| #Pearson correlation coefficient | |
| import argparse | |
| import numpy as np | |
| import json | |
| import pandas as pd | |
| import scipy.stats | |
| import os | |
| import math | |
| def compute_correlation(set1, set2): | |
| corr_coefficient = np.corrcoef(set1, set2)[0, 1] | |
| return corr_coefficient | |
| def compute_max_correlation(fix, var): #(350,3) | |
| set1 = fix[:,0].flatten() | |
| set2 = var[:,0].flatten() | |
| max = np.corrcoef(set1,set2)[0, 1] | |
| selected_human_score = set2 | |
| selected_human_index = np.zeros(num_images) | |
| for i in range(num_images): | |
| for j in range(3): | |
| set2[i] = var[i,j] | |
| corr_coefficient = np.corrcoef(set1, set2)[0, 1] | |
| if corr_coefficient > max: | |
| selected_human_score[i] = var[i,j] | |
| selected_human_index[i] = j | |
| max = corr_coefficient | |
| return max,selected_human_index,selected_human_score | |
| # Normalizing function using Min-Max scaling | |
| def min_max_normalize(scores): | |
| min_score = np.min(scores) | |
| max_score = np.max(scores) | |
| return (scores - min_score) / (max_score - min_score) | |
| def scale_dict_values(input_dict): | |
| # 获取字典中所有值 | |
| values = list(input_dict.values()) | |
| # 找到最小值和最大值 | |
| min_value = min(values) | |
| max_value = max(values) | |
| # 定义缩放函数 | |
| def scale_value(value): | |
| return 1 + (value - min_value) * (5 - 1) / (max_value - min_value) | |
| # 创建一个新的字典，其中所有值都缩放到1到5之间 | |
| scaled_dict = {key: scale_value(value) for key, value in input_dict.items()} | |
| return scaled_dict | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description="Demo") | |
| parser.add_argument( | |
| "--auto_evaluation", | |
| type=str, | |
| help="csv", | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/frame_score.csv" # TODO pkc | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_9_2/frame_score.csv" # TODO | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_2_2/frame_score_mechanism_3_1.csv" # TODO | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_2_2/frame_score_mechanism.csv" # TODO pick | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_6_2_gt/grid_score.csv" # TODO pick | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_6_2_gt/frame_score.csv" | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_6_2/video_score.csv" # | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/grid_score.csv" # TODO pick | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_6_2_gt/clip.csv" # TODO | |
| # default ="/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_9_2_user_study/thresh_04_2_score.csv" | |
| # default ="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_2_2/b_clip.csv" | |
| # default ="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_2_2/clip.csv" | |
| # default ="/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_2_2/video_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_again.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_6_again.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_4_adjust_again.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_8.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/frame_score_8.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/BLIP-vqa/csv_output_1_2_blipvqa.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_5_2/video_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_2_2/frame_score_mechanism_test.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_2_2/grid_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_2_combine_frame.csv", | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/Grounded-Segment-Anything/csv_3_depth_user_study/algorithm_035_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/Grounded-Segment-Anything/csv_3_depth_user_study/algorithm_035_addscore_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_3_2/clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_6_2/b_clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/dot/csv_4_2_user_study/4_2_user_study_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_4_2/clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_4_2/b_clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_4_2/frame_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_4_2/grid_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/frame_score_corrected.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_productscore_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_2_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/Grounded-Segment-Anything/csv_3_depth_user_study/algorithm_035_addscore_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/groundingDINO_3/spatial_final_2.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_corrected_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_corrected_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/csv_output_3_2_user_study_2d/user_study_2d_03_321split_algorithm_035_corrected_2_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/Grounded-Segment-Anything/csv_3_depth_user_study/algorithm_035_corrected_combine_frame.