Intel® Extension for Scikit-learn DBSCAN for spoken arabic digit dataset

from timeit import default_timer as timer
from sklearn.model_selection import train_test_split
from sklearn.metrics import davies_bouldin_score
from sklearn.datasets import fetch_openml
from IPython.display import HTML
import warnings

warnings.filterwarnings("ignore")

Download the data

x, y = fetch_openml(name="spoken-arabic-digit", return_X_y=True)

Preprocessing

Split the data into train and test sets

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)

Normalize the data

from sklearn.preprocessing import MinMaxScaler

scaler_x = MinMaxScaler()

scaler_x.fit(x_train)
x_train = scaler_x.transform(x_train)
x_test = scaler_x.transform(x_test)

Patch original Scikit-learn with Intel® Extension for Scikit-learn

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:

from sklearnex import patch_sklearn

patch_sklearn()

Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)

Intel® Extension for Scikit-learn patching affects performance of specific Scikit-learn functionality. Refer to the list of supported algorithms and parameters 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.

Training of the DBSCAN algorithm with Intel® Extension for Scikit-learn for spoken arabic digit dataset

from sklearn.cluster import DBSCAN

params = {
    "n_jobs": -1,
}
start = timer()
y_pred = DBSCAN(**params).fit_predict(x_train)
train_patched = timer() - start
f"Intel® extension for Scikit-learn time: {train_patched:.2f} s"

'Intel® extension for Scikit-learn time: 6.37 s'

Let’s take a look at Davies-Bouldin score of the DBSCAN algorithm with Intel® Extension for Scikit-learn

score_opt = davies_bouldin_score(x_train, y_pred)
f"Intel® extension for Scikit-learn Davies-Bouldin score: {score_opt}"

'Intel® extension for Scikit-learn Davies-Bouldin score: 0.8542652084275848'

Train the same algorithm with original Scikit-learn

In order to cancel optimizations, we use unpatch_sklearn and reimport the class DBSCAN

from sklearnex import unpatch_sklearn

unpatch_sklearn()

Training of the DBSCAN algorithm with original Scikit-learn library for spoken arabic digit dataset

from sklearn.cluster import DBSCAN

start = timer()
y_pred = DBSCAN(**params).fit_predict(x_train)
train_unpatched = timer() - start
f"Original Scikit-learn time: {train_unpatched:.2f} s"

'Original Scikit-learn time: 469.21 s'

Let’s take a look Davies-Bouldin score of the DBSCAN algorithm with original Scikit-learn

score_original = davies_bouldin_score(x_train, y_pred)
f"Original Scikit-learn Davies-Bouldin score: {score_opt}"

'Original Scikit-learn Davies-Bouldin score: 0.8542652084275848'

HTML(
    f"<h3>Compare Davies-Bouldin score of patched Scikit-learn and original</h3>"
    f"Davies-Bouldin score of patched Scikit-learn: {score_opt} <br>"
    f"Davies-Bouldin score of unpatched Scikit-learn: {score_original} <br>"
    f"Metrics ratio: {score_opt/score_original} <br>"
    f"<h3>With Scikit-learn-intelex patching you can:</h3>"
    f"<ul>"
    f"<li>Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);</li>"
    f"<li>Fast execution training and prediction of Scikit-learn models;</li>"
    f"<li>Get the similar quality</li>"
    f"<li>Get speedup in <strong>{(train_unpatched/train_patched):.1f}</strong> times.</li>"
    f"</ul>"
)

Compare Davies-Bouldin score of patched Scikit-learn and original

Davies-Bouldin score of patched Scikit-learn: 0.8542652084275848
Davies-Bouldin score of unpatched Scikit-learn: 0.8542652084275848
Metrics ratio: 1.0

With Scikit-learn-intelex patching you can:

• Use your Scikit-learn code for training and prediction with minimal changes (a couple of lines of code);
• Fast execution training and prediction of Scikit-learn models;
• Get the similar quality
• Get speedup in 73.6 times.