from evalml.pipelines.components.transformers import Transformer
from evalml.pipelines.components.transformers.imputers import SimpleImputer
from evalml.utils import (
_convert_woodwork_types_wrapper,
_retain_custom_types_and_initalize_woodwork,
infer_feature_types
)
[docs]class Imputer(Transformer):
"""Imputes missing data according to a specified imputation strategy."""
name = "Imputer"
hyperparameter_ranges = {
"categorical_impute_strategy": ["most_frequent"],
"numeric_impute_strategy": ["mean", "median", "most_frequent"]
}
_valid_categorical_impute_strategies = set(["most_frequent", "constant"])
_valid_numeric_impute_strategies = set(["mean", "median", "most_frequent", "constant"])
[docs] def __init__(self, categorical_impute_strategy="most_frequent",
categorical_fill_value=None,
numeric_impute_strategy="mean",
numeric_fill_value=None,
random_seed=0, **kwargs):
"""Initalizes an transformer that imputes missing data according to the specified imputation strategy."
Arguments:
categorical_impute_strategy (string): Impute strategy to use for string, object, boolean, categorical dtypes. Valid values include "most_frequent" and "constant".
numeric_impute_strategy (string): Impute strategy to use for numeric columns. Valid values include "mean", "median", "most_frequent", and "constant".
categorical_fill_value (string): When categorical_impute_strategy == "constant", fill_value is used to replace missing data. The default value of None will fill with the string "missing_value".
numeric_fill_value (int, float): When numeric_impute_strategy == "constant", fill_value is used to replace missing data. The default value of None will fill with 0.
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
if categorical_impute_strategy not in self._valid_categorical_impute_strategies:
raise ValueError(f"{categorical_impute_strategy} is an invalid parameter. Valid categorical impute strategies are {', '.join(self._valid_numeric_impute_strategies)}")
elif numeric_impute_strategy not in self._valid_numeric_impute_strategies:
raise ValueError(f"{numeric_impute_strategy} is an invalid parameter. Valid impute strategies are {', '.join(self._valid_numeric_impute_strategies)}")
parameters = {"categorical_impute_strategy": categorical_impute_strategy,
"numeric_impute_strategy": numeric_impute_strategy,
"categorical_fill_value": categorical_fill_value,
"numeric_fill_value": numeric_fill_value}
parameters.update(kwargs)
self._categorical_imputer = SimpleImputer(impute_strategy=categorical_impute_strategy,
fill_value=categorical_fill_value,
**kwargs)
self._numeric_imputer = SimpleImputer(impute_strategy=numeric_impute_strategy,
fill_value=numeric_fill_value,
**kwargs)
self._all_null_cols = None
self._numeric_cols = None
self._categorical_cols = None
super().__init__(parameters=parameters,
component_obj=None,
random_seed=random_seed)
[docs] def fit(self, X, y=None):
"""Fits imputer to data. 'None' values are converted to np.nan before imputation and are
treated as the same.
Arguments:
X (ww.DataTable, pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features]
y (ww.DataColumn, pd.Series, optional): The target training data of length [n_samples]
Returns:
self
"""
X = infer_feature_types(X)
cat_cols = list(X.select(['category', 'boolean']).columns)
numeric_cols = list(X.select('numeric').columns)
X = _convert_woodwork_types_wrapper(X.to_dataframe())
self._all_null_cols = set(X.columns) - set(X.dropna(axis=1, how='all').columns)
X_copy = X.copy()
X_null_dropped = X_copy.drop(self._all_null_cols, axis=1, errors='ignore')
X_numerics = X_null_dropped[[col for col in numeric_cols if col not in self._all_null_cols]]
if len(X_numerics.columns) > 0:
self._numeric_imputer.fit(X_numerics, y)
self._numeric_cols = X_numerics.columns
X_categorical = X_null_dropped[[col for col in cat_cols if col not in self._all_null_cols]]
if len(X_categorical.columns) > 0:
self._categorical_imputer.fit(X_categorical, y)
self._categorical_cols = X_categorical.columns
return self