added random forest classifier
mahehsma
parent
45195943d7
commit
77c9299308
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 4,
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"id": "initial_id",
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"metadata": {
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"jupyter": {
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@ -12,12 +12,14 @@
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sklearn.preprocessing import MinMaxScaler, StandardScaler"
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"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
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"from sklearn.model_selection import KFold\n",
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"from sklearn import decomposition"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 2,
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"id": "67503952-9074-4cdb-9d7e-d9142f7c319c",
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"metadata": {},
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"outputs": [
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@ -216,7 +218,7 @@
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"[5 rows x 28 columns]"
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]
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},
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"execution_count": 21,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -252,7 +254,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 6,
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"id": "2bbee865-c000-43da-84d9-ce7e04874110",
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"metadata": {},
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"outputs": [],
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@ -271,7 +273,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 20,
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"id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933",
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"metadata": {},
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"outputs": [
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@ -280,56 +282,56 @@
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"output_type": "stream",
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"text": [
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"Training fold 0 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 0: 0.9\n",
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"\tTrain samples:\t267\n",
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"\tTest samples:\t30\n",
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"\tAccuracy of fold 0: 0.8666666666666667\n",
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"Training fold 1 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 1: 0.8666666666666667\n",
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"\tTrain samples:\t267\n",
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"\tTest samples:\t30\n",
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"\tAccuracy of fold 1: 0.8\n",
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"Training fold 2 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 2: 0.8666666666666667\n",
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"\tTrain samples:\t267\n",
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"\tTest samples:\t30\n",
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"\tAccuracy of fold 2: 0.9\n",
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"Training fold 3 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 3: 0.9\n",
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"\tTrain samples:\t267\n",
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"\tTest samples:\t30\n",
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"\tAccuracy of fold 3: 0.9\n",
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"Training fold 4 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 4: 0.9\n",
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"\tTrain samples:\t267\n",
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"\tTest samples:\t30\n",
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"\tAccuracy of fold 4: 0.8666666666666667\n",
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"Training fold 5 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 5: 0.8333333333333334\n",
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"\tTrain samples:\t267\n",
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"\tTest samples:\t30\n",
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"\tAccuracy of fold 5: 0.8\n",
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"Training fold 6 for 20 epochs\n",
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"Train samples:\t267\n",
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"Test samples:\t30\n",
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"Accuracy of fold 6: 0.7666666666666667\n",
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"\tTrain samples:\t267\n",
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"\tTest samples:\t30\n",
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"\tAccuracy of fold 6: 0.8333333333333334\n",
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"Training fold 7 for 20 epochs\n",
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"Train samples:\t268\n",
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"Test samples:\t29\n",
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"Accuracy of fold 7: 0.8275862068965517\n",
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"\tTrain samples:\t268\n",
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"\tTest samples:\t29\n",
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"\tAccuracy of fold 7: 0.8620689655172413\n",
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"Training fold 8 for 20 epochs\n",
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"Train samples:\t268\n",
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"Test samples:\t29\n",
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"Accuracy of fold 8: 0.7586206896551724\n",
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"\tTrain samples:\t268\n",
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"\tTest samples:\t29\n",
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"\tAccuracy of fold 8: 0.7241379310344828\n",
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"Training fold 9 for 20 epochs\n",
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"Train samples:\t268\n",
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"Test samples:\t29\n",
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"Accuracy of fold 9: 0.7586206896551724\n",
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"Avg accuracy 0.837816091954023\n"
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"\tTrain samples:\t268\n",
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"\tTest samples:\t29\n",
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"\tAccuracy of fold 9: 0.896551724137931\n",
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"Avg accuracy 0.8449425287356321\n"
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]
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}
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],
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"source": [
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"from sklearn.model_selection import KFold\n",
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"from sklearn import decomposition\n",
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"import tensorflow as tf\n",
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"\n",
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"use_pca = True\n",
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"# number of components extracted from the pca\n",
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"n_features = 8\n",
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"n_features = n_features if use_pca else len(X.columns)\n",
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"\n",
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"epochs = 20\n",
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"k_folds = 10\n",
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@ -345,27 +347,30 @@
