# Path: src/obsolete/read_labels.py
# def get_features(data, split, classifier_type="C"):
# def features(mode, category, split, problem_type='C', feature_scale=False, count="all", select = "select"):
#     X = []


import os
import numpy as np
import src.main.config
import src.obsolete.utils
from sklearn.externals import joblib
from src.obsolete import read_labels




def final_classifier(mode,category="PN",problem_type="C",normalize="normalize"):
    if category == "PN":
        cat_1 = "positive"
        cat_2 = "negative"
    if mode == "late_fusion":
        X_test = [    [   map(np.asarray, read_labels.features("acoustic", cat_1, "test", problem_type, normalize)),
                      map(np.asarray, read_labels.features("acoustic", cat_2, "test", problem_type, normalize))
                  ],
                  [   map(np.asarray, read_labels.features("visual", cat_1, "test", problem_type, normalize)),
                      map(np.asarray, read_labels.features("visual", cat_2, "test", problem_type, normalize))
                  ],

# Path: src/obsolete/read_labels.py
# def get_features(data, split, classifier_type="C"):
# def features(mode, category, split, problem_type='C', feature_scale=False, count="all", select = "select"):
#     X = []


# Path: src/obsolete/get_prediction_labels.py
import os
import numpy as np
import src.main.config
import src.obsolete.utils
from sklearn.externals import joblib
from src.obsolete import read_labels





def final_classifier(mode,category="PN",problem_type="C",normalize="normalize"):
    if category == "PN":
        cat_1 = "positive"
        cat_2 = "negative"
    if mode == "late_fusion":
        X_test = [    [   map(np.asarray, read_labels.features("acoustic", cat_1, "test", problem_type, normalize)),
                      map(np.asarray, read_labels.features("acoustic", cat_2, "test", problem_type, normalize))
                  ],
                  [   map(np.asarray, read_labels.features("visual", cat_1, "test", problem_type, normalize)),
                      map(np.asarray, read_labels.features("visual", cat_2, "test", problem_type, normalize))
                  ],

                  [   map(np.asarray, read_labels.features("linguistic", cat_1, "test", problem_type, normalize)),