package sklearn

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package sklearn

sklearn
- Sklearn

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val get_py : string -> Py.Object.t

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

module CountVectorizer : sig ... end

module HashingVectorizer : sig ... end

module Mapping : sig ... end

module TfidfTransformer : sig ... end

module TfidfVectorizer : sig ... end

module Itemgetter : sig ... end

module Partial : sig ... end

val check_array : 
  ?accept_sparse:[ `S of string | `StringList of string list | `Bool of bool ] ->
  ?accept_large_sparse:bool ->
  ?dtype:
    [ `S of string
    | `Dtype of Np.Dtype.t
    | `Dtypes of Np.Dtype.t list
    | `None ] ->
  ?order:[ `C | `F ] ->
  ?copy:bool ->
  ?force_all_finite:[ `Allow_nan | `Bool of bool ] ->
  ?ensure_2d:bool ->
  ?allow_nd:bool ->
  ?ensure_min_samples:int ->
  ?ensure_min_features:int ->
  ?estimator:[> `BaseEstimator ] Np.Obj.t ->
  array:Py.Object.t ->
  unit ->
  Py.Object.t

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, np.nan, pd.NA in array. The possibilities are:

True: Force all values of array to be finite.
False: accepts np.inf, np.nan, pd.NA in array.
'allow-nan': accepts only np.nan and pd.NA values in array. Values cannot be infinite.

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

.. versionchanged:: 0.23 Accepts `pd.NA` and converts it into `np.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.

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.

val check_is_fitted : 
  ?attributes:
    [ `S of string
    | `StringList of string list
    | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
  ?msg:string ->
  ?all_or_any:[ `Callable of Py.Object.t | `PyObject of Py.Object.t ] ->
  estimator:[> `BaseEstimator ] Np.Obj.t ->
  unit ->
  Py.Object.t

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.

val normalize : 
  ?norm:[ `L1 | `L2 | `Max ] ->
  ?axis:[ `Zero | `One ] ->
  ?copy:bool ->
  ?return_norm:bool ->
  x:[> `ArrayLike ] Np.Obj.t ->
  unit ->
  [> `ArrayLike ] Np.Obj.t * [> `ArrayLike ] Np.Obj.t

Scale input vectors individually to unit norm (vector length).

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

Parameters ---------- X : array-like, sparse matrix, shape n_samples, n_features The data to normalize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.

norm : 'l1', 'l2', or 'max', optional ('l2' by default) The norm to use to normalize each non zero sample (or each non-zero feature if axis is 0).

axis : 0 or 1, optional (1 by default) axis used to normalize the data along. If 1, independently normalize each sample, otherwise (if 0) normalize each feature.

copy : boolean, optional, default True set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix and if axis is 1).

return_norm : boolean, default False whether to return the computed norms

Returns ------- X : array-like, sparse matrix, shape n_samples, n_features Normalized input X.

norms : array, shape n_samples if axis=1 else n_features An array of norms along given axis for X. When X is sparse, a NotImplementedError will be raised for norm 'l1' or 'l2'.

See also -------- Normalizer: Performs normalization using the ``Transformer`` API (e.g. as part of a preprocessing :class:`sklearn.pipeline.Pipeline`).

Notes ----- For a comparison of the different scalers, transformers, and normalizers, see :ref:`examples/preprocessing/plot_all_scaling.py <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.

val strip_accents_ascii : string -> Py.Object.t

Transform accentuated unicode symbols into ascii or nothing

Warning: this solution is only suited for languages that have a direct transliteration to ASCII symbols.

Parameters ---------- s : string The string to strip

See Also -------- strip_accents_unicode Remove accentuated char for any unicode symbol.

val strip_accents_unicode : string -> Py.Object.t

Transform accentuated unicode symbols into their simple counterpart

Warning: the python-level loop and join operations make this implementation 20 times slower than the strip_accents_ascii basic normalization.

Parameters ---------- s : string The string to strip

See Also -------- strip_accents_ascii Remove accentuated char for any unicode symbol that has a direct ASCII equivalent.

val strip_tags : string -> Py.Object.t

Basic regexp based HTML / XML tag stripper function

For serious HTML/XML preprocessing you should rather use an external library such as lxml or BeautifulSoup.

Parameters ---------- s : string The string to strip