{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ae82c606", "metadata": {}, "outputs": [], "source": [ "# Import dependencies and packages neededfrom sklearn import datasets\n", "\n", "import joblib\n", "from sklearn.neural_network import MLPClassifier, MLPRegressor\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn import metrics\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sn\n", "import matplotlib.pyplot as plt\n", "import warnings\n", "warnings.filterwarnings('ignore', category = FutureWarning)\n", "warnings.filterwarnings('ignore', category = DeprecationWarning)" ] }, { "cell_type": "code", "execution_count": 2, "id": "9d5432cd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 200, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}\n", "\n", "{'activation': 'relu', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.999, 'early_stopping': False, 'epsilon': 1e-08, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 200, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': False, 'warm_start': False}\n" ] } ], "source": [ "# this step is just to demonstrate the adjustable parameters within the algorithm and not a necessary step\n", "print(MLPClassifier().get_params())\n", "print()\n", "print(MLPRegressor().get_params())" ] }, { "cell_type": "code", "execution_count": 3, "id": "6a6fb38c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PatientIdPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
0161487235033.60.627501
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" ], "text/plain": [ " PatientId Pregnancies Glucose BloodPressure SkinThickness Insulin \\\n", "0 1 6 148 72 35 0 \n", "1 2 1 85 66 29 0 \n", "2 3 8 183 64 0 0 \n", "3 4 1 89 66 23 94 \n", "4 5 0 137 40 35 168 \n", "\n", " BMI DiabetesPedigreeFunction Age Outcome \n", "0 33.6 0.627 50 1 \n", "1 26.6 0.351 31 0 \n", "2 23.3 0.672 32 1 \n", "3 28.1 0.167 21 0 \n", "4 43.1 2.288 33 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import the dataset (stored in a variable)\n", "diabetes = pd.read_csv(\"diabetes.csv\")\n", "# Inspect the data(first five rows, or this can be changed by including how many rows within the brackets)\n", "diabetes.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "01ebfb9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 614 entries, 60 to 102\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PatientId 614 non-null int64 \n", " 1 Pregnancies 614 non-null int64 \n", " 2 Glucose 614 non-null int64 \n", " 3 BloodPressure 614 non-null int64 \n", " 4 SkinThickness 614 non-null int64 \n", " 5 Insulin 614 non-null int64 \n", " 6 BMI 614 non-null float64\n", " 7 DiabetesPedigreeFunction 614 non-null float64\n", " 8 Age 614 non-null int64 \n", " 9 Outcome 614 non-null int64 \n", "dtypes: float64(2), int64(8)\n", "memory usage: 52.8 KB\n" ] } ], "source": [ "# split the data into train and test using the SciKitLearn package\n", "from sklearn.model_selection import train_test_split\n", "train,test=train_test_split(diabetes,test_size=0.2,random_state=42)\n", "# view the information\n", "train.info()" ] }, { "cell_type": "code", "execution_count": 5, "id": "6fa51623", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 154 entries, 668 to 462\n", "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Pregnancies 154 non-null int64 \n", " 1 Glucose 154 non-null int64 \n", " 2 BloodPressure 154 non-null int64 \n", " 3 SkinThickness 154 non-null int64 \n", " 4 Insulin 154 non-null int64 \n", " 5 BMI 154 non-null float64\n", " 6 DiabetesPedigreeFunction 154 non-null float64\n", " 7 Age 154 non-null int64 \n", "dtypes: float64(2), int64(6)\n", "memory usage: 10.8 KB\n" ] } ], "source": [ "# Ensure only labels are saved in y variables\n", "y_train=train['Outcome']\n", "y_test=test['Outcome']\n", "#drop the unwanted / irrelevant columns, features are stored in X variables\n", "X_train=train.drop(['PatientId','Outcome'],axis=1)\n", "X_test=test.drop(['PatientId','Outcome'],axis=1)\n", "# view the information\n", "X_test.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "5303cb33", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MLPClassifier()" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a model for Support Vector (Machine) Classifier using default parameters\n", "mlp = MLPClassifier()\n", "# fit the data to the model, both features and labels to train it\n", "mlp.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 7, "id": "ac8118a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n", " dtype=int64)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# use the model to classify the datapoints within the test set\n", "y_pred2=mlp.predict(X_test)\n", "# print predicted classifications\n", "y_pred2" ] }, { "cell_type": "code", "execution_count": 8, "id": "5e3c86af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Actual Predicted\n", "668 0 0\n", "324 0 0\n", "624 0 0\n", "690 0 0\n", "473 0 0\n", ".. ... ...