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

import pandas as pd

from evalml.exceptions import MethodPropertyNotFoundError
from evalml.model_family import ModelFamily
from evalml.pipelines.components import ComponentBase


[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 """ try: X_t = self._component_obj.transform(X) except AttributeError: raise MethodPropertyNotFoundError("Transformer requires a transform method or a component_obj that implements transform") if not isinstance(X_t, pd.DataFrame) and isinstance(X, pd.DataFrame): return pd.DataFrame(X_t, columns=X.columns, index=X.index) return pd.DataFrame(X_t)
[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. DataFrame): Target data Returns: pd.DataFrame: Transformed X """ try: X_t = self._component_obj.fit_transform(X, y) except AttributeError: try: self.fit(X, y) X_t = self.transform(X, y) except MethodPropertyNotFoundError as e: raise e if not isinstance(X_t, pd.DataFrame) and isinstance(X, pd.DataFrame): return pd.DataFrame(X_t, columns=X.columns, index=X.index) return pd.DataFrame(X_t)