BaseFeatureTransformer
Overview¶
A feature_transformer is a component that transforms data into features for a ML model. This is consists of encoding or scaling values to make them better suited for model training. Contrast this with a data_transformer, which contains more of a data engineering-style workflow around reshaping or creating new data.
Feature transformers can either be set at the train pipeline level, or at the model_trainer component level. If set at the pipeline level, the transformer will apply to every model created in the pipeline (e.g. if you are doing hyperparameter tuning across multiple experiments and wish to use the same transformer for each). Setting a feature transformer at the model_trainer level will apply only to that model trainer. This can be useful if you wish to override the pipeline feature transformer for a particular model type.
Attributes¶
BaseDataConnector contains no default attributes.
Configuration¶
BaseDataConnector contains no the following required components:
metadata_trackerresource_version_control
Interface¶
The following methods are part of BaseFeatureTransformer and should be implemented in any class that inherits from this base class:
fit¶
def fit(self, data, *args, **kwargs) -> Any
Arguments:
data(object): The source data to fit the feature transformer on. This should be something like a local python object (pandas.DataFrame).
Returns:
transformer(Any): Returns a fitted feature transformer.
transform¶
Transforms data using the feature transformer.
def transform(self, data, *args, **kwargs) -> Any
Arguments:
data(object): The data to transform with the fitted feature transformer. This could be something like a local python object (pandas.DataFrame).
Returns:
data_out(Any): Returns a data object, such as apandasDataframe, which has been transformed by the feature transformer.
fit_transform¶
Fits the transformer to the provided data, and then transform that data using the fitted feature transformer.
def fit_transform(self, data, *args, **kwargs) -> Any
Arguments:
data(object): The data to fit and transform with the fitted feature transformer. This could be something like a local python object (pandas.DataFrame).
Returns:
data_out(Any): Returns a data object, such as apandasDataframe, which has been transformed by the feature transformer.
Default Methods¶
The following methods are implemented in the base class. You may find a need to overwrite them as you implement your own feature transformers.
save¶
Saves the feature transformer into a resource version control system.
def save(self, experiment, *args, **kwargs) -> Any
Arguments:
experiment(object): The experiment in which to save the feature transformer. This object should be created by themetadata_tracker.
Returns:
- Nothing.