def run_chain_from_automl(train_file_path: str, test_file_path: str, max_run_time: timedelta = timedelta(minutes=10)): train_data = InputData.from_csv(train_file_path) test_data = InputData.from_csv(test_file_path) testing_target = test_data.target chain = Chain() node_tpot = PrimaryNode('tpot') node_tpot.model.params = {'max_run_time_sec': max_run_time.seconds} node_lda = PrimaryNode('lda') node_rf = SecondaryNode('rf') node_rf.nodes_from = [node_tpot, node_lda] chain.add_node(node_rf) chain.fit(train_data) results = chain.predict(test_data) roc_auc_value = roc_auc(y_true=testing_target, y_score=results.predict) print(roc_auc_value) return roc_auc_value
def chain_tuning(nodes_to_tune: str, chain: Chain, train_data: InputData, test_data: InputData, local_iter: int, tuner_iter_num: int = 50) -> (float, list): several_iter_scores_test = [] if nodes_to_tune == 'primary': print('primary_node_tuning') chain_tune_strategy = chain.fine_tune_primary_nodes elif nodes_to_tune == 'root': print('root_node_tuning') chain_tune_strategy = chain.fine_tune_all_nodes else: raise ValueError( f'Invalid type of nodes. Nodes must be primary or root') for iteration in range(local_iter): print(f'current local iteration {iteration}') # Chain tuning chain_tune_strategy(train_data, iterations=tuner_iter_num) # After tuning prediction chain.fit(train_data) after_tuning_predicted = chain.predict(test_data) # Metrics aft_tun_roc_auc = roc_auc(y_true=test_data.target, y_score=after_tuning_predicted.predict) several_iter_scores_test.append(aft_tun_roc_auc) return float(np.mean(several_iter_scores_test)), several_iter_scores_test
def metric_for_nodes(self, metric_function, train_data: InputData, test_data: InputData, is_chain_shared: bool, chain: Chain) -> float: validate(chain) if is_chain_shared: chain = SharedChain(base_chain=chain, shared_cache=self.shared_cache) chain.fit(input_data=train_data) return metric_function(chain, test_data)
def test_secondary_nodes_is_invariant_to_inputs_order(data_setup): data = data_setup train, test = train_test_data_setup(data) first = NodeGenerator.primary_node(model_type=ModelTypesIdsEnum.logit) second = NodeGenerator.primary_node(model_type=ModelTypesIdsEnum.lda) third = NodeGenerator.primary_node(model_type=ModelTypesIdsEnum.knn) final = NodeGenerator.secondary_node(model_type=ModelTypesIdsEnum.xgboost, nodes_from=[first, second, third]) chain = Chain() for node in [first, second, third, final]: chain.add_node(node) first = deepcopy(first) second = deepcopy(second) third = deepcopy(third) final_shuffled = NodeGenerator.secondary_node( model_type=ModelTypesIdsEnum.xgboost, nodes_from=[third, first, second]) chain_shuffled = Chain() # change order of nodes in list for node in [final_shuffled, third, first, second]: chain_shuffled.add_node(node) train_predicted = chain.fit(input_data=train) train_predicted_shuffled = chain_shuffled.fit(input_data=train) # train results should be invariant assert chain.root_node.descriptive_id == chain_shuffled.root_node.descriptive_id assert all( np.equal(train_predicted.predict, train_predicted_shuffled.predict)) test_predicted = chain.predict(input_data=test) test_predicted_shuffled = chain_shuffled.predict(input_data=test) # predict results should be invariant assert all( np.equal(test_predicted.predict, test_predicted_shuffled.predict)) # change parents order for the nodes fitted chain nodes_for_change = chain.nodes[3].nodes_from chain.nodes[3].nodes_from = [ nodes_for_change[2], nodes_for_change[0], nodes_for_change[1] ] chain.nodes[3].cache.clear() chain.fit(train) test_predicted_re_shuffled = chain.predict(input_data=test) # predict results should be invariant assert all( np.equal(test_predicted.predict, test_predicted_re_shuffled.predict))
