simple_imputer¶
Component that imputes missing data according to a specified imputation strategy.
Module Contents¶
Classes Summary¶
Imputes missing data according to a specified imputation strategy. |
Contents¶
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class
evalml.pipelines.components.transformers.imputers.simple_imputer.SimpleImputer(impute_strategy='most_frequent', fill_value=None, random_seed=0, **kwargs)[source]¶ Imputes missing data according to a specified imputation strategy.
- Parameters
impute_strategy (string) – Impute strategy to use. Valid values include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types.
fill_value (string) – When impute_strategy == “constant”, fill_value is used to replace missing data. Defaults to 0 when imputing numerical data and “missing_value” for strings or object data types.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “impute_strategy”: [“mean”, “median”, “most_frequent”]}
model_family
ModelFamily.NONE
modifies_features
True
modifies_target
False
name
Simple Imputer
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. ‘None’ values are converted to np.nan before imputation and are treated as the same.
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 input by imputing missing values. ‘None’ and np.nan values are treated as the same.
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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.
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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
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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
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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.
- Parameters
X (pd.DataFrame or 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
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fit_transform(self, X, y=None)[source]¶ Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform
y (pd.Series, optional) – Target data.
- Returns
Transformed X
- Return type
pd.DataFrame
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static
load(file_path)¶ Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
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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.
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property
parameters(self)¶ Returns the parameters which were used to initialize the component.
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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.