Base

BaseEstimator¶

Module Sklearn.Base.BaseEstimator wraps Python class sklearn.base.BaseEstimator.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Base class for all estimators in scikit-learn

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

get_params¶

method get_params

val get_params :
  ?deep:bool ->
  [> tag] Obj.t ->
  Dict.t

Get parameters for this estimator.

Parameters

deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params : mapping of string to any Parameter names mapped to their values.

set_params¶

method set_params

val set_params :
  ?params:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Parameters

**params : dict Estimator parameters.

Returns

self : object Estimator instance.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

BiclusterMixin¶

Module Sklearn.Base.BiclusterMixin wraps Python class sklearn.base.BiclusterMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Mixin class for all bicluster estimators in scikit-learn

get_indices¶

method get_indices

val get_indices :
  i:int ->
  [> tag] Obj.t ->
  (Py.Object.t * Py.Object.t)

Row and column indices of the i'th bicluster.

Only works if rows_ and columns_ attributes exist.

Parameters

i : int The index of the cluster.

Returns

row_ind : ndarray, dtype=np.intp Indices of rows in the dataset that belong to the bicluster.
col_ind : ndarray, dtype=np.intp Indices of columns in the dataset that belong to the bicluster.

get_shape¶

method get_shape

val get_shape :
  i:int ->
  [> tag] Obj.t ->
  Py.Object.t

Shape of the i'th bicluster.

Parameters

i : int The index of the cluster.

Returns

shape : tuple (int, int) Number of rows and columns (resp.) in the bicluster.

get_submatrix¶

method get_submatrix

val get_submatrix :
  i:int ->
  data:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Return the submatrix corresponding to bicluster i.

Parameters

i : int The index of the cluster.
data : array-like The data.

Returns

submatrix : ndarray The submatrix corresponding to bicluster i.

Notes

Works with sparse matrices. Only works if rows_ and columns_ attributes exist.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

ClassifierMixin¶

Module Sklearn.Base.ClassifierMixin wraps Python class sklearn.base.ClassifierMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Mixin class for all classifiers in scikit-learn.

score¶

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns

score : float Mean accuracy of self.predict(X) wrt. y.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

ClusterMixin¶

Module Sklearn.Base.ClusterMixin wraps Python class sklearn.base.ClusterMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Mixin class for all cluster estimators in scikit-learn.

fit_predict¶

method fit_predict

val fit_predict :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Perform clustering on X and returns cluster labels.

Parameters

X : array-like of shape (n_samples, n_features) Input data.
y : Ignored Not used, present for API consistency by convention.

Returns

labels : ndarray of shape (n_samples,) Cluster labels.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

DensityMixin¶

Module Sklearn.Base.DensityMixin wraps Python class sklearn.base.DensityMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Mixin class for all density estimators in scikit-learn.

score¶

method score

val score :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the score of the model on the data X

Parameters

X : array-like of shape (n_samples, n_features)
y : Ignored Not used, present for API consistency by convention.

Returns

score : float

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

MetaEstimatorMixin¶

Module Sklearn.Base.MetaEstimatorMixin wraps Python class sklearn.base.MetaEstimatorMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

MultiOutputMixin¶

Module Sklearn.Base.MultiOutputMixin wraps Python class sklearn.base.MultiOutputMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Mixin to mark estimators that support multioutput.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

OutlierMixin¶

Module Sklearn.Base.OutlierMixin wraps Python class sklearn.base.OutlierMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Mixin class for all outlier detection estimators in scikit-learn.

fit_predict¶

method fit_predict

val fit_predict :
  ?y:Py.Object.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters

X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
y : Ignored Not used, present for API consistency by convention.

