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)[source]#

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)[source]#

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)[source]#

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.