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_3_2/b_clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_3_2/frame_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_3_2/video_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_4_2/video_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/grid_score_8.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_1_2/frame_score_corrected_8_withinitial.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_1_2/video_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/BLIP-vqa/csv_output_1_2_blipvqa.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_1_2/b_clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_6_2_gt/clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/groundingDINO_3/spatial_method1.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/groundingDINO_3/spatial_method2.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/frame_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/grid_score_old.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/b_clip/csv_output_5_2/b_clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/clip/csv_output_5_2/clip.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/pllava/csv_output_5_2/video_score.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava/csv_output_5_2/frame_score_new.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/consistent_attr_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/dynamic_attr_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/spatial_relationship_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/motion_binding_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/action_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/interaction_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/viclip/numeracy_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/consistent_attr_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/dynamic_attr_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/spatial_relationship_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/motion_binding_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/action_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/interaction_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/EvalCrafter/blipbleu_rebuttal/numeracy_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/rebuttal_models/VPEval/csv_rebuttal/vpeval_user_study_video.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/GroundingDINO/rebuttal_videodirectorgpt_csv/M-GDino_user_study_rebuttal.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/LLaVA/cvpr_csv/csv_6/user_study/user-t0_object_interactions_score_0.0.csv", | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_1_2/frame_score.csv", | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_5_2/frame_score.csv", | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_6_2_gt/frame_score.csv", | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_1_2/frame_score_1st_last_2.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/auto_eval/llava_cvpr/csv_output_3_2/frame_score.csv", | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/metric/models/LLaVA/cvpr_csv/csv_1/user_study/cvpr_conv/user-t005_consistent_attr_score_0.05_4.csv", | |
| ) | |
| parser.add_argument( | |
| "--human_evaluation", | |
| type=str, | |
| help="csv", | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/action_grid_corrected.csv" # pick!!! | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_attr.csv" | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_llava_grid.csv" | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_llava_grid_corrected_gt_filter2.csv" #pick!!! | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_attr_mechanism_corrected.csv" #pick!!! | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_gt.csv" | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent spatial_human.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent spatial_human_corrected2.csv" | |
| # default="/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/numeracy_human.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/numeracy_human_adjust.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_spatial_human_1.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent attribute_human.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent attribute_human_2.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent attribute_human_corrected.csv" | |
| default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent attribute_human_corrected_kaiyue.csv", #ok final | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_attr_mechanism_corrected_kaiyue.csv" #ok final | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/consistent spatial_human_corrected2_kaiyue.csv" #ok final | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/dynamic_spatial_human_1_kaiyue.csv" #ok final | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/action_adjust_kaiyue_arxiv.csv" #ok final | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_llava_grid_corrected_gt_filter2_kaiyue.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/numeracy_human_adjust_corrected_kaiyue.csv" | |
