def cross_validation_error(df_labels, N, df_data, segments):
error_list = {
'anger': 1,
'disgust': 2,
'fear': 3,
'happiness': 4,
'sadness': 5,
'surprise': 6
}
for e in cnst.EMOTIONS_LIST:
total_error_for_emotion = 0
error_list[1] = 2
print("/\ Decision tree building for emotion:", e)
binary_targets = util.filter_for_emotion(df_labels,
cnst.EMOTIONS_DICT[e])
for test_seg in segments:
test_df_data, test_df_targets, train_df_data, train_df_targets = util.divide_data(
test_seg, N, df_data, df_labels)
root = dtree.decision_tree(train_df_data, set(cnst.AU_INDICES),
train_df_targets)
TreeNode.plot_tree(root, e)
# root = decision_tree(df_data, set(cnst.AU_INDICES), binary_targets)
print("/\ Decision tree built.\n")
count = 0
# Counts number of incorrectly predicted tests
for i in test_df_data.index.values:
count += 1 - TreeNode.dfs2(root, test_df_data.loc[i],
test_df_targets.loc[i].at[0])
error = count / len(test_df_targets)
total_error_for_emotion += error
print()
total_error_for_emotion /= 10
error_list[e] = total_error_for_emotion
print()
print("Total error:", total_error_for_emotion)
print()
def visualise(df_labels, df_data, N):
for e in cnst.EMOTIONS_LIST:
root = decision_tree(
df_data, set(cnst.AU_INDICES),
util.filter_for_emotion(df_labels, cnst.EMOTIONS_DICT[e]))
TreeNode.plot_tree(root, e)
