Pipeline

Bunch¶

Module Sklearn.Pipeline.Bunch wraps Python class sklearn.pipeline.Bunch.

type t

create¶

constructor and attributes create

val create :
  ?kwargs:(string * Py.Object.t) list ->
  unit ->
  t

Container object exposing keys as attributes

Bunch objects are sometimes used as an output for functions and methods. They extend dictionaries by enabling values to be accessed by key, bunch['value_key'], or by an attribute, bunch.value_key.

Examples

>>> b = Bunch(a=1, b=2)
>>> b['b']
2
>>> b.b
2
>>> b.a = 3
>>> b['a']
3
>>> b.c = 6
>>> b['c']
6

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.

FeatureUnion¶

Module Sklearn.Pipeline.FeatureUnion wraps Python class sklearn.pipeline.FeatureUnion.

type t

create¶

constructor and attributes create

val create :
  ?n_jobs:int ->
  ?transformer_weights:Dict.t ->
  ?verbose:int ->
  transformer_list:(string * [>`TransformerMixin] Np.Obj.t) list ->
  unit ->
  t

Concatenates results of multiple transformer objects.

This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer.

Parameters of the transformers may be set using its name and the parameter name separated by a '__'. A transformer may be replaced entirely by setting the parameter with its name to another transformer, or removed by setting to 'drop'.

Read more in the :ref:User Guide <feature_union>.

.. versionadded:: 0.13

Parameters

transformer_list : list of (string, transformer) tuples List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer.

.. versionchanged:: 0.22 Deprecated None as a transformer in favor of 'drop'.
n_jobs : int, default=None Number of jobs to run in parallel. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors. See :term:Glossary <n_jobs> for more details.

.. versionchanged:: v0.20 n_jobs default changed from 1 to None
transformer_weights : dict, default=None Multiplicative weights for features per transformer. Keys are transformer names, values the weights.
verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed.

Examples

>>> from sklearn.pipeline import FeatureUnion
>>> from sklearn.decomposition import PCA, TruncatedSVD
>>> union = FeatureUnion([('pca', PCA(n_components=1)),
...                       ('svd', TruncatedSVD(n_components=2))])
>>> X = [[0., 1., 3], [2., 2., 5]]
>>> union.fit_transform(X)
array([[ 1.5       ,  3.0...,  0.8...],
       [-1.5       ,  5.7..., -0.4...]])

fit¶

method fit

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

Fit all transformers using X.

Parameters

X : iterable or array-like, depending on transformers Input data, used to fit transformers.
y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning.

Returns

self : FeatureUnion This estimator

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 all transformers, transform the data and concatenate results.

Parameters

X : iterable or array-like, depending on transformers Input data to be transformed.
y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning.

Returns

X_t : array-like or sparse matrix of shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

get_feature_names¶

method get_feature_names

val get_feature_names :
  [> tag] Obj.t ->
  string list

Get feature names from all transformers.

Returns

feature_names : list of strings Names of the features produced by transform.

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

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params().

Returns

self

transform¶

method transform

val transform :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Transform X separately by each transformer, concatenate results.

Parameters

X : iterable or array-like, depending on transformers Input data to be transformed.

Returns

X_t : array-like or sparse matrix of shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers.

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.

Pipeline¶

Module Sklearn.Pipeline.Pipeline wraps Python class sklearn.pipeline.Pipeline.

type t

create¶

constructor and attributes create

val create :
  ?memory:[`S of string | `Joblib_Memory of Py.Object.t] ->
  ?verbose:bool ->
  steps:(string * [>`BaseEstimator] Np.Obj.t) list ->
  unit ->
  t

Pipeline of transforms with a final estimator.

Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using memory argument.

The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or None.

Read more in the :ref:User Guide <pipeline>.

.. versionadded:: 0.5

Parameters

steps : list List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator.
memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute named_steps or steps to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.
verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed.

Attributes

named_steps : :class:~sklearn.utils.Bunch Dictionary-like object, with the following attributes. Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.

