8000 [WIP] MNT Use isinstance instead of dtype.kind check for scalar validation. by massich · Pull Request #10017 · scikit-learn/scikit-learn · GitHub
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[WIP] MNT Use isinstance instead of dtype.kind check for scalar validation. #10017

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[WIP] MNT Use isinstance instead of dtype.kind check for scalar validation. #10017

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01e776e
MNT Use isinstance(..., float/numbers.Integral)
raghavrv Sep 12, 2016
f838fa9
factorize the reference class to compare to
Oct 26, 2017
1f1aeb8
[PEP8] fix long line
Nov 3, 2017
6a1b5ec
[PEP8] clean up unused variable
Nov 3, 2017
f8b2628
Change (numbers.Integral, np.integer) or INTEGER_TYPES for SCALAR_IN…
Nov 3, 2017
e0e6281
rename floating_types to SCALAR_FLOATING_TYPES
Nov 8, 2017
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8 changes: 5 additions & 3 deletions benchmarks/bench_plot_nmf.py
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Expand Up @@ -16,12 +16,13 @@
import matplotlib.pyplot as plt
import pandas

from ..utils.validation import SCALAR_INTEGER_TYPES
from sklearn.utils.testing import ignore_warnings
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition.nmf import NMF
from sklearn.decomposition.nmf import _initialize_nmf
from sklearn.decomposition.nmf import _beta_divergence
from sklearn.decomposition.nmf import INTEGER_TYPES, _check_init
from sklearn.decomposition.nmf import _check_init
from sklearn.externals.joblib import Memory
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.extmath import safe_sparse_dot, squared_norm
Expand Down Expand Up @@ -237,11 +238,12 @@ def _fit_transform(self, X, y=None, W=None, H=None, update_H=True):
if n_components is None:
n_components = n_features

if (not isinstance(n_components, INTEGER_TYPES) or
if (not isinstance(n_components, SCALAR_INTEGER_TYPES) or
n_components <= 0):
raise ValueError("Number of components must be a positive integer;"
" got (n_components=%r)" % n_components)
if not isinstance(self.max_iter, INTEGER_TYPES) or self.max_iter < 0:
if (not isinstance(self.max_iter, SCALAR_INTEGER_TYPES) or
self.max_iter < 0):
raise ValueError("Maximum number of iterations must be a positive "
"integer; got (max_iter=%r)" % self.max_iter)
if not isinstance(self.tol, numbers.Number) or self.tol < 0:
Expand Down
7 changes: 3 additions & 4 deletions sklearn/decomposition/nmf.py
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Expand Up @@ -22,13 +22,12 @@
from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm
from ..utils.extmath import safe_min
from ..utils.validation import check_is_fitted, check_non_negative
from ..utils.validation import SCALAR_INTEGER_TYPES
from ..exceptions import ConvergenceWarning
from .cdnmf_fast import _update_cdnmf_fast

EPSILON = np.finfo(np.float32).eps

INTEGER_TYPES = (numbers.Integral, np.integer)


def norm(x):
"""Dot product-based Euclidean norm implementation
Expand Down Expand Up @@ -984,10 +983,10 @@ def non_negative_factorization(X, W=None, H=None, n_components=None,
if n_components is None:
n_components = n_features

if not isinstance(n_components, INTEGER_TYPES) or n_components <= 0:
if not isinstance(n_components, SCALAR_INTEGER_TYPES) or n_components <= 0:
raise ValueError("Number of components must be a positive integer;"
" got (n_components=%r)" % n_components)
if not isinstance(max_iter, INTEGER_TYPES) or max_iter < 0:
if not isinstance(max_iter, SCALAR_INTEGER_TYPES) or max_iter < 0:
raise ValueError("Maximum number of iterations must be a positive "
"integer; got (max_iter=%r)" % max_iter)
if not isinstance(tol, numbers.Number) or tol < 0:
Expand Down
7 changes: 3 additions & 4 deletions sklearn/decomposition/pca.py
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Expand Up @@ -11,7 +11,6 @@
# License: BSD 3 clause

from math import log, sqrt
import numbers

import numpy as np
from scipy import linalg
Expand All @@ -28,7 +27,7 @@
from ..utils import check_array
from ..utils.extmath import fast_logdet, randomized_svd, svd_flip
from ..utils.extmath import stable_cumsum
from ..utils.validation import check_is_fitted
from ..utils.validation import check_is_fitted, SCALAR_INTEGER_TYPES


