8000 DOC Adds missing imports into examples and test them (#21186) · samronsin/scikit-learn@82300d7 · GitHub
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DOC Adds missing imports into examples and test them (scikit-learn#21186)
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‎sklearn/compose/_column_transformer.py

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@@ -1002,6 +1002,7 @@ class make_column_selector:
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>>> from sklearn.preprocessing import StandardScaler, OneHotEncoder
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>>> from sklearn.compose import make_column_transformer
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>>> from sklearn.compose import make_column_selector
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>>> import numpy as np
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>>> import pandas as pd # doctest: +SKIP
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>>> X = pd.DataFrame({'city': ['London', 'London', 'Paris', 'Sallisaw'],
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... 'rating': [5, 3, 4, 5]}) # doctest: +SKIP

‎sklearn/conftest.py

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@@ -154,6 +154,13 @@ def pytest_collection_modifyitems(config, items):
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except ImportError:
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pass
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# Normally doctest has the entire module's scope. Here we set globs to an empty dict
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# to remove the module's scope:
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# https://docs.python.org/3/library/doctest.html#what-s-the-execution-context
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for item in items:
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if isinstance(item, DoctestItem):
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item.dtest.globs = {}
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if skip_doctests:
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skip_marker = pytest.mark.skip(reason=reason)
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‎sklearn/feature_selection/_variance_threshold.py

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@@ -58,6 +58,7 @@ class VarianceThreshold(SelectorMixin, BaseEstimator):
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The following dataset has integer features, two of which are the same
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in every sample. These are removed with the default setting for threshold::
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>>> from sklearn.feature_selection import VarianceThreshold
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>>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
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>>> selector = VarianceThreshold()
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>>> selector.fit_transform(X)

‎sklearn/linear_model/_coordinate_descent.py

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@@ -325,6 +325,8 @@ def lasso_path(
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Comparing lasso_path and lars_path with interpolation:
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>>> import numpy as np
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>>> from sklearn.linear_model import lasso_path
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>>> X = np.array([[1, 2, 3.1], [2.3, 5.4, 4.3]]).T
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>>> y = np.array([1, 2, 3.1])
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>>> # Use lasso_path to compute a coefficient path

‎sklearn/metrics/_ranking.py

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@@ -523,6 +523,7 @@ class scores must correspond to the order of ``labels``,
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Multilabel case:
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>>> import numpy as np
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>>> from sklearn.datasets import make_multilabel_classification
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>>> from sklearn.multioutput import MultiOutputClassifier
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>>> X, y = make_multilabel_classification(random_state=0)
@@ -1429,6 +1430,7 @@ def dcg_score(
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.metrics import dcg_score
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>>> # we have groud-truth relevance of some answers to a query:
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>>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
@@ -1578,6 +1580,7 @@ def ndcg_score(y_true, y_score, *, k=None, sample_weight=None, ignore_ties=False
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.metrics import ndcg_score
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>>> # we have groud-truth relevance of some answers to a query:
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>>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])

‎sklearn/model_selection/_search_successive_halving.py

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@@ -1002,6 +1002,7 @@ class HalvingRandomSearchCV(BaseSuccessiveHalving):
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>>> from sklearn.experimental import enable_halving_search_cv # noqa
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>>> from sklearn.model_selection import HalvingRandomSearchCV
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>>> from scipy.stats import randint
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>>> import numpy as np
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...
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>>> X, y = load_iris(return_X_y=True)
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>>> clf = RandomForestClassifier(random_state=0)

‎sklearn/neighbors/_kde.py

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@@ -102,6 +102,7 @@ class KernelDensity(BaseEstimator):
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--------
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Compute a gaussian kernel density estimate with a fixed bandwidth.
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>>> from sklearn.neighbors import KernelDensity
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>>> import numpy as np
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>>> rng = np.random.RandomState(42)
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>>> X = rng.random_sample((100, 3))

‎sklearn/pipeline.py

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@@ -864,6 +864,7 @@ def make_pipeline(*steps, memory=None, verbose=False):
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--------
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>>> from sklearn.naive_bayes import GaussianNB
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>>> from sklearn.preprocessing import StandardScaler
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>>> from sklearn.pipeline import make_pipeline
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>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
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Pipeline(steps=[('standardscaler', StandardScaler()),
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('gaussiannb', GaussianNB())])

‎sklearn/preprocessing/_discretization.py

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@@ -107,6 +107,7 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):
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Examples
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--------
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>>> from sklearn.preprocessing import KBinsDiscretizer
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>>> X = [[-2, 1, -4, -1],
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... [-1, 2, -3, -0.5],
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... [ 0, 3, -2, 0.5],

‎sklearn/random_projection.py

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@@ -96,7 +96,7 @@ def johnson_lindenstrauss_min_dim(n_samples, *, eps=0.1):
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Examples
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--------
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>>> from sklearn.random_projection import johnson_lindenstrauss_min_dim
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>>> johnson_lindenstrauss_min_dim(1e6, eps=0.5)
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