pca¶
Module Contents¶
Contents¶
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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 component to data
Fits on X and transforms X
Loads component at file path
Returns boolean determining if component needs fitting before
Returns the parameters which were used to initialize the component
Saves component at file path
Transforms data X.
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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.
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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
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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
prints and returns dictionary
- Return type
None or dict
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fit(self, X, y=None)[source]¶ Fits component to data
- Parameters
X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]
y (list, pd.Series, np.ndarray, optional) – The target training data of length [n_samples]
- Returns
self
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fit_transform(self, X, y=None)[source]¶ 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
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static
load(file_path)¶ Loads component at file path
- Parameters
file_path (str) – Location to load file
- Returns
ComponentBase object
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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.
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property
parameters(self)¶ Returns the parameters which were used to initialize the component
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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.
- Returns
None