Source code for evalml.pipelines.components.transformers.imputers.simple_imputer
import pandas as pd
from sklearn.impute import SimpleImputer as SkImputer
from evalml.pipelines.components.transformers import Transformer
[docs]class SimpleImputer(Transformer):
"""Imputes missing data with either mean, median and most_frequent for numerical data or most_frequent for categorical data"""
name = 'Simple Imputer'
hyperparameter_ranges = {"impute_strategy": ["mean", "median", "most_frequent"]}
[docs] def __init__(self, impute_strategy="most_frequent"):
parameters = {"impute_strategy": impute_strategy}
imputer = SkImputer(strategy=impute_strategy)
super().__init__(parameters=parameters,
component_obj=imputer,
random_state=0)
[docs] def transform(self, X, y=None):
"""Transforms data X by imputing missing values
Arguments:
X (pd.DataFrame): Data to transform
y (pd.Series, optional): Input Labels
Returns:
pd.DataFrame: Transformed X
"""
X_t = self._component_obj.transform(X)
if not isinstance(X_t, pd.DataFrame) and isinstance(X, pd.DataFrame):
# skLearn's SimpleImputer loses track of column type, so we need to restore
X_t = pd.DataFrame(X_t, columns=X.columns, index=X.index).astype(X.dtypes.to_dict())
return X_t
[docs] def fit_transform(self, X, y=None):
"""Fits imputer on data X then imputes missing values in X
Arguments:
X (pd.DataFrame): Data to fit and transform
y (pd.Series): Labels to fit and transform
Returns:
pd.DataFrame: Transformed X
"""
X_t = self._component_obj.fit_transform(X, y)
if not isinstance(X_t, pd.DataFrame) and isinstance(X, pd.DataFrame):
# skLearn's SimpleImputer loses track of column type, so we need to restore
X_t = pd.DataFrame(X_t, columns=X.columns, index=X.index).astype(X.dtypes.to_dict())
return X_t