] END ....activation=relu, max_iter=400;, score=0.933 total time= 3.4s\n", + "[CV 9/10] END ....activation=relu, max_iter=400;, score=0.868 total time= 1.8s\n", + "[CV 4/10] END ....activation=relu, max_iter=400;, score=0.927 total time= 3.4s\n", + "[CV 1/10] END ....activation=relu, max_iter=400;, score=0.887 total time= 3.4s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 5/10] END activation=logistic, max_iter=400;, score=0.952 total time= 3.9s\n", + "[CV 8/10] END activation=logistic, max_iter=400;, score=0.975 total time= 3.9s\n", + "[CV 7/10] END activation=logistic, max_iter=400;, score=0.951 total time= 3.9s\n", + "[CV 4/10] END activation=logistic, max_iter=400;, score=0.940 total time= 4.0s\n", + "[CV 9/10] END activation=logistic, max_iter=400;, score=0.920 total time= 3.9s\n", + "[CV 10/10] END activation=logistic, max_iter=400;, score=0.959 total time= 3.9s\n", + "[CV 6/10] END activation=logistic, max_iter=400;, score=0.965 total time= 4.0s\n", + "[CV 3/10] END activation=logistic, max_iter=400;, score=0.957 total time= 4.0s\n", + "[CV 2/10] END activation=logistic, max_iter=400;, score=0.962 total time= 4.0s\n", + "[CV 1/10] END activation=logistic, max_iter=400;, score=0.906 total time= 4.0s\n", + "[CV 4/10] END ....activation=tanh, max_iter=400;, score=0.966 total time= 4.0s\n", + "[CV 7/10] END ....activation=relu, max_iter=400;, score=0.915 total time= 2.5s\n", + "[CV 8/10] END ....activation=relu, max_iter=400;, score=0.958 total time= 2.5s\n", + "[CV 5/10] END ....activation=tanh, max_iter=400;, score=0.985 total time= 4.2s\n", + "[CV 10/10] END ...activation=relu, max_iter=400;, score=0.948 total time= 2.6s\n", + "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.976 total time= 4.3s\n", + "[CV 7/10] END ....activation=tanh, max_iter=400;, score=0.977 total time= 4.3s\n", + "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.947 total time= 4.3s\n", + "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.975 total time= 4.4s\n", + "[CV 8/10] END ....activation=tanh, max_iter=400;, score=0.979 total time= 4.4s\n", + "[CV 1/10] END ....activation=tanh, max_iter=400;, score=0.935 total time= 4.4s\n", + "[CV 2/10] END ....activation=tanh, max_iter=400;, score=0.986 total time= 4.4s\n", + "[CV 6/10] END ....activation=relu, max_iter=400;, score=0.958 total time= 2.7s\n", + "[CV 10/10] END ...activation=tanh, max_iter=400;, score=0.967 total time= 4.4s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best CV score: 0.969:\n", + "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.553 total time= 0.0s\n", + "[CV 1/10] END ...............C=0.1, epsilon=0.1;, score=0.634 total time= 0.0s\n", + "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.500 total time= 0.0s\n", + "[CV 4/10] END ...............C=0.1, epsilon=0.1;, score=0.544 total time= 0.0s\n", + "[CV 5/10] END ...............C=0.1, epsilon=0.1;, score=0.602 total time= 0.0s\n", + "[CV 1/10] END .................C=0.1, epsilon=1;, score=0.589 total time= 0.0s\n", + "[CV 7/10] END ...............C=0.1, epsilon=0.1;, score=0.583 total time= 0.0s\n", + "[CV 3/10] END .................C=0.1, epsilon=1;, score=0.496 total time= 0.0s\n", + "[CV 9/10] END ...............C=0.1, epsilon=0.1;, score=0.452 total time= 0.0s\n", + "[CV 6/10] END ...............C=0.1, epsilon=0.1;, score=0.628 total time= 0.1s\n", + "[CV 8/10] END ...............C=0.1, epsilon=0.1;, score=0.606 total time= 0.1s\n", + "[CV 5/10] END .................C=0.1, epsilon=1;, score=0.558 total time= 0.0s\n", + "[CV 4/10] END .................C=0.1, epsilon=1;, score=0.458 total time= 0.0s\n", + "[CV 2/10] END .................C=0.1, epsilon=1;, score=0.512 total time= 0.0s\n", + "[CV 10/10] END ..............C=0.1, epsilon=0.1;, score=0.569 total time= 0.1s\n", + "[CV 6/10] END .................C=0.1, epsilon=1;, score=0.569 total time= 0.0s\n", + "[CV 7/10] END .................C=0.1, epsilon=1;, score=0.503 total time= 0.0s\n", + "[CV 9/10] END .................C=0.1, epsilon=1;, score=0.437 total time= 0.0s\n", + "[CV 8/10] END .................C=0.1, epsilon=1;, score=0.553 total time= 0.0s\n", + "[CV 10/10] END ................C=0.1, epsilon=1;, score=0.516 total time= 0.0s\n", + "[CV 1/10] END ...................C=1, epsilon=1;, score=0.783 total time= 0.0s\n", + "[CV 3/10] END ...................C=1, epsilon=1;, score=0.849 total time= 0.0s\n", + "[CV 5/10] END ...................C=1, epsilon=1;, score=0.819 total time= 0.0s\n", + "[CV 6/10] END ...................C=1, epsilon=1;, score=0.831 total time= 0.0s\n", + "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.869 total time= 0.0s\n", + "[CV 7/10] END ...................C=1, epsilon=1;, score=0.706 total time= 0.0s\n", + "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.734 total time= 0.0s\n", + "[CV 8/10] END ...................C=1, epsilon=1;, score=0.820 total time= 0.0s\n", + "[CV 9/10] END ...................C=1, epsilon=1;, score=0.723 total time= 0.0s\n", + "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.801 total time= 0.1s\n", + "[CV 2/10] END ...................C=1, epsilon=1;, score=0.751 total time= 0.0s\n", + "[CV 4/10] END ...................C=1, epsilon=1;, score=0.598 total time= 0.0s\n", + "[CV 10/10] END ..................C=1, epsilon=1;, score=0.795 total time= 0.0s\n", + "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.713 total time= 0.1s\n", + "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.811 total time= 0.1s\n", + "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.843 total time= 0.1s\n", + "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.811 total time= 0.1s\n", + "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.847 total time= 0.1s\n", + "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.653 total time= 0.1s\n", + "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.852 total time= 0.1s\n", + "Best CV score: 0.793:\n", + "Best parameters: {'C': 1, 'epsilon': 0.1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.666 total time= 0.0s\n", + "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.732 total time= 0.0s\n", + "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.642 total time= 0.0s\n", + "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.655 total time= 0.0s\n", + "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.757 total time= 0.0s\n", + "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.720 total time= 0.0s\n", + "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.680 total time= 0.0s\n", + "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.732 total time= 0.0s\n", + "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.725 total time= 0.0s\n", + "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.477 total time= 0.0s\n", + "Best CV score: 0.679:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.678 total time= 0.0s\n", + "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.718 total time= 0.0s\n", + "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.718 total time= 0.0s\n", + "[CV 5/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.765 total time= 0.0s\n", + "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.821 total time= 0.0s\n", + "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.719 total time= 0.0s\n", + "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.667 total time= 0.0s\n", + "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.796 total time= 0.0s\n", + "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.792 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.678 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.718 total time= 0.0s\n", + "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.754 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.718 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.821 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.0001, l1_ratio=1;, score=0.765 total time= 0.0s\n", + "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.719 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.668 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.796 total time= 0.0s\n", + "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.792 total time= 0.0s\n", + "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.679 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.754 total time= 0.0s\n", + "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.821 total time= 0.0s\n", + "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.718 total time= 0.0s\n", + "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.715 total time= 0.0s\n", + "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.720 total time= 0.0s\n", + "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.664 total time= 0.0s\n", + "[CV 5/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.764 total time= 0.0s\n", + "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.795 total time= 0.0s\n", + "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.756 total time= 0.0s\n", + "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.794 total time= 0.0s\n", + "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.679 total time= 0.0s\n", + "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.717 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.821 total time= 0.0s\n", + "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.718 total time= 0.0s\n", + "[CV 5/10] END ..........alpha=0.001, l1_ratio=1;, score=0.764 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.719 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.666 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.795 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.755 total time= 0.0s\n", + "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.793 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.705 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.820 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.713 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.759 total time= 0.0s\n", + "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.652 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.790 total time= 0.0s\n", + "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.680 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.761 total time= 0.0s\n", + "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.700 total time= 0.0s\n", + "[CV 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score=0.711 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.818 total time= 0.0s\n", + "[CV 5/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.757 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.646 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.788 total time= 0.0s\n", + "[CV 1/10] END ............alpha=0.1, l1_ratio=1;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.699 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.762 total time= 0.0s\n", + "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.815 total time= 0.0s\n", + "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.797 total time= 0.0s\n", + "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.716 total time= 0.0s\n", + "[CV 5/10] END ............alpha=0.1, l1_ratio=1;, score=0.759 total time= 0.0s\n", + "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.718 total time= 0.0s\n", + "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.650 total time= 0.0s\n", + "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.787 total time= 0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 28.912355898621172, tolerance: 0.4275189965039049\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 27.414628175816006, tolerance: 