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MNT add isort to ruff's rules (#26649)
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‎.github/scripts/label_title_regex.py

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@@ -1,10 +1,11 @@
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"""Labels PRs based on title. Must be run in a github action with the
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pull_request_target event."""
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from github import Github
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import os
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import json
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import os
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import re
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from github import Github
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context_dict = json.loads(os.getenv("CONTEXT_GITHUB"))
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repo = context_dict["repository"]

‎.pre-commit-config.yaml

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@@ -14,6 +14,7 @@ repos:
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rev: v0.0.272
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hooks:
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- id: ruff
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args: ["--fix", "--show-source"]
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v1.3.0
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hooks:

‎asv_benchmarks/benchmarks/cluster.py

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@@ -1,7 +1,7 @@
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from sklearn.cluster import KMeans, MiniBatchKMeans
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from .common import Benchmark, Estimator, Predictor, Transformer
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from .datasets import _blobs_dataset, _20newsgroups_highdim_dataset
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from .datasets import _20newsgroups_highdim_dataset, _blobs_dataset
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from .utils import neg_mean_inertia
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‎asv_benchmarks/benchmarks/common.py

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@@ -1,11 +1,11 @@
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import os
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import itertools
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import json
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import timeit
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import os
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import pickle
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import itertools
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import timeit
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from abc import ABC, abstractmethod
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from pathlib import Path
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from multiprocessing import cpu_count
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from pathlib import Path
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import numpy as np
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‎asv_benchmarks/benchmarks/datasets.py

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@@ -1,21 +1,22 @@
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from pathlib import Path
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import numpy as np
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import scipy.sparse as sp
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from joblib import Memory
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from pathlib import Path
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from sklearn.decomposition import TruncatedSVD
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from sklearn.datasets import (
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make_blobs,
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fetch_20newsgroups,
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fetch_olivetti_faces,
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fetch_openml,
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load_digits,
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make_regression,
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make_blobs,
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make_classification,
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fetch_olivetti_faces,
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make_regression,
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)
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from sklearn.preprocessing import MaxAbsScaler, StandardScaler
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from sklearn.decomposition import TruncatedSVD
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MaxAbsScaler, StandardScaler
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# memory location for caching datasets
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M = Memory(location=str(Path(__file__).resolve().parent / "cache"))

‎asv_benchmarks/benchmarks/decomposition.py

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@@ -1,8 +1,8 @@
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from sklearn.decomposition import PCA, DictionaryLearning, MiniBatchDictionaryLearning
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from .common import Benchmark, Estimator, Transformer
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from .datasets import _olivetti_faces_dataset, _mnist_dataset
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from .utils import make_pca_scorers, make_dict_learning_scorers
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from .datasets import _mnist_dataset, _olivetti_faces_dataset
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from .utils import make_dict_learning_scorers, make_pca_scorers
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class PCABenchmark(Transformer, Estimator, Benchmark):

‎asv_benchmarks/benchmarks/ensemble.py

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from sklearn.ensemble import (
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RandomForestClassifier,
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GradientBoostingClassifier,
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HistGradientBoostingClassifier,
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RandomForestClassifier,
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)
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from .common import Benchmark, Estimator, Predictor

‎asv_benchmarks/benchmarks/linear_model.py

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from sklearn.linear_model import (
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LogisticRegression,
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Ridge,
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ElasticNet,
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Lasso,
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LinearRegression,
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LogisticRegression,
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Ridge,
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SGDRegressor,
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)
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‎benchmarks/bench_20newsgroups.py

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@@ -1,18 +1,19 @@
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from time import time
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import argparse
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import numpy as np
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from time import time
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from sklearn.dummy import DummyClassifier
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import numpy as np
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from sklearn.datasets import fetch_20newsgroups_vectorized
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from sklearn.metrics import accuracy_score
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from sklearn.utils.validation import check_array
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.dummy import DummyClassifier
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from sklearn.ensemble import (
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AdaBoostClassifier,
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ExtraTreesClassifier,
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RandomForestClassifier,
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)
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import accuracy_score
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.utils.validation import check_array
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ESTIMATORS = {
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"dummy": DummyClassifier(),

‎benchmarks/bench_covertype.py

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# Arnaud Joly <[email protected]>
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# License: BSD 3 clause
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import argparse
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import os
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from time import time
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import argparse
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import numpy as np
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from joblib import Memory
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from sklearn.datasets import fetch_covtype, get_data_home
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from sklearn.svm import LinearSVC
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from sklearn.linear_model import SGDClassifier, LogisticRegression
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from sklearn.ensemble import (
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ExtraTreesClassifier,
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GradientBoostingClassifier,
59+
RandomForestClassifier,
60+
)
61+
from sklearn.linear_model import LogisticRegression, SGDClassifier
62+
from sklearn.metrics import zero_one_loss
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from sklearn.naive_bayes import GaussianNB
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from sklearn.svm import LinearSVC
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.metrics import zero_one_loss
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from sklearn.utils import check_array
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# Memoize the data extraction and memory map the resulting

‎benchmarks/bench_feature_expansions.py

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from time import time
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import matplotlib.pyplot as plt
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import numpy as np
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import scipy.sparse as sparse
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from sklearn.preprocessing import PolynomialFeatures
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from time import time
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degree = 2
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trials = 3

‎benchmarks/bench_glm.py

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"""
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from datetime import datetime
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import numpy as np
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from sklearn import linear_model
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from sklearn import linear_model
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if __name__ == "__main__":
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import matplotlib.pyplot as plt

