8000 MAINT Complete 0.20 deprecations (#9570) · wdevazelhes/scikit-learn@eec7649 · GitHub
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MAINT Complete 0.20 deprecations (scikit-learn#9570)
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‎.gitignore

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@@ -54,6 +54,7 @@ benchmarks/bench_covertype_data/
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*.prefs
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.pydevproject
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.idea
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.vscode
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*.c
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*.cpp

‎benchmarks/bench_plot_incremental_pca.py

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@@ -13,7 +13,7 @@
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from collections import defaultdict
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import matplotlib.pyplot as plt
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from sklearn.datasets import fetch_lfw_people
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from sklearn.decomposition import IncrementalPCA, RandomizedPCA, PCA
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from sklearn.decomposition import IncrementalPCA, PCA
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def plot_results(X, y, label):
@@ -37,7 +37,6 @@ def plot_feature_times(all_times, batch_size, all_components, data):
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plot_results(all_components, all_times['pca'], label="PCA")
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plot_results(all_components, all_times['ipca'],
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label="IncrementalPCA, bsize=%i" % batch_size)
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plot_results(all_components, all_times['rpca'], label="RandomizedPCA")
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plt.legend(loc="upper left")
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plt.suptitle("Algorithm runtime vs. n_components\n \
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LFW, size %i x %i" % data.shape)
@@ -50,7 +49,6 @@ def plot_feature_errors(all_errors, batch_size, all_components, data):
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plot_results(all_components, all_errors['pca'], label="PCA")
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plot_results(all_components, all_errors['ipca'],
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label="IncrementalPCA, bsize=%i" % batch_size)
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plot_results(all_components, all_errors['rpca'], label="RandomizedPCA")
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plt.legend(loc="lower left")
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plt.suptitle("Algorithm error vs. n_components\n"
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"LFW, size %i x %i" % data.shape)
@@ -61,7 +59,6 @@ def plot_feature_errors(all_errors, batch_size, all_components, data):
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def plot_batch_times(all_times, n_features, all_batch_sizes, data):
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plt.figure()
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plot_results(all_batch_sizes, all_times['pca'], label="PCA")
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plot_results(all_batch_sizes, all_times['rpca'], label="RandomizedPCA")
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plot_results(all_batch_sizes, all_times['ipca'], label="IncrementalPCA")
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plt.legend(loc="lower left")
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plt.suptitle("Algorithm runtime vs. batch_size for n_components %i\n \
@@ -92,11 +89,9 @@ def fixed_batch_size_comparison(data):
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all_errors = defaultdict(list)
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for n_components in all_features:
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pca = PCA(n_components=n_components)
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rpca = RandomizedPCA(n_components=n_components, random_state=1999)
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ipca = IncrementalPCA(n_components=n_components, batch_size=batch_size)
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results_dict = {k: benchmark(est, data) for k, est in [('pca', pca),
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('ipca', ipca),
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('rpca', rpca)]}
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('ipca', ipca)]}
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for k in sorted(results_dict.keys()):
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all_times[k].append(results_dict[k]['time'])
@@ -116,7 +111,8 @@ def variable_batch_size_comparison(data):
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all_times = defaultdict(list)
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all_errors = defaultdict(list)
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pca = PCA(n_components=n_components)
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rpca = RandomizedPCA(n_components=n_components, random_state=1999)
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rpca = PCA(n_components=n_components, svd_solver='randomized',
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random_state=1999)
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results_dict = {k: benchmark(est, data) for k, est in [('pca', pca),
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('rpca', rpca)]}
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@@ -138,8 +134,6 @@ def variable_batch_size_comparison(data):
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all_errors['ipca'].append(results_dict['ipca']['error'])
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plot_batch_times(all_times, n_components, batch_sizes, data)
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# RandomizedPCA error is always worse (approx 100x) than other PCA
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# tests
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plot_batch_errors(all_errors, n_components, batch_sizes, data)
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faces = fetch_lfw_people(resize=.2, min_faces_per_person=5)

‎doc/modules/classes.rst

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datasets.load_mlcomp
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linear_model.lasso_stability_path
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To be removed in 0.20
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---------------------
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.. autosummary::
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:toctree: generated/
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:template: deprecated_class.rst
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cross_validation.KFold
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cross_validation.LabelKFold
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cross_validation.LeaveOneLabelOut
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cross_validation.LeaveOneOut
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cross_validation.LeavePOut
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cross_validation.LeavePLabelOut
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cross_validation.LabelShuffleSplit
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cross_validation.ShuffleSplit
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cross_validation.StratifiedKFold
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cross_validation.StratifiedShuffleSplit
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cross_validation.PredefinedSplit
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decomposition.RandomizedPCA
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gaussian_process.GaussianProcess
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grid_search.ParameterGrid
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grid_search.ParameterSampler
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grid_search.GridSearchCV
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grid_search.RandomizedSearchCV
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mixture.DPGMM
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mixture.GMM
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mixture.VBGMM
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.. autosummary::
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:toctree: generated/
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:template: deprecated_function.rst
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cross_validation.check_cv
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cross_validation.cross_val_predict
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cross_validation.cross_val_score
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cross_validation.permutation_test_score
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cross_validation.train_test_split
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grid_search.fit_grid_point
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learning_curve.learning_curve
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learning_curve.validation_curve

‎doc/modules/preprocessing.rst

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independently, since a downstream model can further make some assumption
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on the linear independence of the features.
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To address this issue you can use :class:`sklearn.decomposition.PCA`
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or :class:`sklearn.decomposition.RandomizedPCA` with ``whiten=True``
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to further remove the linear correlation across features.
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To address this issue you can use :class:`sklearn.decomposition.PCA` with
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``whiten=True`` to further remove the linear correlation across features.
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.. topic:: Scaling a 1D array
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‎doc/tutorial/machine_learning_map/index.rst