0.4351835203188813\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.211560781099706, tolerance: 0.4351835203188813\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102.68962205058529, tolerance: 0.4464705762214399\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 105.71342646149753, tolerance: 0.45070119912621\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62.84988612837935, tolerance: 0.45233045408996186\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94.99680245367381, tolerance: 0.4464705762214399\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39.7396496187331, tolerance: 0.4275189965039049\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100.01943109648846, tolerance: 0.4479692470703709\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100.64747724225492, tolerance: 0.45070119912621\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88.3088581561525, tolerance: 0.43794994720251135\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102.29438370867433, tolerance: 0.46414008999791107\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98.29566453580003, tolerance: 0.42697028233034046\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40.74590311439215, tolerance: 0.4351835203188813\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 89.40215530971864, tolerance: 0.43330549863246526\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.144881080779214, tolerance: 0.45233045408996186\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.054947463228132, tolerance: 0.4464705762214399\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.371206881185572, tolerance: 0.4479692470703709\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.57474306598374, tolerance: 0.4275189965039049\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.141784847107601, tolerance: 0.45070119912621\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.65219515899787, tolerance: 0.43794994720251135\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.48316478295476, tolerance: 0.46414008999791107\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.52700414015294, tolerance: 0.42697028233034046\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.27961589892618, tolerance: 0.4351835203188813\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.411954044651566, tolerance: 0.43330549863246526\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.211560781099706, tolerance: 0.4351835203188813\n", + " positive)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END ...........alpha=0.1, l1_ratio=1;, score=0.797 total time= 0.0s\n", + "Best CV score: 0.743:\n", + "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 1/10] END .....................alpha=0.0001;, score=0.678 total time= 0.0s\n", + "[CV 2/10] END .....................alpha=0.0001;, score=0.718 total time= 0.0s\n", + "[CV 4/10] END .....................alpha=0.0001;, score=0.718 total time= 0.0s\n", + "[CV 3/10] END .....................alpha=0.0001;, score=0.821 total time= 0.0s\n", + "[CV 5/10] END .....................alpha=0.0001;, score=0.765 total time= 0.0s\n", + "[CV 6/10] END .....................alpha=0.0001;, score=0.719 total time= 0.0s\n", + "[CV 7/10] END .....................alpha=0.0001;, score=0.668 total time= 0.0s\n", + "[CV 8/10] END .....................alpha=0.0001;, score=0.796 total time= 0.0s\n", + "[CV 10/10] END ....................alpha=0.0001;, score=0.792 total time= 0.0s\n", + "[CV 1/10] END ......................alpha=0.001;, score=0.679 total time= 0.0s\n", + "[CV 2/10] END ......................alpha=0.001;, score=0.717 total time= 0.0s\n", + "[CV 9/10] END .....................alpha=0.0001;, score=0.754 total time= 0.0s\n", + "[CV 3/10] END ......................alpha=0.001;, score=0.821 total time= 0.0s\n", + "[CV 4/10] END ......................alpha=0.001;, score=0.718 total time= 0.0s\n", + "[CV 5/10] END ......................alpha=0.001;, score=0.764 total time= 0.0s\n", + "[CV 6/10] END ......................alpha=0.001;, score=0.719 total time= 0.0s\n", + "[CV 7/10] END ......................alpha=0.001;, score=0.666 total time= 0.0s\n", + "[CV 8/10] END ......................alpha=0.001;, score=0.795 total time= 0.0s\n", + "[CV 9/10] END ......................alpha=0.001;, score=0.755 total time= 0.0s\n", + "[CV 10/10] END .....................alpha=0.001;, score=0.793 total time= 0.0s\n", + "[CV 1/10] END .......................alpha=0.01;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END .......................alpha=0.01;, score=0.700 total time= 0.0s\n", + "[CV 3/10] END .......................alpha=0.01;, score=0.819 total time= 0.0s\n", + "[CV 4/10] END .......................alpha=0.01;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END .......................alpha=0.01;, score=0.757 total time= 0.0s\n", + "[CV 6/10] END .......................alpha=0.01;, score=0.720 total time= 0.0s\n", + "[CV 7/10] END .......................alpha=0.01;, score=0.646 total time= 0.0s\n", + "[CV 8/10] END .......................alpha=0.01;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END .......................alpha=0.01;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END ......................alpha=0.01;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END ........................alpha=0.1;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END ........................alpha=0.1;, score=0.699 total time= 0.0s\n", + "[CV 3/10] END ........................alpha=0.1;, score=0.815 total time= 0.0s\n", + "[CV 4/10] END ........................alpha=0.1;, score=0.716 total time= 0.0s\n", + "[CV 5/10] END ........................alpha=0.1;, score=0.759 total time= 0.0s\n", + "[CV 6/10] END ........................alpha=0.1;, score=0.718 total time= 0.0s\n", + "[CV 7/10] END ........................alpha=0.1;, score=0.650 total time= 0.0s\n", + "[CV 8/10] END ........................alpha=0.1;, score=0.787 total time= 0.0s\n", + "[CV 9/10] END ........................alpha=0.1;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END .......................alpha=0.1;, score=0.797 total time= 0.0s\n", + "Best CV score: 0.743:\n", + "Best parameters: {'alpha': 0.0001} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.970 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.964 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.990 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.981 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.986 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.993 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.979 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.967 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.975 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.986 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.968 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.980 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.994 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.955 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.977 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.940 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.976 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.980 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.981 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.992 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.979 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.970 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.964 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.990 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.981 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.986 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.993 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.979 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.967 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.975 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.986 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.968 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.980 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.994 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.955 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.977 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.940 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.976 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.980 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.981 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.992 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.979 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.970 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.964 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.990 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.981 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.986 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.993 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.979 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.967 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.975 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.986 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.968 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.980 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.994 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.955 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.983 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.977 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.984 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.940 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.976 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.980 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.981 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.992 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.979 total time= 0.0s\n", + "Best CV score: 0.979:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 2, 'weights': 'distance'} \n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.701 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.819 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.757 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.647 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.701 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.819 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.757 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.647 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.705 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.820 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.714 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.760 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.652 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.790 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.761 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.796 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.705 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.820 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.714 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.760 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.652 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.790 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.761 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.796 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.701 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.819 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.757 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.647 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.701 