{
"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": {
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"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Sex | \n",
" Length | \n",
" Diameter | \n",
" Height | \n",
" Whole weight | \n",
" Shucked weight | \n",
" Viscera weight | \n",
" Shell weight | \n",
" Rings | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" M | \n",
" 0.455 | \n",
" 0.365 | \n",
" 0.095 | \n",
" 0.5140 | \n",
" 0.2245 | \n",
" 0.1010 | \n",
" 0.150 | \n",
" 15 | \n",
"
\n",
" \n",
" 1 | \n",
" M | \n",
" 0.350 | \n",
" 0.265 | \n",
" 0.090 | \n",
" 0.2255 | \n",
" 0.0995 | \n",
" 0.0485 | \n",
" 0.070 | \n",
" 7 | \n",
"
\n",
" \n",
" 2 | \n",
" F | \n",
" 0.530 | \n",
" 0.420 | \n",
" 0.135 | \n",
" 0.6770 | \n",
" 0.2565 | \n",
" 0.1415 | \n",
" 0.210 | \n",
" 9 | \n",
"
\n",
" \n",
" 3 | \n",
" M | \n",
" 0.440 | \n",
" 0.365 | \n",
" 0.125 | \n",
" 0.5160 | \n",
" 0.2155 | \n",
" 0.1140 | \n",
" 0.155 | \n",
" 10 | \n",
"
\n",
" \n",
" 4 | \n",
" I | \n",
" 0.330 | \n",
" 0.255 | \n",
" 0.080 | \n",
" 0.2050 | \n",
" 0.0895 | \n",
" 0.0395 | \n",
" 0.055 | \n",
" 7 | \n",
"
\n",
" \n",
"
\n",
"
"
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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 | 0.081s | 0.081s | COMPLETE |\n",
"| 1 | squared_epsilon_insen.. | 0.0534 | False | 0.3862 | 0.3862 | -2.5926 | -2.5926 | 0.078s | 0.159s | COMPLETE |\n",
"| 2 | squared_epsilon_insen.. | 0.0105 | True | 0.433 | 0.433 | -2.4084 | -2.4084 | 0.070s | 0.229s | COMPLETE |\n",
"| 3 | epsilon_insensitive | 0.6215 | True | 0.4022 | 0.433 | -2.5251 | -2.4084 | 0.064s | 0.293s | COMPLETE |\n",
"| 4 | squared_epsilon_insen.. | 0.0369 | False | 0.4057 | 0.433 | -2.5477 | -2.4084 | 0.058s | 0.351s | COMPLETE |\n",
"| 5 | epsilon_insensitive | 0.0016 | True | -1.5344 | 0.433 | -5.0102 | -2.4084 | 0.052s | 0.403s | COMPLETE |\n",
"| 6 | squared_epsilon_insen.. | 61.5811 | False | 0.4354 | 0.4354 | -2.3845 | -2.3845 | 0.047s | 0.450s | COMPLETE |\n",
"| 7 | squared_epsilon_insen.. | 14.898 | False | 0.4925 | 0.4925 | -2.2628 | -2.2628 | 0.050s | 0.500s | COMPLETE |\n",
"| 8 | epsilon_insensitive | 0.0252 | True | 0.3695 | 0.4925 | -2.6178 | -2.2628 | 0.048s | 0.548s | COMPLETE |\n",
"| 9 | squared_epsilon_insen.. | 0.0294 | True | 0.4767 | 0.4925 | -2.3896 | -2.2628 | 0.055s | 0.604s | COMPLETE |\n",
"Hyperparameter tuning ---------------------------\n",
"Best trial --> 7\n",
"Best parameters:\n",
" --> loss: squared_epsilon_insensitive\n",
" --> C: 14.898\n",
" --> dual: False\n",
"Best evaluation --> r2: 0.4925 rmse: -2.2628\n",
"Time elapsed: 0.604s\n",
"Fit ---------------------------------------------\n",
"Train evaluation --> r2: 0.4592 rmse: -2.3795\n",
"Test evaluation --> r2: 0.4584 rmse: -2.3369\n",
"Time elapsed: 0.112s\n",
"Bootstrap ---------------------------------------\n",
"Evaluation --> r2: 0.4577 ± 0.002 rmse: -2.3384 ± 0.0043\n",
"Time elapsed: 0.131s\n",
"-------------------------------------------------\n",
"Time: 0.847s\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 | 0.933s | 0.933s | COMPLETE |\n",
"| 1 | gamma | 0.5 | 0.242 | 160 | 38 | 3 | 20 | 0.0 | 0.574 | 0.574 | -2.1598 | -2.1398 | 0.166s | 1.099s | COMPLETE |\n",
"| 2 | quantile | 0.4 | 0.2448 | 210 | 12 | 3 | 25 | 0.3 | 0.4714 | 0.574 | -2.3253 | -2.1398 | 0.474s | 1.573s | COMPLETE |\n",
"| 3 | quantile | 0.6 | 0.017 | 480 | 28 | 16 | 13 | 0.1 | 0.5712 | 0.574 | -2.1385 | -2.1385 | 3.667s | 5.241s | COMPLETE |\n",
"| 4 | squared.. | 1.0 | 0.2649 | 70 | 10 | 10 | 28 | 0.8 | 0.5561 | 0.574 | -2.2019 | -2.1385 | 0.167s | 5.408s | COMPLETE |\n",
"| 5 | squared.. | 0.1 | 0.0283 | 360 | 32 | 9 | 11 | 0.5 | 0.5464 | 0.574 | -2.1197 | -2.1197 | 1.312s | 6.720s | COMPLETE |\n",
"| 6 | quantile | 0.4 | 0.1264 | 380 | 37 | 12 | 29 | 1.0 | 0.4417 | 0.574 | -2.3712 | -2.1197 | 3.378s | 10.098s | COMPLETE |\n",
"| 7 | gamma | 0.6 | 0.678 | 330 | 25 | 6 | 12 | 0.8 | 0.4299 | 0.574 | -2.3984 | -2.1197 | 0.807s | 10.904s | COMPLETE |\n",
"| 8 | absolut.. | 0.9 | 0.0831 | 280 | 42 | 16 | 10 | 1.0 | 0.5242 | 0.574 | -2.2742 | -2.1197 | 2.341s | 13.246s | COMPLETE |\n",
"| 9 | absolut.. | 0.6 | 0.0373 | 300 | 40 | 13 | 17 | 0.8 | 0.5685 | 0.574 | -2.17 | -2.1197 | 2.032s | 15.277s | COMPLETE |\n",
"Hyperparameter tuning ---------------------------\n",