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" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
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" y_train, y_test = y[train_idx], y[test_idx]\n",
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"\n",
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" print(f'Train samples:\\t{len(X_train)}')\n",
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" print(f'Test samples:\\t{len(X_test)}')\n",
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" print(f'\\tTrain samples:\\t{len(X_train)}')\n",
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" print(f'\\tTest samples:\\t{len(X_test)}')\n",
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"\n",
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" # do pca based on the train data of the given fold to extract 'n_features'\n",
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" pca = decomposition.PCA(n_components=n_features)\n",
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" pca.fit(X_train)\n",
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" X_train = pca.transform(X_train)\n",
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" if use_pca:\n",
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" # do pca based on the train data of the given fold to extract 'n_features'\n",
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" pca = decomposition.PCA(n_components=n_features)\n",
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" pca.fit(X_train)\n",
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" X_train = pca.transform(X_train)\n",
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"\n",
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" # train the model using the components extracted from pca\n",
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" model = get_model(n_features)\n",
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" model.fit(X_train, y_train, epochs=epochs, verbose=0)\n",
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"\n",
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" # transform test data using on the pca model trained on the train data\n",
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" X_test = pca.transform(X_test)\n",
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" if use_pca:\n",
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" # transform test data using on the pca model trained on the train data\n",
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" X_test = pca.transform(X_test)\n",
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" \n",
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" y_pred = model.predict(X_test, verbose=0)\n",
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" y_pred = y_pred > 0.5\n",
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" y_pred = y_pred > 0.5 # threshold to binarize\n",
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"\n",
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" # calculate the accuracy of the train data for the current fold\n",
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" accuracy = sum(y_pred == y_test)[0] / len(y_pred)\n",
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" accuracies.append(accuracy)\n",
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" print(f'Accuracy of fold {i}: {accuracy}')\n",
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" print(f'\\tAccuracy of fold {i}: {accuracy}')\n",
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"\n",
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"# calculate the average accuracy over all folds\n",
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"avg_accuracy = sum(accuracies) / len(accuracies)\n",
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@ -374,7 +379,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"execution_count": 22,
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"id": "95215693-47c9-4202-92f5-efbc65bc32c9",
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"metadata": {},
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"outputs": [
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@ -383,16 +388,14 @@
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"output_type": "stream",
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"text": [
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"Training fold 0 for 20 epochs\n",
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"Train samples:\t237\n",
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"Test samples:\t60\n"
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"\tTrain samples:\t237\n",
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"\tTest samples:\t60\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
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" warnings.warn(\n",
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
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" warnings.warn(\n"
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]
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@ -401,20 +404,16 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 1 1 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 1\n",
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" 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1]\n",
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"Accuracy of fold 0: 0.5833333333333334\n",
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"\tAccuracy of fold 0: 0.5833333333333334\n",
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"Training fold 1 for 20 epochs\n",
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"Train samples:\t237\n",
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"Test samples:\t60\n"
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"\tTrain samples:\t237\n",
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"\tTest samples:\t60\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
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" warnings.warn(\n",
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
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" warnings.warn(\n"
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]
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@ -423,20 +422,16 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1 0 1 0 1 1 0 0 1 0 0 1 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 1 1\n",
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" 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1]\n",
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"Accuracy of fold 1: 0.5\n",
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"\tAccuracy of fold 1: 0.5\n",
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"Training fold 2 for 20 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n"
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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
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" warnings.warn(\n",
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
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" warnings.warn(\n"
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]
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@ -445,20 +440,16 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0\n",
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" 0 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0]\n",
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"Accuracy of fold 2: 0.559322033898305\n",
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"\tAccuracy of fold 2: 0.559322033898305\n",
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"Training fold 3 for 20 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n"
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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
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" warnings.warn(\n",
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
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" warnings.warn(\n"
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]
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@ -467,20 +458,16 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 1 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1\n",
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" 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 1 1]\n",
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"Accuracy of fold 3: 0.576271186440678\n",