def run_metocean_forecasting_problem(train_file_path, test_file_path, forecast_length=1, max_window_size=64, is_visualise=False): # specify the task to solve task_to_solve = Task( TaskTypesEnum.ts_forecasting, TsForecastingParams(forecast_length=forecast_length, max_window_size=max_window_size)) full_path_train = os.path.join(str(project_root()), train_file_path) dataset_to_train = InputData.from_csv(full_path_train, task=task_to_solve, data_type=DataTypesEnum.ts) # a dataset for a final validation of the composed model full_path_test = os.path.join(str(project_root()), test_file_path) dataset_to_validate = InputData.from_csv(full_path_test, task=task_to_solve, data_type=DataTypesEnum.ts) chain = get_composite_lstm_chain() chain_simple = Chain() node_single = PrimaryNode('ridge') chain_simple.add_node(node_single) chain_lstm = Chain() node_lstm = PrimaryNode('lstm') chain_lstm.add_node(node_lstm) chain.fit(input_data=dataset_to_train, verbose=False) rmse_on_valid = calculate_validation_metric( chain.predict(dataset_to_validate), dataset_to_validate, f'full-composite_{forecast_length}', is_visualise) chain_lstm.fit(input_data=dataset_to_train, verbose=False) rmse_on_valid_lstm_only = calculate_validation_metric( chain_lstm.predict(dataset_to_validate), dataset_to_validate, f'full-lstm-only_{forecast_length}', is_visualise) chain_simple.fit(input_data=dataset_to_train, verbose=False) rmse_on_valid_simple = calculate_validation_metric( chain_simple.predict(dataset_to_validate), dataset_to_validate, f'full-simple_{forecast_length}', is_visualise) print(f'RMSE composite: {rmse_on_valid}') print(f'RMSE simple: {rmse_on_valid_simple}') print(f'RMSE LSTM only: {rmse_on_valid_lstm_only}') return rmse_on_valid_simple
def test_regression_chain_fit_correct(): data = get_synthetic_ts_data() chain = Chain() node_rfr = PrimaryNode('rfr') chain.add_node(node_rfr) train_data, test_data = train_test_data_setup(data) chain.fit(input_data=train_data) _, rmse_on_test = get_rmse_value(chain, train_data, test_data) rmse_threshold = np.std(data.target) * 1.5 assert rmse_on_test < rmse_threshold
def metric_for_nodes(self, metric_function, train_data: InputData, test_data: InputData, is_chain_shared: bool, chain: Chain) -> float: try: validate(chain) if is_chain_shared: chain = SharedChain(base_chain=chain, shared_cache=self.shared_cache) chain.fit(input_data=train_data) return metric_function(chain, test_data) except Exception as ex: print( f'Error in chain assessment during composition: {ex}. Continue.' ) return max_int_value
def test_regression_chain_with_datamodel_fit_correct(): data = get_synthetic_regression_data() train_data, test_data = train_test_data_setup(data) node_data = PrimaryNode('direct_data_model') node_first = PrimaryNode('ridge') node_second = SecondaryNode('lasso') node_second.nodes_from = [node_first, node_data] chain = Chain(node_second) chain.fit(train_data) results = chain.predict(test_data) assert results.predict.shape == test_data.target.shape
def test_chain_hierarchy_fit_correct(data_setup): data = data_setup train, _ = train_test_data_setup(data) first = NodeGenerator.primary_node(model_type=ModelTypesIdsEnum.logit) second = NodeGenerator.secondary_node(model_type=ModelTypesIdsEnum.logit, nodes_from=[first]) third = NodeGenerator.secondary_node(model_type=ModelTypesIdsEnum.logit, nodes_from=[first]) final = NodeGenerator.secondary_node(model_type=ModelTypesIdsEnum.logit, nodes_from=[second, third]) chain = Chain() for node in [first, second, third, final]: chain.add_node(node) train_predicted = chain.fit(input_data=train, use_cache=False) assert chain.root_node.descriptive_id == ( '((/n_ModelTypesIdsEnum.logit_defaultparams;)/' 'n_ModelTypesIdsEnum.logit_defaultparams;;(/' 'n_ModelTypesIdsEnum.logit_defaultparams;)/' 'n_ModelTypesIdsEnum.logit_defaultparams;)/' 'n_ModelTypesIdsEnum.logit_defaultparams') assert chain.length == 4 assert chain.depth == 3 assert train_predicted.predict.shape == train.target.shape
