def hpskForClf(train_arrays, train_labels):
estim = HyperoptEstimator(
classifier=sgd('mySGD'),
preprocessing=[],
algo=tpe.suggest,
max_evals=5,
)
#cross_val_score(estim, train_arrays, train_labels, cv=5, scoring='recall_macro').mean()
estim.fit(train_arrays, train_labels)
print(estim.best_model())
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)
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'
for alg in [select_alg[args.index]]:
tic_mod = time.time()
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
"running on %s" % (alg.name + '.one_V_all'),
"\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
clf_method = one_vs_rest(str(alg.name + '.one_V_all'),
estimator=alg,