{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Multi-metric runs\n", "----------------------------\n", "\n", "This example shows how to evaluate an atom's pipeline on multiple metrics.\n", "\n", "Import the breast cancer dataset from [sklearn.datasets](https://scikit-learn.org/stable/datasets/index.html#wine-dataset). This is a small and easy to train dataset whose goal is to predict whether a patient has breast cancer or not." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import packages\n", "import pandas as pd\n", "from atom import ATOMRegressor" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SexLengthDiameterHeightWhole weightShucked weightViscera weightShell weightRings
0M0.4550.3650.0950.51400.22450.10100.15015
1M0.3500.2650.0900.22550.09950.04850.0707
2F0.5300.4200.1350.67700.25650.14150.2109
3M0.4400.3650.1250.51600.21550.11400.15510
4I0.3300.2550.0800.20500.08950.03950.0557
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" ], "text/plain": [ " Sex Length Diameter Height Whole weight Shucked weight Viscera weight \\\n", "0 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 \n", "1 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 \n", "2 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 \n", "3 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 \n", "4 I 0.330 0.255 0.080 0.2050 0.0895 0.0395 \n", "\n", " Shell weight Rings \n", "0 0.150 15 \n", "1 0.070 7 \n", "2 0.210 9 \n", "3 0.155 10 \n", "4 0.055 7 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load data\n", "X = pd.read_csv(\"./datasets/abalone.csv\")\n", "\n", "# Let's have a look\n", "X.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the pipeline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<< ================== ATOM ================== >>\n", "\n", "Configuration ==================== >>\n", "Algorithm task: Regression.\n", "\n", "Dataset stats ==================== >>\n", "Shape: (4177, 9)\n", "Train set size: 3342\n", "Test set size: 835\n", "-------------------------------------\n", "Memory: 300.88 kB\n", "Scaled: False\n", "Categorical features: 1 (12.5%)\n", "Outlier values: 189 (0.6%)\n", "\n" ] } ], "source": [ "atom = ATOMRegressor(X, n_jobs=1, verbose=2, random_state=1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting Encoder...\n", "Encoding categorical columns...\n", " --> OneHot-encoding feature Sex. Contains 3 classes.\n" ] } ], "source": [ "atom.encode()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training ========================= >>\n", "Models: lSVM, hGBM\n", "Metric: r2, rmse\n", "\n", "\n", "Running hyperparameter tuning for LinearSVM...\n", "| trial | loss | C | dual | r2 | best_r2 | rmse | best_rmse | time_trial | time_ht | state |\n", "| ----- | ----------------------- | ------- | ------- | ------- | ------- | ------- | --------- | ---------- | ------- | -------- |\n", "| 0 | squared_epsilon_insen.. | 0.001 | True | 0.2887 | 0.2887 | -2.6528 | -2.6528 | 0.054s | 0.054s | COMPLETE |\n", "| 1 | squared_epsilon_insen.. | 0.0534 | False | 0.4507 | 0.4507 | -2.3314 | -2.3314 | 0.047s | 0.101s | COMPLETE |\n", "| 2 | squared_epsilon_insen.. | 0.0105 | True | 0.451 | 0.451 | -2.3307 | -2.3307 | 0.056s | 0.157s | COMPLETE |\n", "| 3 | epsilon_insensitive | 0.6215 | True | 0.4266 | 0.451 | -2.3818 | -2.3307 | 0.059s | 0.216s | COMPLETE |\n", "| 4 | squared_epsilon_insen.. | 0.0369 | False | 0.4509 | 0.451 | -2.3308 | -2.3307 | 0.049s | 0.265s | COMPLETE |\n", "| 5 | epsilon_insensitive | 0.0016 | True | -1.5995 | 0.451 | -5.0716 | -2.3307 | 0.049s | 0.314s | COMPLETE |\n", "| 6 | squared_epsilon_insen.. | 61.5811 | False | 0.4499 | 0.451 | -2.333 | -2.3307 | 0.054s | 0.369s | COMPLETE |\n", "| 7 | squared_epsilon_insen.. | 14.898 | False | 0.4499 | 0.451 | -2.333 | -2.3307 | 0.049s | 0.418s | COMPLETE |\n", "| 8 | epsilon_insensitive | 0.0252 | True | 0.3798 | 0.451 | -2.4772 | -2.3307 | 0.051s | 0.469s | COMPLETE |\n", "| 9 | squared_epsilon_insen.. | 0.0294 | True | 0.4512 | 0.4512 | -2.3302 | -2.3302 | 0.051s | 0.520s | COMPLETE |\n", "Hyperparameter tuning ---------------------------\n", "Best trial --> 9\n", "Best parameters:\n", " --> loss: squared_epsilon_insensitive\n", " --> C: 0.0294\n", " --> dual: True\n", "Best evaluation --> r2: 0.4512 rmse: -2.3302\n", "Time elapsed: 0.520s\n", "Fit ---------------------------------------------\n", "Train evaluation --> r2: 0.4587 rmse: -2.3806\n", "Test evaluation --> r2: 0.4586 rmse: -2.3365\n", "Time elapsed: 0.120s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> r2: 0.458 ± 0.0014 rmse: -2.3377 ± 0.0031\n", "Time elapsed: 0.199s\n", "-------------------------------------------------\n", "Time: 0.838s\n", "\n", "\n", "Running hyperparameter tuning for HistGradientBoosting...