def optimize_accuracy_under_constraints( trial, metafeature_values_hold): #todo: transfer use features directly try: gen = SpaceGenerator() space = gen.generate_params() space.sample_parameters(trial) trial.set_user_attr('space', copy.deepcopy(space)) search_time = trial.suggest_int('global_search_time_constraint', 10, search_time_frozen, log=False) evaluation_time = trial.suggest_int( 'global_evaluation_time_constraint', 10, search_time, log=False) memory_limit = trial.suggest_uniform('global_memory_constraint', 0.001, 4) cv = trial.suggest_int('global_cv', 2, 20, log=False) number_of_cvs = trial.suggest_int('global_number_cv', 1, 10, log=False) my_list_constraints_values = [ search_time, evaluation_time, memory_limit, cv, number_of_cvs ] features = space2features(space, my_list_constraints_values, metafeature_values_hold) trial.set_user_attr('features', features) return predict_range(model, features) except Exception as e: print(str(e) + 'except dataset _ accuracy: ' + '\n\n') return 0.0
def optimize_uncertainty(trial): try: gen = SpaceGenerator() space = gen.generate_params() space.sample_parameters(trial) trial.set_user_attr('space', copy.deepcopy(space)) search_time = trial.suggest_int('global_search_time_constraint', 10, total_search_time, log=False) evaluation_time = trial.suggest_int( 'global_evaluation_time_constraint', 10, search_time, log=False) memory_limit = trial.suggest_uniform('global_memory_constraint', 1.5, 4) cv = trial.suggest_int('global_cv', 2, 20, log=False) number_of_cvs = trial.suggest_int('global_number_cv', 1, 10, log=False) dataset_id = trial.suggest_categorical('dataset_id', my_openml_datasets) my_random_seed = int(time.time()) X_train, X_test, y_train, y_test, categorical_indicator, attribute_names = get_data( dataset_id, randomstate=my_random_seed) trial.set_user_attr('data_random_seed', my_random_seed) #add metafeatures of data my_list_constraints_values = [ search_time, evaluation_time, memory_limit, cv, number_of_cvs ] metafeature_values = data2features(X_train, y_train, categorical_indicator) features = space2features(space, my_list_constraints_values, metafeature_values) trial.set_user_attr('features', features) predictions = [] for tree in range(model.n_estimators): predictions.append(predict_range(model.estimators_[tree], features)) stddev_pred = np.std(np.matrix(predictions).transpose(), axis=1) return stddev_pred[0] except Exception as e: print( str(e) + 'except dataset _ uncertainty: ' + str(dataset_id) + '\n\n') return 0.0
def optimize_uncertainty(trial, dataset_id): dataset_id = str(dataset_id) try: gen = SpaceGenerator() space = gen.generate_params() space.sample_parameters(trial) trial.set_user_attr('space', copy.deepcopy(space)) search_time, evaluation_time, memory_limit, privacy_limit, training_time_limit, inference_time_limit, pipeline_size_limit, cv, number_of_cvs, hold_out_fraction, sample_fraction, _ = generate_parameters(trial, total_search_time, my_openml_datasets) my_random_seed = int(time.time()) X_train, X_test, y_train, y_test, categorical_indicator, attribute_names = get_data(dataset_id, randomstate=my_random_seed) trial.set_user_attr('data_random_seed', my_random_seed) trial.set_user_attr('dataset_id', dataset_id) #add metafeatures of data my_list_constraints_values = [search_time, evaluation_time, memory_limit, cv, number_of_cvs, ifNull(privacy_limit, constant_value=1000), ifNull(hold_out_fraction), sample_fraction, training_time_limit, inference_time_limit, pipeline_size_limit] metafeature_values = data2features(X_train, y_train, categorical_indicator) features = space2features(space, my_list_constraints_values, metafeature_values) features = FeatureTransformations().fit(features).transform(features, feature_names=feature_names) trial.set_user_attr('features', features) model = mp_glob.ml_model trial.set_user_attr('predicted_target', model.predict(features)) predictions = [] for tree in range(model.n_estimators): predictions.append(predict_range(model.estimators_[tree], features)) stddev_pred = np.std(np.matrix(predictions).transpose(), axis=1) return stddev_pred[0] except Exception as e: print(str(e) + 'except dataset _ uncertainty: ' + str(dataset_id) + '\n\n') return -np.inf
