code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def check_clusterer_compute_labels_predict(name, clusterer_orig):
"""Check that predict is invariant of compute_labels."""
X, y = make_blobs(n_samples=20, random_state=0)
clusterer = clone(clusterer_orig)
set_random_state(clusterer)
if hasattr(clusterer, "compute_labels"):
# MiniBatchKMeans... | Check that predict is invariant of compute_labels. | check_clusterer_compute_labels_predict | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_classifiers_one_label_sample_weights(name, classifier_orig):
"""Check that classifiers accepting sample_weight fit or throws a ValueError with
an explicit message if the problem is reduced to one class.
"""
error_fit = (
f"{name} failed when fitted on one label after sample_weight trim... | Check that classifiers accepting sample_weight fit or throws a ValueError with
an explicit message if the problem is reduced to one class.
| check_classifiers_one_label_sample_weights | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_classifiers_multilabel_output_format_predict(name, classifier_orig):
"""Check the output of the `predict` method for classifiers supporting
multilabel-indicator targets."""
classifier = clone(classifier_orig)
set_random_state(classifier)
n_samples, test_size, n_outputs = 100, 25, 5
X,... | Check the output of the `predict` method for classifiers supporting
multilabel-indicator targets. | check_classifiers_multilabel_output_format_predict | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_classifiers_multilabel_output_format_predict_proba(name, classifier_orig):
"""Check the output of the `predict_proba` method for classifiers supporting
multilabel-indicator targets."""
classifier = clone(classifier_orig)
set_random_state(classifier)
n_samples, test_size, n_outputs = 100, ... | Check the output of the `predict_proba` method for classifiers supporting
multilabel-indicator targets. | check_classifiers_multilabel_output_format_predict_proba | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_classifiers_multilabel_output_format_decision_function(name, classifier_orig):
"""Check the output of the `decision_function` method for classifiers supporting
multilabel-indicator targets."""
classifier = clone(classifier_orig)
set_random_state(classifier)
n_samples, test_size, n_outputs... | Check the output of the `decision_function` method for classifiers supporting
multilabel-indicator targets. | check_classifiers_multilabel_output_format_decision_function | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_get_feature_names_out_error(name, estimator_orig):
"""Check the error raised by get_feature_names_out when called before fit.
Unfitted estimators with get_feature_names_out should raise a NotFittedError.
"""
estimator = clone(estimator_orig)
err_msg = (
f"Estimator {name} should ... | Check the error raised by get_feature_names_out when called before fit.
Unfitted estimators with get_feature_names_out should raise a NotFittedError.
| check_get_feature_names_out_error | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_estimators_fit_returns_self(name, estimator_orig):
"""Check if self is returned when calling fit."""
X, y = make_blobs(random_state=0, n_samples=21)
X = _enforce_estimator_tags_X(estimator_orig, X)
estimator = clone(estimator_orig)
y = _enforce_estimator_tags_y(estimator, y)
set_rand... | Check if self is returned when calling fit. | check_estimators_fit_returns_self | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_readonly_memmap_input(name, estimator_orig):
"""Check that the estimator can handle readonly memmap backed data.
This is particularly needed to support joblib parallelisation.
"""
X, y = make_blobs(random_state=0, n_samples=21)
X = _enforce_estimator_tags_X(estimator_orig, X)
estimat... | Check that the estimator can handle readonly memmap backed data.
This is particularly needed to support joblib parallelisation.
| check_readonly_memmap_input | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_estimators_unfitted(name, estimator_orig):
"""Check that predict raises an exception in an unfitted estimator.
Unfitted estimators should raise a NotFittedError.
"""
err_msg = (
"Estimator should raise a NotFittedError when calling `{method}` before fit. "
"Either call `check_... | Check that predict raises an exception in an unfitted estimator.
Unfitted estimators should raise a NotFittedError.
| check_estimators_unfitted | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_class_weight_balanced_linear_classifier(name, estimator_orig):
"""Test class weights with non-contiguous class labels."""
# this is run on classes, not instances, though this should be changed
X = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
y = np.array([1, 1, 1, ... | Test class weights with non-contiguous class labels. | check_class_weight_balanced_linear_classifier | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_estimator_cloneable(name, estimator_orig):
"""Checks whether the estimator can be cloned."""
try:
clone(estimator_orig)
except Exception as e:
raise AssertionError(f"Cloning of {name} failed with error: {e}.") from e | Checks whether the estimator can be cloned. | check_estimator_cloneable | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_estimator_repr(name, estimator_orig):
"""Check that the estimator has a functioning repr."""
estimator = clone(estimator_orig)
try:
repr(estimator)
except Exception as e:
raise AssertionError(f"Repr of {name} failed with error: {e}.") from e | Check that the estimator has a functioning repr. | check_estimator_repr | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def param_default_value(p):
"""Identify hyper parameters of an estimator."""
return (
p.name != "self"
and p.kind != p.VAR_KEYWORD
and p.kind != p.VAR_POSITIONAL
# and it should have a default value for this ... | Identify hyper parameters of an estimator. | param_default_value | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def param_required(p):
"""Identify hyper parameters of an estimator."""
return (
p.name != "self"
and p.kind != p.VAR_KEYWORD
# technically VAR_POSITIONAL is also required, but we don't have a
# nice way to c... | Identify hyper parameters of an estimator. | param_required | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_positive_only_tag_during_fit(name, estimator_orig):
"""Test that the estimator correctly sets the tags.input_tags.positive_only
If the tag is False, the estimator should accept negative input regardless of the
tags.input_tags.pairwise flag.
"""
estimator = clone(estimator_orig)
tags =... | Test that the estimator correctly sets the tags.input_tags.positive_only
If the tag is False, the estimator should accept negative input regardless of the
tags.input_tags.pairwise flag.
| check_positive_only_tag_during_fit | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_valid_tag_types(name, estimator):
"""Check that estimator tags are valid."""
assert hasattr(estimator, "__sklearn_tags__"), (
f"Estimator {name} does not have `__sklearn_tags__` method. This method is"
" implemented in BaseEstimator and returns a sklearn.utils.Tags instance."
)
... | Check that estimator tags are valid. | check_valid_tag_types | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def _output_from_fit_transform(transformer, name, X, df, y):
"""Generate output to test `set_output` for different configuration:
- calling either `fit.transform` or `fit_transform`;
- passing either a dataframe or a numpy array to fit;
- passing either a dataframe or a numpy array to transform.
