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# %%
from scipy.interpolate import interp1d
from sklearn.base import BaseEstimator, RegressorMixin, clone
from sklearn.utils.validation import check_is_fitted
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.utils.validation import check_consistent_length
from sklearn.utils import check_random_state
import numpy as np

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# %%
from sklearn import set_config
from sklearn.base import clone

set_config(array_api_dispatch=True)

# %%
try:
    import torch
except ModuleNotFoundError:

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2023-02-24T08:51:55.0856095Z ##[section]Starting: Test loky
2023-02-24T08:51:55.0862853Z ==============================================================================
2023-02-24T08:51:55.0863227Z Task         : Command line
2023-02-24T08:51:55.0863416Z Description  : Run a command line script using Bash on Linux and macOS and cmd.exe on Windows
2023-02-24T08:51:55.0863830Z Version      : 2.212.0
2023-02-24T08:51:55.0864023Z Author       : Microsoft Corporation
2023-02-24T08:51:55.0864198Z Help         : https://docs.microsoft.com/azure/devops/pipelines/tasks/utility/command-line
2023-02-24T08:51:55.0864458Z ==============================================================================
2023-02-24T08:51:55.2236683Z Generating script.
2023-02-24T08:51:55.2246660Z Script contents:

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## Context: the new `global_dtype` fixture and `SKLEARN_RUN_FLOAT32_TESTS` environment variable

Introduction of low-level computational routines for 32bit motivated an extension of tests to run them on 32bit.

In this regards, https://github.com/scikit-learn/scikit-learn/pull/22690 introduced a new `global_dtype` fixture as well has the `SKLEARN_RUN_FLOAT32_TESTS` env. variable to make it possible to run the test on 32bit data.

Running test on 32bit can be done using `SKLEARN_RUN_FLOAT32_TESTS=1`.

For instance, this run the first `global_dtype`-parametrised test:

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from pathlib import Path
import sys
from time import perf_counter
from threadpoolctl import threadpool_limits
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from joblib import Memory
import pandas as pd
import matplotlib.pyplot as plt