ogrisel

# %%
import numpy as np
from numpy.testing import assert_allclose
from sklearn.linear_model import Ridge
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from scipy.optimize import fmin_l_bfgs_b, check_grad

np.random.seed(0)

ogrisel

# %%
from time import perf_counter
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.cluster import MiniBatchKMeans
from sklearn.cluster import kmeans_plusplus
from sklearn.model_selection import train_test_split
from scipy.spatial.distance import cdist
from subprocess import run
from pprint import pprint

ogrisel

NumPy version 0.3.0+24607.gd075ba2ce
NumPy relaxed strides checking option: True
NumPy CPU features: NEON NEON_FP16 NEON_VFPV4 ASIMD ASIMDHP? ASIMDDP?
........................................................................ [  0%]
......................................................x................. [  1%]
........................................................................ [  1%]
................................................x....................... [  2%]
........................................................................ [  2%]
........................................................................ [  3%]
........................................................................ [  3%]

ogrisel

import numpy as np
try:
    import tensorflow as tf
except ImportError:
    tf = None
from time import perf_counter


def timeit(func, *args, **kwargs):
    durations = []

ogrisel

NumPy version 1.20.0
NumPy relaxed strides checking option: True
NumPy CPU features: NEON NEON_FP16 NEON_VFPV4 ASIMD ASIMDHP? ASIMDDP?
........................................................................ [  0%]
......................................................x................. [  1%]
........................................................................ [  1%]
.................................x...................................... [  2%]
........................................................................ [  2%]
..........................sssssssss..................................... [  3%]
........................................................................ [  4%]

ogrisel

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <float.h>
#include "cblas.h"


int main() {
    int found_error;
    int k;

ogrisel

import os
os.environ["OMP_NUM_THREADS"] = "1"  # avoid oversubscription

import pandas as pd
from distributed.client import performance_report
from time import perf_counter
from joblib import Memory, parallel_backend
from distributed import Client, LocalCluster
from sklearn.datasets import make_regression
from sklearn.experimental import enable_hist_gradient_boosting  # noqa

ogrisel

ogrisel

In [19]: from sklearn.preprocessing import StandardScaler

In [20]: from sklearn.linear_model import LogisticRegression

In [21]: from sklearn.pipeline import Pipeline

In [22]: p = Pipeline([("scaler", StandardScaler()), ("classifier", LogisticRegression())])

In [23]: import numpy as np

ogrisel

from sklearn.model_selection import cross_validate
import matplotlib.pyplot as plt
import numpy as np

from sklearn.datasets import fetch_openml
from sklearn.experimental import enable_hist_gradient_boosting  # noqa
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.compose import make_column_transformer
from sklearn.compose import make_column_selector