""... | Generate output to test `set_output` for different configuration:
- calling either `fit.transform` or `fit_transform`;
- passing either a dataframe or a numpy array to fit;
- passing either a dataframe or a numpy array to transform.
| _output_from_fit_transform | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def _check_generated_dataframe(
name,
case,
index,
outputs_default,
outputs_dataframe_lib,
is_supported_dataframe,
create_dataframe,
assert_frame_equal,
):
"""Check if the generated DataFrame by the transformer is valid.
The DataFrame implementation is specified through the para... | Check if the generated DataFrame by the transformer is valid.
The DataFrame implementation is specified through the parameters of this function.
Parameters
----------
name : str
The name of the transformer.
case : str
A single case from the cases generated by `_output_from_fit_tran... | _check_generated_dataframe | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def _check_set_output_transform_dataframe(
name,
transformer_orig,
*,
dataframe_lib,
is_supported_dataframe,
create_dataframe,
assert_frame_equal,
context,
):
"""Check that a transformer can output a DataFrame when requested.
The DataFrame implementation is specified through the... | Check that a transformer can output a DataFrame when requested.
The DataFrame implementation is specified through the parameters of this function.
Parameters
----------
name : str
The name of the transformer.
transformer_orig : estimator
The original transformer instance.
dataf... | _check_set_output_transform_dataframe | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_inplace_ensure_writeable(name, estimator_orig):
"""Check that estimators able to do inplace operations can work on read-only
input data even if a copy is not explicitly requested by the user.
Make sure that a copy is made and consequently that the input array and its
writeability are not modi... | Check that estimators able to do inplace operations can work on read-only
input data even if a copy is not explicitly requested by the user.
Make sure that a copy is made and consequently that the input array and its
writeability are not modified by the estimator.
| check_inplace_ensure_writeable | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_do_not_raise_errors_in_init_or_set_params(name, estimator_orig):
"""Check that init or set_param does not raise errors."""
Estimator = type(estimator_orig)
params = signature(Estimator).parameters
smoke_test_values = [-1, 3.0, "helloworld", np.array([1.0, 4.0]), [1], {}, []]
for value in ... | Check that init or set_param does not raise errors. | check_do_not_raise_errors_in_init_or_set_params | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def check_classifier_not_supporting_multiclass(name, estimator_orig):
"""Check that if the classifier has tags.classifier_tags.multi_class=False,
then it should raise a ValueError when calling fit with a multiclass dataset.
This test is not yielded if the tag is not False.
"""
estimator = clone(est... | Check that if the classifier has tags.classifier_tags.multi_class=False,
then it should raise a ValueError when calling fit with a multiclass dataset.
This test is not yielded if the tag is not False.
| check_classifier_not_supporting_multiclass | python | scikit-learn/scikit-learn | sklearn/utils/estimator_checks.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/estimator_checks.py | BSD-3-Clause |
def squared_norm(x):
"""Squared Euclidean or Frobenius norm of x.
Faster than norm(x) ** 2.
Parameters
----------
x : array-like
The input array which could be either be a vector or a 2 dimensional array.
Returns
-------
float
The Euclidean norm when x is a vector, the... | Squared Euclidean or Frobenius norm of x.
Faster than norm(x) ** 2.
Parameters
----------
x : array-like
The input array which could be either be a vector or a 2 dimensional array.
Returns
-------
float
The Euclidean norm when x is a vector, the Frobenius norm when x
... | squared_norm | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def row_norms(X, squared=False):
"""Row-wise (squared) Euclidean norm of X.
Equivalent to np.sqrt((X * X).sum(axis=1)), but also supports sparse
matrices and does not create an X.shape-sized temporary.
Performs no input validation.
Parameters
----------
X : array-like
The input ar... | Row-wise (squared) Euclidean norm of X.
Equivalent to np.sqrt((X * X).sum(axis=1)), but also supports sparse
matrices and does not create an X.shape-sized temporary.
Performs no input validation.
Parameters
----------
X : array-like
The input array.
squared : bool, default=False
... | row_norms | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def fast_logdet(A):
"""Compute logarithm of determinant of a square matrix.
The (natural) logarithm of the determinant of a square matrix
is returned if det(A) is non-negative and well defined.
If the determinant is zero or negative returns -Inf.
Equivalent to : np.log(np.det(A)) but more robust.
... | Compute logarithm of determinant of a square matrix.
The (natural) logarithm of the determinant of a square matrix
is returned if det(A) is non-negative and well defined.
If the determinant is zero or negative returns -Inf.
Equivalent to : np.log(np.det(A)) but more robust.
Parameters
-------... | fast_logdet | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def density(w):
"""Compute density of a sparse vector.
Parameters
----------
w : {ndarray, sparse matrix}
The input data can be numpy ndarray or a sparse matrix.
Returns
-------
float
The density of w, between 0 and 1.
Examples
--------
>>> from scipy import sp... | Compute density of a sparse vector.
Parameters
----------
w : {ndarray, sparse matrix}
The input data can be numpy ndarray or a sparse matrix.
Returns
-------
float
The density of w, between 0 and 1.
Examples
--------
>>> from scipy import sparse
>>> from sklea... | density | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def safe_sparse_dot(a, b, *, dense_output=False):
"""Dot product that handle the sparse matrix case correctly.
Parameters
----------
a : {ndarray, sparse matrix}
b : {ndarray, sparse matrix}
dense_output : bool, default=False
When False, ``a`` and ``b`` both being sparse will yield spar... | Dot product that handle the sparse matrix case correctly.
Parameters
----------
a : {ndarray, sparse matrix}
b : {ndarray, sparse matrix}
dense_output : bool, default=False
When False, ``a`` and ``b`` both being sparse will yield sparse output.
When True, output will always be a den... | safe_sparse_dot | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def randomized_range_finder(
A, *, size, n_iter, power_iteration_normalizer="auto", random_state=None
):
"""Compute an orthonormal matrix whose range approximates the range of A.
Parameters
----------
A : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data matrix.
... | Compute an orthonormal matrix whose range approximates the range of A.
Parameters
----------
A : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data matrix.
size : int
Size of the return array.
n_iter : int
Number of power iterations used to stabili... | randomized_range_finder | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def randomized_svd(
M,
n_components,
*,
n_oversamples=10,
n_iter="auto",
power_iteration_normalizer="auto",
transpose="auto",
flip_sign=True,
random_state=None,
svd_lapack_driver="gesdd",
):
"""Compute a truncated randomized SVD.
