{
"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": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"UserWarning: The pandas version installed (1.5.3) does not match the supported pandas version in Modin (1.5.2). This may cause undesired side effects!\n"
]
}
],
"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",
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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",
"Algorithm task: regression.\n",
"\n",
"Dataset stats ==================== >>\n",
"Shape: (4177, 9)\n",
"Train set size: 3342\n",
"Test set size: 835\n",
"-------------------------------------\n",
"Memory: 509.72 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, neg_root_mean_squared_error\n",
"\n",
"\n",
"Running hyperparameter tuning for LinearSVM...\n",
"| trial | loss | C | dual | r2 | best_r2 | neg_root_mean_squared_error | best_neg_root_mean_squared_error | time_trial | time_ht | state |\n",
"| ----- | ----------------------- | ------- | ------- | ------- | ------- | --------------------------- | -------------------------------- | ---------- | ------- | -------- |\n",
"| 0 | squared_epsilon_insen.. | 0.001 | True | 0.2887 | 0.2887 | -2.6528 | -2.6528 | 0.041s | 0.041s | COMPLETE |\n",
"| 1 | squared_epsilon_insen.. | 0.0534 | False | 0.3862 | 0.3862 | -2.5926 | -2.5926 | 0.038s | 0.079s | COMPLETE |\n",
"| 2 | squared_epsilon_insen.. | 0.0105 | True | 0.433 | 0.433 | -2.4084 | -2.4084 | 0.041s | 0.120s | COMPLETE |\n",
"| 3 | epsilon_insensitive | 0.6215 | True | 0.4022 | 0.433 | -2.5251 | -2.4084 | 0.048s | 0.168s | COMPLETE |\n",
"| 4 | squared_epsilon_insen.. | 0.0369 | False | 0.4057 | 0.433 | -2.5477 | -2.4084 | 0.039s | 0.207s | COMPLETE |\n",
"| 5 | epsilon_insensitive | 0.0016 | True | -1.5344 | 0.433 | -5.0102 | -2.4084 | 0.039s | 0.246s | COMPLETE |\n",
"| 6 | squared_epsilon_insen.. | 61.5811 | False | 0.4354 | 0.4354 | -2.3845 | -2.3845 | 0.038s | 0.284s | COMPLETE |\n",
"| 7 | squared_epsilon_insen.. | 14.898 | False | 0.4925 | 0.4925 | -2.2628 | -2.2628 | 0.039s | 0.323s | COMPLETE |\n",
"| 8 | epsilon_insensitive | 0.0252 | True | 0.3695 | 0.4925 | -2.6178 | -2.2628 | 0.040s | 0.363s | COMPLETE |\n",
"| 9 | squared_epsilon_insen.. | 0.0294 | True | 0.4767 | 0.4925 | -2.3896 | -2.2628 | 0.044s | 0.407s | 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 neg_root_mean_squared_error: -2.2628\n",
"Time elapsed: 0.407s\n",
"Fit ---------------------------------------------\n",
"Train evaluation --> r2: 0.4592 neg_root_mean_squared_error: -2.3795\n",
"Test evaluation --> r2: 0.4584 neg_root_mean_squared_error: -2.3369\n",
"Time elapsed: 0.027s\n",
"Bootstrap ---------------------------------------\n",
"Evaluation --> r2: 0.4577 ± 0.002 neg_root_mean_squared_error: -2.3384 ± 0.0043\n",
"Time elapsed: 0.097s\n",
"-------------------------------------------------\n",
"Total time: 0.531s\n",
"\n",
"\n",
"Running hyperparameter tuning for HistGradientBoosting...\n",
"| trial | loss | learning_rate | max_iter | max_leaf_nodes | max_depth | min_samples_leaf | l2_regularization | r2 | best_r2 | neg_root_mean_squared_error | best_neg_root_mean_squared_error | time_trial | time_ht | state |\n",
"| ----- | ----------- | ------------- | -------- | -------------- | --------- | ---------------- | ----------------- | ------- | ------- | --------------------------- | -------------------------------- | ---------- | ------- | -------- |\n",
"| 0 | absolute_.. | 0.0402 | 80 | 13 | 15 | 24 | 0.9 | 0.5248 | 0.5248 | -2.1683 | -2.1683 | 0.275s | 0.275s | COMPLETE |\n",
"| 1 | squared_e.. | 0.0219 | 440 | 14 | 9 | 16 | 0.1 | 0.5673 | 0.5673 | -2.1767 | -2.1683 | 1.059s | 1.334s | COMPLETE |\n",
"| 2 | absolute_.. | 0.034 | 250 | 12 | 12 | 26 | 0.4 | 0.5174 | 0.5673 | -2.2218 | -2.1683 | 0.703s | 2.037s | COMPLETE |\n",
"| 3 | absolute_.. | 0.3174 | 370 | 46 | 6 | 10 | 0.6 | 0.5566 | 0.5673 | -2.1746 | -2.1683 | 1.291s | 3.328s | COMPLETE |\n",
"| 4 | poisson | 0.0518 | 460 | 35 | 3 | 15 | 0.9 | 0.5691 | 0.5691 | -2.1695 | -2.1683 | 0.586s | 3.914s | COMPLETE |\n",
"| 5 | poisson | 0.0177 | 140 | 34 | None | 21 | 0.0 | 0.5546 | 0.5691 | -2.1003 | -2.1003 | 0.839s | 4.752s | COMPLETE |\n",
"| 6 | absolute_.. | 0.0255 | 130 | 40 | 15 | 17 | 0.6 | 0.483 | 0.5691 | -2.2817 | -2.1003 | 1.117s | 5.869s | COMPLETE |\n",
"| 7 | squared_e.. | 0.0136 | 190 | 35 | 14 | 22 | 1.0 | 0.5699 | 0.5699 | -2.083 | -2.083 | 1.174s | 7.043s | COMPLETE |\n",
"| 8 | squared_e.. | 0.1919 | 200 | 29 | 5 | 26 | 0.3 | 0.5105 | 0.5699 | -2.3066 | -2.083 | 0.440s | 7.484s | COMPLETE |\n",
"| 9 | squared_e.. | 0.4892 | 460 | 28 | 10 | 24 | 1.0 | 0.4298 | 0.5699 | -2.4942 | -2.083 | 1.545s | 9.029s | COMPLETE |\n",
"Hyperparameter tuning ---------------------------\n",
"Best trial --> 7\n",
"Best parameters:\n",
" --> loss: squared_error\n",
" --> learning_rate: 0.0136\n",
" --> max_iter: 190\n",
" --> max_leaf_nodes: 35\n",
" --> max_depth: 14\n",
" --> min_samples_leaf: 22\n",
" --> l2_regularization: 1.0\n",
"Best evaluation --> r2: 0.5699 neg_root_mean_squared_error: -2.083\n",
"Time elapsed: 9.029s\n",
"Fit ---------------------------------------------\n",
"Train evaluation --> r2: 0.6668 neg_root_mean_squared_error: -1.8677\n",
"Test evaluation --> r2: 0.5583 neg_root_mean_squared_error: -2.1105\n",
"Time elapsed: 1.076s\n",
"Bootstrap ---------------------------------------\n",
"Evaluation --> r2: 0.5388 ± 0.0083 neg_root_mean_squared_error: -2.1565 ± 0.0192\n",
"Time elapsed: 6.803s\n",
