config = utils.load_json("demo_ca.json")
gc = GCForest(config)
datasets = ['cirrhosis', 'obesity', 't2d']
for dataset_idx, name in enumerate(datasets):
thre_features = {}
X = None
Y = None
if name == 'cirrhosis':
X, Y = load.cirrhosis_data()
elif name == 't2d':
X, Y = load.t2d_data()
elif name == 'obesity':
X, Y = load.obesity_data()
else:
raise Exception('the dataset is not defined!!!')
output_features = pd.Series()
for train, test in cv.split(X, Y):
x_train = X.iloc[train]
y_train = Y[train]
x_test = X.iloc[test]
y_test = Y[test]
X_train = x_train.values.reshape(-1, 1, len(x_train.columns))
X_test = x_test.values.reshape(-1, 1, len(x_test.columns))
X_train_enc, _features = gc.fit_transform(X_train, y_train)
data_sets = ["cirrhosis", 'obesity', 't2d']
feature_sets = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
# feature_len = [542, 572, 465]
clf_svm = SVC(kernel='linear')
cv = StratifiedKFold(n_splits=5, shuffle=False, random_state=0)
for k, data_name in enumerate(data_sets):
print("==================")
print(data_name)
ll = []
X, y = None, None
if data_name == 'obesity':
X, y = load.obesity_data()
elif data_name == 'cirrhosis':
X, y = load.cirrhosis_data()
else:
X, y = load.t2d_data()
clf_acc_before = cross_val_score(clf_svm, X, y, cv=cv, scoring='accuracy')
print(np.mean(clf_acc_before))
for feat in feature_sets:
llm = []
print("------------")
# ###### deep forest
output_dir = osp.join("output", "result")
mat = []
for i in range(5):