"Best trial --> 5\n",
"Best parameters:\n",
" --> loss: squared_error\n",
" --> quantile: 0.1\n",
" --> learning_rate: 0.0283\n",
" --> max_iter: 360\n",
" --> max_leaf_nodes: 32\n",
" --> max_depth: 9\n",
" --> min_samples_leaf: 11\n",
" --> l2_regularization: 0.5\n",
"Best evaluation --> r2: 0.5464 rmse: -2.1197\n",
"Time elapsed: 15.277s\n",
"Fit ---------------------------------------------\n",
"Train evaluation --> r2: 0.7959 rmse: -1.4619\n",
"Test evaluation --> r2: 0.5479 rmse: -2.135\n",
"Time elapsed: 1.311s\n",
"Bootstrap ---------------------------------------\n",
"Evaluation --> r2: 0.5259 ± 0.0154 rmse: -2.1862 ± 0.0353\n",
"Time elapsed: 4.073s\n",
"-------------------------------------------------\n",
"Time: 20.661s\n",
"\n",
"\n",
"Final results ==================== >>\n",
"Total time: 22.907s\n",
"-------------------------------------\n",
"LinearSVM --> r2: 0.4577 ± 0.002 rmse: -2.3384 ± 0.0043\n",
"HistGradientBoosting --> r2: 0.5259 ± 0.0154 rmse: -2.1862 ± 0.0353 ~ !\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"
]
},
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"\n",
"\n",
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\n",
" \n",
" \n",
" | \n",
" train_r2 | \n",
" test_r2 | \n",
" train_rmse | \n",
" test_rmse | \n",
" time (s) | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0.796038 | \n",
" 0.542475 | \n",
" -1.453147 | \n",
" -2.195779 | \n",
" 0.640582 | \n",
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\n",
" \n",
" 1 | \n",
" 0.794954 | \n",
" 0.540335 | \n",
" -1.457709 | \n",
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\n",
" \n",
" 2 | \n",
" 0.790722 | \n",
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" -1.492522 | \n",
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\n",
" \n",
" 3 | \n",
" 0.785317 | \n",
" 0.580703 | \n",
" -1.474827 | \n",
" -2.189902 | \n",
" 0.684622 | \n",
"
\n",
" \n",
" 4 | \n",
" 0.795872 | \n",
" 0.547938 | \n",
" -1.461929 | \n",
" -2.135022 | \n",
" 0.741673 | \n",
"
\n",
" \n",
" mean | \n",
" 0.792581 | \n",
" 0.543480 | \n",
" -1.468027 | \n",
" -2.174097 | \n",
" 0.676815 | \n",
"
\n",
" \n",
" std | \n",
" 0.004114 | \n",
" 0.023769 | \n",
" 0.014222 | \n",
" 0.025189 | \n",
" 0.036433 | \n",
"
\n",
" \n",
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\n",
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"
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"text/plain": [
" train_r2 test_r2 train_rmse test_rmse time (s)\n",
"0 0.796038 0.542475 -1.453147 -2.195779 0.640582\n",
"1 0.794954 0.540335 -1.457709 -2.196391 0.644585\n",
"2 0.790722 0.505952 -1.492522 -2.153392 0.672611\n",
"3 0.785317 0.580703 -1.474827 -2.189902 0.684622\n",
"4 0.795872 0.547938 -1.461929 -2.135022 0.741673\n",
"mean 0.792581 0.543480 -1.468027 -2.174097 0.676815\n",
"std 0.004114 0.023769 0.014222 0.025189 0.036433"
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},
"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": [
{
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" r2_ht | \n",
" r2_train | \n",
" r2_test | \n",
" rmse_ht | \n",
" rmse_train | \n",
" rmse_test | \n",
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\n",
" \n",
" \n",
" \n",
" lSVM | \n",
" 0.492530 | \n",
" 0.4583 | \n",
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\n",
" \n",
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" -1.4619 | \n",
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\n",
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"text/plain": [
" r2_ht r2_train r2_test rmse_ht rmse_train rmse_test\n",
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},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The columns in the results dataframe contain one for each metric\n",
"atom.results[[\"r2_ht\", \"r2_train\", \"r2_test\", \"rmse_ht\", \"rmse_train\", \"rmse_test\"]]"
]
},
{
"cell_type": "code",
"execution_count": 8,
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"source": [
"# Some plots allow us to choose the metric we want to show\n",
"with atom.canvas():\n",
" atom.plot_trials(metric=\"r2\", title=\"Hyperparameter tuning performance for R2\")\n",
" atom.plot_trials(metric=\"rmse\", title=\"Hyperparameter tuning performance for RMSE\")"
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