polynomial_detrender

Component that removes trends from time series by fitting a polynomial to the data.

Module Contents

Classes Summary

PolynomialDetrender

Removes trends from time series by fitting a polynomial to the data.

Contents

class evalml.pipelines.components.transformers.preprocessing.polynomial_detrender.PolynomialDetrender(degree=1, random_seed=0, **kwargs)

Removes trends from time series by fitting a polynomial to the data.

Parameters

• degree (int) – Degree for the polynomial. If 1, linear model is fit to the data. If 2, quadratic model is fit, etc. Defaults to 1.

• random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{ “degree”: Integer(1, 3)}
modifies_features	False
modifies_target	True
name	Polynomial Detrender
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 PolynomialDetrender.
fit_transform	Removes fitted trend from target variable.
inverse_transform	Adds back fitted trend to target variable.
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	Removes fitted trend from 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)

Fits the PolynomialDetrender.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable to detrend.

Returns

self

Raises

ValueError – If y is None.

fit_transform(self, X, y=None)

Removes fitted trend from target variable.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable to detrend.

Returns

The first element are the input features returned without modification.

The second element is the target variable y with the fitted trend removed.

Return type

tuple of pd.DataFrame, pd.Series

inverse_transform(self, y)

Adds back fitted trend to target variable.

Parameters

y (pd.Series) – Target variable.

Returns

The first element are the input features returned without modification.

The second element is the target variable y with the trend added back.

Return type

tuple of pd.DataFrame, pd.Series

Raises

ValueError – If y is None.

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)

Removes fitted trend from target variable.

Parameters

• X (pd.DataFrame, optional) – Ignored.

• y (pd.Series) – Target variable to detrend.

Returns

The input features are returned without modification. The target

variable y is detrended

Return type

tuple of pd.DataFrame, pd.Series

previous

lsa

next

text_featurizer