"""Component that imputes missing data according to a specified imputation strategy per column."""
import warnings
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".
impute_all (bool): Whether or not to impute all columns or just the columns that are specified in `impute_strategies`. If True,
columns will be imputed using the strategy in the `impute_strategies` dictionary if specified or using the `default_impute_strategy`.
If False, only columns specified as keys in the `impute_strategies` dictionary are imputed. If False and `impute_strategies` is None,
no columns will be imputed. Defaults to True.
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",
impute_all=True,
random_seed=0,
**kwargs,
):
parameters = {
"impute_strategies": impute_strategies,
"default_impute_strategy": default_impute_strategy,
"impute_all": impute_all,
}
self.imputers = None
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. "
)
self.default_impute_strategy = default_impute_strategy
self.impute_all = impute_all
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()
columns_to_impute = (
X.columns if self.impute_all else self.impute_strategies.keys()
)
if len(columns_to_impute) == 0:
warnings.warn(
"No columns to impute. Please check `impute_strategies` and `impute_all` parameters."
)
for column in columns_to_impute:
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.ww[[column]])
return self