This method solves the fixed-rank approxi... | Compute a truncated randomized SVD.
This method solves the fixed-rank approximation problem described in [1]_
(problem (1.5), p5).
Refer to
:ref:`sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py`
for a typical example where the power iteration algorithm is used to rank web pa... | randomized_svd | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def weighted_mode(a, w, *, axis=0):
"""Return an array of the weighted modal (most common) value in the passed array.
If there is more than one such value, only the first is returned.
The bin-count for the modal bins is also returned.
This is an extension of the algorithm in scipy.stats.mode.
Par... | Return an array of the weighted modal (most common) value in the passed array.
If there is more than one such value, only the first is returned.
The bin-count for the modal bins is also returned.
This is an extension of the algorithm in scipy.stats.mode.
Parameters
----------
a : array-like o... | weighted_mode | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def cartesian(arrays, out=None):
"""Generate a cartesian product of input arrays.
Parameters
----------
arrays : list of array-like
1-D arrays to form the cartesian product of.
out : ndarray of shape (M, len(arrays)), default=None
Array to place the cartesian product in.
Return... | Generate a cartesian product of input arrays.
Parameters
----------
arrays : list of array-like
1-D arrays to form the cartesian product of.
out : ndarray of shape (M, len(arrays)), default=None
Array to place the cartesian product in.
Returns
-------
out : ndarray of shape... | cartesian | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def svd_flip(u, v, u_based_decision=True):
"""Sign correction to ensure deterministic output from SVD.
Adjusts the columns of u and the rows of v such that the loadings in the
columns in u that are largest in absolute value are always positive.
If u_based_decision is False, then the same sign correcti... | Sign correction to ensure deterministic output from SVD.
Adjusts the columns of u and the rows of v such that the loadings in the
columns in u that are largest in absolute value are always positive.
If u_based_decision is False, then the same sign correction is applied to
so that the rows in v that ar... | svd_flip | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def softmax(X, copy=True):
"""
Calculate the softmax function.
The softmax function is calculated by
np.exp(X) / np.sum(np.exp(X), axis=1)
This will cause overflow when large values are exponentiated.
Hence the largest value in each row is subtracted from each data
point to prevent this.
... |
Calculate the softmax function.
The softmax function is calculated by
np.exp(X) / np.sum(np.exp(X), axis=1)
This will cause overflow when large values are exponentiated.
Hence the largest value in each row is subtracted from each data
point to prevent this.
Parameters
----------
... | softmax | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def make_nonnegative(X, min_value=0):
"""Ensure `X.min()` >= `min_value`.
Parameters
----------
X : array-like
The matrix to make non-negative.
min_value : float, default=0
The threshold value.
Returns
-------
array-like
The thresholded array.
Raises
--... | Ensure `X.min()` >= `min_value`.
Parameters
----------
X : array-like
The matrix to make non-negative.
min_value : float, default=0
The threshold value.
Returns
-------
array-like
The thresholded array.
Raises
------
ValueError
When X is sparse.... | make_nonnegative | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def _safe_accumulator_op(op, x, *args, **kwargs):
"""
This function provides numpy accumulator functions with a float64 dtype
when used on a floating point input. This prevents accumulator overflow on
smaller floating point dtypes.
Parameters
----------
op : function
A numpy accumul... |
This function provides numpy accumulator functions with a float64 dtype
when used on a floating point input. This prevents accumulator overflow on
smaller floating point dtypes.
Parameters
----------
op : function
A numpy accumulator function such as np.mean or np.sum.
x : ndarray
... | _safe_accumulator_op | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def _incremental_mean_and_var(
X, last_mean, last_variance, last_sample_count, sample_weight=None
):
"""Calculate mean update and a Youngs and Cramer variance update.
If sample_weight is given, the weighted mean and variance is computed.
Update a given mean and (possibly) variance according to new dat... | Calculate mean update and a Youngs and Cramer variance update.
If sample_weight is given, the weighted mean and variance is computed.
Update a given mean and (possibly) variance according to new data given
in X. last_mean is always required to compute the new mean.
If last_variance is None, no varianc... | _incremental_mean_and_var | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def _deterministic_vector_sign_flip(u):
"""Modify the sign of vectors for reproducibility.
Flips the sign of elements of all the vectors (rows of u) such that
the absolute maximum element of each vector is positive.
Parameters
----------
u : ndarray
Array with vectors as its rows.
... | Modify the sign of vectors for reproducibility.
Flips the sign of elements of all the vectors (rows of u) such that
the absolute maximum element of each vector is positive.
Parameters
----------
u : ndarray
Array with vectors as its rows.
Returns
-------
u_flipped : ndarray wi... | _deterministic_vector_sign_flip | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
"""Use high precision for cumsum and check that final value matches sum.
Warns if the final cumulative sum does not match the sum (up to the chosen
tolerance).
Parameters
----------
arr : array-like
To be cumulatively summed as... | Use high precision for cumsum and check that final value matches sum.
Warns if the final cumulative sum does not match the sum (up to the chosen
tolerance).
Parameters
----------
arr : array-like
To be cumulatively summed as flat.
axis : int, default=None
Axis along which the c... | stable_cumsum | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def _nanaverage(a, weights=None):
"""Compute the weighted average, ignoring NaNs.
Parameters
----------
a : ndarray
Array containing data to be averaged.
weights : array-like, default=None
An array of weights associated with the values in a. Each value in a
contributes to th... | Compute the weighted average, ignoring NaNs.
Parameters
----------
a : ndarray
Array containing data to be averaged.
weights : array-like, default=None
An array of weights associated with the values in a. Each value in a
contributes to the average according to its associated wei... | _nanaverage | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def safe_sqr(X, *, copy=True):
"""Element wise squaring of array-likes and sparse matrices.
Parameters
----------
X : {array-like, ndarray, sparse matrix}
copy : bool, default=True
Whether to create a copy of X and operate on it or to perform
inplace computation (default behaviour)... | Element wise squaring of array-likes and sparse matrices.
Parameters
----------
X : {array-like, ndarray, sparse matrix}
copy : bool, default=True
Whether to create a copy of X and operate on it or to perform
inplace computation (default behaviour).
Returns
-------
X ** 2 ... | safe_sqr | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def _approximate_mode(class_counts, n_draws, rng):
"""Computes approximate mode of multivariate hypergeometric.
This is an approximation to the mode of the multivariate
hypergeometric given by class_counts and n_draws.