| # default = "/group/xihuiliu/sky/T2V-Compbench/user_study/human_eval/interaction_adjust_kaiyue.csv", #ok final | |
| ) | |
| args = parser.parse_args() | |
| return args | |
| if __name__ == "__main__": | |
| args = parse_args() | |
| auto_evaluation = args.auto_evaluation | |
| method = ["videocrafter2", "latte", "opensora11", "opensoraplan", "show1", "modelscope", "gt"] | |
| dic_human = {} # store by model name, for calculating avg score for each model | |
| dic_auto = {}# store by model name | |
| dic_score_category = {} | |
| dic_human_avg = {} # store by video name, for calculating human corr with avg score | |
| dic_auto_avg = {} # store by video name | |
| # hky | |
| # get AMT score | |
| # Define the path to your CSV file | |
| csv_file = args.human_evaluation | |
| # Read the CSV file into a pandas DataFrame | |
| data_frame = pd.read_csv(csv_file) | |
| try: | |
| column_index = data_frame.columns.get_loc('Input.video') | |
| except: | |
| column_index = data_frame.columns.get_loc('Input.url') | |
| column_name = data_frame.iloc[:,column_index] | |
| column_name = column_name.to_numpy() | |
| try: | |
| score_index = data_frame.columns.get_loc('Answer.score') | |
| except: | |
| score_index = data_frame.columns.get_loc('Answer.correctness_answer') | |
| score_AMT = data_frame.iloc[:,score_index] | |
| score_AMT = score_AMT.to_numpy() | |
| score_dic = {} # to store {method: {id: score,... },... } | |
| # read from auto eval | |
| csv_file_auto = args.auto_evaluation | |
| data_frame_auto = pd.read_csv(csv_file_auto) | |
| # read key = id, value = Score from csv | |
| for i in range(len(data_frame_auto)): | |
| # print(data_frame_auto.iloc[i]) | |
| # score_dic[(data_frame_auto.iloc[i]['name'].split(".")[0])] = float(data_frame_auto.iloc[i][-1]) | |
| score_dic[(data_frame_auto.iloc[i]['name'].split(".")[0])] = float(data_frame_auto.iloc[i]['Score']) | |
| # score_dic[(data_frame_auto[0].split(".")[0])] = float(data_frame_auto.iloc[i]['Score']) | |
| # score_dic[(data_frame_auto.iloc[i]['name'])] = float(data_frame_auto.iloc[i]['Score']) | |
| # score_dic[(data_frame_auto.iloc[i]['id'])] = float(data_frame_auto.iloc[i]['Score']) | |
| # # read from human eval's csv (col. "Input.video"), store {method: [score,...],...} | |
| for me in range(len(method)): # initialize | |
| dic_human[method[me]] = [] | |
| dic_auto[method[me]] = [] | |
| dic_score_category[method[me]] = [] | |
| print(score_dic) | |
| record = np.empty(0) | |
| score_human = np.empty(0) # for calculating human corr with all scores | |
| score_norm = np.empty(0) | |
| score_human_avg = np.empty(0) #f or calculating human corr with avg score | |
| score_norm_avg = np.empty(0) | |
| for i in range(len(column_name)): # initialize | |
| c = column_name[i] | |
| c = c.replace(".mp4","") | |
| dic_human_avg[c] = [] | |
| dic_auto_avg[c] = [] | |
| # import pdb; pdb.set_trace() | |
| # score_dic = scale_dict_values(score_dic) # TODO rescale to 1~5 | |
| for i in range(len(column_name)): | |
| c = column_name[i] | |
| c = c.replace(".mp4","") | |
| s = score_AMT[i] | |
| method_name = c.split("_")[-1] | |
| # hky | |
| index_name = method.index(method_name) | |
| # print(method_name, index_num) | |
| # score_norm = np.append(score_norm, score_dic[method_name][index_num]) | |
| # print(score_dic) | |
| # print(c) | |
| # print(score_dic[c]) | |
| try: | |
| score_norm = np.append(score_norm, score_dic[c]) #TODO round or not | |
| # import pdb; pdb.set_trace() | |
| # auto_score = round(score_dic[c]) | |
| # score_norm = np.append(score_norm, auto_score) | |
| score_human = np.append(score_human, (s/5)) | |
| record = np.append(record, c) | |
| dic_human[method_name].append(s/5) # length 150 | |
| dic_auto[method_name].append(score_dic[c]) # | |
| dic_score_category[method_name].append(score_dic[c]) | |
| dic_human_avg[c].append(s/5) | |
| dic_auto_avg[c].append(score_dic[c]) #TODO round or not | |
| # dic_auto_avg[c].append(auto_score) | |
| except: # not for gt | |
| continue | |
| # import pdb; pdb.set_trace() | |
| for k, v in dic_human_avg.items(): | |
| score_human_avg = np.append(score_human_avg, np.average(dic_human_avg[k])) | |
| score_norm_avg = np.append(score_norm_avg, np.average(dic_auto_avg[k])) | |
| Pearson_correlation_coefficient_all = compute_correlation(score_human,score_norm) #TODO change score_human, score_norm | |
| print("Pearson correlation coefficient all:",f'{Pearson_correlation_coefficient_all: .4}') | |
| score_diff = np.empty(0) | |
| # Normalize the scores to the range [0, 1], and pick the larger different score | |
| score_norm_norm = min_max_normalize(score_norm) | |
| score_human_norm = min_max_normalize(score_human) | |
| # Calculate the threshold | |
| threshold = np.mean(np.abs(score_human_norm - score_norm_norm)) | |
