pca

Component that reduces the number of features by using Principal Component Analysis (PCA).

Module Contents

Classes Summary

PCA

Reduces the number of features by using Principal Component Analysis (PCA).

Contents

class evalml.pipelines.components.transformers.dimensionality_reduction.pca.PCA(variance=0.95, n_components=None, random_seed=0, **kwargs)[source]

Reduces the number of features by using Principal Component Analysis (PCA).

Parameters
  • variance (float) – The percentage of the original data variance that should be preserved when reducing the number of features. Defaults to 0.95.

  • n_components (int) – The number of features to maintain after computing SVD. Defaults to None, but will override variance variable if set.

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

Attributes

hyperparameter_ranges

Real(0.25, 1)}:type: {“variance”

model_family

ModelFamily.NONE

modifies_features

True

modifies_target

False

name

PCA Transformer

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 PCA component.

fit_transform

Fit and transform data using the PCA component.

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

Transform data using fitted PCA component.

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 PCA component.

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

ValueError – If input data is not all numeric.

fit_transform(self, X, y=None)[source]

Fit and transform data using the PCA component.

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

Transformed data.

Return type

pd.DataFrame

Raises

ValueError – If input data is not all numeric.

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]

Transform data using fitted PCA component.

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

Transformed data.

Return type

pd.DataFrame

Raises

ValueError – If input data is not all numeric.