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.819 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.757 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.647 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.704 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.820 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.759 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.713 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.651 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.790 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.761 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.820 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.704 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.713 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.759 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.651 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.790 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.761 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.701 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.819 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.757 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.647 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.762 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.819 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.701 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.757 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.711 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.647 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.705 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.820 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.714 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.760 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.652 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.761 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.790 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.796 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.705 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.820 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.760 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.714 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.652 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.790 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.761 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.796 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.701 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.819 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.757 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.647 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.762 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.788 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.701 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.819 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.757 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.647 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.762 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.820 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.704 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.713 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.759 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.651 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.790 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.761 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.704 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.820 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.759 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.713 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.651 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.761 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.790 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.819 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.701 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.711 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.757 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.647 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.819 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.701 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.757 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.711 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.647 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.762 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.788 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.705 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.760 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.820 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.761 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.714 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.652 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.796 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.820 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.790 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.714 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.652 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.705 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.760 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.790 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.761 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.701 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.757 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.819 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.796 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.762 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.711 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.647 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.819 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.788 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.711 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.647 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.701 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.757 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.788 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.762 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.704 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.759 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.820 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.713 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.761 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.651 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.790 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.820 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.713 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.651 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.704 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.759 total time= 0.0s[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.790 total time= 0.0s\n", + "\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.761 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.701 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.757 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.819 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.762 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.711 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.647 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.819 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.788 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.711 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.647 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.701 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.757 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.788 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.762 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.705 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.760 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.820 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.761 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.714 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.652 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.796 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.820 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.790 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.714 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.652 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.705 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.760 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.790 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.761 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.701 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.757 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.796 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.819 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.762 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.711 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.647 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.819 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.788 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.711 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.701 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.757 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.647 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.788 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.762 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.704 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.820 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.713 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.759 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.651 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.790 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.761 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.797 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.680 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.704 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.820 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.713 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.759 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.721 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.651 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.790 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.761 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.797 total time= 0.0s\n", + "Best CV score: 0.740:\n", + "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n", + "butyrate\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "[CV 2/10] END ....activation=relu, max_iter=400;, score=0.854 total time= 1.8s\n", + "[CV 4/10] END ....activation=relu, max_iter=400;, score=0.843 total time= 2.8s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 5/10] END ....activation=relu, max_iter=400;, score=0.783 total time= 3.5s\n", + "[CV 3/10] END ....activation=relu, max_iter=400;, score=0.821 total time= 3.5s\n", + "[CV 1/10] END ....activation=relu, max_iter=400;, score=0.794 total time= 3.6s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 1/10] END activation=logistic, max_iter=400;, score=0.828 total time= 4.1s\n", + "[CV 4/10] END activation=logistic, max_iter=400;, score=0.881 total time= 4.1s\n", + "[CV 10/10] END activation=logistic, max_iter=400;, score=0.817 total time= 