def optimize_uncertainty(trial): try: gen = SpaceGenerator() space = gen.generate_params() space.sample_parameters(trial) trial.set_user_attr('space', copy.deepcopy(space)) search_time, evaluation_time, memory_limit, privacy_limit, cv, number_of_cvs, hold_out_fraction, sample_fraction, dataset_id = generate_parameters( trial) my_random_seed = int(time.time()) trial.set_user_attr('data_random_seed', my_random_seed) return 0 except Exception as e: print(str(e) + 'except dataset _ uncertainty: ' + str(dataset_id) + '\n\n') return 0.0
search_time=search_time_frozen, memory_limit=memory_budget, pipeline_size_limit=pipeline_size) from fastsklearnfeature.declarative_automl.optuna_package.myautoml.utils_model import show_progress #show_progress(search, X_test_hold, y_test_hold, my_scorer) print("test result: " + str(result)) current_dynamic.append(result) except: current_dynamic.append(0.0) print('dynamic: ' + str(current_dynamic)) print('static: ' + str(current_static)) gen_new = SpaceGenerator() space_new = gen_new.generate_params() for pre, _, node in RenderTree(space_new.parameter_tree): if node.status == True: print("%s%s" % (pre, node.name)) try: search = MyAutoML(n_jobs=1, time_search_budget=search_time_frozen, space=space_new, evaluation_budget=int(0.1 * search_time_frozen), main_memory_budget_gb=memory_budget, pipeline_size_limit=pipeline_size, hold_out_fraction=0.33)
def run_AutoML(trial, X_train=None, X_test=None, y_train=None, y_test=None, categorical_indicator=None): space = None search_time = None if not 'space' in trial.user_attrs: # which hyperparameters to use gen = SpaceGenerator() space = gen.generate_params() space.sample_parameters(trial) trial.set_user_attr('space', copy.deepcopy(space)) search_time, evaluation_time, memory_limit, privacy_limit, training_time_limit, inference_time_limit, pipeline_size_limit, cv, number_of_cvs, hold_out_fraction, sample_fraction, dataset_id = generate_parameters(trial, total_search_time, my_openml_datasets) else: space = trial.user_attrs['space'] print(trial.params) #make this a hyperparameter search_time = trial.params['global_search_time_constraint'] evaluation_time = search_time if 'global_evaluation_time_constraint' in trial.params: evaluation_time = trial.params['global_evaluation_time_constraint'] memory_limit = 10 if 'global_memory_constraint' in trial.params: memory_limit = trial.params['global_memory_constraint'] privacy_limit = None if 'privacy_constraint' in trial.params: privacy_limit = trial.params['privacy_constraint'] training_time_limit = search_time if 'training_time_constraint' in trial.params: training_time_limit = trial.params['training_time_constraint'] inference_time_limit = 60 if 'inference_time_constraint' in trial.params: inference_time_limit = trial.params['inference_time_constraint'] pipeline_size_limit = 350000000 if 'pipeline_size_constraint' in trial.params: pipeline_size_limit = trial.params['pipeline_size_constraint'] cv = 1 number_of_cvs = 1 hold_out_fraction = None if 'global_cv' in trial.params: cv = trial.params['global_cv'] if 'global_number_cv' in trial.params: number_of_cvs = trial.params['global_number_cv'] else: hold_out_fraction = trial.params['hold_out_fraction'] sample_fraction = 1.0 if 'sample_fraction' in trial.params: sample_fraction = trial.params['sample_fraction'] if 'dataset_id' in trial.params: dataset_id = trial.params['dataset_id'] else: dataset_id = trial.user_attrs['dataset_id'] for pre, _, node in RenderTree(space.parameter_tree): if node.status == True: print("%s%s" % (pre, node.name)) if type(X_train) == type(None): my_random_seed = int(time.time()) if 'data_random_seed' in trial.user_attrs: my_random_seed = trial.user_attrs['data_random_seed'] X_train, X_test, y_train, y_test, categorical_indicator, attribute_names = get_data(dataset_id, randomstate=my_random_seed) if not isinstance(trial, FrozenTrial): my_list_constraints_values = [search_time, evaluation_time, memory_limit, cv, number_of_cvs, ifNull(privacy_limit, constant_value=1000), ifNull(hold_out_fraction), sample_fraction, training_time_limit, inference_time_limit, pipeline_size_limit] metafeature_values = data2features(X_train, y_train, categorical_indicator) features = space2features(space, my_list_constraints_values, metafeature_values) features = FeatureTransformations().fit(features).transform(features, feature_names=feature_names) trial.set_user_attr('features', features) dynamic_params = [] for random_i in range(5): #5 search = MyAutoML(cv=cv, number_of_cvs=number_of_cvs, n_jobs=1, evaluation_budget=evaluation_time, time_search_budget=search_time, space=space, main_memory_budget_gb=memory_limit, differential_privacy_epsilon=privacy_limit, hold_out_fraction=hold_out_fraction, sample_fraction=sample_fraction, training_time_limit=training_time_limit, inference_time_limit=inference_time_limit, pipeline_size_limit=pipeline_size_limit) test_score = 0.0 try: search.fit(X_train, y_train, categorical_indicator=categorical_indicator, scorer=my_scorer) best_pipeline = search.get_best_pipeline() if type(best_pipeline) != type(None): test_score = my_scorer(search.get_best_pipeline(), X_test, y_test) except: pass dynamic_params.append(test_score) count_success = 0 for i_run in range(len(dynamic_params)): if dynamic_params[i_run] > 0.0: count_success += 1 success_rate = float(count_success) / float(len(dynamic_params)) return success_rate, search
from fastsklearnfeature.declarative_automl.optuna_package.myautoml.Space_GenerationTree import SpaceGenerator auc = make_scorer(roc_auc_score, greater_is_better=True, needs_threshold=True) #dataset = openml.datasets.get_dataset(40536) dataset = openml.datasets.get_dataset(31) #dataset = openml.datasets.get_dataset(1590) X, y, categorical_indicator, attribute_names = dataset.get_data( dataset_format='array', target=dataset.default_target_attribute) X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split( X, y, random_state=1, stratify=y, train_size=0.6) gen = SpaceGenerator() space = gen.generate_params() print('number hyperparameters: ' + str(len(space.name2node))) from anytree import Node, RenderTree for pre, _, node in RenderTree(space.parameter_tree): print("%s%s: %s" % (pre, node.name, node.status)) my_study = optuna.create_study(direction='maximize') validation_scores = [] test_scores = [] #add Caruana ensemble with replacement # save pipelines to disk
def run_AutoML(trial, X_train=None, X_test=None, y_train=None, y_test=None, categorical_indicator=None): space = None search_time = None if not 'space' in trial.user_attrs: # which hyperparameters to use gen = SpaceGenerator() space = gen.generate_params() space.sample_parameters(trial) trial.set_user_attr('space', copy.deepcopy(space)) # which constraints to use search_time = trial.suggest_int('global_search_time_constraint', 10, total_search_time, log=False) # how much time for each evaluation evaluation_time = trial.suggest_int('global_evaluation_time_constraint', 10, search_time, log=False) # how much memory is allowed memory_limit = trial.suggest_uniform('global_memory_constraint', 1.5, 4) # how many cvs should be used cv = trial.suggest_int('global_cv', 2, 20, log=False) #todo: calculate minimum number of splits based on y number_of_cvs = trial.suggest_int('global_number_cv', 1, 10, log=False) dataset_id = trial.suggest_categorical('dataset_id', my_openml_datasets) else: space = trial.user_attrs['space'] print(trial.params) #make this a hyperparameter search_time = trial.params['global_search_time_constraint'] evaluation_time = trial.params['global_evaluation_time_constraint'] memory_limit = trial.params['global_memory_constraint'] cv = trial.params['global_cv'] number_of_cvs = trial.params['global_number_cv'] if 'dataset_id' in trial.params: