package sklearn

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

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.

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.

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.

Compute class priors from multioutput-multiclass target data

Parameters ---------- y : array like or 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 ------- classes : list of size n_outputs of arrays of size (n_classes,) List of classes for each column.

n_classes : list of integers of size n_outputs Number of classes in each column

class_prior : list of size n_outputs of arrays of size (n_classes,) Class distribution of each column.