package sklearn

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

A decorator indicating abstract methods.

Requires that the metaclass is ABCMeta or derived from it. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods are overridden. The abstract methods can be called using any of the normal 'super' call mechanisms. abstractmethod() may be used to declare abstract methods for properties and descriptors.

Usage:

class C(metaclass=ABCMeta): @abstractmethod def my_abstract_method(self, ...): ...

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 multi-label y, set multi_output=True to allow 2D and sparse y. If the dtype of X is object, attempt converting to float, raising on failure.

Parameters ---------- X : nd-array, list or sparse matrix Input data.

y : nd-array, list or sparse matrix Labels.

accept_sparse : string, boolean or list of string (default=False) Strings representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error.

accept_large_sparse : bool (default=True) If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse will cause it to be accepted only if its indices are stored with a 32-bit dtype.

.. versionadded:: 0.20

dtype : string, type, list of types or None (default='numeric') Data type of result. If None, the dtype of the input is preserved. If 'numeric', dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list.

order : 'F', 'C' or None (default=None) Whether an array will be forced to be fortran or c-style.

copy : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in X. This parameter does not influence whether y can have np.inf or np.nan values. The possibilities are:

• True: Force all values of X to be finite.
• False: accept both np.inf and np.nan in X.
• 'allow-nan': accept only np.nan values in X. Values cannot be infinite.

.. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``.

ensure_2d : boolean (default=True) Whether to raise a value error if X is not 2D.

allow_nd : boolean (default=False) Whether to allow X.ndim > 2.

multi_output : boolean (default=False) Whether to allow 2D y (array or sparse matrix). If false, y will be validated as a vector. y cannot have np.nan or np.inf values if multi_output=True.

ensure_min_samples : int (default=1) Make sure that X has a minimum number of samples in its first axis (rows for a 2D array).

ensure_min_features : int (default=1) Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when X has effectively 2 dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0 disables this check.

y_numeric : boolean (default=False) Whether to ensure that y has a numeric type. If dtype of y is object, it is converted to float64. Should only be used for regression algorithms.

warn_on_dtype : boolean or None, optional (default=None) Raise DataConversionWarning if the dtype of the input data structure does not match the requested dtype, causing a memory copy.

.. deprecated:: 0.21 ``warn_on_dtype`` is deprecated in version 0.21 and will be removed in 0.23.

estimator : str or estimator instance (default=None) If passed, include the name of the estimator in warning messages.

Returns ------- X_converted : object The converted and validated X.

y_converted : object The converted and validated y.

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 object to check / convert.

accept_sparse : string, boolean or list/tuple of strings (default=False) Strings representing allowed sparse matrix formats, such as 'csc', 'csr', etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error.

accept_large_sparse : bool (default=True) If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse=False will cause it to be accepted only if its indices are stored with a 32-bit dtype.

.. versionadded:: 0.20

dtype : string, type, list of types or None (default='numeric') Data type of result. If None, the dtype of the input is preserved. If 'numeric', dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list.

order : 'F', 'C' or None (default=None) Whether an array will be forced to be fortran or c-style. When order is None (default), then if copy=False, nothing is ensured about the memory layout of the output array; otherwise (copy=True) the memory layout of the returned array is kept as close as possible to the original array.

copy : boolean (default=False) Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

force_all_finite : boolean or 'allow-nan', (default=True) Whether to raise an error on np.inf and np.nan in array. The possibilities are:

• True: Force all values of array to be finite.
• False: accept both np.inf and np.nan in array.
• 'allow-nan': accept only np.nan values in array. Values cannot be infinite.

For object dtyped data, only np.nan is checked and not np.inf.

.. versionadded:: 0.20 ``force_all_finite`` accepts the string ``'allow-nan'``.

ensure_2d : boolean (default=True) Whether to raise a value error if array is not 2D.

allow_nd : boolean (default=False) Whether to allow array.ndim > 2.

ensure_min_samples : int (default=1) Make sure that the array has a minimum number of samples in its first axis (rows for a 2D array). Setting to 0 disables this check.

ensure_min_features : int (default=1) Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when the input data has effectively 2 dimensions or is originally 1D and ``ensure_2d`` is True. Setting to 0 disables this check.

warn_on_dtype : boolean or None, optional (default=None) Raise DataConversionWarning if the dtype of the input data structure does not match the requested dtype, causing a memory copy.

.. deprecated:: 0.21 ``warn_on_dtype`` is deprecated in version 0.21 and will be removed in 0.23.

estimator : str or estimator instance (default=None) If passed, include the name of the estimator in warning messages.

Returns ------- array_converted : object The converted and validated array.

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

Perform is_fitted validation for estimator.

Checks if the estimator is fitted by verifying the presence of fitted attributes (ending with a trailing underscore) and otherwise raises a NotFittedError with the given message.