4.0s\n", + "[CV 8/10] END activation=logistic, max_iter=400;, score=0.857 total time= 4.0s\n", + "[CV 3/10] END activation=logistic, max_iter=400;, score=0.862 total time= 4.1s\n", + "[CV 9/10] END activation=logistic, max_iter=400;, score=0.851 total time= 4.0s\n", + "[CV 5/10] END activation=logistic, max_iter=400;, score=0.824 total time= 4.1s\n", + "[CV 2/10] END activation=logistic, max_iter=400;, score=0.900 total time= 4.1s\n", + "[CV 5/10] END ....activation=tanh, max_iter=400;, score=0.914 total time= 4.1s\n", + "[CV 7/10] END activation=logistic, max_iter=400;, score=0.934 total time= 4.1s\n", + "[CV 6/10] END activation=logistic, max_iter=400;, score=0.927 total time= 4.1s\n", + "[CV 9/10] END ....activation=relu, max_iter=400;, score=0.817 total time= 2.4s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/neural_network/_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CV 4/10] END ....activation=tanh, max_iter=400;, score=0.914 total time= 4.5s\n", + "[CV 3/10] END ....activation=tanh, max_iter=400;, score=0.893 total time= 4.5s\n", + "[CV 2/10] END ....activation=tanh, max_iter=400;, score=0.931 total time= 4.5s\n", + "[CV 1/10] END ....activation=tanh, max_iter=400;, score=0.870 total time= 4.5s\n", + "[CV 7/10] END ....activation=tanh, max_iter=400;, score=0.964 total time= 4.5s\n", + "[CV 6/10] END ....activation=tanh, max_iter=400;, score=0.937 total time= 4.5s\n", + "[CV 10/10] END ...activation=tanh, max_iter=400;, score=0.840 total time= 4.5s\n", + "[CV 8/10] END ....activation=tanh, max_iter=400;, score=0.892 total time= 4.5s\n", + "[CV 9/10] END ....activation=tanh, max_iter=400;, score=0.887 total time= 4.5s\n", + "[CV 6/10] END ....activation=relu, max_iter=400;, score=0.904 total time= 2.8s\n", + "[CV 7/10] END ....activation=relu, max_iter=400;, score=0.881 total time= 2.8s\n", + "[CV 8/10] END ....activation=relu, max_iter=400;, score=0.837 total time= 2.8s\n", + "[CV 10/10] END ...activation=relu, max_iter=400;, score=0.802 total time= 2.8s\n", + "Best CV score: 0.904:\n", + "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 1/10] END ...............C=0.1, epsilon=0.1;, score=0.143 total time= 0.0s\n", + "[CV 2/10] END ...............C=0.1, epsilon=0.1;, score=0.235 total time= 0.0s\n", + "[CV 3/10] END ...............C=0.1, epsilon=0.1;, score=0.175 total time= 0.0s\n", + "[CV 4/10] END ...............C=0.1, epsilon=0.1;, score=0.190 total time= 0.0s\n", + "[CV 5/10] END ...............C=0.1, epsilon=0.1;, score=0.088 total time= 0.0s\n", + "[CV 7/10] END ...............C=0.1, epsilon=0.1;, score=0.187 total time= 0.1s\n", + "[CV 8/10] END ...............C=0.1, epsilon=0.1;, score=0.215 total time= 0.1s\n", + "[CV 1/10] END .................C=0.1, epsilon=1;, score=0.199 total time= 0.0s\n", + "[CV 3/10] END .................C=0.1, epsilon=1;, score=0.181 total time= 0.0s\n", + "[CV 4/10] END .................C=0.1, epsilon=1;, score=0.185 total time= 0.0s\n", + "[CV 2/10] END .................C=0.1, epsilon=1;, score=0.238 total time= 0.0s\n", + "[CV 8/10] END .................C=0.1, epsilon=1;, score=0.177 total time= 0.0s\n", + "[CV 6/10] END .................C=0.1, epsilon=1;, score=0.235 total time= 0.0s\n", + "[CV 6/10] END ...............C=0.1, epsilon=0.1;, score=0.250 total time= 0.1s\n", + "[CV 7/10] END .................C=0.1, epsilon=1;, score=0.248 total time= 0.0s\n", + "[CV 10/10] END ................C=0.1, epsilon=1;, score=0.168 total time= 0.0s\n", + "[CV 5/10] END .................C=0.1, epsilon=1;, score=0.138 total time= 0.1s\n", + "[CV 10/10] END ..............C=0.1, epsilon=0.1;, score=0.131 total time= 0.1s\n", + "[CV 9/10] END .................C=0.1, epsilon=1;, score=0.183 total time= 0.1s\n", + "[CV 9/10] END ...............C=0.1, epsilon=0.1;, score=0.171 total time= 0.1s\n", + "[CV 2/10] END .................C=1, epsilon=0.1;, score=0.477 total time= 0.1s\n", + "[CV 3/10] END ...................C=1, epsilon=1;, score=0.474 total time= 0.0s\n", + "[CV 4/10] END .................C=1, epsilon=0.1;, score=0.486 total time= 0.1s\n", + "[CV 8/10] END ...................C=1, epsilon=1;, score=0.466 total time= 0.0s\n", + "[CV 7/10] END ...................C=1, epsilon=1;, score=0.462 total time= 0.0s\n", + "[CV 2/10] END ...................C=1, epsilon=1;, score=0.501 total time= 0.0s\n", + "[CV 7/10] END .................C=1, epsilon=0.1;, score=0.441 total time= 0.1s\n", + "[CV 6/10] END ...................C=1, epsilon=1;, score=0.554 total time= 0.0s\n", + "[CV 10/10] END ..................C=1, epsilon=1;, score=0.440 total time= 0.0s\n", + "[CV 5/10] END ...................C=1, epsilon=1;, score=0.192 total time= 0.0s\n", + "[CV 1/10] END ...................C=1, epsilon=1;, score=0.482 total time= 0.1s\n", + "[CV 6/10] END .................C=1, epsilon=0.1;, score=0.557 total time= 0.1s\n", + "[CV 9/10] END ...................C=1, epsilon=1;, score=0.475 total time= 0.0s\n", + "[CV 1/10] END .................C=1, epsilon=0.1;, score=0.449 total time= 0.1s\n", + "[CV 8/10] END .................C=1, epsilon=0.1;, score=0.480 total time= 0.1s\n", + "[CV 10/10] END ................C=1, epsilon=0.1;, score=0.402 total time= 0.1s\n", + "[CV 3/10] END .................C=1, epsilon=0.1;, score=0.475 total time= 0.1s\n", + "[CV 4/10] END ...................C=1, epsilon=1;, score=0.468 total time= 0.1s\n", + "[CV 9/10] END .................C=1, epsilon=0.1;, score=0.436 total time= 0.1s\n", + "[CV 5/10] END .................C=1, epsilon=0.1;, score=0.176 total time= 0.1s\n", + "Best CV score: 0.452:\n", + "Best parameters: {'C': 1, 'epsilon': 1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "[CV 2/10] END .....max_depth=2, n_estimators=10;, score=0.488 total time= 0.0s\n", + "[CV 4/10] END .....max_depth=2, n_estimators=10;, score=0.466 total time= 0.0s\n", + "[CV 3/10] END .....max_depth=2, n_estimators=10;, score=0.537 total time= 0.0s\n", + "[CV 7/10] END .....max_depth=2, n_estimators=10;, score=0.352 total time= 0.0s\n", + "[CV 8/10] END .....max_depth=2, n_estimators=10;, score=0.414 total time= 0.0s\n", + "[CV 9/10] END .....max_depth=2, n_estimators=10;, score=0.567 total time= 0.0s\n", + "[CV 10/10] END ....max_depth=2, n_estimators=10;, score=0.475 total time= 0.0s\n", + "[CV 1/10] END .....max_depth=2, n_estimators=10;, score=0.496 total time= 0.0s\n", + "[CV 5/10] END .....max_depth=2, n_estimators=10;, score=0.150 total time= 0.0s\n", + "[CV 6/10] END .....max_depth=2, n_estimators=10;, score=0.542 total time= 0.0s\n", + "Best CV score: 0.449:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "[CV 3/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.522 total time= 0.0s\n", + "[CV 1/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.471 total time= 0.0s\n", + "[CV 2/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.448 total time= 0.0s\n", + "[CV 6/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.513 total time= 0.0s\n", + "[CV 4/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.406 total time= 0.0s\n", + "[CV 5/10] END ......alpha=0.0001, l1_ratio=0.1;, score=-0.027 total time= 0.0s\n", + "[CV 7/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.416 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.0001, l1_ratio=1;, score=0.471 total time= 0.0s\n", + "[CV 8/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.374 total time= 0.0s\n", + "[CV 9/10] END .......alpha=0.0001, l1_ratio=0.1;, score=0.494 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.0001, l1_ratio=1;, score=0.448 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.0001, l1_ratio=1;, score=0.522 total time= 0.0s\n", + "[CV 10/10] END ......alpha=0.0001, l1_ratio=0.1;, score=0.445 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.0001, l1_ratio=1;, score=0.406 total time= 0.0s\n", + "[CV 5/10] END ........alpha=0.0001, l1_ratio=1;, score=-0.027 total time= 0.0s\n", + "[CV 6/10] END .........alpha=0.0001, l1_ratio=1;, score=0.512 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.0001, l1_ratio=1;, score=0.416 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.0001, l1_ratio=1;, score=0.374 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.0001, l1_ratio=1;, score=0.494 total time= 0.0s\n", + "[CV 10/10] END ........alpha=0.0001, l1_ratio=1;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.474 total time= 0.0s\n", + "[CV 2/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.447 total time= 0.0s\n", + "[CV 3/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.524 total time= 0.0s\n", + "[CV 4/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.403 total time= 0.0s\n", + "[CV 5/10] END .......alpha=0.001, l1_ratio=0.1;, score=-0.029 total time= 0.0s\n", + "[CV 6/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.515 total time= 0.0s\n", + "[CV 7/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.373 total time= 0.0s\n", + "[CV 1/10] END ..........alpha=0.001, l1_ratio=1;, score=0.472 total time= 0.0s\n", + "[CV 9/10] END ........alpha=0.001, l1_ratio=0.1;, score=0.493 total time= 0.0s\n", + "[CV 2/10] END ..........alpha=0.001, l1_ratio=1;, score=0.448 total time= 0.0s\n", + "[CV 10/10] END .......alpha=0.001, l1_ratio=0.1;, score=0.445 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.001, l1_ratio=1;, score=0.523 total time= 0.0s\n", + "[CV 4/10] END ..........alpha=0.001, l1_ratio=1;, score=0.403 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.001, l1_ratio=1;, score=-0.028 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.001, l1_ratio=1;, score=0.513 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.001, l1_ratio=1;, score=0.416 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.001, l1_ratio=1;, score=0.373 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.001, l1_ratio=1;, score=0.493 total time= 0.0s\n", + "[CV 10/10] END .........alpha=0.001, l1_ratio=1;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.478 total time= 0.0s\n", + "[CV 2/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.444 total time= 0.0s\n", + "[CV 3/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.528 total time= 0.0s\n", + "[CV 4/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.395 total time= 0.0s\n", + "[CV 5/10] END ........alpha=0.01, l1_ratio=0.1;, score=-0.035 total time= 0.0s\n", + "[CV 6/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.519 total time= 0.0s\n", + "[CV 7/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.414 total time= 0.0s\n", + "[CV 1/10] END ...........alpha=0.01, l1_ratio=1;, score=0.479 total time= 0.0s\n", + "[CV 8/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.369 total time= 0.0s\n", + "[CV 2/10] END ...........alpha=0.01, l1_ratio=1;, score=0.442 total time= 0.0s\n", + "[CV 9/10] END .........alpha=0.01, l1_ratio=0.1;, score=0.490 total time= 0.0s\n", + "[CV 3/10] END ...........alpha=0.01, l1_ratio=1;, score=0.528 total time= 0.0s\n", + "[CV 10/10] END ........alpha=0.01, l1_ratio=0.1;, score=0.445 total time= 0.0s\n", + "[CV 4/10] END ...........alpha=0.01, l1_ratio=1;, score=0.392 total time= 0.0s\n", + "[CV 5/10] END ..........alpha=0.01, l1_ratio=1;, score=-0.038 total time= 0.0s\n", + "[CV 6/10] END ...........alpha=0.01, l1_ratio=1;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END ...........alpha=0.01, l1_ratio=1;, score=0.413 total time= 0.0s\n", + "[CV 8/10] END ...........alpha=0.01, l1_ratio=1;, score=0.367 total time= 0.0s\n", + "[CV 9/10] END ...........alpha=0.01, l1_ratio=1;, score=0.488 total time= 0.0s\n", + "[CV 10/10] END ..........alpha=0.01, l1_ratio=1;, score=0.444 total time= 0.0s\n", + "[CV 1/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.441 total time= 0.0s\n", + "[CV 3/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.527 