"-------------------------------------------------\n",
"Total time: 16.908s\n",
"\n",
"\n",
"Final results ==================== >>\n",
"Total time: 17.610s\n",
"-------------------------------------\n",
"LinearSVM --> r2: 0.4577 ± 0.002 neg_root_mean_squared_error: -2.3384 ± 0.0043\n",
"HistGradientBoosting --> r2: 0.5388 ± 0.0083 neg_root_mean_squared_error: -2.1565 ± 0.0192 !\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",
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" | \n",
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" time (s) | \n",
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\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0.672482 | \n",
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\n",
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\n",
" \n",
" mean | \n",
" 0.669035 | \n",
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" -2.164841 | \n",
" 1.100801 | \n",
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\n",
" \n",
" std | \n",
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\n",
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\n",
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"text/plain": [
" train_r2 test_r2 train_neg_root_mean_squared_error \\\n",
"0 0.672482 0.534480 -1.841417 \n",
"1 0.669693 0.541603 -1.850140 \n",
"2 0.674650 0.525120 -1.860947 \n",
"3 0.661519 0.579041 -1.851866 \n",
"4 0.666829 0.558253 -1.867706 \n",
"mean 0.669035 0.547699 -1.854415 \n",
"std 0.004587 0.019055 0.009089 \n",
"\n",
" test_neg_root_mean_squared_error time (s) \n",
"0 -2.214880 1.079982 \n",
"1 -2.193359 1.113014 \n",
"2 -2.111204 1.109009 \n",
"3 -2.194239 1.065969 \n",
"4 -2.110524 1.136033 \n",
"mean -2.164841 1.100801 \n",
"std 0.044741 0.024918 "
]
},
"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",
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" \n",
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\n",
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"text/plain": [
" score_ht score_train \\\n",
"lSVM [0.4925303455788521, -2.262753922393612] [0.4592, -2.3795] \n",
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"\n",
" score_test \n",
"lSVM [0.4584, -2.3369] \n",
"hGBM [0.5583, -2.1105] "
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"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The columns in the results dataframe contain a list of\n",
"# scores, one for each metric (in the same order as called)\n",
"atom.results[[\"score_ht\", \"score_train\", \"score_test\"]]"
]
},
{
"cell_type": "code",
"execution_count": 8,
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"boxpoints": "outliers",
"legendgroup": "r2",
"marker": {
"color": "rgb(0, 98, 98)"
},
"name": "r2",
"orientation": "h",
"showlegend": true,
"type": "box",
"x": [
0.4575562001062955,
0.458340904925999,
0.45684280677070677,
0.46113826422776416,
0.45724897839794176,
0.45516891136266535,
0.536584648360881,
0.543985237241343,
0.5383790777848179,
0.5431746768421848,
0.5468136441055751,
0.5237852578664706
],
"xaxis": "x",
"y": [
"lSVM",
"lSVM",
"lSVM",
"lSVM",
"lSVM",
"lSVM",
"hGBM",
"hGBM",
"hGBM",
"hGBM",
"hGBM",
"hGBM"
],
"yaxis": "y"
}
],
"layout": {
"bargroupgap": 0.05,
"boxmode": "group",
"font": {
"size": 12
},
"height": 500,
"hoverlabel": {
"font": {
"size": 16
}
},
"legend": {
"bgcolor": "rgba(255, 255, 255, 0.5)",
"font": {
"size": 16
},
"groupclick": "toggleitem",
"traceorder": "grouped",
"x": 0.99,
"xanchor": "right",
"y": 0.01,
"yanchor": "bottom"
},
"margin": {
"b": 50,
"l": 50,
"pad": 0,
"r": 0,
"t": 35
},
"showlegend": true,
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
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0,
"#0d0887"
],
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"#46039f"
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],
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],
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],
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"#d8576b"
],
[
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],
[
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"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
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],
[
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[
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],
[
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],
[
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]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
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[
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],
[
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[
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],
[
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],
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]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
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},
"colorscale": [
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],
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",
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""
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"atom.plot_results(metric=\"r2\")"
]
}
],
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"language": "python",
"name": "python3"
},
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"version": 3
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}
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}