pca¶
Component that reduces the number of features by using Principal Component Analysis (PCA).
Module Contents¶
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
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the PCA component.
Fit and transform data using the PCA component.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
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.