total time= 0.0s\n", + "[CV 4/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.394 total time= 0.0s\n", + "[CV 5/10] END .........alpha=0.1, l1_ratio=0.1;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.519 total time= 0.0s\n", + "[CV 7/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.414 total time= 0.0s\n", + "[CV 1/10] END ............alpha=0.1, l1_ratio=1;, score=0.475 total time= 0.0s\n", + "[CV 8/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.366 total time= 0.0s\n", + "[CV 9/10] END ..........alpha=0.1, l1_ratio=0.1;, score=0.486 total time= 0.0s\n", + "[CV 2/10] END ............alpha=0.1, l1_ratio=1;, score=0.439 total time= 0.0s\n", + "[CV 10/10] END .........alpha=0.1, l1_ratio=0.1;, score=0.444 total time= 0.0s\n", + "[CV 3/10] END ............alpha=0.1, l1_ratio=1;, score=0.523 total time= 0.0s\n", + "[CV 4/10] END ............alpha=0.1, l1_ratio=1;, score=0.401 total time= 0.0s\n", + "[CV 5/10] END ...........alpha=0.1, l1_ratio=1;, score=-0.029 total time= 0.0s\n", + "[CV 6/10] END ............alpha=0.1, l1_ratio=1;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END ............alpha=0.1, l1_ratio=1;, score=0.420 total time= 0.0s\n", + "[CV 8/10] END ............alpha=0.1, l1_ratio=1;, score=0.360 total time= 0.0s\n", + "[CV 9/10] END ............alpha=0.1, l1_ratio=1;, score=0.484 total time= 0.0s\n", + "[CV 10/10] END ...........alpha=0.1, l1_ratio=1;, score=0.441 total time= 0.0s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.632035032778731, tolerance: 0.3485204612649598\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.155936566667606, tolerance: 0.3539494224997914\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38.519076627995446, tolerance: 0.36078944497708576\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.9988426704092035, tolerance: 0.3709440982510048\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.873875892601745, tolerance: 0.3638014392671959\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19.986358536062653, tolerance: 0.3585677869785208\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.30358975494255, tolerance: 0.3423723737711889\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11.370452510840778, tolerance: 0.3430130256193086\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.887658076375828, tolerance: 0.3485204612649598\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.179017229232613, tolerance: 0.3539494224997914\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.951285348593274, tolerance: 0.35186466114232334\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45.95855518995154, tolerance: 0.36078944497708576\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.01677052387663, tolerance: 0.3709440982510048\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.371907593092828, tolerance: 0.3494529278766458\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29.256674670356006, tolerance: 0.3638014392671959\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29.358168703854744, tolerance: 0.3585677869785208\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16.563424193644778, tolerance: 0.3423723737711889\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20.191094319963668, tolerance: 0.3430130256193086\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38.339777716322715, tolerance: 0.3485204612649598\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34.7716966240323, tolerance: 0.3539494224997914\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31.08431058282349, tolerance: 0.35186466114232334\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60.926646143130256, tolerance: 0.36078944497708576\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30.86634830157095, tolerance: 0.3709440982510048\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 35.89329211475774, tolerance: 0.3494529278766458\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55.971282581429705, tolerance: 0.3638014392671959\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53.114596727344406, tolerance: 0.3585677869785208\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37.40582784868843, tolerance: 0.3423723737711889\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45.244191490476396, tolerance: 0.3430130256193086\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17.002534131717994, tolerance: 0.3485204612649598\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13.7458842802057, tolerance: 0.3539494224997914\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.037063022743268, tolerance: 0.35186466114232334\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21.988467954298585, tolerance: 0.36078944497708576\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12.707045390143776, tolerance: 0.3709440982510048\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15.601003509890688, tolerance: 0.3494529278766458\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22.304027229350368, tolerance: 0.3638014392671959\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20.85403737098477, tolerance: 0.3585677869785208\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.681665032243927, tolerance: 0.3423723737711889\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18.308516748228612, tolerance: 0.3430130256193086\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.974590094593623, tolerance: 0.3975429358853114\n", + " positive)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best CV score: 0.406:\n", + "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "[CV 1/10] END .....................alpha=0.0001;, score=0.471 total time= 0.0s\n", + "[CV 2/10] END .....................alpha=0.0001;, score=0.448 total time= 0.0s\n", + "[CV 3/10] END .....................alpha=0.0001;, score=0.522 total time= 0.0s\n", + "[CV 4/10] END .....................alpha=0.0001;, score=0.406 total time= 0.0s\n", + "[CV 5/10] END ....................alpha=0.0001;, score=-0.027 total time= 0.0s\n", + "[CV 6/10] END .....................alpha=0.0001;, score=0.512 total time= 0.0s\n", + "[CV 7/10] END .....................alpha=0.0001;, score=0.416 total time= 0.0s\n", + "[CV 8/10] END .....................alpha=0.0001;, score=0.374 total time= 0.0s\n", + "[CV 9/10] END .....................alpha=0.0001;, score=0.494 total time= 0.0s\n", + "[CV 10/10] END ....................alpha=0.0001;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END ......................alpha=0.001;, score=0.472 total time= 0.0s\n", + "[CV 2/10] END ......................alpha=0.001;, score=0.448 total time= 0.0s\n", + "[CV 3/10] END ......................alpha=0.001;, score=0.523 total time= 0.0s\n", + "[CV 4/10] END ......................alpha=0.001;, score=0.403 total time= 0.0s\n", + "[CV 5/10] END .....................alpha=0.001;, score=-0.028 total time= 0.0s\n", + "[CV 6/10] END ......................alpha=0.001;, score=0.513 total time= 0.0s\n", + "[CV 7/10] END ......................alpha=0.001;, score=0.416 total time= 0.0s\n", + "[CV 8/10] END ......................alpha=0.001;, score=0.373 total time= 0.0s\n", + "[CV 9/10] END ......................alpha=0.001;, score=0.493 total time= 0.0s\n", + "[CV 10/10] END .....................alpha=0.001;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END .......................alpha=0.01;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END .......................alpha=0.01;, score=0.442 total time= 0.0s\n", + "[CV 3/10] END .......................alpha=0.01;, score=0.528 total time= 0.0s\n", + "[CV 4/10] END .......................alpha=0.01;, score=0.392 total time= 0.0s\n", + "[CV 5/10] END ......................alpha=0.01;, score=-0.038 total time= 0.0s\n", + "[CV 6/10] END .......................alpha=0.01;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END .......................alpha=0.01;, score=0.413 total time= 0.0s\n", + "[CV 8/10] END .......................alpha=0.01;, score=0.367 total time= 0.0s\n", + "[CV 9/10] END .......................alpha=0.01;, score=0.488 total time= 0.0s\n", + "[CV 10/10] END ......................alpha=0.01;, score=0.444 total time= 0.0s\n", + "[CV 1/10] END ........................alpha=0.1;, score=0.475 total time= 0.0s\n", + "[CV 2/10] END ........................alpha=0.1;, score=0.439 total time= 0.0s\n", + "[CV 3/10] END ........................alpha=0.1;, score=0.523 total time= 0.0s\n", + "[CV 4/10] END ........................alpha=0.1;, score=0.401 total time= 0.0s\n", + "[CV 5/10] END .......................alpha=0.1;, score=-0.029 total time= 0.0s\n", + "[CV 6/10] END ........................alpha=0.1;, score=0.511 total time= 0.0s\n", + "[CV 7/10] END ........................alpha=0.1;, score=0.420 total time= 0.0s\n", + "[CV 8/10] END ........................alpha=0.1;, score=0.360 total time= 0.0s\n", + "[CV 9/10] END ........................alpha=0.1;, score=0.484 total time= 0.0s\n", + "[CV 10/10] END .......................alpha=0.1;, score=0.441 total time= 0.0s\n", + "Best CV score: 0.406:\n", + "Best parameters: {'alpha': 0.0001} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.832 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.930 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.930 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.862 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.925 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.927 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=2, weights=distance;, score=0.888 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.842 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.941 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.932 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.904 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.931 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.939 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.904 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.914 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=3, weights=distance;, score=0.874 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.889 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.933 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.934 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.895 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.948 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.928 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.885 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.946 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=4, n_neighbors=4, weights=distance;, score=0.861 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.832 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.930 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.930 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.862 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.925 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.927 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=2, weights=distance;, score=0.888 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.842 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.941 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.932 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.904 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.931 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.939 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.904 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.914 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=3, weights=distance;, score=0.874 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.889 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.933 