8000 MAINT Remove outdated numpy and scipy backports (#16725) · gio8tisu/scikit-learn@ecb72f7 · GitHub
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MAINT Remove outdated numpy and scipy backports (scikit-learn#16725)
* Remove unecessary numpy & scipy backports * More fixes * Remove unused imports
1 parent d3f9be0 commit ecb72f7

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17 files changed

+19
-174
lines changed

17 files changed

+19
-174
lines changed

‎sklearn/decomposition/_lda.py

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@@ -13,12 +13,11 @@
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import numpy as np
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import scipy.sparse as sp
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from scipy.special import gammaln
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from scipy.special import gammaln, logsumexp
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from joblib import Parallel, delayed, effective_n_jobs
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from ..base import BaseEstimator, TransformerMixin
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from ..utils import check_random_state, gen_batches, gen_even_slices
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from ..utils.fixes import logsumexp
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from ..utils.validation import check_non_negative
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from ..utils.validation import check_is_fitted
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‎sklearn/ensemble/_gb_losses.py

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@@ -6,10 +6,9 @@
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from abc import abstractmethod
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import numpy as np
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from scipy.special import expit
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from scipy.special import expit, logsumexp
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from ..tree._tree import TREE_LEAF
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from ..utils.fixes import logsumexp
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from ..utils.stats import _weighted_percentile
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from ..dummy import DummyClassifier
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from ..dummy import DummyRegressor

‎sklearn/ensemble/_hist_gradient_boosting/loss.py

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@@ -9,11 +9,7 @@
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from abc import ABC, abstractmethod
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import numpy as np
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from scipy.special import expit
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try: # logsumexp was moved from mist to special in 0.19
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from scipy.special import logsumexp
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except ImportError:
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from scipy.misc import logsumexp
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from scipy.special import expit, logsumexp
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from .common import Y_DTYPE
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from .common import G_H_DTYPE

‎sklearn/ensemble/tests/test_forest.py

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@@ -20,6 +20,7 @@
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from scipy.sparse import csr_matrix
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from scipy.sparse import csc_matrix
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from scipy.sparse import coo_matrix
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from scipy.special import comb
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import pytest
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@@ -47,7 +48,6 @@
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from sklearn.model_selection import GridSearchCV
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from sklearn.svm import LinearSVC
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from sklearn.utils.validation import check_random_state
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from sklearn.utils.fixes import comb
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from sklearn.tree._classes import SPARSE_SPLITTERS
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‎sklearn/impute/_iterative.py

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@@ -1,10 +1,8 @@
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from time import time
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from distutils.version import LooseVersion
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from collections import namedtuple
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import warnings
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import scipy
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from scipy import stats
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import numpy as np
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@@ -329,19 +327,10 @@ def _impute_one_feature(self,
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a = (self._min_value[feat_idx] - mus) / sigmas
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b = (self._max_value[feat_idx] - mus) / sigmas
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if scipy.__version__ < LooseVersion('0.18'):
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# bug with vector-valued `a` in old scipy
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imputed_values[inrange_mask] = [
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stats.truncnorm(a=a_, b=b_,
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loc=loc_, scale=scale_).rvs(
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random_state=self.random_state_)
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for a_, b_, loc_, scale_
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in zip(a, b, mus, sigmas)]
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else:
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truncated_normal = stats.truncnorm(a=a, b=b,
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loc=mus, scale=sigmas)
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imputed_values[inrange_mask] = truncated_normal.rvs(
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random_state=self.random_state_)
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truncated_normal = stats.truncnorm(a=a, b=b,
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loc=mus, scale=sigmas)
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imputed_values[inrange_mask] = truncated_normal.rvs(
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random_state=self.random_state_)
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else:
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imputed_values = estimator.predict(X_test)
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imputed_values = np.clip(imputed_values,

‎sklearn/linear_model/_logistic.py

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@@ -15,7 +15,7 @@
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import numpy as np
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from scipy import optimize, sparse
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from scipy.special import expit
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from scipy.special import expit, logsumexp
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from joblib import Parallel, delayed, effective_n_jobs
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from ._base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator
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from ..utils.extmath import (log_logistic, safe_sparse_dot, softmax,
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squared_norm)
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from ..utils.extmath import row_norms
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from ..utils.fixes import logsumexp
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from ..utils.optimize import _newton_cg, _check_optimize_result
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from ..utils.validation import check_is_fitted, _check_sample_weight
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from ..utils.multiclass import check_classification_targets

‎sklearn/linear_model/tests/test_sag.py

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@@ -7,14 +7,14 @@
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import pytest
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import numpy as np
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import scipy.sparse as sp
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from scipy.special import logsumexp
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from sklearn.linear_model._sag import get_auto_step_size
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from sklearn.linear_model._sag_fast import _multinomial_grad_loss_all_samples
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from sklearn.linear_model import LogisticRegression, Ridge
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from sklearn.linear_model._base import make_dataset
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from sklearn.linear_model._logistic import _multinomial_loss_grad
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from sklearn.utils.fixes import logsumexp
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from sklearn.utils.extmath import row_norms
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from sklearn.utils._testing import assert_almost_equal
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from sklearn.utils._testing import assert_array_almost_equal

‎sklearn/metrics/cluster/_supervised.py

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@@ -19,10 +19,11 @@
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import numpy as np
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from scipy import sparse as sp
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from scipy.special import comb
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from ._expected_mutual_info_fast import expected_mutual_information
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from ...utils.validation import check_array, check_consistent_length
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from ...utils.fixes import comb, _astype_copy_false
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from ...utils.fixes import _astype_copy_false
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def _comb2(n):

‎sklearn/mixture/_base.py

Lines cha 8000 nged: 1 addition & 1 deletion
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@@ -9,14 +9,14 @@
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from time import time
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import numpy as np
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from scipy.special import logsumexp
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from .. import cluster
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from ..base import BaseEstimator
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from ..base import DensityMixin
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from ..exceptions import ConvergenceWarning
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from ..utils import check_array, check_random_state
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from ..utils.validation import check_is_fitted
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from ..utils.fixes import logsumexp
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def _check_shape(param, param_shape, name):

‎sklearn/model_selection/_search.py

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@@ -21,6 +21,7 @@
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import warnings
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import numpy as np
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from numpy.ma import MaskedArray
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from scipy.stats import rankdata
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from ..base import BaseEstimator, is_classifier, clone
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from ..exceptions import NotFittedError
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from joblib import Parallel, delayed
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from ..utils import check_random_state
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from ..utils.fixes import MaskedArray
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from ..utils.random import sample_without_replacement
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from ..utils.validation import indexable, check_is_fitted, _check_fit_params
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from ..utils.metaestimators import if_delegate_has_method

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