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

import pandas as pd

from evalml.pipelines.components.transformers import Transformer
from evalml.pipelines.components.transformers.imputers.simple_imputer import (

[docs]class PerColumnImputer(Transformer): """Imputes missing data according to a specified imputation strategy per column""" name = 'Per Column Imputer' hyperparameter_ranges = {}
[docs] def __init__(self, impute_strategies=None, default_impute_strategy="most_frequent", random_state=0, **kwargs): """Initializes a transformer that imputes missing data according to the specified imputation strategy per column." Arguments: 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 "most_frequent" for all columns. 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. 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" """ 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_state=random_state)
[docs] def fit(self, X, y=None): """Fits imputers on input data Arguments: X (pd.DataFrame): Data to fit y (pd.Series, optional): Ignored. Returns: self """ if not isinstance(X, pd.DataFrame): X = pd.DataFrame(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():[[column]]) return self
[docs] def transform(self, X, y=None): """Transforms input data by imputing missing values Arguments: X (pd.DataFrame): Data to transform y (pd.Series, optional): Ignored. Returns: pd.DataFrame: Transformed X """ if not isinstance(X, pd.DataFrame): X = pd.DataFrame(X) X_t = X.copy() cols_to_drop = [] for column, imputer in self.imputers.items(): transformed = imputer.transform(X[[column]]) if transformed.empty: cols_to_drop.append(column) else: X_t[column] = transformed X_t = X_t.drop(cols_to_drop, axis=1) return X_t
[docs] def fit_transform(self, X, y=None): """Fits imputer and imputes missing values in input data. Arguments: X (pd.DataFrame): Data to fit and transform y (pd.Series): Target data. Returns: pd.DataFrame: Transformed X """, y) return self.transform(X, y)