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# %% 
import sys, os

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
    sys.path.append(nb_dir)

from machine_learning.cross_validation import CrossValidation
from machine_learning.preprocessing import Preprocessing

# %% 
df = pd.read_csv("../data/stressfulness_event_with_speech/input_appraisal_stressfulness_event_mean.csv")
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
df.set_index(index_columns, inplace=True)

cv = CrossValidation(data=df, cv_method="logo")

categorical_columns = ["gender", "startlanguage", "mostcommonactivity", "homelabel"]
interval_feature_list, other_feature_list = [], []

print(df.columns.tolist())

for split in cv.get_splits():
    train_X, train_y, test_X, test_y = cv.get_train_test_sets(split)
    pre = Preprocessing(train_X, train_y, test_X, test_y)
    pre.one_hot_encode_train_and_test_sets(categorical_columns)
    train_X, train_y, test_X, test_y = pre.get_train_test_sets()
    break

# %%