text_transformer#

Base class for all transformers working with text features.

Module Contents#

Classes Summary#

TextTransformer

Base class for all transformers working with text features.

Contents#

class evalml.pipelines.components.transformers.preprocessing.text_transformer.TextTransformer(component_obj=None, random_seed=0, **kwargs)[source]#

Base class for all transformers working with text features.

Parameters
  • component_obj (obj) – Third-party objects useful in component implementation. Defaults to None.

  • random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

modifies_features

True

modifies_target

False

training_only

False

Methods

clone

Constructs a new component with the same parameters and random state.

default_parameters

Returns the default parameters for this component.

describe

Describe a component and its parameters.

fit

Fits component to data.

fit_transform

Fits on X and transforms X.

load

Loads component at file path.

name

Returns string name of this component.

needs_fitting

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

parameters

Returns the parameters which were used to initialize the component.

save

Saves component at file path.

transform

Transforms data X.

clone(self)#

Constructs a new component with the same parameters and random state.

Returns

A new instance of this component with identical parameters and random state.

default_parameters(cls)#

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns

Default parameters for this component.

Return type

dict

describe(self, print_name=False, return_dict=False)#

Describe a component and its parameters.

Parameters
  • print_name (bool, optional) – whether to print name of component

  • return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

fit(self, X, y=None)#

Fits component to data.

Parameters
  • X (pd.DataFrame) – The input training data of shape [n_samples, n_features]

  • y (pd.Series, optional) – The target training data of length [n_samples]

Returns

self

Raises

MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.

fit_transform(self, X, y=None)#

Fits on X and transforms X.

Parameters
  • X (pd.DataFrame) – Data to fit and transform.

  • y (pd.Series) – Target data.

Returns

Transformed X.

Return type

pd.DataFrame

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.

static load(file_path)#

Loads component at file path.

Parameters

file_path (str) – Location to load file.

Returns

ComponentBase object

property name(cls)#

Returns string name of this component.

needs_fitting(self)#

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns

True.

property parameters(self)#

Returns the parameters which were used to initialize the component.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#

Saves component at file path.

Parameters
  • file_path (str) – Location to save file.

  • pickle_protocol (int) – The pickle data stream format.

abstract transform(self, X, y=None)#

Transforms data X.

Parameters
  • X (pd.DataFrame) – Data to transform.

  • y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.