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?
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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?
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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

ogrisel

import numpy as np
import pytest
from sklearn.datasets import load_breast_cancer
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, roc_curve

ogrisel

@pytest.mark.parametrize("loss", ['huber', 'ls', 'lad', 'quantile'])
@pytest.mark.parametrize("use_sample_weight", [False, True])
def test_regressor_train_loss_convergence(loss, use_sample_weight):
    rng = np.random.RandomState(42)
    n_samples, n_features = 30, 5
    n_estimators = 300

    # Make random data (without duplicated samples) to make sure
    # it's possible to build an invertible (overfitting) mapping
    # from X to y that therefore should lead to a regression loss