decomposer

Component that removes trends from time series and returns the decomposed components.

Module Contents

Classes Summary

Decomposer

Component that removes trends and seasonality from time series and returns the decomposed components.

Contents

class evalml.pipelines.components.transformers.preprocessing.decomposer.Decomposer(parameters=None, component_obj=None, random_seed=0, **kwargs)

Component that removes trends and seasonality from time series and returns the decomposed components.

Parameters

• parameters (dict) – Dictionary of parameters to pass to component object.

• component_obj (class) – Instance of a detrender/deseasonalizer class.

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

Attributes

hyperparameter_ranges	None
modifies_features	False
modifies_target	True
name	Decomposer
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.
determine_periodicity	Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal.
fit	Fits component to data.
fit_transform	Fits on X and transforms X.
get_trend_dataframe	Return a list of dataframes, each with 3 columns: trend, seasonality, residual.
inverse_transform	Add the trend + seasonality back to y.
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.
set_seasonal_period	Function to set the component's seasonal period based on the target's seasonality.
transform	Transforms data X.

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

determine_periodicity(self, X, y, method='autocorrelation')

Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal.

Parameters

• X (pandas.DataFrame) – The feature data of the time series problem.

• y (pandas.Series) – The target data of a time series problem.

• method (str) – Either “autocorrelation” or “partial-autocorrelation”. The method by which to determine the first period of the seasonal part of the target signal. “partial-autocorrelation” should currently not be used. Defaults to “autocorrelation”.

Returns

The integer numbers of entries in time series data over which the seasonal part of the target data

repeats. If the time series data is in days, then this is the number of days that it takes the target’s seasonal signal to repeat. Note: the target data can contain multiple seasonal signals. This function will only return the first, and thus, shortest period. E.g. if the target has both weekly and yearly seasonality, the function will only return “7” and not return “365”. If no period is detected, returns [None].

Return type

(list[int])

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)

Fits on X and transforms X.

Parameters

• X (pd.DataFrame) – Data to fit and transform.

• y (pd.Series) – Target data.

Returns

Transformed X.

Return type

pd.DataFrame

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.

abstract get_trend_dataframe(self, y)

Return a list of dataframes, each with 3 columns: trend, seasonality, residual.

abstract inverse_transform(self, y)

Add the trend + seasonality back to y.

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.

set_seasonal_period(self, X, y)

Function to set the component’s seasonal period based on the target’s seasonality.

Parameters

• X (pandas.DataFrame) – The feature data of the time series problem.

• y (pandas.Series) – The target data of a time series problem.

abstract transform(self, X, y=None)

Transforms data X.

Parameters

• X (pd.DataFrame) – Data to transform.

• y (pd.Series, optional) – Target data.

Returns

Transformed X

Return type

pd.DataFrame

Raises

MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.