"""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(X.ww.select(["category"], return_schema=True).columns)
bool_cols = list(
X.ww.select(["BooleanNullable", "Boolean"], return_schema=True).columns,
)
numeric_cols = list(X.ww.select(["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:
self._numeric_imputer.fit(X_numerics, 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:
self._categorical_imputer.fit(X_categorical, 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:
self._boolean_imputer.fit(X_boolean, y)
self._boolean_cols = X_boolean.columns
return self
