def bench_classifiers(name):
classifiers = [
ada_boost(name + '.ada_boost'), # boo
gaussian_nb(name + '.gaussian_nb'), # eey
knn(name + '.knn', sparse_data=True), # eey
linear_discriminant_analysis(name + '.linear_discriminant_analysis', n_components=1), # eey
random_forest(name + '.random_forest'), # boo
sgd(name + '.sgd') # eey
]
if xgboost:
classifiers.append(xgboost_classification(name + '.xgboost')) # boo
return hp.choice('%s' % name, classifiers)
Y_train_mini.sum(axis=0),
Y_val_mini.sum(axis=0),
Y_test_mini.sum(axis=0)
])))
print(seed_val)
print("\ndata is loaded - next step > model testing\n")
n_job = 6
select_classes = [0, 1, 2, 3, 4, 5]
val_dist = X_val_mini.shape[0] / X_train_mini.shape[0]
name = 'my_est_oVa'
tic_mod_all = time.time()
select_alg = [
ada_boost(name + '.ada_boost'),
gaussian_nb(name + '.gaussian_nb'),
knn(name + '.knn', sparse_data=True),
linear_discriminant_analysis(name +
'.linear_discriminant_analysis',
n_components=1),
random_forest(name + '.random_forest'),
sgd(name + '.sgd'),
xgboost_classification(name + '.xgboost')
]
# fitting models
estim_one_vs_rest = dict()
# scoring models
algo_scoring = dict()
save_score_path = r'C:/Users/anden/PycharmProjects/NovelEEG/results'
X_val_mini.shape, Y_val_mini.shape,
X_test_mini.shape, Y_test_mini.shape,
X_model_mini.shape, Y_model_mini.shape,
np.array([Y_train_mini.sum(axis=0), Y_val_mini.sum(axis=0), Y_test_mini.sum(axis=0)])))
print("\ndata is loaded - next step > model testing\n")
print('model:%i\nrun_nr:%s_subsample_%.2f' %
(args.index, args.reruns, args.subsample))
n_job = 5
select_classes = [0, 1, 2, 3, 4, 5]
val_dist = X_val_mini.shape[0] / X_train_mini.shape[0]
name = 'my_est_oVa'
tic_mod_all = time.time()
select_alg = [ada_boost(name + '.ada_boost'),
gaussian_nb(name + '.gaussian_nb'),
knn(name + '.knn', sparse_data=True),
linear_discriminant_analysis(name + '.linear_discriminant_analysis', n_components=1),
random_forest(name + '.random_forest'),
sgd(name + '.sgd'),
xgboost_classification(name + '.xgboost')]
# fitting models and score initialization
estim_one_vs_rest = dict()
algo_scoring = dict()
lars_table = {"model names": [], "wF1": [], "acc": [], "balanced acc": [], "sens": [],
"sens-eyem": [], "sens-chew": [], "sens-shiv": [],
"sens-elpp": [], "sens-musc": [], "sens-null": [],
"acc-eyem": [], "acc-chew": [], "acc-shiv": [], "acc-elpp": [], "acc-musc": [], "acc-null": []}