It shouldn't be off by more than one.
It is the mostly likely outcome of drawin... | Computes approximate mode of multivariate hypergeometric.
This is an approximation to the mode of the multivariate
hypergeometric given by class_counts and n_draws.
It shouldn't be off by more than one.
It is the mostly likely outcome of drawing n_draws many
samples from the population given by cl... | _approximate_mode | python | scikit-learn/scikit-learn | sklearn/utils/extmath.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/extmath.py | BSD-3-Clause |
def _yeojohnson_lambda(_neg_log_likelihood, x):
"""Estimate the optimal Yeo-Johnson transformation parameter (lambda).
This function provides a compatibility workaround for versions of SciPy
older than 1.9.0, where `scipy.stats.yeojohnson` did not return
the estimated lambda directly.
Parameters
... | Estimate the optimal Yeo-Johnson transformation parameter (lambda).
This function provides a compatibility workaround for versions of SciPy
older than 1.9.0, where `scipy.stats.yeojohnson` did not return
the estimated lambda directly.
Parameters
----------
_neg_log_likelihood : callable
... | _yeojohnson_lambda | python | scikit-learn/scikit-learn | sklearn/utils/fixes.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/fixes.py | BSD-3-Clause |
def _preserve_dia_indices_dtype(
sparse_container, original_container_format, requested_sparse_format
):
"""Preserve indices dtype for SciPy < 1.12 when converting from DIA to CSR/CSC.
For SciPy < 1.12, DIA arrays indices are upcasted to `np.int64` that is
inconsistent with DIA matrices. We downcast th... | Preserve indices dtype for SciPy < 1.12 when converting from DIA to CSR/CSC.
For SciPy < 1.12, DIA arrays indices are upcasted to `np.int64` that is
inconsistent with DIA matrices. We downcast the indices dtype to `np.int32` to
be consistent with DIA matrices.
The converted indices arrays are affected... | _preserve_dia_indices_dtype | python | scikit-learn/scikit-learn | sklearn/utils/fixes.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/fixes.py | BSD-3-Clause |
def _smallest_admissible_index_dtype(arrays=(), maxval=None, check_contents=False):
"""Based on input (integer) arrays `a`, determine a suitable index data
type that can hold the data in the arrays.
This function returns `np.int64` if it either required by `maxval` or based on the
largest precision of ... | Based on input (integer) arrays `a`, determine a suitable index data
type that can hold the data in the arrays.
This function returns `np.int64` if it either required by `maxval` or based on the
largest precision of the dtype of the arrays passed as argument, or by their
contents (when `check_contents ... | _smallest_admissible_index_dtype | python | scikit-learn/scikit-learn | sklearn/utils/fixes.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/fixes.py | BSD-3-Clause |
def _in_unstable_openblas_configuration():
"""Return True if in an unstable configuration for OpenBLAS"""
# Import libraries which might load OpenBLAS.
import numpy # noqa: F401
import scipy # noqa: F401
modules_info = _get_threadpool_controller().info()
open_blas_used = any(info["internal_... | Return True if in an unstable configuration for OpenBLAS | _in_unstable_openblas_configuration | python | scikit-learn/scikit-learn | sklearn/utils/fixes.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/fixes.py | BSD-3-Clause |
def single_source_shortest_path_length(graph, source, *, cutoff=None):
"""Return the length of the shortest path from source to all reachable nodes.
Parameters
----------
graph : {array-like, sparse matrix} of shape (n_nodes, n_nodes)
Adjacency matrix of the graph. Sparse matrix of format LIL i... | Return the length of the shortest path from source to all reachable nodes.
Parameters
----------
graph : {array-like, sparse matrix} of shape (n_nodes, n_nodes)
Adjacency matrix of the graph. Sparse matrix of format LIL is
preferred.
source : int
Start node for path.
cutoff... | single_source_shortest_path_length | python | scikit-learn/scikit-learn | sklearn/utils/graph.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/graph.py | BSD-3-Clause |
def _fix_connected_components(
X,
graph,
n_connected_components,
component_labels,
mode="distance",
metric="euclidean",
**kwargs,
):
"""Add connections to sparse graph to connect unconnected components.
For each pair of unconnected components, compute all pairwise distances
from... | Add connections to sparse graph to connect unconnected components.
For each pair of unconnected components, compute all pairwise distances
from one component to the other, and add a connection on the closest pair
of samples. This is a hacky way to get a graph with a single connected
component, which is... | _fix_connected_components | python | scikit-learn/scikit-learn | sklearn/utils/graph.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/graph.py | BSD-3-Clause |
def _safe_split(estimator, X, y, indices, train_indices=None):
"""Create subset of dataset and properly handle kernels.
Slice X, y according to indices for cross-validation, but take care of
precomputed kernel-matrices or pairwise affinities / distances.
If ``estimator._pairwise is True``, X needs to ... | Create subset of dataset and properly handle kernels.
Slice X, y according to indices for cross-validation, but take care of
precomputed kernel-matrices or pairwise affinities / distances.
If ``estimator._pairwise is True``, X needs to be square and
we slice rows and columns. If ``train_indices`` is n... | _safe_split | python | scikit-learn/scikit-learn | sklearn/utils/metaestimators.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/metaestimators.py | BSD-3-Clause |
def unique_labels(*ys):
"""Extract an ordered array of unique labels.
We don't allow:
- mix of multilabel and multiclass (single label) targets
- mix of label indicator matrix and anything else,
because there are no explicit labels)
- mix of label indicator matrices of differe... | Extract an ordered array of unique labels.
We don't allow:
- mix of multilabel and multiclass (single label) targets
- mix of label indicator matrix and anything else,
because there are no explicit labels)
- mix of label indicator matrices of different sizes
- mix of strin... | unique_labels | python | scikit-learn/scikit-learn | sklearn/utils/multiclass.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/multiclass.py | BSD-3-Clause |
def is_multilabel(y):
"""Check if ``y`` is in a multilabel format.
Parameters
----------
y : ndarray of shape (n_samples,)
Target values.
Returns
-------
out : bool
Return ``True``, if ``y`` is in a multilabel format, else ``False``.
Examples
--------
>>> impor... | Check if ``y`` is in a multilabel format.
Parameters
----------
y : ndarray of shape (n_samples,)
Target values.
Returns
-------
out : bool
Return ``True``, if ``y`` is in a multilabel format, else ``False``.
