# # 将数据点和MD一起聚类
# import os
# import numpy as np
# import pandas as pd
# from matplotlib import pyplot as plt
#
# from draw_md_cluster import DBSCAN
# from ml_er.ml_entity_resolver import build_col_pairs_sim_tensor_dict
#
#
# def plot(md_keys_, md_data_, pre_match_points_, pre_mismatch_points_, labels_, output_path_):
#     clusterNum = len(set(labels_))
#     fig = plt.figure()
#     scatterColors = ['black', 'blue', 'green', 'yellow', 'red', 'purple', 'orange', 'brown']
#     ax = fig.add_subplot(111, projection='3d')
#     for i in range(-1, clusterNum):
#         colorStyle = scatterColors[i % len(scatterColors)]
#         subCluster = md_data_[np.where(labels_ == i)]
#         ax.scatter(subCluster[:, 0], subCluster[:, 1], subCluster[:, 2], c=colorStyle, s=12)
#     ax.scatter(pre_match_points_[:, 0], pre_match_points_[:, 1], pre_match_points_[:, 2], c='#66CCFF', s=12, marker='x')
#     if pre_mismatch_points_.shape[0] > 0:
#         ax.scatter(pre_mismatch_points_[:, 0], pre_mismatch_points_[:, 1], pre_mismatch_points_[:, 2], c='#006666', s=12, marker='x')
#     ax.set_xlabel(md_keys_[0], rotation=0)  # 设置标签角度
#     ax.set_ylabel(md_keys_[1], rotation=-45)
#     ax.set_zlabel(md_keys_[2], rotation=0)
#     plt.title(output_path_.split('\\')[-1].split('.')[0])
#     plt.savefig(output_path_, dpi=500)
#     plt.show()
#
#
# if __name__ == '__main__':
#     outcome_path = r'E:\Data\Research\Outcome'
#     config_dir = r'\Magellan+Smac+roberta-large-nli-stsb-mean-tokens+inter-0.5'
#     dataset_name_list = [f.name for f in os.scandir(outcome_path) if f.is_dir()]
#     for dataset_name in dataset_name_list:
#         absolute_path = outcome_path + rf'\{dataset_name}' + config_dir + r'\mds.txt'  # MD路径
#         predictions = outcome_path + rf'\{dataset_name}' + config_dir + r'\predictions.csv'  # prediction路径
#         pred = pd.read_csv(predictions)
#         pred = pred.astype(str)
#         # pred = pred[pred['predicted'] == str(1)]
#         sim_tensor_dict = build_col_pairs_sim_tensor_dict(pred)
#         # 选取的三个字段
#         md_keys = []
#         with open(absolute_path, 'r') as f:
#             # 读取每一行的md，加入该文件的md列表
#             md_data = []
#             for line in f.readlines():
#                 md_metadata = line.strip().split('\t')
#                 md_tuple = eval(md_metadata[1])
#                 md_keys = list(md_tuple[0].keys())[1:4]
#                 md_values = list(md_tuple[0].values())
#                 md_data.append(md_values[1:4])
#                 if len(md_data) == 10000:
#                     break
#
#         pre_match_points = []
#         pre_mismatch_points = []
#         for _ in pred.itertuples():
#             data_point_value = []
#             for key in md_keys:
#                 sim_tensor = sim_tensor_dict[key]
#                 data_point_value.append(round(float(sim_tensor[_[0]]), 4))
#             if getattr(_, 'predicted') == str(1):
#                 pre_match_points.append(data_point_value)
#             elif getattr(_, 'predicted') == str(0):
#                 pre_mismatch_points.append(data_point_value)
#
#         md_data = np.array(md_data, dtype=np.float32)
#         pre_match_points = np.array(pre_match_points, dtype=np.float32)
#         pre_mismatch_points = np.array(pre_mismatch_points, dtype=np.float32)
#         labels = DBSCAN(md_data, 0.5, 30)
#         output_path = outcome_path + rf'\{dataset_name}_MD&data.png'
#         plot(md_keys, md_data, pre_match_points, pre_mismatch_points, labels, output_path)