time_series_imputer

Component that imputes missing data according to a specified timeseries-specific imputation strategy.

Module Contents

Classes Summary

TimeSeriesImputer

Imputes missing data according to a specified timeseries-specific imputation strategy.

Contents

class evalml.pipelines.components.transformers.imputers.time_series_imputer.TimeSeriesImputer(categorical_impute_strategy='forwards_fill', numeric_impute_strategy='interpolate', target_impute_strategy='forwards_fill', random_seed=0, **kwargs)

Imputes missing data according to a specified timeseries-specific imputation strategy.

This Transformer should be used after the TimeSeriesRegularizer in order to impute the missing values that were added to X and y (if passed).

Parameters

• categorical_impute_strategy (string) – Impute strategy to use for string, object, boolean, categorical dtypes. Valid values include “backwards_fill” and “forwards_fill”. Defaults to “forwards_fill”.

• numeric_impute_strategy (string) – Impute strategy to use for numeric columns. Valid values include “backwards_fill”, “forwards_fill”, and “interpolate”. Defaults to “interpolate”.

• target_impute_strategy (string) – Impute strategy to use for the target column. Valid values include “backwards_fill”, “forwards_fill”, and “interpolate”. Defaults to “forwards_fill”.

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

Raises

ValueError – If categorical_impute_strategy, numeric_impute_strategy, or target_impute_strategy is not one of the valid values.

Attributes

hyperparameter_ranges	{ “categorical_impute_strategy”: [“backwards_fill”, “forwards_fill”], “numeric_impute_strategy”: [“backwards_fill”, “forwards_fill”, “interpolate”], “target_impute_strategy”: [“backwards_fill”, “forwards_fill”, “interpolate”],}
modifies_features	True
modifies_target	True
name	Time Series Imputer
training_only	True

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 imputer to data.
fit_transform	Fits on X and transforms X.
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	Transforms data X by imputing missing values using specified timeseries-specific strategies. 'None' values are converted to np.nan before imputation and are treated as the same.
update_parameters	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)

Fits imputer to data.

‘None’ values are converted to np.nan before imputation and are treated as the same. If a value is missing at the beginning or end of a column, that value will be imputed using backwards fill or forwards fill as necessary, respectively.

Parameters

• X (pd.DataFrame, np.ndarray) – The input training data of shape [n_samples, n_features]

• y (pd.Series, optional) – The target training data of length [n_samples]

Returns

self

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.

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)

Transforms data X by imputing missing values using specified timeseries-specific strategies. ‘None’ values are converted to np.nan before imputation and are treated as the same.

Parameters

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

• y (pd.Series, optional) – Optionally, target data to transform.

Returns

Transformed X and y

Return type

pd.DataFrame

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.