Examples
--------
>>> import numpy as np
>>> from sk... | is_multilabel | python | scikit-learn/scikit-learn | sklearn/utils/multiclass.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/multiclass.py | BSD-3-Clause |
def check_classification_targets(y):
"""Ensure that target y is of a non-regression type.
Only the following target types (as defined in type_of_target) are allowed:
'binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences'
Parameters
----------
... | Ensure that target y is of a non-regression type.
Only the following target types (as defined in type_of_target) are allowed:
'binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences'
Parameters
----------
y : array-like
Target values.
... | check_classification_targets | python | scikit-learn/scikit-learn | sklearn/utils/multiclass.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/multiclass.py | BSD-3-Clause |
def type_of_target(y, input_name="", raise_unknown=False):
"""Determine the type of data indicated by the target.
Note that this type is the most specific type that can be inferred.
For example:
* ``binary`` is more specific but compatible with ``multiclass``.
* ``multiclass`` of integers is more ... | Determine the type of data indicated by the target.
Note that this type is the most specific type that can be inferred.
For example:
* ``binary`` is more specific but compatible with ``multiclass``.
* ``multiclass`` of integers is more specific but compatible with ``continuous``.
* ``multilabel-in... | type_of_target | python | scikit-learn/scikit-learn | sklearn/utils/multiclass.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/multiclass.py | BSD-3-Clause |
def _raise_or_return():
"""Depending on the value of raise_unknown, either raise an error or return
'unknown'.
"""
if raise_unknown:
input = input_name if input_name else "data"
raise ValueError(f"Unknown label type for {input}: {y!r}")
else:
r... | Depending on the value of raise_unknown, either raise an error or return
'unknown'.
| _raise_or_return | python | scikit-learn/scikit-learn | sklearn/utils/multiclass.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/multiclass.py | BSD-3-Clause |
def _check_partial_fit_first_call(clf, classes=None):
"""Private helper function for factorizing common classes param logic.
Estimators that implement the ``partial_fit`` API need to be provided with
the list of possible classes at the first call to partial_fit.
Subsequent calls to partial_fit should ... | Private helper function for factorizing common classes param logic.
Estimators that implement the ``partial_fit`` API need to be provided with
the list of possible classes at the first call to partial_fit.
Subsequent calls to partial_fit should check that ``classes`` is still
consistent with a previou... | _check_partial_fit_first_call | python | scikit-learn/scikit-learn | sklearn/utils/multiclass.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/multiclass.py | BSD-3-Clause |
def class_distribution(y, sample_weight=None):
"""Compute class priors from multioutput-multiclass target data.
Parameters
----------
y : {array-like, sparse matrix} of size (n_samples, n_outputs)
The labels for each example.
sample_weight : array-like of shape (n_samples,), default=None
... | Compute class priors from multioutput-multiclass target data.
Parameters
----------
y : {array-like, sparse matrix} of size (n_samples, n_outputs)
The labels for each example.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
cl... | class_distribution | python | scikit-learn/scikit-learn | sklearn/utils/multiclass.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/multiclass.py | BSD-3-Clause |
def _ovr_decision_function(predictions, confidences, n_classes):
"""Compute a continuous, tie-breaking OvR decision function from OvO.
It is important to include a continuous value, not only votes,
to make computing AUC or calibration meaningful.
Parameters
----------
predictions : array-like ... | Compute a continuous, tie-breaking OvR decision function from OvO.
It is important to include a continuous value, not only votes,
to make computing AUC or calibration meaningful.
Parameters
----------
predictions : array-like of shape (n_samples, n_classifiers)
Predicted classes for each b... | _ovr_decision_function | python | scikit-learn/scikit-learn | sklearn/utils/multiclass.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/multiclass.py | BSD-3-Clause |
def _line_search_wolfe12(
f, fprime, xk, pk, gfk, old_fval, old_old_fval, verbose=0, **kwargs
):
"""
Same as line_search_wolfe1, but fall back to line_search_wolfe2 if
suitable step length is not found, and raise an exception if a
suitable step length is not found.
Raises
------
_LineSe... |
Same as line_search_wolfe1, but fall back to line_search_wolfe2 if
suitable step length is not found, and raise an exception if a
suitable step length is not found.
Raises
------
_LineSearchError
If no suitable step size is found.
| _line_search_wolfe12 | python | scikit-learn/scikit-learn | sklearn/utils/optimize.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/optimize.py | BSD-3-Clause |
def _cg(fhess_p, fgrad, maxiter, tol, verbose=0):
"""
Solve iteratively the linear system 'fhess_p . xsupi = fgrad'
with a conjugate gradient descent.
Parameters
----------
fhess_p : callable
Function that takes the gradient as a parameter and returns the
matrix product of the H... |
Solve iteratively the linear system 'fhess_p . xsupi = fgrad'
with a conjugate gradient descent.
Parameters
----------
fhess_p : callable
Function that takes the gradient as a parameter and returns the
matrix product of the Hessian and gradient.
fgrad : ndarray of shape (n_fea... | _cg | python | scikit-learn/scikit-learn | sklearn/utils/optimize.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/optimize.py | BSD-3-Clause |
def _newton_cg(
grad_hess,
func,
grad,
x0,
args=(),
tol=1e-4,
maxiter=100,
maxinner=200,
line_search=True,
warn=True,
verbose=0,
):
"""
Minimization of scalar function of one or more variables using the
Newton-CG algorithm.
Parameters
----------
grad_... |
Minimization of scalar function of one or more variables using the
Newton-CG algorithm.
Parameters
----------
grad_hess : callable
Should return the gradient and a callable returning the matvec product
of the Hessian.
func : callable
Should return the value of the func... | _newton_cg | python | scikit-learn/scikit-learn | sklearn/utils/optimize.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/optimize.py | BSD-3-Clause |
def _check_optimize_result(solver, result, max_iter=None, extra_warning_msg=None):
"""Check the OptimizeResult for successful convergence
Parameters
----------
solver : str
Solver name. Currently only `lbfgs` is supported.
result : OptimizeResult
Result of the scipy.optimize.minimize... | Check the OptimizeResult for successful convergence
Parameters
----------
solver : str
Solver name. Currently only `lbfgs` is supported.
result : OptimizeResult
Result of the scipy.optimize.minimize function.
max_iter : int, default=None
Expected maximum number of iterations.... | _check_optimize_result | python | scikit-learn/scikit-learn | sklearn/utils/optimize.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/optimize.py | BSD-3-Clause |
def _with_config_and_warning_filters(delayed_func, config, warning_filters):
"""Helper function that intends to attach a config to a delayed function."""
if hasattr(delayed_func, "with_config_and_warning_filters"):
return delayed_func.with_config_and_warning_filters(config, warning_filters)
else:
... | Helper function that intends to attach a config to a delayed function. | _with_config_and_warning_filters | python | scikit-learn/scikit-learn | sklearn/utils/parallel.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/parallel.py | BSD-3-Clause |
def __call__(self, iterable):
"""Dispatch the tasks and return the results.
