{
"cells": [
{
"cell_type": "markdown",
"id": "f5c4abc0",
"metadata": {},
"source": [
"# Intel® Extension for Scikit-learn NuSVR for Medical Charges dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "27b99b44",
"metadata": {},
"outputs": [],
"source": [
"from timeit import default_timer as timer\n",
"from sklearn.datasets import fetch_openml\n",
"from sklearn.model_selection import train_test_split\n",
"from IPython.display import HTML\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"id": "adf9ffe9",
"metadata": {},
"source": [
"### Download the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a9b315cc",
"metadata": {},
"outputs": [],
"source": [
"x, y = fetch_openml(name=\"medical_charges_nominal\", return_X_y=True)"
]
},
{
"cell_type": "markdown",
"id": "49fbf604",
"metadata": {},
"source": [
"### Preprocessing"
]
},
{
"cell_type": "markdown",
"id": "fafea10b",
"metadata": {},
"source": [
"Encode categorical features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f77c30f2",
"metadata": {},
"outputs": [],
"source": [
"cat_columns = x.select_dtypes([\"category\"]).columns\n",
"x[cat_columns] = x[cat_columns].apply(lambda x: x.cat.codes)"
]
},
{
"cell_type": "markdown",
"id": "cd8d3b6d",
"metadata": {},
"source": [
"Split the data into train and test sets"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "96e14dd7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((48919, 11), (114146, 11), (48919,), (114146,))"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.3, random_state=42)\n",
"x_train.shape, x_test.shape, y_train.shape, y_test.shape"
]
},
{
"cell_type": "markdown",
"id": "0341cac9",
"metadata": {},
"source": [
"### Patch original Scikit-learn with Intel® Extension for Scikit-learn\n",
"Intel® Extension for Scikit-learn (previously known as daal4py) contains drop-in replacement functionality for the stock Scikit-learn package. You can take advantage of the performance optimizations of Intel® Extension for Scikit-learn by adding just two lines of code before the usual Scikit-learn imports:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "244c5bc9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Intel(R) Extension for Scikit-learn* enabled (https://github.com/uxlfoundation/scikit-learn-intelex)\n"
]
}
],
"source": [
"from sklearnex import patch_sklearn\n",
"\n",
"patch_sklearn()"
]
},
{
"cell_type": "markdown",
"id": "6bb14ac8",
"metadata": {},
"source": [
"Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the [list of supported algorithms and parameters](https://uxlfoundation.github.io/scikit-learn-intelex/latest/algorithms.html) for details. In cases when unsupported parameters are used, the package fallbacks into original Scikit-learn. If the patching does not cover your scenarios, [submit an issue on GitHub](https://github.com/uxlfoundation/scikit-learn-intelex/issues)."
]
},
{
"cell_type": "markdown",
"id": "693b4e26",
"metadata": {},
"source": [
"Training of the NuSVR algorithm with Intel® Extension for Scikit-learn for Medical Charges dataset"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e9b8f06b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Intel® extension for Scikit-learn time: 24.69 s'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.svm import NuSVR\n",
"\n",
"params = {\n",
" \"nu\": 0.4,\n",
" \"C\": y_train.mean(),\n",
" \"degree\": 2,\n",
" \"kernel\": \"poly\",\n",
"}\n",
"start = timer()\n",
"nusvr = NuSVR(**params).fit(x_train, y_train)\n",
"train_patched = timer() - start\n",
"f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
]
},
{
"cell_type": "markdown",
"id": "d01cdabc",
"metadata": {},
"source": [
"Predict and get a result of the NuSVR algorithm with Intel® Extension for Scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9ead2a44",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'Intel® extension for Scikit-learn R2 score: 0.8635974264586637'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_opt = nusvr.score(x_test, y_test)\n",
"f\"Intel® extension for Scikit-learn R2 score: {score_opt}\""
]
},
{
"cell_type": "markdown",
"id": "bd8e7b0b",
"metadata": {},
"source": [
"### Train the same algorithm with original Scikit-learn\n",
"In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class NuSVR"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5bb884d5",
"metadata": {},
"outputs": [],
"source": [
"from sklearnex import unpatch_sklearn\n",
"\n",
"unpatch_sklearn()"
]
},
{
"cell_type": "markdown",
"id": "8cfa0dba",
"metadata": {},
"source": [
"Training of the NuSVR algorithm with original Scikit-learn library for Medical Charges dataset"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ae421d8e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Original Scikit-learn time: 331.85 s'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.svm import NuSVR\n",
"\n",
"start = timer()\n",
"nusvr = NuSVR(**params).fit(x_train, y_train)\n",
"train_unpatched = timer() - start\n",
"f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
]
},
{
"cell_type": "markdown",
"id": "23b8faa6",
"metadata": {},
"source": [
"Predict and get a result of the NuSVR algorithm with original Scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7644999d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Original Scikit-learn R2 score: 0.8636031741516902'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_original = nusvr.score(x_test, y_test)\n",
"f\"Original Scikit-learn R2 score: {score_original}\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3a704d51",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>Compare R2 score of patched Scikit-learn and original</h3>R2 score of patched Scikit-learn: 0.8635974264586637 <br>R2 score of unpatched Scikit-learn: 0.8636031741516902 <br>Metrics ratio: 0.999993344520726 <br><h3>With Scikit-learn-intelex patching you can:</h3><ul><li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li><li>Fast execution training and prediction of Scikit-learn models;</li><li>Get the similar quality</li><li>Get speedup in <strong>13.4</strong> times.</li></ul>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(\n",
" f\"<h3>Compare R2 score of patched Scikit-learn and original</h3>\"\n",
" f\"R2 score of patched Scikit-learn: {score_opt} <br>\"\n",
" f\"R2 score of unpatched Scikit-learn: {score_original} <br>\"\n",
" f\"Metrics ratio: {score_opt/score_original} <br>\"\n",
" f\"<h3>With Scikit-learn-intelex patching you can:</h3>\"\n",
" f\"<ul>\"\n",
" f\"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>\"\n",
" f\"<li>Fast execution training and prediction of Scikit-learn models;</li>\"\n",
" f\"<li>Get the similar quality</li>\"\n",
" f\"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>\"\n",
" f\"</ul>\"\n",
")"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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