from sklearn import datasets
from sklearn.linear_model import LogisticRegression
wine = datasets.load_wine()
X = wine.data # rows of features
y = wine.target # integer labels

num_features = len(X[0])
features = []
for i in range(7):
    selected_feature = None
    max_precision = -1
    for j in range(num_features):
        if j in features:
            continue
        current_features = features.copy() # copy the current selection
        current_features.append(j) # and add what we are considering now
        sel_X = X[:, current_features]

        # 1. sel_X로 logistic regression classifier 학습

        # 2. classifier 정확도 평가

        # 3. 해당 feature(j)를 고를건지 말건지 판단

        print('>', current_features, score)
        #################################
    if selected_feature is not None:
        features.append(selected_feature)
    print(features)