Predictor Interface

The PredictorInterface creates the skeletal structure around basic functionality of Lightwood.

class api.predictor.PredictorInterface[source]

Abstraction of a Lightwood predictor. The PredictorInterface encompasses how Lightwood interacts with the full ML pipeline. Internally,

The PredictorInterface class must have several expected functions:

  • analyze_data: Peform a statistical analysis on the unprocessed data; this helps inform downstream encoders and mixers on how to treat the data types.

  • preprocess: Apply cleaning functions to each of the columns within the dataset to prepare them for featurization

  • split: Split the input dataset into a train/dev/test set according to your splitter function

  • prepare: Create and, if necessary, train your encoders to create feature representations from each column of your data.

  • featurize: For input, pre-processed data, create feature vectors

  • fit: Train your mixer models to yield predictions from featurized data

  • analyze_ensemble: Evaluate the quality of fit for your mixer models

  • adjust: Incorporate new data to update pre-existing model(s).

For simplification, we offer an end-to-end approach that allows you to input raw data and follow every step of the process until you reach a trained predictor with the learn function:

  • learn: An end-to-end technique specifying how to pre-process, featurize, and train the model(s) of interest. The expected input is raw, untrained data. No explicit output is provided, but the Predictor object will “host” the trained model thus.

You can also use the predictor to now estimate new data:

  • predict: Deploys the chosen best model, and evaluates the given data to provide target estimates.

  • save: Saves the Predictor object for further use.

The PredictorInterface is created via J{ai}son’s custom code creation. A problem inherits from this class with pre-populated routines to fill out expected results, given the nature of each problem type.

adjust(new_data, old_data=None)[source]

Adjusts a previously trained model on new data. Adopts the same process as learn but with the exception that the adjust function expects the best model to have been already trained.

Warning

This is experimental and subject to change.

Parameters
  • new_data (DataFrame) – New data used to adjust a previously trained model.

  • old_data (Optional[DataFrame]) – In some situations, the old data is still required to train a model (i.e. Regression mixer) to ensure the new data doesn’t entirely override it.

Return type

None

Returns

Nothing; adjusts best-fit model

analyze_data(data)[source]

Performs a statistical analysis on the data to identify distributions, imbalanced classes, and other nuances within the data.

Parameters

data (DataFrame) – Data used in training the model(s).

Return type

None

analyze_ensemble(enc_data)[source]

Evaluate the quality of mixers within an ensemble of models.

Parameters

enc_data (Dict[str, DataFrame]) – Pre-processed and featurized data, split into the relevant train/test splits.

Return type

None

featurize(split_data)[source]

Provides an encoded representation for each dataset in split_data. Requires self.encoders to be prepared.

Parameters

split_data (Dict[str, DataFrame]) – Pre-processed data from the dataset, split into train/test (or any other keys relevant)

Returns

For each dataset provided in split_data, the encoded representations of the data.

fit(enc_data)[source]

Fits “mixer” models to train predictors on the featurized data. Instantiates a set of trained mixers and an ensemble of them.

Parameters

enc_data (Dict[str, DataFrame]) – Pre-processed and featurized data, split into the relevant train/test splits. Keys expected are “train”, “dev”, and “test”

Return type

None

learn(data)[source]

Trains the attribute model starting from raw data. Raw data is pre-processed and cleaned accordingly. As data is assigned a particular type (ex: numerical, categorical, etc.), the respective feature encoder will convert it into a representation useable for training ML models. Of all ML models requested, these models are compiled and fit on the training data.

This step amalgates preprocess -> featurize -> fit with the necessary splitting + analyze_data that occurs.

Parameters

data (DataFrame) – (Unprocessed) Data used in training the model(s).

Return type

None

Returns

Nothing; instantiates with best fit model from ensemble.

predict(data, args={})[source]

Intakes raw data to provide predicted values for your trained model.

Parameters
  • data (DataFrame) – Data (n_samples, n_columns) that the model(s) will evaluate on and provide the target prediction.

  • args (Dict[str, object]) – parameters needed to update the predictor PredictionArguments object, which holds any parameters relevant for prediction.

Return type

DataFrame

Returns

A dataframe of predictions of the same length of input.

prepare(data)[source]

Prepares the encoders for each column of data.

Parameters

data (Dict[str, DataFrame]) – Pre-processed data that has been split into train/test. Explicitly uses “train” and/or “dev” in preparation of encoders.

Return type

None

Returns

Nothing; prepares the encoders for learned representations.

preprocess(data)[source]

Cleans the unprocessed dataset provided.

Parameters

data (DataFrame) – (Unprocessed) Data used in training the model(s).

Return type

DataFrame

Returns

The cleaned data frame

save(file_path)[source]

With a provided file path, saves the Predictor instance for later use.

Parameters

file_path (str) – Location to store your Predictor Instance.

Return type

None

Returns

Saves Predictor instance.

split(data)[source]

Categorizes the data into a training/testing split; if data is a classification problem, will stratify the data.

Parameters

data (DataFrame) – Pre-processed data, but generically any dataset to split into train/dev/test.

Return type

Dict[str, DataFrame]

Returns

Dictionary containing training/testing fraction