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ahuber21 merged 3 commits into from
Mar 13, 2025
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Add Jupyter Notebook examples #2343

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504e49c
Add Jupyter Notebook example - RandomForestClassifier (#1)
tanannie22 Mar 5, 2025
0ef48b9
Add Ridge Regression examples using Intel® Extension for Scikit-learn…
nandried Mar 5, 2025
9f17f17
update README.md
tanannie22 Mar 5, 2025
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Add Ridge Regression examples using Intel® Extension for Scikit-learn (… 
…#2)
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@nandried
nandried authored Mar 5, 2025
commit 0ef48b9bbd1c76a02c4c3ce9adcaaed66a536616
373 changes: 373 additions & 0 deletions examples/notebooks/ridge_regression_bike_sharing.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "c44da9e1-d76e-4956-8c19-9dade31e2bc5",
"metadata": {},
"source": [
"# Intel® Extension for Scikit-learn Ridge Regression for New York City Bike Share dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "963c5285-6470-474c-9550-6990029fa6a3",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from timeit import default_timer as timer\n",
"from sklearn import metrics\n",
"from sklearn.model_selection import train_test_split\n",
"import warnings\n",
"from sklearn.datasets import fetch_openml\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from IPython.display import HTML\n",
"\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"id": "421d17b2-a02f-464d-b196-9e46cd0bb396",
"metadata": {},
"source": [
"### Download the data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d0b94c65-bd7c-480e-bd56-e5adc711585f",
"metadata": {},
"outputs": [],
"source": [
"dataset = fetch_openml(data_id=43526, as_frame=True)"
]
},
{
"cell_type": "markdown",
"id": "bcb6b5bc-5a76-4741-b350-518cd9c43f9c",
"metadata": {},
"source": [
"### Preprocessing\n",
"Let's encode categorical features with LabelEncoder"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a3995621-d516-470f-a8d1-78d635435bb7",
"metadata": {},
"outputs": [],
"source": [
"# Access the data as a DataFrame\n",
"data = dataset.frame\n",
"\n",
"# Convert date columns to datetime\n",
"data['Start_Time'] = pd.to_datetime(data['Start_Time'])\n",
"data['Stop_Time'] = pd.to_datetime(data['Stop_Time'])\n",
"\n",
"# Extract useful features from datetime columns\n",
"data['Start_Year'] = data['Start_Time'].dt.year\n",
"data['Start_Month'] = data['Start_Time'].dt.month\n",
"data['Start_Day'] = data['Start_Time'].dt.day\n",
"data['Start_Hour'] = data['Start_Time'].dt.hour\n",
"\n",
"data['Stop_Year'] = data['Stop_Time'].dt.year\n",
"data['Stop_Month'] = data['Stop_Time'].dt.month\n",
"data['Stop_Day'] = data['Stop_Time'].dt.day\n",
"data['Stop_Hour'] = data['Stop_Time'].dt.hour\n",
"\n",
"# Drop the original datetime columns\n",
"data = data.drop(columns=['Start_Time', 'Stop_Time'])\n",
"\n",
"# Encode categorical variables\n",
"for col in ['Start_Station_Name', 'End_Station_Name', 'Gender', 'User_Type']:\n",
" le = LabelEncoder().fit(data[col])\n",
" data[col] = le.transform(data[col])\n",
"\n",
"# Set the target variable\n",
"data['target'] = data['Trip_Duration']\n",
"\n",
"# Separate features and target\n",
"x = data.drop(columns=['target', 'Trip_Duration'])\n",
"y = data['target']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d21fbc59-1f27-4114-a54e-51a5263a5d31",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(661951, 22) (73551, 22) (661951,) (73551,)\n"
]
}
],
"source": [
"# Ensure x and y are defined and not None\n",
"if x is not None and y is not None:\n",
" for col in ['User_Type', 'Gender']:\n",
" if col in x.columns:\n",
" le = LabelEncoder().fit(x[col])\n",
" x[col] = le.transform(x[col])\n",
" else:\n",
" print(f\"Column {col} does not exist in the DataFrame.\")\n",
"\n",
" # Split the data\n",
" x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=0)\n",
" print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
"else:\n",