def _assess_dimension_(spectrum, rank, n_samples, n_features):
Expand Down Expand Up @@ -423,7 +422,7 @@ def _fit_full(self, X, n_components):
"svd_solver='full'"
% (n_components, min(n_samples, n_features)))
elif n_components >= 1:
if not isinstance(n_components, (numbers.Integral, np.integer)):
if not isinstance(n_components, SCALAR_INTEGER_TYPES):
raise ValueError("n_components=%r must be of type int "
"when greater than or equal to 1, "
"was of type=%r"
Expand Down Expand Up @@ -488,7 +487,7 @@ def _fit_truncated(self, X, n_components, svd_solver):
"svd_solver='%s'"
% (n_components, min(n_samples, n_features),
svd_solver))
elif not isinstance(n_components, (numbers.Integral, np.integer)):
elif not isinstance(n_components, SCALAR_INTEGER_TYPES):
raise ValueError("n_components=%r must be of type int "
"when greater than or equal to 1, was of type=%r"
% (n_components, type(n_components)))
Expand Down
7 changes: 3 additions & 4 deletions sklearn/ensemble/bagging.py
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Expand Up @@ -6,7 +6,6 @@
from __future__ import division

import itertools
import numbers
import numpy as np
from warnings import warn
from abc import ABCMeta, abstractmethod
Expand All @@ -20,6 +19,7 @@
from ..utils import check_random_state, check_X_y, check_array, column_or_1d
from ..utils.random import sample_without_replacement
from ..utils.validation import has_fit_parameter, check_is_fitted
from ..utils.validation import SCALAR_INTEGER_TYPES
from ..utils import indices_to_mask, check_consistent_length
from ..utils.metaestimators import if_delegate_has_method
from ..utils.multiclass import check_classification_targets
Expand Down Expand Up @@ -299,7 +299,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
# Validate max_samples
if max_samples is None:
67E6 max_samples = self.max_samples
elif not isinstance(max_samples, (numbers.Integral, np.integer)):
elif not isinstance(max_samples, SCALAR_INTEGER_TYPES):
max_samples = int(max_samples * X.shape[0])

if not (0 < max_samples <= X.shape[0]):
Expand All @@ -309,7 +309,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
self._max_samples = max_samples

# Validate max_features
if isinstance(self.max_features, (numbers.Integral, np.integer)):
if isinstance(self.max_features, SCALAR_INTEGER_TYPES):
max_features = self.max_features
else: # float
max_features = int(self.max_features * self.n_features_)
Expand Down Expand Up @@ -578,7 +578,6 @@ def _validate_estimator(self):
def _set_oob_score(self, X, y):
n_samples = y.shape[0]
n_classes_ = self.n_classes_
classes_ = self.classes_

predictions = np.zeros((n_samples, n_classes_))

Expand Down
4 changes: 2 additions & 2 deletions sklearn/ensemble/base.py
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Expand Up @@ -6,12 +6,12 @@
# License: BSD 3 clause

import numpy as np
import numbers

from ..base import clone
from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..utils import _get_n_jobs, check_random_state
from ..utils.validation import SCALAR_INTEGER_TYPES
from ..externals import six
from abc import ABCMeta, abstractmethod

Expand Down Expand Up @@ -100,7 +100,7 @@ def __init__(self, base_estimator, n_estimators=10,
def _validate_estimator(self, default=None):
"""Check the estimator and the n_estimator attribute, set the
`base_estimator_` attribute."""
if not isinstance(self.n_estimators, (numbers.Integral, np.integer)):
if not isinstance(self.n_estimators, SCALAR_INTEGER_TYPES):
raise ValueError("n_estimators must be an integer, "
"got {0}.".format(type(self.n_estimators)))

Expand Down
8 changes: 4 additions & 4 deletions sklearn/ensemble/gradient_boosting.py
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Original file line nu F438 mber Diff line number Diff line change
Expand Up @@ -35,7 +35,6 @@
from ._gradient_boosting import predict_stage
from ._gradient_boosting import _random_sample_mask

import numbers
import numpy as np

from scipy import stats
Expand All @@ -59,6 +58,7 @@
from ..utils.fixes import logsumexp
from ..utils.stats import _weighted_percentile
from ..utils.validation import check_is_fitted
from ..utils.validation import SCALAR_INTEGER_TYPES
from ..utils.multiclass import check_classification_targets
from ..exceptions import NotFittedError

