{
"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 | 0.054s | 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": {
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"\n",
"\n",
" \n",
" \n",
" | \n",
" train_r2 | \n",
" test_r2 | \n",
" train_rmse | \n",
" test_rmse | \n",
" time | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0.719472 | \n",
" 0.535855 | \n",
" -1.704209 | \n",
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\n",
" \n",
" 1 | \n",
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\n",
" \n",
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\n",
" \n",
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\n",
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" 4 | \n",
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" 0.542704 | \n",
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" -2.147347 | \n",
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\n",
" \n",
" mean | \n",
" 0.717879 | \n",
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\n",
" \n",
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\n"
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""
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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",
" 0.451203 | \n",
" 0.4580 | \n",
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\n",
" \n",
" hGBM | \n",
" 0.556021 | \n",
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" 0.5427 | \n",
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" -1.7178 | \n",
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\n",
" \n",
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\n",
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"
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"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",
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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.data[[\"r2_ht\", \"r2_train\", \"r2_test\", \"rmse_ht\", \"rmse_train\", \"rmse_test\"]]"
]
},
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"cell_type": "code",
"execution_count": 8,
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"# Some plots allow us to choose the metric we want to show\n",
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" atom.plot_trials(metric=\"rmse\", title=\"Hyperparameter tuning performance for RMSE\")"
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