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.934 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.895 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.948 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.928 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.885 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.946 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=5, n_neighbors=4, weights=distance;, score=0.861 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.832 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.958 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.930 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.930 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.929 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.862 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.951 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.925 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.927 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=2, weights=distance;, score=0.888 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.842 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.941 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.932 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.904 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.931 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.917 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.939 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.904 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.914 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=3, weights=distance;, score=0.874 total time= 0.0s\n", + "[CV 1/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.889 total time= 0.0s\n", + "[CV 2/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.933 total time= 0.0s\n", + "[CV 3/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.934 total time= 0.0s\n", + "[CV 4/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.895 total time= 0.0s\n", + "[CV 5/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.948 total time= 0.0s\n", + "[CV 6/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.928 total time= 0.0s\n", + "[CV 7/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.960 total time= 0.0s\n", + "[CV 8/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.885 total time= 0.0s\n", + "[CV 9/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.946 total time= 0.0s\n", + "[CV 10/10] END algorithm=ball_tree, leaf_size=6, n_neighbors=4, weights=distance;, score=0.861 total time= 0.0s\n", + "Best CV score: 0.918:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.478 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.442 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.394 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.519 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.414 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.366 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.478 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.442 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.394 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.519 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.414 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.366 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.444 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.528 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.393 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.368 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.444 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.528 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.393 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.368 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.478 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.441 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.395 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.518 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.365 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.478 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.441 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.395 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.518 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.365 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.528 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.393 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.368 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.528 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.393 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.368 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.478 total time= 0.0s[CV 10/10] END alpha_1=0.1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.445 total time= 0.0s\n", + "\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.442 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.394 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.519 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.414 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.366 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.478 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.442 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.394 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.519 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.414 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.366 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.479 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.528 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.444 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.393 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.520 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.368 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.479 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.528 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.444 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.393 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.368 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.478 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.441 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.395 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.518 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.365 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.478 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.441 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.395 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.518 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.365 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.479 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.528 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.393 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.520 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.368 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.528 total time= 0.0s\n", + "[CV 4/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.393 total time= 0.0s\n", + "[CV 5/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 7/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 6/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.520 total time= 0.0s\n", + "[CV 8/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.368 total time= 0.0s\n", + "[CV 9/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 10/10] END alpha_1=0.1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.478 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.442 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.394 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.414 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.519 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.366 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.478 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.442 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.394 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.519 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.414 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.366 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.444 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.528 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.520 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.393 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.445 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.528 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.368 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.479 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.393 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.444 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.368 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.478 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.520 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.442 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.445 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.518 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.395 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.365 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.478 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.395 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.442 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.365 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.479 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.518 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.528 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.520 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.393 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.445 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.528 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.368 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.479 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.393 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.368 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.478 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.520 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.442 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=0.1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.445 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.519 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.394 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.414 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=300;, score=0.366 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.478 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.394 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.414 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.442 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.366 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.479 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.519 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.444 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=0.1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.528 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.520 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.413 total time= 0.0s[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.393 total time= 0.0s\n", + "\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.445 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=300;, score=0.368 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.528 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.479 