Parameters
----------
iterable : iterable
Iterable containing tuples of (delayed_function, args, kwargs) that should
be consumed.
Returns
-------
results : list
... | Dispatch the tasks and return the results.
Parameters
----------
iterable : iterable
Iterable containing tuples of (delayed_function, args, kwargs) that should
be consumed.
Returns
-------
results : list
List of results of the tasks.
... | __call__ | python | scikit-learn/scikit-learn | sklearn/utils/parallel.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/parallel.py | BSD-3-Clause |
def delayed(function):
"""Decorator used to capture the arguments of a function.
This alternative to `joblib.delayed` is meant to be used in conjunction
with `sklearn.utils.parallel.Parallel`. The latter captures the scikit-
learn configuration by calling `sklearn.get_config()` in the current
threa... | Decorator used to capture the arguments of a function.
This alternative to `joblib.delayed` is meant to be used in conjunction
with `sklearn.utils.parallel.Parallel`. The latter captures the scikit-
learn configuration by calling `sklearn.get_config()` in the current
thread, prior to dispatching the fi... | delayed | python | scikit-learn/scikit-learn | sklearn/utils/parallel.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/parallel.py | BSD-3-Clause |
def _get_threadpool_controller():
"""Return the global threadpool controller instance."""
global _threadpool_controller
if _threadpool_controller is None:
_threadpool_controller = ThreadpoolController()
return _threadpool_controller | Return the global threadpool controller instance. | _get_threadpool_controller | python | scikit-learn/scikit-learn | sklearn/utils/parallel.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/parallel.py | BSD-3-Clause |
def _random_choice_csc(n_samples, classes, class_probability=None, random_state=None):
"""Generate a sparse random matrix given column class distributions
Parameters
----------
n_samples : int,
Number of samples to draw in each column.
classes : list of size n_outputs of arrays of size (n_... | Generate a sparse random matrix given column class distributions
Parameters
----------
n_samples : int,
Number of samples to draw in each column.
classes : list of size n_outputs of arrays of size (n_classes,)
List of classes for each column.
class_probability : list of size n_out... | _random_choice_csc | python | scikit-learn/scikit-learn | sklearn/utils/random.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/random.py | BSD-3-Clause |
def _raise_typeerror(X):
"""Raises a TypeError if X is not a CSR or CSC matrix"""
input_type = X.format if sp.issparse(X) else type(X)
err = "Expected a CSR or CSC sparse matrix, got %s." % input_type
raise TypeError(err) | Raises a TypeError if X is not a CSR or CSC matrix | _raise_typeerror | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def inplace_csr_column_scale(X, scale):
"""Inplace column scaling of a CSR matrix.
Scale each feature of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
... | Inplace column scaling of a CSR matrix.
Scale each feature of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix to normalize using the variance ... | inplace_csr_column_scale | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def inplace_csr_row_scale(X, scale):
"""Inplace row scaling of a CSR matrix.
Scale each sample of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
... | Inplace row scaling of a CSR matrix.
Scale each sample of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix to be scaled. It should be of CSR fo... | inplace_csr_row_scale | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def mean_variance_axis(X, axis, weights=None, return_sum_weights=False):
"""Compute mean and variance along an axis on a CSR or CSC matrix.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It can be of CSR or CSC format.
axis : {0, 1}
Axis along ... | Compute mean and variance along an axis on a CSR or CSC matrix.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It can be of CSR or CSC format.
axis : {0, 1}
Axis along which the axis should be computed.
weights : ndarray of shape (n_samples,) ... | mean_variance_axis | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def incr_mean_variance_axis(X, *, axis, last_mean, last_var, last_n, weights=None):
"""Compute incremental mean and variance along an axis on a CSR or CSC matrix.
last_mean, last_var are the statistics computed at the last step by this
function. Both must be initialized to 0-arrays of the proper size, i.e.... | Compute incremental mean and variance along an axis on a CSR or CSC matrix.
last_mean, last_var are the statistics computed at the last step by this
function. Both must be initialized to 0-arrays of the proper size, i.e.
the number of features in X. last_n is the number of samples encountered
until now... | incr_mean_variance_axis | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def inplace_column_scale(X, scale):
"""Inplace column scaling of a CSC/CSR matrix.
Scale each feature of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
... | Inplace column scaling of a CSC/CSR matrix.
Scale each feature of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix to normalize using the varia... | inplace_column_scale | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def inplace_row_scale(X, scale):
"""Inplace row scaling of a CSR or CSC matrix.
Scale each row of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
... | Inplace row scaling of a CSR or CSC matrix.
Scale each row of the data matrix by multiplying with specific scale
provided by the caller assuming a (n_samples, n_features) shape.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix to be scaled. It should be of CS... | inplace_row_scale | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def inplace_swap_row_csc(X, m, n):
"""Swap two rows of a CSC matrix in-place.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix whose two rows are to be swapped. It should be of
CSC format.
m : int
Index of the row of X to be swapped.
n : ... | Swap two rows of a CSC matrix in-place.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix whose two rows are to be swapped. It should be of
CSC format.
m : int
Index of the row of X to be swapped.
n : int
Index of the row of X to be sw... | inplace_swap_row_csc | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def inplace_swap_row_csr(X, m, n):
"""Swap two rows of a CSR matrix in-place.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix whose two rows are to be swapped. It should be of
CSR format.
m : int
Index of the row of X to be swapped.
n : ... | Swap two rows of a CSR matrix in-place.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix whose two rows are to be swapped. It should be of
CSR format.
m : int
Index of the row of X to be swapped.
n : int
Index of the row of X to be sw... | inplace_swap_row_csr | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def inplace_swap_row(X, m, n):
"""
Swap two rows of a CSC/CSR matrix in-place.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix whose two rows are to be swapped. It should be of CSR or
CSC format.
m : int
Index of the row of X to be swappe... |
Swap two rows of a CSC/CSR matrix in-place.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix whose two rows are to be swapped. It should be of CSR or
CSC format.
m : int
Index of the row of X to be swapped.
n : int
Index of the r... | inplace_swap_row | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def inplace_swap_column(X, m, n):
"""
Swap two columns of a CSC/CSR matrix in-place.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix whose two columns are to be swapped. It should be of
CSR or CSC format.
m : int
Index of the column of X ... |
Swap two columns of a CSC/CSR matrix in-place.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Matrix whose two columns are to be swapped. It should be of
CSR or CSC format.
m : int
Index of the column of X to be swapped.
n : int
Index... | inplace_swap_column | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def min_max_axis(X, axis, ignore_nan=False):
"""Compute minimum and maximum along an axis on a CSR or CSC matrix.