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"\tAccuracy of fold 3: 0.576271186440678\n",
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"Training fold 4 for 20 epochs\n",
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"Train samples:\t238\n",
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"Test samples:\t59\n"
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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
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" warnings.warn(\n",
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"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
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" warnings.warn(\n"
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]
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1 1 1 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 1\n",
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" 1 0 1 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1]\n",
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"Accuracy of fold 4: 0.5254237288135594\n",
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"\tAccuracy of fold 4: 0.5254237288135594\n",
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"Avg accuracy 0.5488700564971751\n"
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]
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}
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"source": [
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"from sklearn.cluster import KMeans\n",
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"\n",
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"use_pca = True\n",
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"# number of components extracted from the pca\n",
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"n_features = 10\n",
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"\n",
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" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
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" y_train, y_test = y[train_idx], y[test_idx]\n",
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"\n",
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" print(f'Train samples:\\t{len(X_train)}')\n",
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" print(f'Test samples:\\t{len(X_test)}')\n",
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" print(f'\\tTrain samples:\\t{len(X_train)}')\n",
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" print(f'\\tTest samples:\\t{len(X_test)}')\n",
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"\n",
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" # do pca based on the train data of the given fold to extract 'n_features'\n",
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" #pca = decomposition.PCA(n_components=n_features)\n",
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" #pca.fit(X_train)\n",
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" #X_train = pca.transform(X_train)\n",
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" if use_pca:\n",
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" # do pca based on the train data of the given fold to extract 'n_features'\n",
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" pca = decomposition.PCA(n_components=n_features)\n",
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" pca.fit(X_train)\n",
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" X_train = pca.transform(X_train)\n",
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"\n",
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" model = KMeans(n_clusters=2)\n",
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" model = KMeans(n_clusters=2, n_init=10)\n",
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" model.fit(X_train)\n",
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"\n",
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" #X_test = pca.transform(X_test)\n",
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" if use_pca:\n",
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" X_test = pca.transform(X_test)\n",
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" \n",
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" y_pred = model.predict(X_test)\n",
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" print(y_pred)\n",
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" \n",
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"\n",
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" # calculate the accuracy of the train data for the current fold\n",
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" accuracy1 = sum(y_pred == y_test)[0] / len(y_pred)\n",
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" accuracy2 = sum(y_pred != y_test)[0] / len(y_pred)\n",
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" accuracy = max(accuracy1, accuracy2)\n",
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" accuracies.append(accuracy)\n",
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" print(f'Accuracy of fold {i}: {accuracy}')\n",
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" print(f'\\tAccuracy of fold {i}: {accuracy}')\n",
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"\n",
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"# calculate the average accuracy over all folds\n",
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"avg_accuracy = sum(accuracies) / len(accuracies)\n",
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@ -545,11 +532,85 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 23,
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"id": "880302e4-82c1-47b9-9fe3-cb3567511639",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Training fold 0 for 20 epochs\n",
|
||||
"\tTrain samples:\t237\n",
|
||||
"\tTest samples:\t60\n",
|
||||
"\tAccuracy of fold 0: 0.85\n",
|
||||
"Training fold 1 for 20 epochs\n",
|
||||
"\tTrain samples:\t237\n",
|
||||
"\tTest samples:\t60\n",
|
||||
"\tAccuracy of fold 1: 0.9\n",
|
||||
"Training fold 2 for 20 epochs\n",
|
||||
"\tTrain samples:\t238\n",
|
||||
"\tTest samples:\t59\n",
|
||||
"\tAccuracy of fold 2: 0.847457627118644\n",
|
||||
"Training fold 3 for 20 epochs\n",
|
||||
"\tTrain samples:\t238\n",
|
||||
"\tTest samples:\t59\n",
|
||||
"\tAccuracy of fold 3: 0.7627118644067796\n",
|
||||
"Training fold 4 for 20 epochs\n",
|
||||
"\tTrain samples:\t238\n",
|
||||
"\tTest samples:\t59\n",
|
||||
"\tAccuracy of fold 4: 0.7796610169491526\n",
|
||||
"Avg accuracy 0.8279661016949152\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||||
"\n",
|
||||
"use_pca = True\n",
|
||||
"# number of components extracted from the pca\n",
|
||||
"n_features = 10\n",
|
||||
"\n",
|
||||
"k_folds = 5\n",
|
||||
"\n",
|
||||
"# used to split the dataset into k folds\n",
|
||||
"kf = KFold(n_splits=k_folds)\n",
|
||||
"\n",
|
||||
"accuracies = []\n",
|
||||
"for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n",
|
||||
" print(f'Training fold {i} for {epochs} epochs')\n",
|
||||
"\n",
|
||||
" # extract train and test data from the cleaned dataset\n",
|
||||
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
|
||||
" y_train, y_test = y[train_idx], y[test_idx]\n",
|
||||
" y_train, y_test = y_train[:, 0], y_test[:, 0]\n",
|
||||
"\n",
|
||||
" print(f'\\tTrain samples:\\t{len(X_train)}')\n",
|
||||
" print(f'\\tTest samples:\\t{len(X_test)}')\n",
|
||||
"\n",
|
||||
" if use_pca:\n",
|
||||
" # do pca based on the train data of the given fold to extract 'n_features'\n",
|
||||
" pca = decomposition.PCA(n_components=n_features)\n",
|
||||
" pca.fit(X_train)\n",
|
||||
" X_train = pca.transform(X_train)\n",
|
||||
"\n",
|
||||
" model = RandomForestClassifier(max_depth=2, random_state=0)\n",
|
||||
" model.fit(X_train, y_train)\n",
|
||||
"\n",
|
||||
" if use_pca:\n",
|
||||
" X_test = pca.transform(X_test)\n",
|
||||
" \n",
|
||||
" y_pred = model.predict(X_test)\n",
|
||||
"\n",
|
||||
" # calculate the accuracy of the train data for the current fold\n",
|
||||
" accuracy = sum(y_pred == y_test) / len(y_pred)\n",
|
||||
" accuracies.append(accuracy)\n",
|
||||
" print(f'\\tAccuracy of fold {i}: {accuracy}')\n",
|
||||
"\n",
|
||||
"# calculate the average accuracy over all folds\n",
|
||||
"avg_accuracy = sum(accuracies) / len(accuracies)\n",
|
||||
"print(f'Avg accuracy {avg_accuracy}')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue