profiling reports

bench_knn.py

import numpy as np
from subprocess import check_output
import threadpoolctl
from pprint import pprint
from time import perf_counter
from pathlib import Path
import sklearn
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split


sklearn_path = Path(sklearn.__file__).parent
sklearn_version = sklearn.__version__
sklearn_git_branch = (
    check_output(
        f'(cd {sklearn_path} && git symbolic-ref --short HEAD) || echo ""',
        shell=True,
    )
    .decode("utf-8")
    .strip()
)
if sklearn_git_branch:
    sklearn_version = f"{sklearn_version} (git: {sklearn_git_branch})"
print(f"sklearn {sklearn_version} at {sklearn_path}")
pprint(threadpoolctl.threadpool_info())

train_size = int(1e5)
test_size = int(1e4)
n_features = 100
X, y = make_classification(
    n_samples=train_size + test_size,
    n_features=n_features,
    n_redundant=0,
    n_informative=n_features,
    random_state=0,
)
X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    train_size=train_size,
    test_size=test_size,
    random_state=0,
)

if sklearn_git_branch == "pairwise_aggregation_cython":
    metric = "fast_sqeuclidean"
else:
    metric = "minkowski"

clf = KNeighborsClassifier(n_neighbors=10, metric=metric).fit(X_train, y_train)
tic = perf_counter()
y_pred = clf.predict(X_test)
toc = perf_counter()
assert y_pred.shape == y_test.shape
accuracy = np.mean(y_pred == y_test)
print(
    f"n_samples_train: {X_train.shape[0]}, n_samples_test: {X_test.shape[0]}, acc:"
    f" {accuracy:.3f}, duration: {toc - tic:.3f} s"
)

knn-sklearn-20254.speedscope

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knn-sklearn-intelex.speedscope

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