\n", "355 1 1\n", "534 0 0\n", "344 0 0\n", "296 1 0\n", "462 0 0\n", "\n", "[154 rows x 2 columns]\n" ] } ], "source": [ "# use Pandas to create a dataframe to view the ground truth and classifications side by side\n", "data = {'Actual': y_test,\n", " 'Predicted': y_pred2}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 9, "id": "c2d56594", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.660 0.960 0.782 99\n", " 1 0.600 0.109 0.185 55\n", "\n", " accuracy 0.656 154\n", " macro avg 0.630 0.534 0.483 154\n", "weighted avg 0.638 0.656 0.569 154\n", "\n" ] } ], "source": [ "# Use Seaborn to create a confusion matrix heatmap and MatPlotLib to display\n", "confusion_matrix = pd.crosstab(df['Actual'], df['Predicted'], rownames=['Actual'], colnames=['Predicted'])\n", "sn.heatmap(confusion_matrix, annot=True)\n", "plt.show()\n", "print(metrics.classification_report(y_test, y_pred2, digits=3))" ] }, { "cell_type": "code", "execution_count": 10, "id": "799e3236", "metadata": {}, "outputs": [], "source": [ "# Create a function to show the results of the classifier under different parameter values\n", "def print_results(results):\n", " print('Best Parameters: {}\\n'.format(results.best_params_))\n", " \n", " means = results.cv_results_['mean_test_score']\n", " stds = results.cv_results_['std_test_score']\n", " for mean, std, params in zip(means, stds, results.cv_results_['params']):\n", " print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))" ] }, { "cell_type": "code", "execution_count": 11, "id": "b7fc0497", "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:372: FitFailedWarning: \n", "45 fits failed out of a total of 180.\n", "The score on these train-test partitions for these parameters will be set to nan.\n", "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", "\n", "Below are more details about the failures:\n", "--------------------------------------------------------------------------------\n", "45 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 680, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", " File \"C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\", line 752, in fit\n", " return self._fit(X, y, incremental=False)\n", " File \"C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\", line 384, in _fit\n", " self._validate_hyperparameters()\n", " File \"C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\", line 493, in _validate_hyperparameters\n", " raise ValueError(\n", "ValueError: The activation 'sigmoid' is not supported. Supported activations are ['identity', 'logistic', 'relu', 'softmax', 'tanh'].\n", "\n", " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:969: UserWarning: One or more of the test scores are non-finite: [0.65641743 0.65807011 0.66453419 0.70525123 0.6970945 0.68087432\n", " 0.67429028 0.7100893 0.70687725 0.64984673 0.67277089 0.65635079\n", " 0.70194589 0.69222977 0.7019992 0.71179528 0.70855658 0.69874717\n", " 0.67261096 0.65636412 0.65960283 0.71506064 0.71989871 0.71507397\n", " 0.74434226 0.73618553 0.74270292 nan nan nan\n", " nan nan nan nan nan nan]\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, estimator=MLPClassifier(),\n", " param_grid={'activation': ['relu', 'tanh', 'logistic', 'sigmoid'],\n", " 'hidden_layer_sizes': [(10,), (50,), (100,)],\n", " 'learning_rate': ['constant', 'adaptive',\n", " 'invscaling']})" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mlp_clf = MLPClassifier()\n", "parameters = {\n", " 'hidden_layer_sizes': [(10,), (50,), (100,)],\n", " 'activation': ['relu', 'tanh', 'logistic', 'sigmoid'],\n", " 'learning_rate': ['constant', 'adaptive', 'invscaling'],\n", "}\n", "\n", "cv = GridSearchCV(mlp_clf, parameters, cv = 5)\n", "cv.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 12, "id": "ecdc0f65", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate': 'constant'}\n", "\n", "0.656 (+/-0.075) for {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant'}\n", "0.658 (+/-0.1) for {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'adaptive'}\n", "0.665 (+/-0.078) for {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling'}\n", "0.705 (+/-0.057) for {'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant'}\n", "0.697 (+/-0.076) for {'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive'}\n", "0.681 (+/-0.066) for {'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate': 'invscaling'}\n", "0.674 (+/-0.026) for {'activation': 'relu', 'hidden_layer_sizes': (100,), 'learning_rate': 'constant'}\n", "0.71 (+/-0.017) for {'activation': 'relu', 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive'}\n", "0.707 (+/-0.063) for {'activation': 'relu', 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling'}\n", "0.65 (+/-0.042) for {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant'}\n", "0.673 (+/-0.106) for {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate': 'adaptive'}\n", "0.656 (+/-0.083) for {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling'}\n", "0.702 (+/-0.049) for {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant'}\n", "0.692 (+/-0.061) for {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive'}\n", "0.702 (+/-0.066) for {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate': 'invscaling'}\n", "0.712 (+/-0.077) for {'activation': 'tanh', 'hidden_layer_sizes': (100,), 'learning_rate': 'constant'}\n", "0.709 (+/-0.069) for {'activation': 'tanh', 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive'}\n", "0.699 (+/-0.055) for {'activation': 'tanh', 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling'}\n", "0.673 (+/-0.037) for {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant'}\n", "0.656 (+/-0.01) for {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate': 'adaptive'}\n", "0.66 (+/-0.026) for {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling'}\n", "0.715 (+/-0.072) for {'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant'}\n", "0.72 (+/-0.041) for {'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive'}\n", "0.715 (+/-0.066) for {'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate': 'invscaling'}\n", "0.744 (+/-0.073) for {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate': 'constant'}\n", "0.736 (+/-0.054) for {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive'}\n", "0.743 (+/-0.04) for {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (10,), 'learning_rate': 'constant'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (10,), 'learning_rate': 'adaptive'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (10,), 'learning_rate': 'invscaling'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (50,), 'learning_rate': 'constant'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (50,), 'learning_rate': 'adaptive'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (50,), 'learning_rate': 'invscaling'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (100,), 'learning_rate': 'constant'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive'}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (100,), 'learning_rate': 'invscaling'}\n" ] } ], "source": [ "print_results(cv)" ] }, { "cell_type": "code", "execution_count": 13, "id": "9b0e76b6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:372: FitFailedWarning: \n", "75 fits failed out of a total of 300.\n", "The score on these train-test partitions for these parameters will be set to nan.\n", "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", "\n", "Below are more details about the failures:\n", "--------------------------------------------------------------------------------\n", "75 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 680, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", " File \"C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\", line 752, in fit\n", " return self._fit(X, y, incremental=False)\n", " File \"C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\", line 384, in _fit\n", " self._validate_hyperparameters()\n", " File \"C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py\", line 493, in _validate_hyperparameters\n", " raise ValueError(\n", "ValueError: The activation 'sigmoid' is not supported. Supported activations are ['identity', 'logistic', 'relu', 'softmax', 'tanh'].\n", "\n", " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", "C:\\Users\\Mark Huntly\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:969: UserWarning: One or more of the test scores are non-finite: [0.66126883 0.67590297 0.65960283 0.65309876 0.65309876 0.66779955\n", " 0.60922298 0.67423697 0.65309876 0.59293616 0.70365187 0.66617353\n", " 0.67277089 0.65309876 0.65309876 0.65797681 0.67275756 0.65309876\n", " 0.59293616 0.53064108 0.70362522 0.68415301 0.63206717 0.64984673\n", " 0.57992803 0.70521125 0.70521125 0.62549647 0.52738904 0.59080368\n", " 0.67103825 0.67595628 0.6368386 0.65309876 0.59131014 0.7264161\n", " 0.72473677 0.64334266 0.59456218 0.46885246 0.73129415 0.71666\n", " 0.64822071 0.65147274 0.65309876 nan nan nan\n", " nan nan nan nan nan nan\n", " nan nan nan nan nan nan]\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=5, estimator=MLPClassifier(),\n", " param_grid={'activation': ['relu', 'tanh', 'logistic', 'sigmoid'],\n", " 'hidden_layer_sizes': [(10,), (50,), (100,)],\n", " 'learning_rate_init': [0.001, 0.01, 0.1, 1, 10]})" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mlp_clf_const = MLPClassifier()\n", "parameters = {\n", " 'hidden_layer_sizes': [(10,), (50,), (100,)],\n", " 'activation': ['relu', 'tanh', 'logistic', 'sigmoid'],\n", " 'learning_rate_init': [0.001, 0.01, 0.1, 1, 10],\n", "}\n", "\n", "cv = GridSearchCV(mlp_clf, parameters, cv = 5)\n", "cv.