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

"""Component that imputes missing data according to a specified imputation strategy per column."""
from evalml.pipelines.components.transformers import Transformer
from evalml.pipelines.components.transformers.imputers.simple_imputer import (
    SimpleImputer,
)
from evalml.utils import infer_feature_types


[docs]class PerColumnImputer(Transformer): """Imputes missing data according to a specified imputation strategy per column. Args: impute_strategies (dict): Column and {"impute_strategy": strategy, "fill_value":value} pairings. Valid values for impute strategy include "mean", "median", "most_frequent", "constant" for numerical data, and "most_frequent", "constant" for object data types. Defaults to None, which uses "most_frequent" for all columns. When impute_strategy == "constant", fill_value is used to replace missing data. When None, uses 0 when imputing numerical data and "missing_value" for strings or object data types. default_impute_strategy (str): Impute strategy to fall back on when none is provided for a certain column. Valid values include "mean", "median", "most_frequent", "constant" for numerical data, and "most_frequent", "constant" for object data types. Defaults to "most_frequent". random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Per Column Imputer" hyperparameter_ranges = {} """{}""" def __init__( self, impute_strategies=None, default_impute_strategy="most_frequent", random_seed=0, **kwargs, ): parameters = { "impute_strategies": impute_strategies, "default_impute_strategy": default_impute_strategy, } self.imputers = None self.default_impute_strategy = default_impute_strategy self.impute_strategies = impute_strategies or dict() if not isinstance(self.impute_strategies, dict): raise ValueError( "`impute_strategies` is not a dictionary. Please provide in Column and {`impute_strategy`: strategy, `fill_value`:value} pairs. " ) super().__init__( parameters=parameters, component_obj=None, random_seed=random_seed )
[docs] def fit(self, X, y=None): """Fits imputers on input data. Args: X (pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features] to fit. y (pd.Series, optional): The target training data of length [n_samples]. Ignored. Returns: self """ X = infer_feature_types(X) self.imputers = dict() for column in X.columns: strategy_dict = self.impute_strategies.get(column, dict()) strategy = strategy_dict.get( "impute_strategy", self.default_impute_strategy ) fill_value = strategy_dict.get("fill_value", None) self.imputers[column] = SimpleImputer( impute_strategy=strategy, fill_value=fill_value ) for column, imputer in self.imputers.items(): imputer.fit(X[[column]]) return self
[docs] def transform(self, X, y=None): """Transforms input data by imputing missing values. Args: X (pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features] to transform. y (pd.Series, optional): The target training data of length [n_samples]. Ignored. Returns: pd.DataFrame: Transformed X """ X_ww = infer_feature_types(X) original_schema = X_ww.ww.schema cols_to_drop = [] for column, imputer in self.imputers.items(): transformed = imputer.transform(X_ww[[column]]) if transformed.empty: cols_to_drop.append(column) else: X_ww.ww[column] = transformed[column] X_t = X_ww.ww.drop(cols_to_drop) X_t.ww.init(schema=original_schema._get_subset_schema(X_t.columns)) return X_t