Source code for evalml.pipelines.components.transformers.transformer

from abc import abstractmethod

import pandas as pd

from evalml.exceptions import MethodPropertyNotFoundError
from evalml.model_family import ModelFamily
from evalml.pipelines.components import ComponentBase
from evalml.utils import (
    _retain_custom_types_and_initalize_woodwork,
    infer_feature_types,
)


[docs]class Transformer(ComponentBase): """A component that may or may not need fitting that transforms data. These components are used before an estimator. To implement a new Transformer, define your own class which is a subclass of Transformer, including a name and a list of acceptable ranges for any parameters to be tuned during the automl search (hyperparameters). Define an `__init__` method which sets up any necessary state and objects. Make sure your `__init__` only uses standard keyword arguments and calls `super().__init__()` with a parameters dict. You may also override the `fit`, `transform`, `fit_transform` and other methods in this class if appropriate. To see some examples, check out the definitions of any Transformer component. """ model_family = ModelFamily.NONE
[docs] def transform(self, X, y=None): """Transforms data X. Arguments: X (pd.DataFrame): Data to transform. y (pd.Series, optional): Target data. Returns: pd.DataFrame: Transformed X """ X_ww = infer_feature_types(X) if y is not None: y = infer_feature_types(y) try: X_t = self._component_obj.transform(X, y) except AttributeError: raise MethodPropertyNotFoundError( "Transformer requires a transform method or a component_obj that implements transform" ) X_t_df = pd.DataFrame(X_t, columns=X_ww.columns, index=X_ww.index) return _retain_custom_types_and_initalize_woodwork( X_ww.ww.logical_types, X_t_df )
[docs] def fit_transform(self, X, y=None): """Fits on X and transforms X Arguments: X (pd.DataFrame): Data to fit and transform y (pd.Series): Target data Returns: pd.DataFrame: Transformed X """ X_ww = infer_feature_types(X) if y is not None: y_ww = infer_feature_types(y) try: X_t = self._component_obj.fit_transform(X_ww, y_ww) return _retain_custom_types_and_initalize_woodwork( X_ww.ww.logical_types, X_t ) except AttributeError: try: return self.fit(X, y).transform(X, y) except MethodPropertyNotFoundError as e: raise e
def _get_feature_provenance(self): return {}
class TargetTransformer(Transformer): """A component that transforms the target.""" @abstractmethod def inverse_transform(self, y): """Inverts the transformation done by the transform method. Arguments: y (pd.Series): Target transformed by this component. Returns: pd.Seriesø: Target without the transformation. """