Optionally ignore NaN values.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It should be of CSR or CSC format.
axis : {0, 1}
... | Compute minimum and maximum along an axis on a CSR or CSC matrix.
Optionally ignore NaN values.
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It should be of CSR or CSC format.
axis : {0, 1}
Axis along which the axis should be computed.
... | min_max_axis | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def count_nonzero(X, axis=None, sample_weight=None):
"""A variant of X.getnnz() with extension to weighting on axis 0.
Useful in efficiently calculating multilabel metrics.
Parameters
----------
X : sparse matrix of shape (n_samples, n_labels)
Input data. It should be of CSR format.
a... | A variant of X.getnnz() with extension to weighting on axis 0.
Useful in efficiently calculating multilabel metrics.
Parameters
----------
X : sparse matrix of shape (n_samples, n_labels)
Input data. It should be of CSR format.
axis : {0, 1}, default=None
The axis on which the dat... | count_nonzero | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def _get_median(data, n_zeros):
"""Compute the median of data with n_zeros additional zeros.
This function is used to support sparse matrices; it modifies data
in-place.
"""
n_elems = len(data) + n_zeros
if not n_elems:
return np.nan
n_negative = np.count_nonzero(data < 0)
middl... | Compute the median of data with n_zeros additional zeros.
This function is used to support sparse matrices; it modifies data
in-place.
| _get_median | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def _get_elem_at_rank(rank, data, n_negative, n_zeros):
"""Find the value in data augmented with n_zeros for the given rank"""
if rank < n_negative:
return data[rank]
if rank - n_negative < n_zeros:
return 0
return data[rank - n_zeros] | Find the value in data augmented with n_zeros for the given rank | _get_elem_at_rank | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def csc_median_axis_0(X):
"""Find the median across axis 0 of a CSC matrix.
It is equivalent to doing np.median(X, axis=0).
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It should be of CSC format.
Returns
-------
median : ndarray of shap... | Find the median across axis 0 of a CSC matrix.
It is equivalent to doing np.median(X, axis=0).
Parameters
----------
X : sparse matrix of shape (n_samples, n_features)
Input data. It should be of CSC format.
Returns
-------
median : ndarray of shape (n_features,)
Median.
... | csc_median_axis_0 | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def _implicit_column_offset(X, offset):
"""Create an implicitly offset linear operator.
This is used by PCA on sparse data to avoid densifying the whole data
matrix.
Params
------
X : sparse matrix of shape (n_samples, n_features)
offset : ndarray of shape (n_features,)
Return... | Create an implicitly offset linear operator.
This is used by PCA on sparse data to avoid densifying the whole data
matrix.
Params
------
X : sparse matrix of shape (n_samples, n_features)
offset : ndarray of shape (n_features,)
Returns
-------
centered : LinearOperator
... | _implicit_column_offset | python | scikit-learn/scikit-learn | sklearn/utils/sparsefuncs.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/sparsefuncs.py | BSD-3-Clause |
def _weighted_percentile(array, sample_weight, percentile_rank=50, xp=None):
"""Compute the weighted percentile with method 'inverted_cdf'.
When the percentile lies between two data points of `array`, the function returns
the lower value.
If `array` is a 2D array, the `values` are selected along axis ... | Compute the weighted percentile with method 'inverted_cdf'.
When the percentile lies between two data points of `array`, the function returns
the lower value.
If `array` is a 2D array, the `values` are selected along axis 0.
`NaN` values are ignored by setting their weights to 0. If `array` is 2D, th... | _weighted_percentile | python | scikit-learn/scikit-learn | sklearn/utils/stats.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/stats.py | BSD-3-Clause |
def _deprecate_positional_args(func=None, *, version="1.3"):
"""Decorator for methods that issues warnings for positional arguments.
Using the keyword-only argument syntax in pep 3102, arguments after the
* will issue a warning when passed as a positional argument.
Parameters
----------
func :... | Decorator for methods that issues warnings for positional arguments.
Using the keyword-only argument syntax in pep 3102, arguments after the
* will issue a warning when passed as a positional argument.
Parameters
----------
func : callable, default=None
Function to check arguments on.
... | _deprecate_positional_args | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def assert_all_finite(
X,
*,
allow_nan=False,
estimator_name=None,
input_name="",
):
"""Throw a ValueError if X contains NaN or infinity.
Parameters
----------
X : {ndarray, sparse matrix}
The input data.
allow_nan : bool, default=False
If True, do not throw err... | Throw a ValueError if X contains NaN or infinity.
Parameters
----------
X : {ndarray, sparse matrix}
The input data.
allow_nan : bool, default=False
If True, do not throw error when `X` contains NaN.
estimator_name : str, default=None
The estimator name, used to construct ... | assert_all_finite | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def as_float_array(
X, *, copy=True, force_all_finite="deprecated", ensure_all_finite=None
):
"""Convert an array-like to an array of floats.
The new dtype will be np.float32 or np.float64, depending on the original
type. The function can create a copy or modify the argument depending
on the argume... | Convert an array-like to an array of floats.
The new dtype will be np.float32 or np.float64, depending on the original
type. The function can create a copy or modify the argument depending
on the argument copy.
Parameters
----------
X : {array-like, sparse matrix}
The input data.
... | as_float_array | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def _is_arraylike(x):
"""Returns whether the input is array-like."""
if sp.issparse(x):
return False
return hasattr(x, "__len__") or hasattr(x, "shape") or hasattr(x, "__array__") | Returns whether the input is array-like. | _is_arraylike | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def _num_features(X):
"""Return the number of features in an array-like X.
This helper function tries hard to avoid to materialize an array version
of X unless necessary. For instance, if X is a list of lists,
this function will return the length of the first element, assuming
that subsequent eleme... | Return the number of features in an array-like X.
