MLAlgo

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

Abstract class for machine learning algorithm. Assume that features are already selected, but parameters my be tuned and set before training.

property name

Get model name.

property features

Get list of features.

property is_fitted

Get flag is the model fitted or not.

property params

Get model’s params dict.

init_params_on_input(train_valid_iterator)[source]

Init params depending on input data.

Parameters: train_valid_iterator (TrainValidIterator) – Classic cv-iterator.
Return type: dict
Returns: Dict with model hyperparameters.

__init__(default_params=None, freeze_defaults=True, timer=None, optimization_search_space={})[source]

Parameters

default_params (Optional[dict]) – Algo hyperparams.
freeze_defaults (bool) –
- True : params may be rewrited depending on dataset.
- False: params may be changed only manually or with tuning.
timer (Optional[TaskTimer]) – Timer for Algo.

abstract fit_predict(train_valid_iterator)[source]

Abstract method.

Fit new algo on iterated datasets and predict on valid parts.

Parameters: train_valid_iterator (TrainValidIterator) – Classic cv-iterator.
Return type: LAMLDataset

abstract predict(test)[source]

Predict target for input data.

score(dataset)[source]

Score prediction on dataset with defined metric.

Parameters: dataset (LAMLDataset) – Dataset with ground truth and predictions.
Return type: float
Returns: Metric value.

set_prefix(prefix)[source]

Set prefix to separate models from different levels/pipelines.

set_timer(timer)[source]

Set timer.