"""Component that imputes missing data according to a specified timeseries-specific imputation strategy."""
import pandas as pd
import woodwork as ww
from evalml.pipelines.components.transformers import Transformer
from evalml.utils import infer_feature_types
[docs]class TimeSeriesImputer(Transformer):
"""Imputes missing data according to a specified timeseries-specific imputation strategy.
This Transformer should be used after the `TimeSeriesRegularizer` in order to impute the missing values that were
added to X and y (if passed).
Args:
categorical_impute_strategy (string): Impute strategy to use for string, object, boolean, categorical dtypes.
Valid values include "backwards_fill" and "forwards_fill". Defaults to "forwards_fill".
numeric_impute_strategy (string): Impute strategy to use for numeric columns. Valid values include
"backwards_fill", "forwards_fill", and "interpolate". Defaults to "interpolate".
target_impute_strategy (string): Impute strategy to use for the target column. Valid values include
"backwards_fill", "forwards_fill", and "interpolate". Defaults to "forwards_fill".
random_seed (int): Seed for the random number generator. Defaults to 0.
Raises:
ValueError: If categorical_impute_strategy, numeric_impute_strategy, or target_impute_strategy is not one of the valid values.
"""
modifies_features = True
modifies_target = True
training_only = True
name = "Time Series Imputer"
hyperparameter_ranges = {
"categorical_impute_strategy": ["backwards_fill", "forwards_fill"],
"numeric_impute_strategy": ["backwards_fill", "forwards_fill", "interpolate"],
"target_impute_strategy": ["backwards_fill", "forwards_fill", "interpolate"],
}
"""{
"categorical_impute_strategy": ["backwards_fill", "forwards_fill"],
"numeric_impute_strategy": ["backwards_fill", "forwards_fill", "interpolate"],
"target_impute_strategy": ["backwards_fill", "forwards_fill", "interpolate"],
}"""
_valid_categorical_impute_strategies = set(["backwards_fill", "forwards_fill"])
_valid_numeric_impute_strategies = set(
["backwards_fill", "forwards_fill", "interpolate"],
)
_valid_target_impute_strategies = set(
["backwards_fill", "forwards_fill", "interpolate"],
)
def __init__(
self,
categorical_impute_strategy="forwards_fill",
numeric_impute_strategy="interpolate",
target_impute_strategy="forwards_fill",
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 impute strategies are {', '.join(self._valid_numeric_impute_strategies)}",
)
elif numeric_impute_strategy not in self._valid_numeric_impute_strategies:
raise ValueError(
f"{numeric_impute_strategy} is an invalid parameter. Valid numeric impute strategies are {', '.join(self._valid_numeric_impute_strategies)}",
)
elif target_impute_strategy not in self._valid_target_impute_strategies:
raise ValueError(
f"{target_impute_strategy} is an invalid parameter. Valid target column impute strategies are {', '.join(self._valid_target_impute_strategies)}",
)
parameters = {
"categorical_impute_strategy": categorical_impute_strategy,
"numeric_impute_strategy": numeric_impute_strategy,
"target_impute_strategy": target_impute_strategy,
}
parameters.update(kwargs)
self._all_null_cols = None
self._forwards_cols = None
self._backwards_cols = None
self._interpolate_cols = None
self._impute_target = 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.
If a value is missing at the beginning or end of a column, that value will be imputed using
backwards fill or forwards fill as necessary, respectively.
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)
nan_ratio = X.ww.describe().loc["nan_count"] / X.shape[0]
self._all_null_cols = nan_ratio[nan_ratio == 1].index.tolist()
def _filter_cols(impute_strat, X):
"""Function to return which columns of the dataset to impute given the impute strategy."""
cols = []
if self.parameters["categorical_impute_strategy"] == impute_strat:
if self.parameters["numeric_impute_strategy"] == impute_strat:
cols = list(X.columns)
else:
cols = list(X.ww.select(exclude=["numeric"]).columns)
elif self.parameters["numeric_impute_strategy"] == impute_strat:
cols = list(X.ww.select(include=["numeric"]).columns)
X_cols = [col for col in cols if col not in self._all_null_cols]
if len(X_cols) > 0:
return X_cols
self._forwards_cols = _filter_cols("forwards_fill", X)
self._backwards_cols = _filter_cols("backwards_fill", X)
self._interpolate_cols = _filter_cols("interpolate", X)
if y is not None:
y = infer_feature_types(y)
if y.isnull().any():
self._impute_target = self.parameters["target_impute_strategy"]
return self