‎benchmarks/bench_glmnet.py

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In both cases, only 10% of the features are informative.
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"""
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import numpy as np
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import gc
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from time import time
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import numpy as np
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from sklearn.datasets import make_regression
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alpha = 0.1
@@ -45,11 +47,11 @@ def bench(factory, X, Y, X_test, Y_test, ref_coef):
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4648

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if __name__ == "__main__":
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from glmnet.elastic_net import Lasso as GlmnetLasso
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from sklearn.linear_model import Lasso as ScikitLasso
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# Delayed import of matplotlib.pyplot
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import matplotlib.pyplot as plt
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from glmnet.elastic_net import Lasso as GlmnetLasso
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from sklearn.linear_model import Lasso as ScikitLasso
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scikit_results = []
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glmnet_results = []

‎benchmarks/bench_hist_gradient_boosting.py

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from time import time
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import argparse
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from time import time
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.datasets import make_classification
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from sklearn.datasets import make_regression
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from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator
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7+
from sklearn.datasets import make_classification, make_regression
8+
from sklearn.ensemble import (
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HistGradientBoostingClassifier,
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HistGradientBoostingRegressor,
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)
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from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator
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from sklearn.model_selection import train_test_split
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parser = argparse.ArgumentParser()
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parser.add_argument("--n-leaf-nodes", type=int, default=31)

‎benchmarks/bench_hist_gradient_boosting_adult.py

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import numpy as np
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import pandas as pd
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7-
from sklearn.model_selection import train_test_split
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from sklearn.compose import make_column_transformer, make_column_selector
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from sklearn.compose import make_column_selector, make_column_transformer
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from sklearn.datasets import fetch_openml
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from sklearn.metrics import accuracy_score, roc_auc_score
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator
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from sklearn.metrics import accuracy_score, roc_auc_score
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import OrdinalEncoder
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15-
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parser = argparse.ArgumentParser()
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parser.add_argument("--n-leaf-nodes", type=int, default=31)
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parser.add_argument("--n-trees", type=int, default=100)

‎benchmarks/bench_hist_gradient_boosting_categorical_only.py

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import argparse
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from time import time
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4-
from sklearn.preprocessing import KBinsDiscretizer
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from sklearn.datasets import make_classification
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator
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7+
from sklearn.preprocessing import KBinsDiscretizer
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parser = argparse.ArgumentParser()
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parser.add_argument("--n-leaf-nodes", type=int, default=31)

‎benchmarks/bench_hist_gradient_boosting_higgsboson.py

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from urllib.request import urlretrieve
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import argparse
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import os
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from gzip import GzipFile
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from time import time
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import argparse
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from urllib.request import urlretrieve
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import numpy as np
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import pandas as pd
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from joblib import Memory
10-
from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, roc_auc_score
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator
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from sklearn.metrics import accuracy_score, roc_auc_score
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from sklearn.model_selection import train_test_split
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parser = argparse.ArgumentParser()
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parser.add_argument("--n-leaf-nodes", type=int, default=31)

‎benchmarks/bench_hist_gradient_boosting_threading.py

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from time import time
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import argparse
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import os
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from pprint import pprint
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from time import time
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import numpy as np
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from threadpoolctl import threadpool_limits
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import sklearn
9-
from sklearn.model_selection import train_test_split
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.datasets import make_classification
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from sklearn.datasets import make_regression
10+
from sklearn.datasets import make_classification, make_regression
11+
from sklearn.ensemble import (
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HistGradientBoostingClassifier,
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HistGradientBoostingRegressor,
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)
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from sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator
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from sklearn.model_selection import train_test_split
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parser = argparse.ArgumentParser()
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parser.add_argument("--n-leaf-nodes", type=int, default=31)
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292293
if args.plot or args.plot_filename:
293-
import matplotlib.pyplot as plt
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import matplotlib
295+
import matplotlib.pyplot as plt
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fig, axs = plt.subplots(2, figsize=(12, 12))
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‎benchmarks/bench_isolation_forest.py

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"""
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from time import time
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import numpy as np
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import matplotlib.pyplot as plt
22+
import numpy as np
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24+
from sklearn.datasets import fetch_covtype, fetch_kddcup99, fetch_openml
2325
from sklearn.ensemble import IsolationForest
24-
from sklearn.metrics import roc_curve, auc
25-
from sklearn.datasets import fetch_kddcup99, fetch_covtype, fetch_openml
26+
from sklearn.metrics import auc, roc_curve
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from sklearn.preprocessing import LabelBinarizer
2728
from sklearn.utils import shuffle as sh
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‎benchmarks/bench_isotonic.py

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This allows the scaling of the algorithm with the problem size to be
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visualized and understood.
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"""
13-
import numpy as np
13+
import argparse
1414
import gc
1515
from datetime import datetime
16-
from sklearn.isotonic import isotonic_regression
17-
from scipy.special import expit
16+
1817
import matplotlib.pyplot as plt
19-
import argparse
18+
import numpy as np
19+
from scipy.special import expit
20+
21+
from sklearn.isotonic import isotonic_regression
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def generate_perturbed_logarithm_dataset(size):

‎benchmarks/bench_kernel_pca_solvers_time_vs_n_components.py

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import time
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import numpy as np
4342
import matplotlib.pyplot as plt
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43+
import numpy as np
4544
from numpy.testing import assert_array_almost_equal
46-
from sklearn.decomposition import KernelPCA
47-
from sklearn.datasets import make_circles
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46+
from sklearn.datasets import make_circles
47+
from sklearn.decomposition import KernelPCA
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print(__doc__)
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