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 BaseEstimator : sig ... end

module BiclusterMixin : sig ... end

module ClassifierMixin : sig ... end

module ClusterMixin : sig ... end

module DensityMixin : sig ... end

module MetaEstimatorMixin : sig ... end

module MultiOutputMixin : sig ... end

module OutlierMixin : sig ... end

module RegressorMixin : sig ... end

module TransformerMixin : sig ... end

module Defaultdict : sig ... end

val check_X_y : 
  ?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 ->
  ?multi_output:bool ->
  ?ensure_min_samples:int ->
  ?ensure_min_features:int ->
  ?y_numeric:bool ->
  ?estimator:[> `BaseEstimator ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  unit ->
  Py.Object.t * Py.Object.t

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, np.nan, pd.NA in X. This parameter does not influence whether y can have np.inf, np.nan, pd.NA values. The possibilities are:

True: Force all values of X to be finite.
False: accepts np.inf, np.nan, pd.NA in X.
'allow-nan': accepts only np.nan or pd.NA values in X. 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 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.

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.

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

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 clone : 
  ?safe:bool ->
  estimator:[> `BaseEstimator ] Np.Obj.t ->
  unit ->
  Py.Object.t

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 :

st, tuple, set

}

of estimator objects or estimator object The estimator or group of estimators to be cloned.

safe : bool, default=True If safe is false, clone will fall back to a deep copy on objects that are not estimators.

val estimator_html_repr : [> `BaseEstimator ] Np.Obj.t -> string

Build a HTML representation of an estimator.

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

Parameters ---------- estimator : estimator object The estimator to visualize.

Returns ------- html: str HTML representation of estimator.

val get_config : unit -> Dict.t

Retrieve current values for configuration set by :func:`set_config`

Returns ------- config : dict Keys are parameter names that can be passed to :func:`set_config`.

See Also -------- config_context: Context manager for global scikit-learn configuration set_config: Set global scikit-learn configuration

val is_classifier : [> `BaseEstimator ] Np.Obj.t -> bool

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.

val is_outlier_detector : [> `BaseEstimator ] Np.Obj.t -> bool

Return True if the given estimator is (probably) an outlier detector.

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

Returns ------- out : bool True if estimator is an outlier detector and False otherwise.

val is_regressor : [> `BaseEstimator ] Np.Obj.t -> bool

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

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

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