\n", "| trial | loss | quantile | learning_rate | max_iter | max_leaf_nodes | max_depth | min_samples_leaf | l2_regularization | r2 | best_r2 | rmse | best_rmse | time_trial | time_ht | state |\n", "| ----- | --------- | -------- | ------------- | -------- | -------------- | --------- | ---------------- | ----------------- | ------- | ------- | ------- | --------- | ---------- | ------- | -------- |\n", "| 0 | absolut.. | 0.1 | 0.0236 | 180 | 26 | 12 | 11 | 0.0 | 0.5373 | 0.5373 | -2.1398 | -2.1398 | 0.928s | 0.928s | COMPLETE |\n", "| 1 | gamma | 0.5 | 0.242 | 160 | 38 | 3 | 20 | 0.0 | 0.556 | 0.556 | -2.0959 | -2.0959 | 0.178s | 1.106s | COMPLETE |\n", "| 2 | quantile | 0.4 | 0.2448 | 210 | 12 | 3 | 25 | 0.3 | 0.4906 | 0.556 | -2.245 | -2.0959 | 0.391s | 1.497s | COMPLETE |\n", "| 3 | quantile | 0.6 | 0.017 | 480 | 28 | 16 | 13 | 0.1 | 0.5535 | 0.556 | -2.1018 | -2.0959 | 2.741s | 4.239s | COMPLETE |\n", "| 4 | squared.. | 1.0 | 0.2649 | 70 | 10 | 10 | 28 | 0.8 | 0.5403 | 0.556 | -2.1327 | -2.0959 | 0.117s | 4.356s | COMPLETE |\n", "| 5 | squared.. | 0.1 | 0.0283 | 360 | 32 | 9 | 11 | 0.5 | 0.5466 | 0.556 | -2.118 | -2.0959 | 0.863s | 5.219s | COMPLETE |\n", "| 6 | quantile | 0.4 | 0.1264 | 380 | 37 | 12 | 29 | 1.0 | 0.4977 | 0.556 | -2.2292 | -2.0959 | 2.699s | 7.918s | COMPLETE |\n", "| 7 | gamma | 0.6 | 0.678 | 330 | 25 | 6 | 12 | 0.8 | 0.3783 | 0.556 | -2.4802 | -2.0959 | 0.593s | 8.511s | COMPLETE |\n", "| 8 | absolut.. | 0.9 | 0.0831 | 280 | 42 | 16 | 10 | 1.0 | 0.5285 | 0.556 | -2.16 | -2.0959 | 1.647s | 10.158s | COMPLETE |\n", "| 9 | absolut.. | 0.6 | 0.0373 | 300 | 40 | 13 | 17 | 0.8 | 0.5381 | 0.556 | -2.1378 | -2.0959 | 1.939s | 12.098s | COMPLETE |\n", "Hyperparameter tuning ---------------------------\n", "Best trial --> 1\n", "Best parameters:\n", " --> loss: gamma\n", " --> quantile: 0.5\n", " --> learning_rate: 0.242\n", " --> max_iter: 160\n", " --> max_leaf_nodes: 38\n", " --> max_depth: 3\n", " --> min_samples_leaf: 20\n", " --> l2_regularization: 0.0\n", "Best evaluation --> r2: 0.556 rmse: -2.0959\n", "Time elapsed: 12.098s\n", "Fit ---------------------------------------------\n", "Train evaluation --> r2: 0.7182 rmse: -1.7178\n", "Test evaluation --> r2: 0.5427 rmse: -2.1473\n", "Time elapsed: 0.232s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> r2: 0.5241 ± 0.022 rmse: -2.19 ± 0.0504\n", "Time elapsed: 0.782s\n", "-------------------------------------------------\n", "Time: 13.111s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 15.597s\n", "-------------------------------------\n", "LinearSVM --> r2: 0.458 ± 0.0014 rmse: -2.3377 ± 0.0031\n", "HistGradientBoosting --> r2: 0.5241 ± 0.022 rmse: -2.19 ± 0.0504 ~ !\n" ] } ], "source": [ "# For every step of the BO, both metrics are calculated,\n", "# but only the first is used for optimization!\n", "atom.run(\n", " models=[\"lsvm\", \"hGBM\"],\n", " metric=(\"r2\", \"rmse\"),\n", " n_trials=10,\n", " n_bootstrap=6,\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Applying cross-validation...\n" ] }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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 train_r2test_r2train_rmsetest_rmsetime
00.7194720.535855-1.704209-2.2116070.135122
10.7267600.539374-1.682744-2.1986860.135123
20.7231680.515653-1.716590-2.1321450.143130
30.7018270.596559-1.738110-2.1480980.116105
40.7181670.542704-1.717794-2.1473470.148133
mean0.7178790.546029-1.711889-2.1675770.135523
\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the robustness of the pipeline using cross-validation\n", "atom.winner.cross_validate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyze the results" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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r2_htr2_trainr2_testrmse_htrmse_trainrmse_test
lSVM0.4512030.45800.4565-2.330239-2.3823-2.3411
hGBM0.5560210.71820.5427-2.095926-1.7178-2.1473
\n", "
" ], "text/plain": [ " r2_ht r2_train r2_test rmse_ht rmse_train rmse_test\n", "lSVM 0.451203 0.4580 0.4565 -2.330239 -2.3823 -2.3411\n", "hGBM 0.556021 0.7182 0.5427 -2.095926 -1.7178 -2.1473" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The columns in the results dataframe contain one for each metric\n", "atom.results.data[[\"r2_ht\", \"r2_train\", \"r2_test\", \"rmse_ht\", \"rmse_train\", \"rmse_test\"]]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "legendgroup": "lSVM", "legendgrouptitle": { "font": { "size": 16 }, "text": "lSVM" }, "line": { "color": "rgb(0, 98, 98)", "dash": "solid", "width": 2 }, "marker": { "color": "rgb(0, 98, 98)", "line": { "color": "rgba(255, 255, 255, 0.9)", "width": 1 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