Expand Down Expand Up @@ -870,7 +870,7 @@ def _check_params(self):
"or 'log2'." % self.max_features)
elif self.max_features is None:
max_features = self.n_features_
elif isinstance(self.max_features, (numbers.Integral, np.integer)):
elif isinstance(self.max_features, SCALAR_INTEGER_TYPES):
max_features = self.max_features
else: # float
if 0. < self.max_features <= 1.:
Expand All @@ -881,8 +881,8 @@ def _check_params(self):

self.max_features_ = max_features

if not isinstance(self.n_iter_no_change,
(numbers.Integral, np.integer, type(None))):
if not (isinstance(self.n_iter_no_change, SCALAR_INTEGER_TYPES) or
self.n_iter_no_change is None):
raise ValueError("n_iter_no_change should either be None or an "
"integer. %r was passed"
% self.n_iter_no_change)
Expand Down
11 changes: 5 additions & 6 deletions sklearn/ensemble/iforest.py
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Expand Up @@ -11,17 +11,15 @@

from scipy.sparse import issparse

import numbers
from ..externals import six
from ..tree import ExtraTreeRegressor
from ..utils import check_random_state, check_array
from ..utils.validation import SCALAR_INTEGER_TYPES

from .bagging import BaseBagging

__all__ = ["IsolationForest"]

INTEGER_TYPES = (numbers.Integral, np.integer)


class IsolationForest(BaseBagging):
"""Isolation Forest Algorithm
Expand Down Expand Up @@ -179,7 +177,7 @@ def fit(self, X, y=None, sample_weight=None):
'Valid choices are: "auto", int or'
'float' % self.max_samples)

elif isinstance(self.max_samples, INTEGER_TYPES):
elif isinstance(self.max_samples, SCALAR_INTEGER_TYPES):
if self.max_samples > n_samples:
warn("max_samples (%s) is greater than the "
"total number of samples (%s). max_samples "
Expand Down Expand Up @@ -278,7 +276,8 @@ def decision_function(self, X):

depths += _average_path_length(n_samples_leaf)

scores = 2 ** (-depths.mean(axis=1) / _average_path_length(self.max_samples_))
scores = 2 ** (-depths.mean(axis=1) /
_average_path_length(self.max_samples_))

# Take the opposite of the scores as bigger is better (here less
# abnormal) and add 0.5 (this value plays a special role as described
Expand All @@ -301,7 +300,7 @@ def _average_path_length(n_samples_leaf):
average_path_length : array, same shape as n_samples_leaf

"""
if isinstance(n_samples_leaf, INTEGER_TYPES):
if isinstance(n_samples_leaf, SCALAR_INTEGER_TYPES):
if n_samples_leaf <= 1:
return 1.
else:
Expand Down
4 changes: 2 additions & 2 deletions sklearn/feature_extraction/hashing.py
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@@ -1,14 +1,14 @@
# Author: Lars Buitinck
# License: BSD 3 clause

import numbers
import warnings

import numpy as np
import scipy.sparse as sp

from . import _hashing
from ..base import BaseEstimator, TransformerMixin
from ..utils.validation import SCALAR_INTEGER_TYPES


def _iteritems(d):
Expand Down Expand Up @@ -103,7 +103,7 @@ def __init__(self, n_features=(2 ** 20), input_type="dict",
def _validate_params(n_features, input_type):
# strangely, np.int16 instances are not instances of Integral,
# while np.int64 instances are...
if not isinstance(n_features, (numbers.Integral, np.integer)):
if not isinstance(n_features, SCALAR_INTEGER_TYPES):
raise TypeError("n_features must be integral, got %r (%s)."
% (n_features, type(n_features)))
elif n_features < 1 or n_features >= 2 ** 31:
Expand Down
30 changes: 16 additions & 14 deletions sklearn/model_selection/_split.py
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Expand Up @@ -17,14 +17,14 @@
from itertools import chain, combinations
from collections import Iterable
from math import ceil, floor
import numbers
from abc import ABCMeta, abstractmethod

import numpy as np

from ..utils import indexable, check_random_state, safe_indexing
from ..utils.validation import _num_samples, column_or_1d
from ..utils.validation import check_array
from ..utils.validation import SCALAR_INTEGER_TYPES, SCALAR_FLOATING_TYPES
from ..utils.multiclass import type_of_target
from ..externals.six import with_metaclass
from ..externals.six.moves import zip
Expand Down Expand Up @@ -271,7 +271,7 @@ class _BaseKFold(with_metaclass(ABCMeta, BaseCrossValidator)):

