lsa¶

Transformer to calculate the Latent Semantic Analysis Values of text input.

Module Contents¶

Classes Summary¶

LSA

Transformer to calculate the Latent Semantic Analysis Values of text input.

Contents¶

class evalml.pipelines.components.transformers.preprocessing.lsa.LSA(random_seed=0, **kwargs)[source]¶

Transformer to calculate the Latent Semantic Analysis Values of text input.

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

Attributes

hyperparameter_ranges	{}
modifies_features	True
modifies_target	False
name	LSA Transformer
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 the input data.
`fit_transform`	Fits on X and transforms X.
`load`	Loads component at file path.
`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 by applying the LSA pipeline.

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)[source]¶

Fits the input data.

Parameters

X (pd.DataFrame) – The data to transform.
y (pd.Series, optional) – Ignored.

Returns

self

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

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.

transform(self, X, y=None)[source]¶

Transforms data X by applying the LSA pipeline.

Parameters

X (pd.DataFrame) – The data to transform.
y (pd.Series, optional) – Ignored.

Returns

Transformed X. The original column is removed and replaced with two columns of the: format LSA(original_column_name)[feature_number], where feature_number is 0 or 1.

Return type

pd.DataFrame

log_transformer

natural_language_featurizer