Source code for evalml.pipelines.components.transformers.imputers.imputer

"""Component that imputes missing data according to a specified imputation strategy."""
import pandas as pd

from evalml.pipelines.components.transformers import Transformer
from evalml.pipelines.components.transformers.imputers import KNNImputer, SimpleImputer
from evalml.utils import infer_feature_types

[docs]class Imputer(Transformer): """Imputes missing data according to a specified imputation strategy. Args: 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". boolean_impute_strategy (string): Impute strategy to use for boolean columns. Valid values include "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. boolean_fill_value (bool): When boolean_impute_strategy == "constant", fill_value is used to replace missing data. The default value of None will fill with True. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Imputer" hyperparameter_ranges = { "categorical_impute_strategy": ["most_frequent"], "numeric_impute_strategy": ["mean", "median", "most_frequent", "knn"], "boolean_impute_strategy": ["most_frequent"], } """{ "categorical_impute_strategy": ["most_frequent"], "numeric_impute_strategy": ["mean", "median", "most_frequent", "knn"], "boolean_impute_strategy": ["most_frequent"] }""" _valid_categorical_impute_strategies = set(["most_frequent", "constant"]) _valid_numeric_impute_strategies = set( ["mean", "median", "most_frequent", "constant", "knn"], ) _valid_boolean_impute_strategies = set(["most_frequent", "constant"]) def __init__( self, categorical_impute_strategy="most_frequent", categorical_fill_value=None, numeric_impute_strategy="mean", numeric_fill_value=None, boolean_impute_strategy="most_frequent", boolean_fill_value=None, random_seed=0, **kwargs, ): if categorical_impute_strategy not in self._valid_categorical_impute_strategies: raise ValueError( f"{categorical_impute_strategy} is an invalid parameter. Valid categorical imputation strategies are {', '.join(self._valid_numeric_impute_strategies)}", ) if numeric_impute_strategy not in self._valid_numeric_impute_strategies: raise ValueError( f"{numeric_impute_strategy} is an invalid parameter. Valid numeric imputation strategies are {', '.join(self._valid_numeric_impute_strategies)}", ) if boolean_impute_strategy not in self._valid_boolean_impute_strategies: raise ValueError( f"{boolean_impute_strategy} is an invalid parameter. Valid boolean imputation strategies are {', '.join(self._valid_boolean_impute_strategies)}", ) parameters = { "categorical_impute_strategy": categorical_impute_strategy, "numeric_impute_strategy": numeric_impute_strategy, "boolean_impute_strategy": boolean_impute_strategy, "categorical_fill_value": categorical_fill_value, "numeric_fill_value": numeric_fill_value, "boolean_fill_value": boolean_fill_value, } parameters.update(kwargs) self._categorical_imputer = SimpleImputer( impute_strategy=categorical_impute_strategy, fill_value=categorical_fill_value, **kwargs, ) self._boolean_imputer = SimpleImputer( impute_strategy=boolean_impute_strategy, fill_value=boolean_fill_value, **kwargs, ) if numeric_impute_strategy == "knn": self._numeric_imputer = KNNImputer( number_neighbors=3, **kwargs, ) else: 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 self._boolean_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. Args: 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 """ X = infer_feature_types(X) cat_cols = list(["category"], return_schema=True).columns) bool_cols = list(["BooleanNullable", "Boolean"], return_schema=True).columns, ) numeric_cols = list(["numeric"], return_schema=True).columns) nan_ratio = X.isna().sum() / X.shape[0] self._all_null_cols = nan_ratio[nan_ratio == 1].index.tolist() X_numerics = X.ww[ [col for col in numeric_cols if col not in self._all_null_cols] ] if len(X_numerics.columns) > 0:, y) self._numeric_cols = X_numerics.columns X_categorical = X.ww[ [col for col in cat_cols if col not in self._all_null_cols] ] if len(X_categorical.columns) > 0:, y) self._categorical_cols = X_categorical.columns X_boolean = X.ww[[col for col in bool_cols if col not in self._all_null_cols]] if len(X_boolean.columns) > 0:, y) self._boolean_cols = X_boolean.columns return self
[docs] def transform(self, X, y=None): """Transforms data X by imputing missing values. Args: X (pd.DataFrame): Data to transform y (pd.Series, optional): Ignored. Returns: pd.DataFrame: Transformed X """ X = infer_feature_types(X) if len(self._all_null_cols) == X.shape[1]: df = pd.DataFrame(index=X.index) df.ww.init() return df X_no_all_null = X.ww.drop(self._all_null_cols) original_schema = X_no_all_null.ww.schema new_ltypes = None if self._numeric_cols is not None and len(self._numeric_cols) > 0: X_numeric = X.ww[self._numeric_cols.tolist()] imputed = self._numeric_imputer.transform(X_numeric) X_no_all_null[X_numeric.columns] = imputed # Numeric imputing may have changed logical types because of use of _get_new_logical_types_for_imputed_data if imputed.ww.schema is None: imputed.ww.init() new_ltypes = imputed.ww.logical_types if self._categorical_cols is not None and len(self._categorical_cols) > 0: X_categorical = X.ww[self._categorical_cols.tolist()] imputed = self._categorical_imputer.transform(X_categorical) X_no_all_null[X_categorical.columns] = imputed if self._boolean_cols is not None and len(self._boolean_cols) > 0: X_boolean = X.ww[self._boolean_cols.tolist()] imputed = self._boolean_imputer.transform(X_boolean) X_no_all_null[X_boolean.columns] = imputed X_no_all_null.ww.init(schema=original_schema, logical_types=new_ltypes) return X_no_all_null