def test_chain_with_datamodel_fit_correct(data_setup): data = data_setup train_data, test_data = train_test_data_setup(data) chain = Chain() node_data = PrimaryNode('direct_data_model') node_first = PrimaryNode('bernb') node_second = SecondaryNode('rf') node_second.nodes_from = [node_first, node_data] chain.add_node(node_data) chain.add_node(node_first) chain.add_node(node_second) chain.fit(train_data) results = np.asarray(probs_to_labels(chain.predict(test_data).predict)) assert results.shape == test_data.target.shape
def test_chain_with_custom_params_for_model(data_setup): data = data_setup custom_params = dict(n_neighbors=1, weights='uniform', p=1) first = PrimaryNode(model_type='logit') second = PrimaryNode(model_type='lda') final = SecondaryNode(model_type='knn', nodes_from=[first, second]) chain = Chain() chain.add_node(final) chain_default_params = deepcopy(chain) chain.root_node.custom_params = custom_params chain_default_params.fit(data) chain.fit(data) custom_params_prediction = chain.predict(data).predict default_params_prediction = chain_default_params.predict(data).predict assert not np.array_equal(custom_params_prediction, default_params_prediction)
def run_tpot_vs_fedot_example(train_file_path: str, test_file_path: str): train_data = InputData.from_csv(train_file_path) test_data = InputData.from_csv(test_file_path) training_features = train_data.features testing_features = test_data.features training_target = train_data.target testing_target = test_data.target # Average CV score on the training set was: 0.93755 exported_pipeline = make_pipeline( StackingEstimator(estimator=BernoulliNB()), RandomForestClassifier()) # Fix random state for all the steps in exported pipeline set_param_recursive(exported_pipeline.steps, 'random_state', 1) exported_pipeline.fit(training_features, training_target) results = exported_pipeline.predict_proba(testing_features)[:, 1] roc_auc_value = roc_auc(y_true=testing_target, y_score=results) print(roc_auc_value) chain = Chain() node_first = PrimaryNode('direct_data_model') node_second = PrimaryNode('bernb') node_third = SecondaryNode('rf') node_third.nodes_from.append(node_first) node_third.nodes_from.append(node_second) chain.add_node(node_third) chain.fit(train_data) results = chain.predict(test_data) roc_auc_value = roc_auc(y_true=testing_target, y_score=results.predict) print(roc_auc_value) return roc_auc_value
def test_chain_sequential_fit_correct(data_setup): data = data_setup train, _ = train_test_data_setup(data) first = PrimaryNode(model_type='logit') second = SecondaryNode(model_type='logit', nodes_from=[first]) third = SecondaryNode(model_type='logit', nodes_from=[second]) final = SecondaryNode(model_type='logit', nodes_from=[third]) chain = Chain() for node in [first, second, third, final]: chain.add_node(node) train_predicted = chain.fit(input_data=train, use_cache=False) assert chain.root_node.descriptive_id == ('(((/n_logit_default_params;)/' 'n_logit_default_params;)/' 'n_logit_default_params;)/' 'n_logit_default_params') assert chain.length == 4 assert chain.depth == 4 assert train_predicted.predict.shape[0] == train.target.shape[0] assert final.cache.actual_cached_state is not None
from core.models.model import * from benchmark.benchmark_utils import get_scoring_case_data_paths train_file_path, test_file_path = get_scoring_case_data_paths() train_data = InputData.from_csv(train_file_path) test_data = InputData.from_csv(test_file_path) training_features = train_data.features testing_features = test_data.features training_target = train_data.target testing_target = test_data.target chain = Chain() node0 = NodeGenerator.primary_node(ModelTypesIdsEnum.tpot) node1 = NodeGenerator.primary_node(ModelTypesIdsEnum.lda) node2 = NodeGenerator.secondary_node(ModelTypesIdsEnum.rf) node2.nodes_from.append(node0) node2.nodes_from.append(node1) chain.add_node(node0) chain.add_node(node1) chain.add_node(node2) chain.fit(train_data) results = chain.predict(test_data) roc_auc_value = roc_auc(y_true=testing_target, y_score=results.predict) print(roc_auc_value)