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

"""Component that imputes missing data according to a specified imputation strategy."""
import pandas as pd
import woodwork
from sklearn.impute import SimpleImputer as SkImputer
from woodwork.logical_types import Double

from evalml.pipelines.components.transformers import Transformer
from evalml.pipelines.components.utils import (
    drop_natural_language_columns,
    set_boolean_columns_to_categorical,
)
from evalml.utils import infer_feature_types
from evalml.utils.gen_utils import is_categorical_actually_boolean


[docs]class SimpleImputer(Transformer): """Imputes missing data according to a specified imputation strategy. Natural language columns are ignored. Args: impute_strategy (string): Impute strategy to use. Valid values include "mean", "median", "most_frequent", "constant" for numerical data, and "most_frequent", "constant" for object data types. fill_value (string): 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. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Simple Imputer" hyperparameter_ranges = {"impute_strategy": ["mean", "median", "most_frequent"]} """{ "impute_strategy": ["mean", "median", "most_frequent"] }""" def __init__( self, impute_strategy="most_frequent", fill_value=None, random_seed=0, **kwargs ): parameters = {"impute_strategy": impute_strategy, "fill_value": fill_value} parameters.update(kwargs) self.impute_strategy = impute_strategy imputer = SkImputer( strategy=impute_strategy, fill_value=fill_value, missing_values=pd.NA, **kwargs, ) self._all_null_cols = None super().__init__( parameters=parameters, component_obj=imputer, 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 or 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 Raises: ValueError: if the SimpleImputer receives a dataframe with both Boolean and Categorical data. """ X = infer_feature_types(X) if set([lt.type_string for lt in X.ww.logical_types.values()]) == { "boolean", "categorical", } and not all( [ is_categorical_actually_boolean(X, col) for col in X.ww.select("Categorical") ], ): raise ValueError( "SimpleImputer cannot handle dataframes with both boolean and categorical features. Use Imputer instead.", ) nan_ratio = X.isna().sum() / X.shape[0] self._all_null_cols = nan_ratio[nan_ratio == 1].index.tolist() X, _ = drop_natural_language_columns(X) X = set_boolean_columns_to_categorical(X) # If the Dataframe only had natural language columns, do nothing. if X.shape[1] == 0: return self self._component_obj.fit(X, y) return self
[docs] def transform(self, X, y=None): """Transforms input by imputing missing values. 'None' and np.nan values are treated as the same. Args: X (pd.DataFrame): Data to transform. y (pd.Series, optional): Ignored. Returns: pd.DataFrame: Transformed X """ X = infer_feature_types(X) original_schema = X.ww.schema # Return early since bool dtype doesn't support nans and sklearn errors if all cols are bool if (X.dtypes == bool).all(): return X not_all_null_cols = [col for col in X.columns if col not in self._all_null_cols] original_index = X.index # Drop natural language columns and transform the other columns X_t, natural_language_cols = drop_natural_language_columns(X) if X_t.shape[1] == 0: return X not_all_null_or_natural_language_cols = [ col for col in not_all_null_cols if col not in natural_language_cols ] X_t = self._component_obj.transform(X_t) X_t = pd.DataFrame(X_t, columns=not_all_null_or_natural_language_cols) new_schema = original_schema.get_subset_schema(X_t.columns) # TODO: Fix this after WW adds inference of object type booleans to BooleanNullable # Iterate through categorical columns that might have been boolean and convert them back to boolean for col in X.ww.select(["Categorical"], return_schema=True).columns: if is_categorical_actually_boolean(X, col): X_t[col] = X_t[col].astype(bool) # Convert Nullable Integers to Doubles for the "mean" and "median" strategies if self.impute_strategy in ["mean", "median"]: nullable_int_cols = X.ww.select(["IntegerNullable"], return_schema=True) nullable_int_cols = [x for x in nullable_int_cols.columns.keys()] for col in nullable_int_cols: new_schema.set_types({col: Double}) X_t.ww.init(schema=new_schema) # Add back in natural language columns, unchanged if len(natural_language_cols) > 0: X_t = woodwork.concat_columns([X_t, X[natural_language_cols]]) if not_all_null_or_natural_language_cols: X_t.index = original_index return X_t
[docs] def fit_transform(self, X, y=None): """Fits on X and transforms X. Args: X (pd.DataFrame): Data to fit and transform y (pd.Series, optional): Target data. Returns: pd.DataFrame: Transformed X """ return self.fit(X, y).transform(X, y)