Returns

y : ndarray of shape (n_samples,) 1 for inliers, -1 for outliers.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

RegressorMixin¶

Module Sklearn.Base.RegressorMixin wraps Python class sklearn.base.RegressorMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Mixin class for all regression estimators in scikit-learn.

score¶

method score

val score :
  ?sample_weight:[>`ArrayLike] Np.Obj.t ->
  x:[>`ArrayLike] Np.Obj.t ->
  y:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  float

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters

X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns

score : float R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of :func:~sklearn.metrics.r2_score. This influences the score method of all the multioutput regressors (except for :class:~sklearn.multioutput.MultiOutputRegressor).

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

TransformerMixin¶

Module Sklearn.Base.TransformerMixin wraps Python class sklearn.base.TransformerMixin.

type t

create¶

constructor and attributes create

val create :
  unit ->
  t

Mixin class for all transformers in scikit-learn.

fit_transform¶

method fit_transform

val fit_transform :
  ?y:[>`ArrayLike] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
y : ndarray of shape (n_samples,), default=None Target values.
**fit_params : dict Additional fit parameters.

Returns

X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

Defaultdict¶

Module Sklearn.Base.Defaultdict wraps Python class sklearn.base.defaultdict.

type t

get_item¶

method get_item

val get_item :
  y:Py.Object.t ->
  [> tag] Obj.t ->
  Py.Object.t

x.getitem(y) <==> x[y]

iter¶

method iter

val iter :
  [> tag] Obj.t ->
  Dict.t Seq.t

Implement iter(self).

setitem¶

method setitem

val __setitem__ :
  key:Py.Object.t ->
  value:Py.Object.t ->
  [> tag] Obj.t ->
  Py.Object.t

Set self[key] to value.

fromkeys¶

method fromkeys

val fromkeys :
  ?value:Py.Object.t ->
  iterable:Py.Object.t ->
  [> tag] Obj.t ->
  Py.Object.t

Create a new dictionary with keys from iterable and values set to value.

get¶

method get

val get :
  ?default:Py.Object.t ->
  key:Py.Object.t ->
  [> tag] Obj.t ->
  Py.Object.t

Return the value for key if key is in the dictionary, else default.

pop¶

method pop

val pop :
  ?d:Py.Object.t ->
  k:Py.Object.t ->
  [> tag] Obj.t ->
  Py.Object.t

D.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised

popitem¶

method popitem

val popitem :
  [> tag] Obj.t ->
  Py.Object.t

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault¶

method setdefault

val setdefault :
  ?default:Py.Object.t ->
  key:Py.Object.t ->
  [> tag] Obj.t ->
  Py.Object.t

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update¶

method update

val update :
  ?e:Py.Object.t ->
  ?f:(string * Py.Object.t) list ->
  [> tag] Obj.t ->
  Py.Object.t

D.update([E, ]**F) -> None. Update D from dict/iterable E and F. If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

to_string¶

method to_string

val to_string: t -> string

Print the object to a human-readable representation.

show¶

method show

val show: t -> string

Print the object to a human-readable representation.

pp¶

method pp

val pp: Format.formatter -> t -> unit

Pretty-print the object to a formatter.

check_X_y¶

function check_X_y

val check_X_y :
  ?accept_sparse:[`S of string | `StringList of string list | `Bool of bool] ->
  ?accept_large_sparse:bool ->
  ?dtype:[`Dtypes of Np.Dtype.t list | `S of string | `Dtype of Np.Dtype.t | `None] ->
  ?order:[`F | `C] ->
  ?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) String[s] 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.

check_array¶

function check_array

val check_array :
  ?accept_sparse:[`S of string | `StringList of string list | `Bool of bool] ->
  ?accept_large_sparse:bool ->
  ?dtype:[`Dtypes of Np.Dtype.t list | `S of string | `Dtype of Np.Dtype.t | `None] ->
  ?order:[`F | `C] ->
  ?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) String[s] 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.

clone¶

function clone

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 : {list, 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.

estimator_html_repr¶

function estimator_html_repr

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.

get_config¶

function get_config

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.

is_classifier¶

function is_classifier

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.

is_outlier_detector¶

function is_outlier_detector

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.

is_regressor¶

function is_regressor

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.