time_series_imputer#
Component that imputes missing data according to a specified timeseries-specific imputation strategy.
Module Contents#
Classes Summary#
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
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits imputer to data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
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.
- 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) – Ignored.
- Returns
Transformed X
- Return type
pd.DataFrame