standard_scaler#
A transformer that standardizes input features by removing the mean and scaling to unit variance.
Module Contents#
Classes Summary#
A transformer that standardizes input features by removing the mean and scaling to unit variance. |
Contents#
- class evalml.pipelines.components.transformers.scalers.standard_scaler.StandardScaler(random_seed=0, **kwargs)[source]#
A transformer that standardizes input features by removing the mean and scaling to unit variance.
- Parameters
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Standard Scaler
training_only
False
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.
Fit and transform data using the standard scaler 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 the fitted standard scaler.
- 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)#
Fits component to data.
- 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
MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.
- fit_transform(self, X, y=None)[source]#
Fit and transform data using the standard scaler 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
- 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.