def _analyze_hybrid_as_model(model: HybridModel, evaluater: Evaluation, train, test)\
-> Tuple[EvaluationResult, EvaluationResult, EvaluationResult]:
model.fit_init(*train)
result_before_x = evaluater.evaluate_hybrid(model, *test)
model.fit_cross()
result_hybrid = evaluater.evaluate(model, *test)
return result_hybrid, result_before_x.cf, result_before_x.md
y_test = y[xval_test]
train = ([inds_u_train, inds_i_train], y_train)
test = ([inds_u_test, inds_i_test], y_test)
hybrid_model = HybridModel(users_features,
items_features,
hybrid_config,
verbose=0)
hybrid_model.fit_init(*train)
result_model = evaluation.evaluate_hybrid(hybrid_model, *test)
results_models[0].add(result_model)
result_hybrid = evaluation.evaluate(hybrid_model, *test)
results_hybrid[0].add(result_hybrid)
for e in range(epochs):
hybrid_model.fit_cross_epoch()
result = evaluation.evaluate_hybrid(hybrid_model, *test)
results_models[e + 1].add(result)
result_hybrid = evaluation.evaluate(hybrid_model, *test)
results_hybrid[e + 1].add(result_hybrid)
rmses_cf = []
rmses_md = []
rmses_hybrid = []
def _analyze_model(model, evaluation: Evaluation, train, test)\
-> EvaluationResult:
model.fit(*train)
result = evaluation.evaluate(model, *test)
return result