@abstractmethod
def __init__(self, n_splits, shuffle, random_state):
if not isinstance(n_splits, numbers.Integral):
if not isinstance(n_splits, SCALAR_INTEGER_TYPES):
raise ValueError('The number of folds must be of Integral type. '
'%s of type %s was passed.'
% (n_splits, type(n_splits)))
Expand Down Expand Up @@ -989,7 +989,7 @@ class _RepeatedSplits(with_metaclass(ABCMeta)):
and shuffle.
"""
def __init__(self, cv, n_repeats=10, random_state=None, **cvargs):
if not isinstance(n_repeats, (np.integer, numbers.Integral)):
if not isinstance(n_repeats, SCALAR_INTEGER_TYPES):
raise ValueError("Number of repetitions must be of Integral type.")

if n_repeats <= 0:
Expand Down Expand Up @@ -1643,27 +1643,27 @@ def _validate_shuffle_split_init(test_size, train_size):
raise ValueError('test_size and train_size can not both be None')

if test_size is not None:
if np.asarray(test_size).dtype.kind == 'f':
if isinstance(test_size, SCALAR_FLOATING_TYPES):
if test_size >= 1.:
raise ValueError(
'test_size=%f should be smaller '
'than 1.0 or be an integer' % test_size)
elif np.asarray(test_size).dtype.kind != 'i':
elif not isinstance(test_size, SCALAR_INTEGER_TYPES):
# int values are checked during split based on the input
raise ValueError("Invalid value for test_size: %r" % test_size)

if train_size is not None:
if np.asarray(train_size).dtype.kind == 'f':
if isinstance(train_size, SCALAR_FLOATING_TYPES):
if train_size >= 1.:
raise ValueError("train_size=%f should be smaller "
"than 1.0 or be an integer" % train_size)
elif (np.asarray(test_size).dtype.kind == 'f' and
elif (isinstance(test_size, SCALAR_FLOATING_TYPES) and
(train_size + test_size) > 1.):
raise ValueError('The sum of test_size and train_size = %f, '
'should be smaller than 1.0. Reduce '
'test_size and/or train_size.' %
(train_size + test_size))
elif np.asarray(train_size).dtype.kind != 'i':
elif not isinstance(train_size, SCALAR_INTEGER_TYPES):
# int values are checked during split based on the input
raise ValueError("Invalid value for train_size: %r" % train_size)

Expand All @@ -1672,30 +1672,32 @@ def _validate_shuffle_split(n_samples, test_size, train_size):
"""
Validation helper to check if the test/test sizes are meaningful wrt to the
size of the data (n_samples)

test_size, defaults to 0.1
"""
if (test_size is not None and
np.asarray(test_size).dtype.kind == 'i' and
isinstance(test_size, SCALAR_INTEGER_TYPES) and
test_size >= n_samples):
raise ValueError('test_size=%d should be smaller than the number of '
'samples %d' % (test_size, n_samples))

if (train_size is not None and
np.asarray(train_size).dtype.kind == 'i' and
isinstance(train_size, SCALAR_INTEGER_TYPES) and
train_size >= n_samples):
raise ValueError("train_size=%d should be smaller than the number of"
" samples %d" % (train_size, n_samples))

if test_size == "default":
test_size = 0.1

if np.asarray(test_size).dtype.kind == 'f':
if isinstance(test_size, SCALAR_FLOATING_TYPES):
n_test = ceil(test_size * n_samples)
elif np.asarray(test_size).dtype.kind == 'i':
elif isinstance(test_size, SCALAR_INTEGER_TYPES):
n_test = float(test_size)

if train_size is None:
n_train = n_samples - n_test
elif np.asarray(train_size).dtype.kind == 'f':
elif isinstance(train_size, SCALAR_FLOATING_TYPES):
n_train = floor(train_size * n_samples)
else:
n_train = float(train_size)
Expand Down Expand Up @@ -1900,7 +1902,7 @@ def check_cv(cv=3, y=None, classifier=False):
if cv is None:
cv = 3

if isinstance(cv, numbers.Integral):
if isinstance(cv, SCALAR_INTEGER_TYPES):
if (classifier and (y is not None) and
(type_of_target(y) in ('binary', 'multiclass'))):
return StratifiedKFold(cv)
Expand Down
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