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@@ -100,7 +100,7 @@ Click on any estimator in the chart below to see its documentation.
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<area href="../../modules/svm.html#classification" title="SVC Documentation" shape="poly" coords="210,157, 210,157, 210,194, 210,194, 210,194, 333,194, 333,194, 333,194, 333,157, 333,157, 333,157, 210,157, 210,157" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
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<area href="../../modules/svm.html#regression" title="SVR Documentation" shape="poly" coords="1696,692, 1696,692, 1696,732, 1696,732, 1696,732, 1890,732, 1890,732, 1890,732, 1890,692, 1890,692, 1890,692, 1696,692, 1696,692" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
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<area href="../../modules/svm.html#regression" title="SVR Documentation" shape="poly" coords="1831,458, 1831,458, 1831,496, 1831,496, 1831,496, 2052,496, 2052,496, 2052,496, 2052,458, 2052,458, 2052,458, 1831,458, 1831,458" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
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<area href="../../modules/mixture.html#vbgmm-classifier-variational-gaussian-mixtures" title=" VBGMM Documentation" shape="poly" coords="562,994, 562,994, 562,1026, 562,1026, 562,1026, 682,1026, 682,1026, 682,1026, 682,994, 682,994, 682,994, 562,994, 562,994" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
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<area href="../../modules/mixture.html#bgmm" title=" Bayesian GMM Documentation" shape="poly" coords="562,994, 562,994, 562,1026, 562,1026, 562,1026, 682,1026, 682,1026, 682,1026, 682,994, 682,994, 682,994, 562,994, 562,994" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
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</map>
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</img>
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</p>

‎examples/model_selection/plot_nested_cross_validation_iris.py

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# Choose cross-validation techniques for the inner and outer loops,
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# independently of the dataset.
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# E.g "LabelKFold", "LeaveOneOut", "LeaveOneLabelOut", etc.
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# E.g "GroupKFold", "LeaveOneOut", "LeaveOneGroupOut", etc.
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inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)
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outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)
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‎sklearn/__init__.py

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__check_build # avoid flakes unused variable error
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__all__ = ['calibration', 'cluster', 'covariance', 'cross_decomposition',
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'cross_validation', 'datasets', 'decomposition', 'dummy',
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'ensemble', 'exceptions', 'externals', 'feature_extraction',
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'feature_selection', 'gaussian_process', 'grid_search',
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'isotonic', 'kernel_approximation', 'kernel_ridge',
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'learning_curve', 'linear_model', 'manifold', 'metrics',
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'datasets', 'decomposition', 'dummy', 'ensemble', 'exceptions',
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'externals', 'feature_extraction', 'feature_selection',
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'gaussian_process', 'isotonic', 'kernel_approximation',
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'kernel_ridge', 'linear_model', 'manifold', 'metrics',
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'mixture', 'model_selection', 'multiclass', 'multioutput',
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'naive_bayes', 'neighbors', 'neural_network', 'pipeline',
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'preprocessing', 'random_projection', 'semi_supervised',

‎sklearn/base.py

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for name in new_object_params:
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param1 = new_object_params[name]
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param2 = params_set[name]
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if param1 is param2:
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# this should always happen
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continue
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if isinstance(param1, np.ndarray):
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# For most ndarrays, we do not test for complete equality
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if not isinstance(param2, type(param1)):
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equality_test = False
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elif (param1.ndim > 0
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and param1.shape[0] > 0
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and isinstance(param2, np.ndarray)
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and param2.ndim > 0
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and param2.shape[0] > 0):
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equality_test = (
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param1.shape == param2.shape
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and param1.dtype == param2.dtype
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and (_first_and_last_element(param1) ==
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_first_and_last_element(param2))
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)
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else:
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equality_test = np.all(param1 == param2)
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elif sparse.issparse(param1):
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# For sparse matrices equality doesn't work
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if not sparse.issparse(param2):
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equality_test = False
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elif param1.size == 0 or param2.size == 0:
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equality_test = (
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param1.__class__ == param2.__class__
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and param1.size == 0
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and param2.size == 0
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)
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else:
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equality_test = (
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param1.__class__ == param2.__class__
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and (_first_and_last_element(param1) ==
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_first_and_last_element(param2))
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and param1.nnz == param2.nnz
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and param1.shape == param2.shape
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)
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else:
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# fall back on standard equality
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equality_test = param1 == param2
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if equality_test:
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warnings.warn("Estimator %s modifies parameters in __init__."
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" This behavior is deprecated as of 0.18 and "
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"support for this behavior will be removed in 0.20."
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% type(estimator).__name__, DeprecationWarning)
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else:
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if param1 is not param2:
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raise RuntimeError('Cannot clone object %s, as the constructor '
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'does not seem to set parameter %s' %
72+
'either does not set or modifies parameter %s' %
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(estimator, name))
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return new_object
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‎sklearn/cluster/tests/test_hierarchical.py

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assert_equal(n_leaves, n_leaves_t)
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assert_equal(parent, parent_t)
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54+
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def test_linkage_misc():
5556
# Misc tests on linkage
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rng = np.random.RandomState(42)
@@ -511,7 +512,8 @@ def test_connectivity_callable():
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connectivity = kneighbors_graph(X, 3, include_self=False)
512513
aglc1 = AgglomerativeClustering(connectivity=connectivity)
513514
aglc2 = AgglomerativeClustering(
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connectivity=partial(kneighbors_graph, n_neighbors=3, include_self=False))
515+
connectivity=partial(kneighbors_graph, n_neighbors=3,
516+
include_self=False))
515517
aglc1.fit(X)
516518
aglc2.fit(X)
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assert_array_equal(aglc1.labels_, aglc2.labels_)

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