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Hyperparameters tuning in the Fedot

Consider learning quality improvement it is needed to pay attention to different sub-processes besides model selection. One of that sub-processes of hyper-parameter tuning was implemented in FEDOT framework.

Since the result of the composing a tree-based structure, it was suggested to tune the leaf and root nodes separately. Leaf nodes or “primary” in terms of FEDOT contain the initial data and models in that nodes can be tuned to the original problem while the model located in the root is tuned to the modified data.

The different tuning approaches in Fedot:


The first two methods were used from sklearn package as RandomizedSearchCV and BayesSearchCV algorithms. The customized approach has a random choice as a base, includes cross-validation technique and works as follow:

The example of tuning in Fedot:

chain = get_regr_chain()

# Before tuning prediction
chain.fit(train_data, use_cache=False)
before_tuning_predicted = chain.predict(test_data)

# Chain tuning
chain.fine_tune_primary_nodes(train_data, max_lead_time=timedelta(minutes=1), iterations=10)

# After tuning prediction
after_tuning_predicted = chain.predict(test_data)

The description of tuning API is available in the documentation.