simplified output messages
mahehsma
parent
77c9299308
commit
84e749f9c0
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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": 4,
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"execution_count": 1,
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"id": "initial_id",
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"metadata": {
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"jupyter": {
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@ -254,7 +254,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 3,
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"id": "2bbee865-c000-43da-84d9-ce7e04874110",
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"metadata": {},
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"outputs": [],
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@ -273,7 +273,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 4,
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"id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933",
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"metadata": {},
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"outputs": [
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@ -281,47 +281,40 @@
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"name": "stdout",
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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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"\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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"\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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"\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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"\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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"\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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"\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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"\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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"\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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"\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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"\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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"Training 10 folds for 20 epochs\n",
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"Fold 0\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 90.000%\n",
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"Fold 1\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 80.000%\n",
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"Fold 2\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 90.000%\n",
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"Fold 3\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 90.000%\n",
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"Fold 4\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000023D0BD63C40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
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"\tAccuracy: 90.000%\n",
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"Fold 5\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x0000023D0D548CC0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
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"\tAccuracy: 86.667%\n",
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"Fold 6\n",
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"\tTrain samples:\t267\tTest samples:\t30\n",
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"\tAccuracy: 80.000%\n",
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"Fold 7\n",
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"\tTrain samples:\t268\tTest samples:\t29\n",
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"\tAccuracy: 86.207%\n",
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"Fold 8\n",
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"\tTrain samples:\t268\tTest samples:\t29\n",
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"\tAccuracy: 79.310%\n",
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"Fold 9\n",
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"\tTrain samples:\t268\tTest samples:\t29\n",
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"\tAccuracy: 82.759%\n",
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"Avg accuracy 85.494%\n"
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]
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}
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],
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@ -340,15 +333,16 @@
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"kf = KFold(n_splits=k_folds)\n",
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"\n",
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"accuracies = []\n",
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"print(f'Training {k_folds} folds for {epochs} epochs')\n",
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"for i, (train_idx, test_idx) in enumerate(kf.split(X)):\n",
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" print(f'Training fold {i} for {epochs} epochs')\n",
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"\n",
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" print(f'Fold {i}')\n",
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" \n",
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" # extract train and test data from the cleaned dataset\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'\\tTrain samples:\\t{len(X_train)}')\n",
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" print(f'\\tTest samples:\\t{len(X_test)}')\n",
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" print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n",
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"\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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@ -370,16 +364,16 @@
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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'\\tAccuracy of fold {i}: {accuracy}')\n",
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" print(f'\\tAccuracy: {accuracy:.3%}')\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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"print(f'Avg accuracy {avg_accuracy}')"
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"print(f'Avg accuracy {avg_accuracy:.3%}')"
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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": 22,
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"execution_count": 5,
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"id": "95215693-47c9-4202-92f5-efbc65bc32c9",
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"metadata": {},
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"outputs": [
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@ -387,9 +381,9 @@
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"name": "stdout",
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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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"\tTrain samples:\t237\n",
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"\tTest samples:\t60\n"
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"Training 5 folds\n",
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"Fold 0\n",
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"\tTrain samples:\t237\tTest samples:\t60\n"
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]
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},
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{
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@ -404,10 +398,10 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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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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"\tTrain samples:\t237\n",
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"\tTest samples:\t60\n"
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"\tAccuracy 58.333%\n",
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"\n",
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"Fold 1\n",
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"\tTrain samples:\t237\tTest samples:\t60\n"
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]
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},
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{
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@ -422,10 +416,10 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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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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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n"
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"\tAccuracy 50.000%\n",
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"\n",
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"Fold 2\n",
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"\tTrain samples:\t238\tTest samples:\t59\n"
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]
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},
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{
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@ -440,10 +434,10 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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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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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n"
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"\tAccuracy 55.932%\n",
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"\n",