dataset_id = trial.params['dataset_id'] #get same random seed else: dataset_id = 31 for pre, _, node in RenderTree(space.parameter_tree): print("%s%s: %s" % (pre, node.name, node.status)) # which dataset to use #todo: add more datasets if type(X_train) == type(None): X_train, X_test, y_train, y_test, categorical_indicator, attribute_names = get_data(dataset_id, randomstate=int(time.time())) if not isinstance(trial, FrozenTrial): my_list_constraints_values = [search_time, evaluation_time, memory_limit, cv, number_of_cvs] metafeature_values = data2features(X_train, y_train, categorical_indicator) features = space2features(space, my_list_constraints_values, metafeature_values) trial.set_user_attr('features', features) search = MyAutoML(cv=cv, number_of_cvs=number_of_cvs, n_jobs=1, evaluation_budget=evaluation_time, time_search_budget=search_time, space=space, main_memory_budget_gb=memory_limit) search.fit(X_train, y_train, categorical_indicator=categorical_indicator, scorer=auc) best_pipeline = search.get_best_pipeline() test_score = 0.0 if type(best_pipeline) != type(None): test_score = auc(search.get_best_pipeline(), X_test, y_test) return test_score
X_train_hold, X_test_hold, y_train_hold, y_test_hold, categorical_indicator_hold, attribute_names_hold = get_data(test_holdout_dataset_id, randomstate=42) metafeature_values_hold = data2features(X_train_hold, y_train_hold, categorical_indicator_hold) auc=make_scorer(roc_auc_score, greater_is_better=True, needs_threshold=True) total_search_time = 60*60#10 * 60 my_openml_datasets = [3, 4, 13, 15, 24, 25, 29, 31, 37, 38, 40, 43, 44, 49, 50, 51, 52, 53, 55, 56, 59, 151, 152, 153, 161, 162, 164, 172, 179, 310, 311, 312, 316, 333, 334, 335, 336, 337, 346, 444, 446, 448, 450, 451, 459, 461, 463, 464, 465, 466, 467, 470, 472, 476, 479, 481, 682, 683, 747, 803, 981, 993, 1037, 1038, 1039, 1040, 1042, 1045, 1046, 1048, 1049, 1050, 1053, 1054, 1055, 1056, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1071, 1073, 1075, 1085, 1101, 1104, 1107, 1111, 1112, 1114, 1116, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1169, 1216, 1235, 1236, 1237, 1238, 1240, 1412, 1441, 1442, 1443, 1444, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1455, 1458, 1460, 1461, 1462, 1463, 1464, 1467, 1471, 1473, 1479, 1480, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1494, 1495, 1496, 1498, 1502, 1504, 1506, 1507, 1510, 1511, 1547, 1561, 1562, 1563, 1564, 1597, 4134, 4135, 4154, 4329, 4534, 23499, 40536, 40645, 40646, 40647, 40648, 40649, 40650, 40660, 40665, 40666, 40669, 40680, 40681, 40690, 40693, 40701, 40705, 40706, 40710, 40713, 40714, 40900, 40910, 40922, 40999, 41005, 41007, 41138, 41142, 41144, 41145, 41146, 41147, 41150, 41156, 41158, 41159, 41160, 41161, 41162, 41228, 41430, 41521, 41538, 41976, 42172, 42477] my_openml_datasets.remove(test_holdout_dataset_id) mgen = SpaceGenerator() mspace = mgen.generate_params() my_list = list(mspace.name2node.keys()) my_list.sort() my_list_constraints = ['global_search_time_constraint', 'global_evaluation_time_constraint', 'global_memory_constraint', 'global_cv', 'global_number_cv'] def run_AutoML(trial, X_train=None, X_test=None, y_train=None, y_test=None, categorical_indicator=None): space = None search_time = None if not 'space' in trial.user_attrs: # which hyperparameters to use gen = SpaceGenerator() space = gen.generate_params()
def optimize_accuracy_under_constraints(trial, metafeature_values_hold, search_time, model, memory_limit=10, privacy_limit=None, evaluation_time=None, hold_out_fraction=None): try: gen = SpaceGenerator() space = gen.generate_params() space.sample_parameters(trial) trial.set_user_attr('space', copy.deepcopy(space)) if type(evaluation_time) == type(None): evaluation_time = search_time if trial.suggest_categorical('use_evaluation_time_constraint', [True, False]): evaluation_time = trial.suggest_int( 'global_evaluation_time_constraint', 10, search_time, log=False) else: trial.set_user_attr('evaluation_time', evaluation_time) # how