This helper function tries hard to avoid to materialize an array version
of X unless necessary. For instance, if X is a list of lists,
this function will return the length of the first element, assuming
that subsequent elements are all lists of the same... | _num_features | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def _num_samples(x):
"""Return number of samples in array-like x."""
message = "Expected sequence or array-like, got %s" % type(x)
if hasattr(x, "fit") and callable(x.fit):
# Don't get num_samples from an ensembles length!
raise TypeError(message)
if _use_interchange_protocol(x):
... | Return number of samples in array-like x. | _num_samples | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def check_memory(memory):
"""Check that ``memory`` is joblib.Memory-like.
joblib.Memory-like means that ``memory`` can be converted into a
joblib.Memory instance (typically a str denoting the ``location``)
or has the same interface (has a ``cache`` method).
Parameters
----------
memory : N... | Check that ``memory`` is joblib.Memory-like.
joblib.Memory-like means that ``memory`` can be converted into a
joblib.Memory instance (typically a str denoting the ``location``)
or has the same interface (has a ``cache`` method).
Parameters
----------
memory : None, str or object with the jobli... | check_memory | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def check_consistent_length(*arrays):
"""Check that all arrays have consistent first dimensions.
Checks whether all objects in arrays have the same shape or length.
Parameters
----------
*arrays : list or tuple of input objects.
Objects that will be checked for consistent length.
Exam... | Check that all arrays have consistent first dimensions.
Checks whether all objects in arrays have the same shape or length.
Parameters
----------
*arrays : list or tuple of input objects.
Objects that will be checked for consistent length.
Examples
--------
>>> from sklearn.utils.... | check_consistent_length | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def _make_indexable(iterable):
"""Ensure iterable supports indexing or convert to an indexable variant.
Convert sparse matrices to csr and other non-indexable iterable to arrays.
Let `None` and indexable objects (e.g. pandas dataframes) pass unchanged.
Parameters
----------
iterable : {list, d... | Ensure iterable supports indexing or convert to an indexable variant.
Convert sparse matrices to csr and other non-indexable iterable to arrays.
Let `None` and indexable objects (e.g. pandas dataframes) pass unchanged.
Parameters
----------
iterable : {list, dataframe, ndarray, sparse matrix} or N... | _make_indexable | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def indexable(*iterables):
"""Make arrays indexable for cross-validation.
Checks consistent length, passes through None, and ensures that everything
can be indexed by converting sparse matrices to csr and converting
non-iterable objects to arrays.
Parameters
----------
*iterables : {lists,... | Make arrays indexable for cross-validation.
Checks consistent length, passes through None, and ensures that everything
can be indexed by converting sparse matrices to csr and converting
non-iterable objects to arrays.
Parameters
----------
*iterables : {lists, dataframes, ndarrays, sparse matr... | indexable | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def _ensure_sparse_format(
sparse_container,
accept_sparse,
dtype,
copy,
ensure_all_finite,
accept_large_sparse,
estimator_name=None,
input_name="",
):
"""Convert a sparse container to a given format.
Checks the sparse format of `sparse_container` and converts if necessary.
... | Convert a sparse container to a given format.
Checks the sparse format of `sparse_container` and converts if necessary.
Parameters
----------
sparse_container : sparse matrix or array
Input to validate and convert.
accept_sparse : str, bool or list/tuple of str
String[s] represent... | _ensure_sparse_format | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def _pandas_dtype_needs_early_conversion(pd_dtype):
"""Return True if pandas extension pd_dtype need to be converted early."""
# Check these early for pandas versions without extension dtypes
from pandas import SparseDtype
from pandas.api.types import (
is_bool_dtype,
is_float_dtype,
... | Return True if pandas extension pd_dtype need to be converted early. | _pandas_dtype_needs_early_conversion | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def check_array(
array,
accept_sparse=False,
*,
accept_large_sparse=True,
dtype="numeric",
order=None,
copy=False,
force_writeable=False,
force_all_finite="deprecated",
ensure_all_finite=None,
ensure_non_negative=False,
ensure_2d=True,
allow_nd=False,
ensure_min_s... | Input validation on an array, list, sparse matrix or similar.
By default, the input is checked to be a non-empty 2D array containing
only finite values. If the dtype of the array is object, attempt
converting to float, raising on failure.
Parameters
----------
array : object
Input obje... | check_array | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def _check_large_sparse(X, accept_large_sparse=False):
"""Raise a ValueError if X has 64bit indices and accept_large_sparse=False"""
if not accept_large_sparse:
supported_indices = ["int32"]
if X.format == "coo":
index_keys = ["col", "row"]
elif X.format in ["csr", "csc", "bs... | Raise a ValueError if X has 64bit indices and accept_large_sparse=False | _check_large_sparse | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def check_X_y(
X,
y,
accept_sparse=False,
*,
accept_large_sparse=True,
dtype="numeric",
order=None,
copy=False,
force_writeable=False,
force_all_finite="deprecated",
ensure_all_finite=None,
ensure_2d=True,
allow_nd=False,
multi_output=False,
ensure_min_samples... | Input validation for standard estimators.
Checks X and y for consistent length, enforces X to be 2D and y 1D. By
default, X is checked to be non-empty and containing only finite values.
Standard input checks are also applied to y, such as checking that y
does not have np.nan or np.inf targets. For mult... | check_X_y | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def _check_y(y, multi_output=False, y_numeric=False, estimator=None):
"""Isolated part of check_X_y dedicated to y validation"""
if multi_output:
y = check_array(
y,
accept_sparse="csr",
ensure_all_finite=True,
ensure_2d=False,
dtype=None,
... | Isolated part of check_X_y dedicated to y validation | _check_y | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
def column_or_1d(y, *, dtype=None, warn=False, device=None):
"""Ravel column or 1d numpy array, else raises an error.
Parameters
----------
y : array-like
Input data.
dtype : data-type, default=None
Data type for `y`.
.. versionadded:: 1.2
warn : bool, default=False
... | Ravel column or 1d numpy array, else raises an error.
Parameters
----------
y : array-like
Input data.
dtype : data-type, default=None
Data type for `y`.
.. versionadded:: 1.2
warn : bool, default=False
To control display of warnings.
device : device, default=N... | column_or_1d | python | scikit-learn/scikit-learn | sklearn/utils/validation.py | https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py | BSD-3-Clause |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.