log_transformer#
Component that applies a log transformation to the target data.
Module Contents#
Classes Summary#
Applies a log transformation to the target data. |
Contents#
- class evalml.pipelines.components.transformers.preprocessing.log_transformer.LogTransformer(random_seed=0)[source]#
Applies a log transformation to the target data.
Attributes
hyperparameter_ranges
{}
modifies_features
False
modifies_target
True
name
Log Transformer
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 the LogTransformer.
Log transforms the target variable.
Apply exponential to target data.
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.
Log transforms the target variable.
Updates the parameter dictionary of the 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 LogTransformer.
- Parameters
X (pd.DataFrame or np.ndarray) – Ignored.
y (pd.Series, optional) – Ignored.
- Returns
self
- fit_transform(self, X, y=None)[source]#
Log transforms the target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable to log transform.
- Returns
- The input features are returned without modification. The target
variable y is log transformed.
- Return type
tuple of pd.DataFrame, pd.Series
- inverse_transform(self, y)[source]#
Apply exponential to target data.
- Parameters
y (pd.Series) – Target variable.
- Returns
Target with exponential applied.
- Return type
pd.Series
- 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]#
Log transforms the target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target data to log transform.
- Returns
- The input features are returned without modification. The target
variable y is log transformed.
- Return type
tuple of pd.DataFrame, pd.Series
- update_parameters(self, update_dict, reset_fit=True)#
Updates the parameter dictionary of the component.
- Parameters
update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.