This utility is meant to be used internally by estimators themselves, typically in their own predict / transform methods.

Parameters ---------- estimator : estimator instance. estimator instance for which the check is performed.

attributes : str, list or tuple of str, default=None Attribute name(s) given as string or a list/tuple of strings Eg.: ``'coef_', 'estimator_', ..., 'coef_'``

If `None`, `estimator` is considered fitted if there exist an attribute that ends with a underscore and does not start with double underscore.

msg : string The default error message is, 'This %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.'

For custom messages if '%(name)s' is present in the message string, it is substituted for the estimator name.

Eg. : 'Estimator, %(name)s, must be fitted before sparsifying'.

all_or_any : callable, all, any, default all Specify whether all or any of the given attributes must exist.

Returns ------- None

Raises ------ NotFittedError If the attributes are not found.

Turn seed into a np.random.RandomState instance

Parameters ---------- seed : None | int | instance of RandomState If seed is None, return the RandomState singleton used by np.random. If seed is an int, return a new RandomState instance seeded with seed. If seed is already a RandomState instance, return it. Otherwise raise ValueError.

Constructs a new estimator with the same parameters.

Clone does a deep copy of the model in an estimator without actually copying attached data. It yields a new estimator with the same parameters that has not been fit on any data.

Parameters ---------- estimator : estimator object, or list, tuple or set of objects The estimator or group of estimators to be cloned

safe : boolean, optional If safe is false, clone will fall back to a deep copy on objects that are not estimators.

Generate cross-validated estimates for each input data point

The data is split according to the cv parameter. Each sample belongs to exactly one test set, and its prediction is computed with an estimator fitted on the corresponding training set.

Passing these predictions into an evaluation metric may not be a valid way to measure generalization performance. Results can differ from :func:`cross_validate` and :func:`cross_val_score` unless all tests sets have equal size and the metric decomposes over samples.

Read more in the :ref:`User Guide <cross_validation>`.

Parameters ---------- estimator : estimator object implementing 'fit' and 'predict' The object to use to fit the data.

X : array-like The data to fit. Can be, for example a list, or an array at least 2d.

y : array-like, optional, default: None The target variable to try to predict in the case of supervised learning.

groups : array-like, with shape (n_samples,), optional Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a 'Group' :term:`cv` instance (e.g., :class:`GroupKFold`).

cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are:

• None, to use the default 5-fold cross validation,
• integer, to specify the number of folds in a `(Stratified)KFold`,
• :term:`CV splitter`,
• An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here.

.. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold.

n_jobs : int or None, optional (default=None) The number of CPUs to use to do the computation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

verbose : integer, optional The verbosity level.

fit_params : dict, optional Parameters to pass to the fit method of the estimator.

pre_dispatch : int, or string, optional Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

• None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs

• An int, giving the exact number of total jobs that are spawned

• A string, giving an expression as a function of n_jobs, as in '2*n_jobs'

method : string, optional, default: 'predict' Invokes the passed method name of the passed estimator. For method='predict_proba', the columns correspond to the classes in sorted order.

Returns ------- predictions : ndarray This is the result of calling ``method``

See also -------- cross_val_score : calculate score for each CV split

cross_validate : calculate one or more scores and timings for each CV split

Notes ----- In the case that one or more classes are absent in a training portion, a default score needs to be assigned to all instances for that class if ``method`` produces columns per class, as in 'decision_function', 'predict_proba', 'predict_log_proba'. For ``predict_proba`` this value is 0. In order to ensure finite output, we approximate negative infinity by the minimum finite float value for the dtype in other cases.

Examples -------- >>> from sklearn import datasets, linear_model >>> from sklearn.model_selection import cross_val_predict >>> diabetes = datasets.load_diabetes() >>> X = diabetes.data:150 >>> y = diabetes.target:150 >>> lasso = linear_model.Lasso() >>> y_pred = cross_val_predict(lasso, X, y, cv=3)

Decorator used to capture the arguments of a function.

Checks whether the estimator's fit method supports the given parameter.

Parameters ---------- estimator : object An estimator to inspect.

parameter : str The searched parameter.

Returns ------- is_parameter: bool Whether the parameter was found to be a named parameter of the estimator's fit method.

Examples -------- >>> from sklearn.svm import SVC >>> has_fit_parameter(SVC(), 'sample_weight') True

Create a decorator for methods that are delegated to a sub-estimator

This enables ducktyping by hasattr returning True according to the sub-estimator.

Parameters ---------- delegate : string, list of strings or tuple of strings Name of the sub-estimator that can be accessed as an attribute of the base object. If a list or a tuple of names are provided, the first sub-estimator that is an attribute of the base object will be used.

Return True if the given estimator is (probably) a classifier.

Parameters ---------- estimator : object Estimator object to test.

Returns ------- out : bool True if estimator is a classifier and False otherwise.