Examples

>>> from sklearn.svm import SVC
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.pipeline import Pipeline
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
...                                                     random_state=0)
>>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())])
>>> # The pipeline can be used as any other estimator
>>> # and avoids leaking the test set into the train set
>>> pipe.fit(X_train, y_train)
Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())])
>>> pipe.score(X_test, y_test)
0.88

get_item¶

method get_item

val get_item :
  ind:[`I of int | `S of string | `Slice of Np.Wrap_utils.Slice.t] ->
  [> tag] Obj.t ->
  Py.Object.t

Returns a sub-pipeline or a single esimtator in the pipeline

Indexing with an integer will return an estimator; using a slice returns another Pipeline instance which copies a slice of this Pipeline. This copy is shallow: modifying (or fitting) estimators in the sub-pipeline will affect the larger pipeline and vice-versa. However, replacing a value in step will not affect a copy.

decision_function¶

method decision_function

val decision_function :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Apply transforms, and decision_function of the final estimator

Parameters

X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_score : array-like of shape (n_samples, n_classes)

fit¶

method fit

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

Fit the model

Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator.

Parameters

X : iterable Training data. Must fulfill input requirements of first step of the pipeline.
y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_params : dict of string -> object Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

self : Pipeline This estimator

fit_predict¶

method fit_predict

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

Applies fit_predict of last step in pipeline after transforms.

Applies fit_transforms of a pipeline to the data, followed by the fit_predict method of the final estimator in the pipeline. Valid only if the final estimator implements fit_predict.

Parameters

X : iterable Training data. Must fulfill input requirements of first step of the pipeline.
y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_params : dict of string -> object Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

y_pred : array-like

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 the model and transform with the final estimator

Fits all the transforms one after the other and transforms the data, then uses fit_transform on transformed data with the final estimator.

Parameters

X : iterable Training data. Must fulfill input requirements of first step of the pipeline.
y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_params : dict of string -> object Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p.

Returns

Xt : array-like of shape (n_samples, n_transformed_features) Transformed samples

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.

inverse_transform¶

method inverse_transform

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

Apply inverse transformations in reverse order

All estimators in the pipeline must support inverse_transform.

Parameters

Xt : array-like of shape (n_samples, n_transformed_features) Data samples, where n_samples is the number of samples and n_features is the number of features. Must fulfill input requirements of last step of pipeline's inverse_transform method.

Returns

Xt : array-like of shape (n_samples, n_features)

predict¶

method predict

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

Apply transforms to the data, and predict with the final estimator

Parameters

X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
**predict_params : dict of string -> object Parameters to the predict called at the end of all transformations in the pipeline. Note that while this may be used to return uncertainties from some models with return_std or return_cov, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator.

.. versionadded:: 0.20

Returns

y_pred : array-like

predict_log_proba¶

method predict_log_proba

val predict_log_proba :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Apply transforms, and predict_log_proba of the final estimator

Parameters

X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_score : array-like of shape (n_samples, n_classes)

predict_proba¶

method predict_proba

val predict_proba :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Apply transforms, and predict_proba of the final estimator

Parameters

X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_proba : array-like of shape (n_samples, n_classes)

score¶

method score

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

Apply transforms, and score with the final estimator

Parameters

X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.
y : iterable, default=None Targets used for scoring. Must fulfill label requirements for all steps of the pipeline.
sample_weight : array-like, default=None If not None, this argument is passed as sample_weight keyword argument to the score method of the final estimator.

Returns

score : float

score_samples¶

method score_samples

val score_samples :
  x:[>`ArrayLike] Np.Obj.t ->
  [> tag] Obj.t ->
  [>`ArrayLike] Np.Obj.t

Apply transforms, and score_samples of the final estimator.

Parameters

X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline.

Returns

y_score : ndarray of shape (n_samples,)

set_params¶

method set_params

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

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params().

Returns

self

named_steps¶

attribute named_steps

val named_steps : t -> Dict.t
val named_steps_opt : t -> (Dict.t) option

This attribute is documented in create above. The first version raises Not_found if the attribute is None. The _opt version returns an option.

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.

