log_transformer#

Component that applies a log transformation to the target data.

Module Contents#

Classes Summary#

LogTransformer

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

`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 LogTransformer.
`fit_transform`	Log transforms the target variable.
`inverse_transform`	Apply exponential to target data.
`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`	Log transforms the target variable.

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