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.393 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.444 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.368 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.478 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.520 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.442 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=0.1, lambda_2=1, n_iter=500;, score=0.445 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.527 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.518 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.486 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.395 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.415 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.527 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=300;, score=0.365 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.478 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.395 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.442 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.518 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.415 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.479 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.365 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.443 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.486 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.528 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=0.1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.393 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.413 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.368 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.489 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=300;, score=0.445 total time= 0.0s\n", + "[CV 1/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.479 total time= 0.0s\n", + "[CV 2/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.443 total time= 0.0s\n", + "[CV 3/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.528 total time= 0.0s\n", + "[CV 4/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.393 total time= 0.0s\n", + "[CV 5/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=-0.037 total time= 0.0s\n", + "[CV 6/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.520 total time= 0.0s\n", + "[CV 7/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.413 total time= 0.0s\n", + "[CV 8/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.368 total time= 0.0s\n", + "[CV 9/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.489 total time= 0.0s\n", + "[CV 10/10] END alpha_1=1, alpha_2=1, lambda_1=1, lambda_2=1, n_iter=500;, score=0.445 total time= 0.0s\n", + "Best CV score: 0.404:\n", + "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n" + ] + } + ], + "source": [ + "# define a dictionary to hold results for all outputs\n", + "trained_model_dictionary = {}\n", + "\n", + "# define a scaler to standardize the input values of all features between 0 and 1\n", + "Scaler = sklearn.preprocessing.MinMaxScaler()\n", + "X = Scaler.fit_transform(X_train, y_train)\n", + "\n", + "# loop over outputs\n", + "for index, output in enumerate(['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']):\n", + " print(f'{output}\\n')\n", + " \n", + " # define a dictionary to hold results for a single output\n", + " trained_models = {} \n", + " \n", + " # loop over models\n", + " for model_name, model_conf in model_cfgs.items():\n", + " print (model_name)\n", + " \n", + " # define grid search parameters\n", + " search = sklearn.model_selection.GridSearchCV(\n", + " estimator = model_conf[\"estimator\"], \n", + " param_grid = model_conf[\"param_grid\"], \n", + " scoring = \"r2\",\n", + " refit = True,\n", + " cv = sklearn.model_selection.ShuffleSplit(n_splits=10, test_size=0.1, random_state=0), \n", + " n_jobs=30, # This is a limitation of the server I am using. -gr\n", + " verbose=3\n", + " )\n", + "\n", + " # output array is a vector of a single output, not 2d array of all outputs\n", + " y_output=y_train[:,index]\n", + "\n", + " # run grid search\n", + " search.fit(X_train, y_output)\n", + " \n", + " # report results\n", + " print(\"Best CV score: %0.3f:\" % search.best_score_)\n", + " print(\"Best parameters:\", search.best_params_, '\\n')\n", + " \n", + " # save results of each model to a dictionary\n", + " trained_models[model_name] = search \n", + "\n", + " # save results from each output to a dictionary\n", + " trained_model_dictionary[output] = trained_models" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "# back up results from non-Bayesian models \n", + "biomass\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 4 out of 30 | elapsed: 1.4s remaining: 9.3s\n", + "[Parallel(n_jobs=30)]: Done 15 out of 30 | elapsed: 2.2s remaining: 2.2s\n", + "[Parallel(n_jobs=30)]: Done 26 out of 30 | elapsed: 3.5s remaining: 0.5s\n", + "[Parallel(n_jobs=30)]: Done 30 out of 30 | elapsed: 4.2s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "\n", + "Best CV score: 0.470:\n", + "Best parameters: {'activation': 'logistic', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.125:\n", + "Best parameters: {'C': 1, 'epsilon': 0.1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "Best CV score: 0.319:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.1s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 3 out of 10 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 7 out of 10 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 10 out of 10 | elapsed: 0.0s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 21 out of 80 | elapsed: 0.1s remaining: 0.3s\n", + "[Parallel(n_jobs=30)]: Done 48 out of 80 | elapsed: 0.1s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 80 out of 80 | elapsed: 0.1s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.0s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 31 out of 90 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 90 out of 90 | elapsed: 0.1s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "\n", + "Best CV score: 0.261:\n", + "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.260:\n", + "Best parameters: {'alpha': 0.0001} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "Best CV score: 0.837:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n", + "\n", + "ethanol\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Done 4 out of 30 | elapsed: 11.4s remaining: 1.2min\n", + "[Parallel(n_jobs=30)]: Done 15 out of 30 | elapsed: 13.0s remaining: 13.0s\n", + "[Parallel(n_jobs=30)]: Done 26 out of 30 | elapsed: 14.5s remaining: 2.2s\n", + "[Parallel(n_jobs=30)]: Done 30 out of 30 | elapsed: 14.8s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.2s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.1s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.1s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 3 out of 10 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 7 out of 10 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 10 out of 10 | elapsed: 0.0s finished\n", + "\n", + "Best CV score: 0.895:\n", + "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.294:\n", + "Best parameters: {'C': 1, 'epsilon': 1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "Best CV score: 0.686:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 21 out of 80 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 48 out of 80 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 80 out of 80 | elapsed: 0.1s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097.125190323121, tolerance: 5.923503688869955\n", + " positive)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.0s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "\n", + "Best CV score: 0.552:\n", + "Best parameters: {'alpha': 0.001, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.552:\n", + "Best parameters: {'alpha': 0.01} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "Best CV score: 0.933:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n", + "\n", + "acetate\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Done 31 out of 90 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 90 out of 90 | elapsed: 0.1s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 4 out of 30 | elapsed: 10.9s remaining: 1.2min\n", + "[Parallel(n_jobs=30)]: Done 15 out of 30 | elapsed: 12.6s remaining: 12.6s\n", + "[Parallel(n_jobs=30)]: Done 26 out of 30 | elapsed: 14.1s remaining: 2.2s\n", + "[Parallel(n_jobs=30)]: Done 30 out of 30 | elapsed: 14.3s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.1s remaining: 0.3s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.1s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.1s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 3 out of 10 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 7 out of 10 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 10 out of 10 | elapsed: 0.0s finished\n", + "\n", + "Best CV score: 0.850:\n", + "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: -0.043:\n", + "Best parameters: {'C': 1, 'epsilon': 1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "Best CV score: 0.621:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "Best CV score: 0.480:\n", + "Best parameters: {'alpha': 0.1, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.480:\n", + "Best parameters: {'alpha': 0.1} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 21 out of 80 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 48 out of 80 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 80 out of 80 | elapsed: 0.1s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42000.21451921737, tolerance: 27.122111107765193\n", + " positive)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.0s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 31 out of 90 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 90 out of 90 | elapsed: 0.1s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "\n", + "Best CV score: 0.819:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n", + "\n", + "butanol\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Done 4 out of 30 | elapsed: 9.6s remaining: 1.0min\n", + "[Parallel(n_jobs=30)]: Done 15 out of 30 | elapsed: 12.3s remaining: 12.3s\n", + "[Parallel(n_jobs=30)]: Done 26 out of 30 | elapsed: 13.7s remaining: 2.1s\n", + "[Parallel(n_jobs=30)]: Done 30 out of 30 | elapsed: 14.2s finished\n", + "\n", + "Best CV score: 0.968:\n", + "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.643:\n", + "Best parameters: {'C': 1, 'epsilon': 0.1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.1s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.1s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 3 out of 10 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 7 out of 10 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 10 out of 10 | elapsed: 0.0s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 21 out of 80 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 48 out of 80 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 80 out of 80 | elapsed: 0.1s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.0s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "\n", + "Best CV score: 0.684:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "Best CV score: 0.743:\n", + "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.743:\n", + "Best parameters: {'alpha': 0.0001} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Done 31 out of 90 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 62 out of 90 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 90 out of 90 | elapsed: 0.1s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "\n", + "Best CV score: 0.979:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 2, 'weights': 'distance'} \n", + "\n", + "butyrate\n", + "\n", + "nn\n", + "Fitting 10 folds for each of 3 candidates, totalling 30 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Done 4 out of 30 | elapsed: 9.2s remaining: 59.7s\n", + "[Parallel(n_jobs=30)]: Done 15 out of 30 | elapsed: 12.8s remaining: 12.8s\n", + "[Parallel(n_jobs=30)]: Done 26 out of 30 | elapsed: 13.7s remaining: 2.1s\n", + "[Parallel(n_jobs=30)]: Done 30 out of 30 | elapsed: 14.2s finished\n", + "\n", + "Best CV score: 0.902:\n", + "Best parameters: {'activation': 'tanh', 'max_iter': 400} \n", + "\n", + "svm_rbf\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.650:\n", + "Best parameters: {'C': 1, 'epsilon': 0.1} \n", + "\n", + "rf\n", + "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.2s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.1s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.1s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n", + " \"avoid this warning.