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”
modifies_features	True
modifies_target	False
name	PCA 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 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.

lda

encoders