" print(\"x or y is None. Please check your data.\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "862647f0-e3cc-486f-b40f-c9d013b2b973",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
"\n",
"scaler_x = MinMaxScaler()\n",
"scaler_y = StandardScaler()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7fabcc1c-1632-45bb-aa8e-cc6edd864a51",
"metadata": {},
"outputs": [],
"source": [
"y_train = y_train.to_numpy().reshape(-1, 1)\n",
"y_test = y_test.to_numpy().reshape(-1, 1)\n",
"\n",
"scaler_x.fit(x_train)\n",
"x_train = scaler_x.transform(x_train)\n",
"x_test = scaler_x.transform(x_test)\n",
"\n",
"scaler_y.fit(y_train)\n",
"y_train = scaler_y.transform(y_train).ravel()\n",
"y_test = scaler_y.transform(y_test).ravel()"
]
},
{
"cell_type": "markdown",
"id": "b6e77f12-027c-4519-940c-26c9b127daac",
"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": 7,
"id": "4fe10be8-83a0-4681-8ecc-6f8b09eb1e13",
"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": "code",
"execution_count": 8,
"id": "60c6aa9b-748b-4a02-87b1-600d1130c4d8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Intel® extension for Scikit-learn time: 0.04 s'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import Ridge\n",
"\n",
"params = {\n",
" \"alpha\": 0.3,\n",
" \"fit_intercept\": False,\n",
" \"random_state\": 0,\n",
" \"copy_X\": False,\n",
"}\n",
"start = timer()\n",
"model = Ridge(random_state=0).fit(x_train, y_train)\n",
"train_patched = timer() - start\n",
"f\"Intel® extension for Scikit-learn time: {train_patched:.2f} s\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e3b5e2bc-a5fe-4f1b-90ab-b3119ad77b4b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Patched Scikit-learn MSE: 0.29078674972552815'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_predict = model.predict(x_test)\n",
"mse_metric_opt = metrics.mean_squared_error(y_test, y_predict)\n",
"f\"Patched Scikit-learn MSE: {mse_metric_opt}\""
]
},
{
"cell_type": "markdown",
"id": "177a3800-4ee6-421f-8cd0-149beebb460a",
"metadata": {},
"source": [
"### Train the same algorithm with original Scikit-learn\n",
"In order to cancel optimizations, we use *unpatch_sklearn* and reimport the class Ridge"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7dbf40f4-7cb6-4ad5-a5d1-a07793780b23",
"metadata": {},
"outputs": [],
"source": [
"from sklearnex import unpatch_sklearn\n",
"\n",
"unpatch_sklearn()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "eb46f060-dbde-43a3-b173-6d1c02fd9b9c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Original Scikit-learn time: 0.19 s'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import Ridge\n",
"\n",
"start = timer()\n",
"model = Ridge(random_state=0).fit(x_train, y_train)\n",
"train_unpatched = timer() - start\n",
"f\"Original Scikit-learn time: {train_unpatched:.2f} s\""
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "6f55a543-d85a-4e76-95d1-4fef83b59d7e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Original Scikit-learn MSE: 0.29078674972650354'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_predict = model.predict(x_test)\n",
"mse_metric_original = metrics.mean_squared_error(y_test, y_predict)\n",
"f\"Original Scikit-learn MSE: {mse_metric_original}\""
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "aa1355e0-b70c-448b-99d8-eee19f2b1f36",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>Compare MSE metric of patched Scikit-learn and original</h3>MSE metric of patched Scikit-learn: 0.29078674972552815 <br>MSE metric of unpatched Scikit-learn: 0.29078674972650354 <br>Metrics ratio: 0.9999999999966457 <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>4.7</strong> times.</li></ul>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(\n",
" f\"<h3>Compare MSE metric of patched Scikit-learn and original</h3>\"\n",
" f\"MSE metric of patched Scikit-learn: {mse_metric_opt} <br>\"\n",
" f\"MSE metric of unpatched Scikit-learn: {mse_metric_original} <br>\"\n",
" f\"Metrics ratio: {mse_metric_opt/mse_metric_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",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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