| # Iterate through the scores and apply the condition | |
| for i in range(len(score_norm)): | |
| score_diff = np.append(score_diff, score_human_norm[i] - score_norm_norm[i]) | |
| if abs(score_norm_norm[i] - score_human_norm[i]) > threshold * 2: | |
| score_diff = np.append(score_diff, [score_norm[i], score_human[i]]) | |
| print("Not aligned:", i, "score ori:", score_norm[i], "score from AMT", score_human[i]) | |
| print("##############################") | |
| # score_diff = np.reshape(score_diff,(num_images,3)) | |
| # best_index = np.argmin(score_diff, axis=1) | |
| # #score_diff_best = np.choose(best_index,score_diff.T) | |
| # score_norm_reshape = np.reshape(score_norm,(num_images,3)) | |
| # score_norm_best = np.choose(best_index,score_norm_reshape.T) | |
| # score_human_reshape = np.reshape(score_human,(num_images,3)) | |
| # score_human_best = np.choose(best_index,score_human_reshape.T) * 5 #best human score | |
| # Pearson_correlation_coefficient_abs_diff = compute_correlation(score_human_best,score_norm_best) | |
| # print("Pearson correlation coefficient with min abs diff:",f'{Pearson_correlation_coefficient_abs_diff: .4}') | |
| # Pearson_correlation_coefficient_search_max,selected_human_index,selected_human_score = compute_max_correlation(score_norm_reshape, score_human_reshape) #(350,3) | |
| # print("Pearson correlation coefficient with search max:",f'{Pearson_correlation_coefficient_search_max: .4}') | |
| # score_norm_average = np.average(score_norm_reshape, axis=1) | |
| # score_human_average = np.average(score_human_reshape, axis=1) | |
| Pearson_correlation_coefficient_all = compute_correlation(score_norm, score_human) | |
| print("Pearson correlation coefficient all:",f'{Pearson_correlation_coefficient_all: .4}') | |
| Pearson_correlation_coefficient_avg = compute_correlation(score_norm_avg, score_human_avg) | |
| print("Pearson correlation coefficient avg:",f'{Pearson_correlation_coefficient_avg: .4}') | |
| kendalltau_all,p_value_all = scipy.stats.kendalltau(score_human, score_norm) | |
| print("kendall's Tau_all: ", f'{kendalltau_all: .4}', " p_value_all: ", f'{p_value_all: .4}') | |
| # kendalltau_abs_diff,p_value_abs_diff = scipy.stats.kendalltau(score_human_best,score_norm_best) | |
| # print("kendall's Tau with abs diff: ", f'{kendalltau_abs_diff: .4}', " p_value: ", f'{p_value_abs_diff: .4}') | |
| #kendalltau_search_max = scipy.stats.kendalltau(x, y) | |
| kendalltau_avg, p_value_avg = scipy.stats.kendalltau(score_human_avg, score_norm_avg) | |
| print("kendall's Tau avg: ", f'{kendalltau_avg: .4}', " p_value: ", f'{p_value_avg: .4}') | |
| spearmanr_all = scipy.stats.spearmanr(score_human,score_norm) | |
| print("spearmanr's rho all", f'{spearmanr_all.statistic: .4}', f'{spearmanr_all.pvalue: .4}') | |
| # spearmanr_abs_diff = scipy.stats.spearmanr(score_human_best, score_norm_best) | |
| # print("spearmanr's rho with abs diff", f'{spearmanr_abs_diff.statistic: .4}', f'{spearmanr_abs_diff.pvalue: .4}') | |
| spearmanr_avg = scipy.stats.spearmanr(score_human_avg, score_norm_avg) | |
| print("spearmanr's rho avg", f'{spearmanr_avg.statistic: .4}', f'{spearmanr_avg.pvalue: .4}') | |
| #auto avg per model | |
| print("##############################") | |
| # import pdb; pdb.set_trace() | |
| for i in range(len(method)): | |
| auto_avg = np.average(dic_auto[method[i]]) | |
| human_avg = np.average(dic_human[method[i]]) | |
| print(method[i], " auto_avg: ", f'{auto_avg: .4}', " human_avg:", f'{human_avg: .4}'," human eval length / method", len(dic_human[method[i]])) | |
| # for i in range (len(method)): | |
| # #corr within per model | |
| # mPearson_all = compute_correlation(dic_human[method[i]], dic_score_category[method[i]]) | |
| # mkendalltau_all, mp_value_all = scipy.stats.kendalltau(dic_human[method[i]], dic_score_category[method[i]]) | |
| # mspearmanr_all = scipy.stats.spearmanr(dic_human[method[i]], dic_score_category[method[i]]) | |
| # m_human_reshape = np.reshape(dic_human[method[i]], (50, 3)) | |
| # m_auto_reshape = np.reshape(dic_score_category[method[i]], (50, 3)) | |
| # m_human_average = np.average(m_human_reshape, axis=1) | |
| # m_auto_average = np.average(m_auto_reshape, axis=1) | |
| # mPearson_average = compute_correlation(m_human_average, m_auto_average) | |
| # mkendalltau_avg, mp_value_avg = scipy.stats.kendalltau(m_human_average, m_auto_average) | |
| # mspearmanr_avg = scipy.stats.spearmanr(m_human_average, m_auto_average) | |
| # print(method[i]) | |
| # print(" Pearson all:", f'{mPearson_all: .4}', " Pearson avg", f'{mPearson_average: .4}') | |
| # print(" kendall all:", f'{mkendalltau_all: .4}', " p value", f'{mp_value_all: .4}',end=" ") | |
| # print(" spearman all:", f'{mspearmanr_all.statistic: .4}', f'{mspearmanr_all.pvalue: .4}') | |
| # print(" kendall avg:", f'{mkendalltau_avg: .4}', " p value", f'{mp_value_avg: .4}',end=" ") | |
| # print(" spearman avg:", f'{mspearmanr_avg.statistic: .4}',f'{mspearmanr_avg.pvalue: .4}') |