TabularMLAlgo

class lightautoml.ml_algo.base.TabularMLAlgo(default_params=None, freeze_defaults=True, timer=None, optimization_search_space={})[source]

Bases: lightautoml.ml_algo.base.MLAlgo

Machine learning algorithms that accepts numpy arrays as input.

fit_predict_single_fold(train, valid)[source]

Train on train dataset and predict on holdout dataset.

Parameters

train (Union[NumpyDataset, PandasDataset]) – Train Dataset.
valid (Union[NumpyDataset, PandasDataset]) – Validation Dataset.

Return type

Tuple[Any, ndarray]

Returns

Target predictions for valid dataset.

fit_predict(train_valid_iterator)[source]

Fit and then predict accordig the strategy that uses train_valid_iterator.

If item uses more then one time it will predict mean value of predictions. If the element is not used in training then the prediction will be numpy.nan for this item

Parameters: train_valid_iterator (TrainValidIterator) – Classic cv-iterator.
Return type: NumpyDataset
Returns: Dataset with predicted values.

predict_single_fold(model, dataset)[source]

Implements prediction on single fold.

Parameters

model (Any) – Model uses to predict.
dataset (Union[NumpyDataset, PandasDataset]) – Dataset used for prediction.

Return type

ndarray

Returns

Predictions for input dataset.

predict(dataset)[source]

Mean prediction for all fitted models.

Parameters: dataset (Union[NumpyDataset, PandasDataset]) – Dataset used for prediction.
Return type: NumpyDataset
Returns: Dataset with predicted values.