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

ogrisel

(conda-forge-compilers) 0 [~/code/scikit-learn (master)]$ pip install -e . -v
Created temporary directory: /private/var/folders/69/7jxl92h50w10b4v998qt4tj00000gn/T/pip-ephem-wheel-cache-cn0u3xn5
Created temporary directory: /private/var/folders/69/7jxl92h50w10b4v998qt4tj00000gn/T/pip-req-tracker-7xtixh31
Created requirements tracker '/private/var/folders/69/7jxl92h50w10b4v998qt4tj00000gn/T/pip-req-tracker-7xtixh31'
Created temporary directory: /private/var/folders/69/7jxl92h50w10b4v998qt4tj00000gn/T/pip-install-q8mggn78
Obtaining file:///Users/ogrisel/code/scikit-learn
  Added file:///Users/ogrisel/code/scikit-learn to build tracker '/private/var/folders/69/7jxl92h50w10b4v998qt4tj00000gn/T/pip-req-tracker-7xtixh31'
    Running setup.py (path:/Users/ogrisel/code/scikit-learn/setup.py) egg_info for package from file:///Users/ogrisel/code/scikit-learn
    Running command python setup.py egg_info
    running egg_info

ogrisel

from time import time
from pprint import pprint

import numpy as np
import pandas as pd
from scipy.stats import expon, randint, uniform

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder

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ogrisel

Starting Microsoft Python language server.
##########Linting Output - flake8##########
Microsoft Python Language Server version 0.1.75.0
Initializing for /opt/venvs/py37/bin/python
Loading files from /home/ogrisel/code/scikit-learn
Parsing document file:///home/ogrisel/code/scikit-learn/setup.py
Parse complete for file:///home/ogrisel/code/scikit-learn/setup.py at version -1
Analysis queued for file:///home/ogrisel/code/scikit-learn/setup.py
Parsing document file:///home/ogrisel/code/scikit-learn/conftest.py
Parse complete for file:///home/ogrisel/code/scikit-learn/conftest.py at version -1