\", FutureWarning)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 3 out of 10 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 7 out of 10 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 10 out of 10 | elapsed: 0.0s finished\n", + "\n", + "Best CV score: 0.438:\n", + "Best parameters: {'max_depth': 2, 'n_estimators': 10} \n", + "\n", + "en\n", + "Fitting 10 folds for each of 8 candidates, totalling 80 fits\n", + "Best CV score: 0.406:\n", + "Best parameters: {'alpha': 0.0001, 'l1_ratio': 0.1} \n", + "\n", + "lasso\n", + "Fitting 10 folds for each of 4 candidates, totalling 40 fits\n", + "Best CV score: 0.406:\n", + "Best parameters: {'alpha': 0.0001} \n", + "\n", + "knn\n", + "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n", + "\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 21 out of 80 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 48 out of 80 | elapsed: 0.1s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 80 out of 80 | elapsed: 0.1s finished\n", + "/usr/local/share/jupyteruser/.pyenv/versions/biod_3.7/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14.974590054853707, tolerance: 0.3975429358853114\n", + " positive)\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "[Parallel(n_jobs=30)]: Done 9 out of 40 | elapsed: 0.0s remaining: 0.1s\n", + "[Parallel(n_jobs=30)]: Done 23 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 37 out of 40 | elapsed: 0.0s remaining: 0.0s\n", + "[Parallel(n_jobs=30)]: Done 40 out of 40 | elapsed: 0.0s finished\n", + "[Parallel(n_jobs=30)]: Using backend LokyBackend with 30 concurrent workers.\n", + "\n", + "Best CV score: 0.918:\n", + "Best parameters: {'algorithm': 'ball_tree', 'leaf_size': 4, 'n_neighbors': 4, 'weights': 'distance'} \n", + "\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "Bayesian result\n", + "\n", + "biomass\n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "Best CV score: 0.261:\n", + "Best parameters: {'alpha_1': 0.1, 'alpha_2': 1, 'lambda_1': 1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n", + "ethanol\n", + "\n", + "bayesian\n", + "\n", + "Best CV score: 0.546:\n", + "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n", + "acetate\n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "Best CV score: 0.480:\n", + "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n", + "butanol\n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "Best CV score: 0.740:\n", + "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n", + "butyrate\n", + "\n", + "bayesian\n", + "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n", + "Best CV score: 0.404:\n", + "Best parameters: {'alpha_1': 1, 'alpha_2': 0.1, 'lambda_1': 0.1, 'lambda_2': 1, 'n_iter': 300} \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train and serialize models with best hyperparameters\n", + "These parameters come from running the parameter search with the full grid\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "optimized_parameters = {\n", + " \"acetate\": {\n", + " \"nn_fine\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [50, 40],\n", + " max_iter = 5000\n", + " ),\n", + " \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True,\n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [40, 20],\n", + " max_iter = 5000\n", + " ),\n", + " \"svm_rbf\": sklearn.svm.SVR(\n", + " kernel = 'rbf', \n", + " C = 10000, \n", + " epsilon = 0.1, \n", + " gamma = 0.01\n", + " ),\n", + " 'rf': sklearn.ensemble.RandomForestRegressor(\n", + " max_depth = 32,\n", + " # max_samples = 0.5,\n", + " n_estimators = 130\n", + " ),\n", + " 'en': sklearn.linear_model.ElasticNet(\n", + " alpha = 0.1,\n", + " l1_ratio = 0.4\n", + " ),\n", + " 'lasso': sklearn.linear_model.Lasso(\n", + " alpha = 0.1\n", + " ),\n", + " 'knn': sklearn.neighbors.KNeighborsRegressor(\n", + " algorithm = 'ball_tree',\n", + " leaf_size = 5,\n", + " n_neighbors = 4,\n", + " weights = 'distance'\n", + " ),\n", + " 'bayesian': sklearn.linear_model.BayesianRidge(\n", + " alpha_1 = 1, \n", + " alpha_2 = 0.1, \n", + " lambda_1 = 0.1, \n", + " lambda_2 = 1, \n", + " n_iter = 300\n", + " ),\n", + " },\n", + " \"biomass\": {\n", + " \"nn_fine\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [100, 80, 60, 70],\n", + " max_iter = 5000\n", + " ),\n", + " \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [100, 100, 60, 80],\n", + " max_iter = 5000\n", + " ),\n", + " \"svm_rbf\": sklearn.svm.SVR(\n", + " kernel = 'rbf', \n", + " C = 10000,\n", + " epsilon = 0.0001, \n", + " gamma = 0.001\n", + " ),\n", + " 'rf': sklearn.ensemble.RandomForestRegressor(\n", + " max_depth = 32,\n", + " # max_samples = 0.5,\n", + " n_estimators = 80\n", + " ),\n", + " 'en': sklearn.linear_model.ElasticNet(\n", + " alpha = 1e-05, \n", + " l1_ratio = 0.1\n", + " ),\n", + " 'lasso': sklearn.linear_model.Lasso(\n", + " alpha = 1e-06\n", + " ),\n", + " 'knn': sklearn.neighbors.KNeighborsRegressor(\n", + " algorithm = 'ball_tree',\n", + " leaf_size = 5,\n", + " n_neighbors = 4,\n", + " weights = 'distance'\n", + " ),\n", + " 'bayesian': sklearn.linear_model.BayesianRidge(\n", + " alpha_1 = 0.1, \n", + " alpha_2 = 1, \n", + " lambda_1 = 1, \n", + " lambda_2 = 1, \n", + " n_iter = 300\n", + " ),\n", + " },\n", + " \"butanol\": {\n", + " \"nn_fine\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [90, 60, 10, 80],\n", + " max_iter = 5000\n", + " ),\n", + " \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [60, 20],\n", + " max_iter = 5000\n", + " ),\n", + " \"svm_rbf\": sklearn.svm.SVR(\n", + " kernel = 'rbf', \n", + " C = 1000, \n", + " epsilon = 0.01, \n", + " gamma = 0.01\n", + " ),\n", + " 'rf': sklearn.ensemble.RandomForestRegressor(\n", + " max_depth = 28,\n", + " n_estimators = 120\n", + " ),\n", + " 'en': sklearn.linear_model.ElasticNet(\n", + " alpha = 1e-10,\n", + " l1_ratio = 0.1\n", + " ),\n", + " 'lasso': sklearn.linear_model.Lasso(\n", + " alpha = 1e-10\n", + " ),\n", + " 'knn': sklearn.neighbors.KNeighborsRegressor(\n", + " algorithm = 'ball_tree',\n", + " leaf_size = 5,\n", + " n_neighbors = 2,\n", + " weights = 'distance'\n", + " ),\n", + " 'bayesian': sklearn.linear_model.BayesianRidge(\n", + " alpha_1 = 1, \n", + " alpha_2 = 0.1, \n", + " lambda_1 = 0.1, \n", + " lambda_2 = 1, \n", + " n_iter = 300\n", + " ),\n", + " },\n", + " \"butyrate\": {\n", + " \"nn_fine\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [90, 30, 20],\n", + " max_iter = 5000\n", + " ),\n", + " \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [60, 20],\n", + " max_iter = 5000\n", + " ),\n", + " \"svm_rbf\": sklearn.svm.SVR(\n", + " kernel = 'rbf', \n", + " C = 10000, \n", + " epsilon = 0.01, \n", + " gamma = 0.01\n", + " ),\n", + " 'rf': sklearn.ensemble.RandomForestRegressor(\n", + " max_depth = 22,\n", + " # max_samples = 0.5,\n", + " n_estimators = 130\n", + " ),\n", + " 'en': sklearn.linear_model.ElasticNet(\n", + " alpha = 0.0001,\n", + " l1_ratio = 0.1\n", + " ),\n", + " 'lasso': sklearn.linear_model.Lasso(\n", + " alpha = 1e-10\n", + " ),\n", + " 'knn': sklearn.neighbors.KNeighborsRegressor(\n", + " algorithm = 'ball_tree',\n", + " leaf_size = 5,\n", + " n_neighbors = 4,\n", + " weights = 'distance'\n", + " ),\n", + " 'bayesian': sklearn.linear_model.BayesianRidge(\n", + " alpha_1 = 1, \n", + " alpha_2 = 0.1, \n", + " lambda_1 = 0.1, \n", + " lambda_2 = 1, \n", + " n_iter = 300\n", + " ),\n", + " },\n", + " \"ethanol\": {\n", + " \"nn_fine\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [80, 50],\n", + " max_iter = 5000\n", + " ),\n", + " \"nn_coarse\": sklearn.neural_network.MLPRegressor(\n", + " shuffle=True, \n", + " activation = 'tanh', \n", + " hidden_layer_sizes = [80, 60],\n", + " max_iter = 5000\n", + " ),\n", + " \"svm_rbf\": sklearn.svm.SVR(\n", + " kernel = 'rbf', \n", + " C = 10000, \n", + " epsilon = 0.0001, \n", + " gamma = 0.001\n", + " ),\n", + " 'rf': sklearn.ensemble.RandomForestRegressor(\n", + " max_depth = 22,\n", + " # max_samples = 0.5,\n", + " n_estimators = 100\n", + " ),\n", + " 'en': sklearn.linear_model.ElasticNet(\n", + " alpha = 0.001,\n", + " l1_ratio = 0.1\n", + " ),\n", + " 'lasso': sklearn.linear_model.Lasso(\n", + " alpha = 0.01\n", + " ),\n", + " 'knn': sklearn.neighbors.KNeighborsRegressor(\n", + " algorithm = 'ball_tree',\n", + " leaf_size = 5,\n", + " n_neighbors = 4,\n", + " weights = 'distance'\n", + " ),\n", + " 'bayesian': sklearn.linear_model.BayesianRidge(\n", + " alpha_1 = 1, \n", + " alpha_2 = 0.1, \n", + " lambda_1 = 0.1, \n", + " lambda_2 = 1, \n", + " n_iter = 300\n", + " ),\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "biomass\n", + "nn_fine\n", + "nn_coarse\n", + "svm_rbf\n", + "rf\n", + "en\n", + "lasso\n", + "knn\n", + "bayesian\n", + "ethanol\n", + "nn_fine\n", + "nn_coarse\n", + "svm_rbf\n", + "rf\n", + "en\n", + "lasso\n", + "knn\n", + "bayesian\n", + "acetate\n", + "nn_fine\n", + "nn_coarse\n", + "svm_rbf\n", + "rf\n", + "en\n", + "lasso\n", + "knn\n", + "bayesian\n", + "butanol\n", + "nn_fine\n", + "nn_coarse\n", + "svm_rbf\n", + "rf\n", + "en\n", + "lasso\n", + "knn\n", + "bayesian\n", + "butyrate\n", + "nn_fine\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79.51816940132096, tolerance: 0.4890807573234609\n", + " positive)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nn_coarse\n", + "svm_rbf\n", + "rf\n", + "en\n", + "lasso\n", + "knn\n", + "bayesian\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 680.3520407331906, tolerance: 0.3975429358853114\n", + " positive)\n", + "/Users/garrettroell/syngas_project/SyngasMachineLearning/venv/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52.524119578237105, tolerance: 0.3975429358853114\n", + " positive)\n" + ] + } + ], + "source": [ + "# Scale X training data\n", + "X_scaled_train = Scaler.fit_transform(X_train, y_train)\n", + "\n", + "for index, output in enumerate(['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']):\n", + " print(output)\n", + " \n", + " # separate out the output of interest\n", + " y_train_output=y_train[:,index]\n", + "\n", + " \n", + " for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n", + " print(algorithm)\n", + "\n", + " # train the model\n", + " model = optimized_parameters[output][algorithm].fit(X_scaled_train, y_train_output)\n", + "\n", + " # serialize the model\n", + " filename = f'../trained_models/{output}/{algorithm}.pkl'\n", + "\n", + " with open(filename, 'wb') as file: \n", + " pickle.dump(model, file)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.9 ('venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + }, + "vscode": { + "interpreter": { + "hash": "b3f28f6adfd1eea15c9e00a250e9367c9752811d529c8274081719d51bde14e5" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}