def test_trees(T_P, x2):
T, P = zip(*T_P)
predictions = []
for i in x2.index.values:
example = x2.loc[i]
T_P_D = []
for j in range(0, len(T_P)):
prediction, depth = TreeNode.dfs_with_depth(T[j], example)
T_P_D.append([prediction, P[j], depth])
prediction_choice = choose_prediction_optimised(T_P_D)
predictions.append(prediction_choice + 1)
return pd.DataFrame(predictions)
def test_forest_trees(forest_T, x2):
predictions = []
for i in x2.index.values:
example = x2.loc[i]
all_emotion_prediction = []
for T in forest_T:
emotion_prediction = []
depths = []
for tree in T:
# how emotion votes
prediction, depth = TreeNode.dfs_with_depth(tree, example)
emotion_prediction.append(prediction)
depths.append(depth)
sum_per_emotion = sum(emotion_prediction)
sum_depths = sum(depths)
all_emotion_prediction.append((sum_per_emotion, sum_depths))
prediction_choice = choose_majority_vote_optimised(
all_emotion_prediction)
predictions.append(prediction_choice + 1)
return pd.DataFrame(predictions)