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 14, "id": "aab80f4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.001}\n", "\n", "0.661 (+/-0.065) for {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.001}\n", "0.676 (+/-0.06) for {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.01}\n", "0.66 (+/-0.028) for {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.1}\n", "0.653 (+/-0.007) for {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate_init': 1}\n", "0.653 (+/-0.007) for {'activation': 'relu', 'hidden_layer_sizes': (10,), 'learning_rate_init': 10}\n", "0.668 (+/-0.047) for {'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.001}\n", "0.609 (+/-0.131) for {'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.01}\n", "0.674 (+/-0.062) for {'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.1}\n", "0.653 (+/-0.007) for {'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate_init': 1}\n", "0.593 (+/-0.243) for {'activation': 'relu', 'hidden_layer_sizes': (50,), 'learning_rate_init': 10}\n", "0.704 (+/-0.064) for {'activation': 'relu', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.001}\n", "0.666 (+/-0.035) for {'activation': 'relu', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.01}\n", "0.673 (+/-0.082) for {'activation': 'relu', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.1}\n", "0.653 (+/-0.007) for {'activation': 'relu', 'hidden_layer_sizes': (100,), 'learning_rate_init': 1}\n", "0.653 (+/-0.007) for {'activation': 'relu', 'hidden_layer_sizes': (100,), 'learning_rate_init': 10}\n", "0.658 (+/-0.027) for {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.001}\n", "0.673 (+/-0.076) for {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.01}\n", "0.653 (+/-0.007) for {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.1}\n", "0.593 (+/-0.243) for {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate_init': 1}\n", "0.531 (+/-0.3) for {'activation': 'tanh', 'hidden_layer_sizes': (10,), 'learning_rate_init': 10}\n", "0.704 (+/-0.042) for {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.001}\n", "0.684 (+/-0.074) for {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.01}\n", "0.632 (+/-0.101) for {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.1}\n", "0.65 (+/-0.017) for {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate_init': 1}\n", "0.58 (+/-0.237) for {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate_init': 10}\n", "0.705 (+/-0.026) for {'activation': 'tanh', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.001}\n", "0.705 (+/-0.012) for {'activation': 'tanh', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.01}\n", "0.625 (+/-0.119) for {'activation': 'tanh', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.1}\n", "0.527 (+/-0.301) for {'activation': 'tanh', 'hidden_layer_sizes': (100,), 'learning_rate_init': 1}\n", "0.591 (+/-0.247) for {'activation': 'tanh', 'hidden_layer_sizes': (100,), 'learning_rate_init': 10}\n", "0.671 (+/-0.054) for {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.001}\n", "0.676 (+/-0.065) for {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.01}\n", "0.637 (+/-0.038) for {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.1}\n", "0.653 (+/-0.007) for {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate_init': 1}\n", "0.591 (+/-0.242) for {'activation': 'logistic', 'hidden_layer_sizes': (10,), 'learning_rate_init': 10}\n", "0.726 (+/-0.051) for {'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.001}\n", "0.725 (+/-0.032) for {'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.01}\n", "0.643 (+/-0.017) for {'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.1}\n", "0.595 (+/-0.245) for {'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate_init': 1}\n", "0.469 (+/-0.297) for {'activation': 'logistic', 'hidden_layer_sizes': (50,), 'learning_rate_init': 10}\n", "0.731 (+/-0.059) for {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.001}\n", "0.717 (+/-0.052) for {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.01}\n", "0.648 (+/-0.023) for {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.1}\n", "0.651 (+/-0.011) for {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate_init': 1}\n", "0.653 (+/-0.007) for {'activation': 'logistic', 'hidden_layer_sizes': (100,), 'learning_rate_init': 10}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.001}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.01}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (10,), 'learning_rate_init': 0.1}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (10,), 'learning_rate_init': 1}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (10,), 'learning_rate_init': 10}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.001}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.01}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.1}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (50,), 'learning_rate_init': 1}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (50,), 'learning_rate_init': 10}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.001}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.01}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (100,), 'learning_rate_init': 0.1}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (100,), 'learning_rate_init': 1}\n", "nan (+/-nan) for {'activation': 'sigmoid', 'hidden_layer_sizes': (100,), 'learning_rate_init': 10}\n" ] } ], "source": [ "print_results(cv)" ] }, { "cell_type": "code", "execution_count": null, "id": "ac795dab", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }