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