Islice¶

Module Sklearn.Pipeline.Islice wraps Python class sklearn.pipeline.islice.

type t

create¶

constructor and attributes create

val create :
  iterable:Py.Object.t ->
  stop:Py.Object.t ->
  unit ->
  t

islice(iterable, stop) --> islice object islice(iterable, start, stop[, step]) --> islice object

Return an iterator whose next() method returns selected values from an iterable. If start is specified, will skip all preceding elements; otherwise, start defaults to zero. Step defaults to one. If specified as another value, step determines how many values are skipped between successive calls. Works like a slice() on a list but returns an iterator.

iter¶

method iter

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

Implement iter(self).

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_memory¶

function check_memory

val check_memory :
  [`Object_with_the_joblib_Memory_interface of Py.Object.t | `S of string | `None] ->
  Py.Object.t

Check that memory is joblib.Memory-like.

joblib.Memory-like means that memory can be converted into a joblib.Memory instance (typically a str denoting the location) or has the same interface (has a cache method).

Parameters

memory : None, str or object with the joblib.Memory interface

Returns

memory : object with the joblib.Memory interface

Raises

ValueError If memory is not joblib.Memory-like.

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.

delayed¶

function delayed

val delayed :
  ?check_pickle:Py.Object.t ->
  function_:Py.Object.t ->
  unit ->
  Py.Object.t

Decorator used to capture the arguments of a function.

if_delegate_has_method¶

function if_delegate_has_method

val if_delegate_has_method :
  [`S of string | `StringList of string list] ->
  Py.Object.t

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.

make_pipeline¶

function make_pipeline

val make_pipeline :
  ?kwargs:(string * Py.Object.t) list ->
  [>`BaseEstimator] Np.Obj.t list ->
  Pipeline.t

Construct a Pipeline from the given estimators.

This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically.

Parameters

*steps : list of estimators.
memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute named_steps or steps to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.
verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed.

Examples

>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('gaussiannb', GaussianNB())])

Returns

p : Pipeline

make_union¶

function make_union

val make_union :
  ?kwargs:(string * Py.Object.t) list ->
  [>`BaseEstimator] Np.Obj.t list ->
  FeatureUnion.t

Construct a FeatureUnion from the given transformers.

This is a shorthand for the FeatureUnion constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their types. It also does not allow weighting.

Parameters

*transformers : list of estimators
n_jobs : int, default=None Number of jobs to run in parallel. None means 1 unless in a :obj:joblib.parallel_backend context. -1 means using all processors. See :term:Glossary <n_jobs> for more details.

.. versionchanged:: v0.20 n_jobs default changed from 1 to None
verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed.

Returns

f : FeatureUnion

Examples

>>> from sklearn.decomposition import PCA, TruncatedSVD
>>> from sklearn.pipeline import make_union
>>> make_union(PCA(), TruncatedSVD())
 FeatureUnion(transformer_list=[('pca', PCA()),
                               ('truncatedsvd', TruncatedSVD())])

Pipeline

Bunch¶

create¶

Examples

to_string¶

show¶

pp¶

FeatureUnion¶

create¶

Parameters

See Also

Examples

fit¶

Parameters

Returns

fit_transform¶

Parameters

Returns

get_feature_names¶

Returns

get_params¶

Parameters

Returns

set_params¶

Returns

transform¶

Parameters

Returns

to_string¶

show¶

pp¶

Pipeline¶

create¶

Parameters

Attributes

See Also

Examples

get_item¶

decision_function¶

Parameters

Returns

fit¶

Parameters

Returns

fit_predict¶

Parameters

Returns

fit_transform¶

Parameters

Returns

get_params¶

Parameters

Returns

inverse_transform¶

Parameters

Returns

predict¶

Parameters

Returns

predict_log_proba¶

Parameters

Returns

predict_proba¶

Parameters

Returns

score¶

Parameters

Returns

score_samples¶

Parameters

Returns

set_params¶

Returns

named_steps¶

to_string¶

show¶

pp¶

Islice¶

create¶

iter¶