log_transformer¶

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	{}
model_family	ModelFamily.NONE
modifies_features	False
modifies_target	True
name	Log Transformer

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`	Inverts the transformation done by the transform method.
`load`	Loads component at file path
`needs_fitting`	Returns boolean determining if component needs fitting before
`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

prints and returns dictionary

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]¶

Inverts the transformation done by the transform method.

Arguments:
y (pd.Series): Target transformed by this component.

Returns: Target without the transformation.
Return type: pd.Seriesø

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.

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.

Returns

None

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

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