TSColumnTransformer¶

class hcrystalball.compose.TSColumnTransformer(transformers, *, remainder='drop', sparse_threshold=0.3, n_jobs=None, transformer_weights=None, verbose=False)[source]¶

Bases: sklearn.compose._column_transformer.ColumnTransformer

Time Series compatible ColumnTransformer.

Allow usage of hcrystalball wrappers and index based transformers. See also: sklearn.compose.ColumnTransformer

Returns: Data transformed on given column
Return type: pandas.DataFrame
Raises: ValueError – If remainder=='passthrough' is set. Use passthrough as an identity estimator If sparse output is requested, but not all columns are numeric

Attributes Summary

`named_transformers_`	Access the fitted transformer by name.
`remainder`	Access to original remainder

Methods Summary

`fit`(X[, y])	Fit all transformers using X.
`fit_transform`(X[, y])	Run index aware fit_transform
`get_feature_names`()	Get feature names from all transformers.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**kwargs)	Set the parameters of this estimator.
`transform`(X)	Run index aware transform

Attributes Documentation

named_transformers_¶

Access the fitted transformer by name.

Read-only attribute to access any transformer by given name. Keys are transformer names and values are the fitted transformer objects.

remainder¶: Access to original remainder

Methods Documentation

fit(X, y=None)¶

Fit all transformers using X.

Parameters

X ({array-like, dataframe} of shape (n_samples, n_features)) – Input data, of which specified subsets are used to fit the transformers.
y (array-like of shape (n_samples,..), default=None) – Targets for supervised learning.

Returns

self – This estimator

Return type

ColumnTransformer

fit_transform(X, y=None)[source]¶

Run index aware fit_transform

Parameters

X (pandas.DataFrame) – Input features.
y (pandas.Series or numpy.array) – Target values

Returns

Transformed data by given transformer on given column

Return type

pandas.DataFrame

get_feature_names()[source]¶

Get feature names from all transformers.

Returns: feature_names – Names of the features produced by transform.
Return type: list of strings

get_params(deep=True)¶

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 – Parameter names mapped to their values.
Return type: dict

set_params(**kwargs)¶

Set the parameters of this estimator.

Valid parameter keys can be listed with get_params().

Returns
Return type: self

transform(X)[source]¶

Run index aware transform

Parameters: X (pandas.DataFrame) – Input features.
Returns: Transformed data by given transformer on given column
Return type: pandas.DataFrame