many cvs should be used cv = 1 number_of_cvs = 1 if type(hold_out_fraction) == type(None): hold_out_fraction = None if trial.suggest_categorical('use_hold_out', [True, False]): hold_out_fraction = trial.suggest_uniform( 'hold_out_fraction', 0, 1) else: cv = trial.suggest_int( 'global_cv', 2, 20, log=False ) # todo: calculate minimum number of splits based on y number_of_cvs = 1 if trial.suggest_categorical('use_multiple_cvs', [True, False]): number_of_cvs = trial.suggest_int('global_number_cv', 2, 10, log=False) else: trial.set_user_attr('hold_out_fraction', hold_out_fraction) sample_fraction = 1.0 #if trial.suggest_categorical('use_sampling', [True, False]): # sample_fraction = trial.suggest_uniform('sample_fraction', 0, 1) my_list_constraints_values = [ search_time, evaluation_time, memory_limit, cv, number_of_cvs, ifNull(privacy_limit, constant_value=1000), ifNull(hold_out_fraction), sample_fraction ] features = space2features(space, my_list_constraints_values, metafeature_values_hold) feature_names, _ = get_feature_names() features = FeatureTransformations().fit(features).transform( features, feature_names=feature_names) trial.set_user_attr('features', features) return predict_range(model, features) except Exception as e: print(str(e) + 'except dataset _ accuracy: ' + '\n\n') return 0.0
def run_AutoML(trial, X_train=None, X_test=None, y_train=None, y_test=None, categorical_indicator=None): space = None search_time = None if not 'space' in trial.user_attrs: # which hyperparameters to use gen = SpaceGenerator() space = gen.generate_params() space.sample_parameters(trial) trial.set_user_attr('space', copy.deepcopy(space)) search_time, evaluation_time, memory_limit, privacy_limit, cv, number_of_cvs, hold_out_fraction, sample_fraction, dataset_id = generate_parameters(trial) else: space = trial.user_attrs['space'] print(trial.params) #make this a hyperparameter search_time = total_search_time evaluation_time = search_time memory_limit = 4 privacy_limit = None cv = 1 number_of_cvs = 1 hold_out_fraction = None if 'global_cv' in trial.params: cv = trial.params['global_cv'] if 'global_number_cv' in trial.params: number_of_cvs = trial.params['global_number_cv'] else: hold_out_fraction = trial.params['hold_out_fraction'] sample_fraction = 1.0 if 'sample_fraction' in trial.params: sample_fraction = trial.params['sample_fraction'] if 'dataset_id' in trial.params: dataset_id = trial.params['dataset_id'] #get same random seed else: dataset_id = 31 for pre, _, node in RenderTree(space.parameter_tree): if node.status == True: print("%s%s" % (pre, node.name)) if type(X_train) == type(None): my_random_seed = int(time.time()) if 'data_random_seed' in trial.user_attrs: my_random_seed = trial.user_attrs['data_random_seed'] X_train, X_test, y_train, y_test, categorical_indicator, attribute_names = get_data(dataset_id, randomstate=my_random_seed) if not isinstance(trial, FrozenTrial): my_list_constraints_values = [search_time, evaluation_time, memory_limit, cv, number_of_cvs, ifNull(privacy_limit, constant_value=1000), ifNull(hold_out_fraction), sample_fraction] metafeature_values = data2features(X_train, y_train, categorical_indicator) features = space2features(space, my_list_constraints_values, metafeature_values) features = FeatureTransformations().fit(features).transform(features, feature_names=feature_names) trial.set_user_attr('features', features) search = MyAutoML(cv=cv, number_of_cvs=number_of_cvs, n_jobs=1, evaluation_budget=evaluation_time, time_search_budget=search_time, space=space, main_memory_budget_gb=memory_limit, differential_privacy_epsilon=privacy_limit, hold_out_fraction=hold_out_fraction, sample_fraction=sample_fraction) search.fit(X_train, y_train, categorical_indicator=categorical_indicator, scorer=my_scorer) best_pipeline = search.get_best_pipeline() test_score = 0.0 if type(best_pipeline) != type(None): test_score = my_scorer(search.get_best_pipeline(), X_test, y_test) return test_score, search