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"Fold 3\n",
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"\tTrain samples:\t238\tTest samples:\t59\n"
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]
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},
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{
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@ -458,10 +452,10 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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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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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n"
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"\tAccuracy 57.627%\n",
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"\n",
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"Fold 4\n",
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"\tTrain samples:\t238\tTest samples:\t59\n"
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]
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},
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{
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@ -476,8 +470,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\tAccuracy of fold 4: 0.5254237288135594\n",
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"Avg accuracy 0.5488700564971751\n"
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"\tAccuracy 52.542%\n",
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"\n",
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"Avg accuracy 54.887%\n"
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]
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}
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],
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@ -494,15 +489,16 @@
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"kf = KFold(n_splits=k_folds)\n",
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"\n",
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"accuracies = []\n",
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"print(f'Training {k_folds} folds')\n",
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"for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n",
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" print(f'Training fold {i} for {epochs} epochs')\n",
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"\n",
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" print(f'Fold {i}')\n",
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" \n",
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" # extract train and test data from the cleaned dataset\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'\\tTrain samples:\\t{len(X_train)}')\n",
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" print(f'\\tTest samples:\\t{len(X_test)}')\n",
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" print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n",
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"\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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@ -523,16 +519,17 @@
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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'\\tAccuracy of fold {i}: {accuracy}')\n",
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" print(f'\\tAccuracy {accuracy:.3%}')\n",
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" print()\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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"print(f'Avg accuracy {avg_accuracy}')"
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"print(f'Avg accuracy {avg_accuracy:.3%}')"
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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": 23,
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"execution_count": 6,
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"id": "880302e4-82c1-47b9-9fe3-cb3567511639",
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"metadata": {},
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"outputs": [
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@ -540,27 +537,28 @@
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"name": "stdout",
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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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"\tTrain samples:\t237\n",
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"\tTest samples:\t60\n",
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"\tAccuracy of fold 0: 0.85\n",
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"Training fold 1 for 20 epochs\n",
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"\tTrain samples:\t237\n",
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"\tTest samples:\t60\n",
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"\tAccuracy of fold 1: 0.9\n",
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"Training fold 2 for 20 epochs\n",
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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n",
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"\tAccuracy of fold 2: 0.847457627118644\n",
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"Training fold 3 for 20 epochs\n",
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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n",
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"\tAccuracy of fold 3: 0.7627118644067796\n",
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"Training fold 4 for 20 epochs\n",
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"\tTrain samples:\t238\n",
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"\tTest samples:\t59\n",
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"\tAccuracy of fold 4: 0.7796610169491526\n",
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"Avg accuracy 0.8279661016949152\n"
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"Training 5 folds\n",
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"Fold 0\n",
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"\tTrain samples:\t237\tTest samples:\t60\n",
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"\tAccuracy 85.000%\n",
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"\n",
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"Fold 1\n",
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"\tTrain samples:\t237\tTest samples:\t60\n",
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"\tAccuracy 90.000%\n",
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"\n",
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"Fold 2\n",
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"\tTrain samples:\t238\tTest samples:\t59\n",
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"\tAccuracy 84.746%\n",
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"\n",
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"Fold 3\n",
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"\tTrain samples:\t238\tTest samples:\t59\n",
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"\tAccuracy 76.271%\n",
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"\n",
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"Fold 4\n",
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"\tTrain samples:\t238\tTest samples:\t59\n",
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"\tAccuracy 77.966%\n",
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"\n",
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"Avg accuracy 82.797%\n"
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]
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}
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],
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"kf = KFold(n_splits=k_folds)\n",
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"\n",
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"accuracies = []\n",
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"print(f'Training {k_folds} folds')\n",
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"for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n",
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" print(f'Training fold {i} for {epochs} epochs')\n",
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" print(f'Fold {i}')\n",
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"\n",
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" # extract train and test data from the cleaned dataset\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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" y_train, y_test = y_train[:, 0], y_test[:, 0]\n",
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"\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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" print(f'\\tTrain samples:\\t{len(X_train)}\\tTest samples:\\t{len(X_test)}')\n",
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"\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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@ -605,11 +603,12 @@
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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) / len(y_pred)\n",
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" accuracies.append(accuracy)\n",
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" print(f'\\tAccuracy of fold {i}: {accuracy}')\n",
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" print(f'\\tAccuracy {accuracy:.3%}')\n",
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" print()\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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"print(f'Avg accuracy {avg_accuracy}')"
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"print(f'Avg accuracy {avg_accuracy:.3%}')"
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]
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}
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],
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Reference in New Issue