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# https://github.com/GISerWang/Spatio-temporal-Clustering.git
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.spatial.distance import pdist
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from scipy.spatial.distance import squareform
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def DBSCAN(data_, eps, minPts):
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# 获得距离矩阵
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disMat = squareform(pdist(data_, metric='euclidean'))
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# 获得数据的行和列(一共有n条数据)
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n, m = data_.shape
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# 将矩阵的中小于eps的数赋予1, 大于eps的数置0, 按行求和, 求核心点坐标的索引
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core_points_index = np.where(np.sum(np.where(disMat <= eps, 1, 0), axis=1) >= minPts)[0]
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# 初始化类别，-1代表未分类。
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labels = np.full((n,), -1)
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clusterId = 0
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# 遍历所有的核心点
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for pointId in core_points_index:
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# 如果核心点未被分类，将其作为的种子点，开始寻找相应簇集
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if labels[pointId] == -1:
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# 首先将点pointId标记为当前类别(即标识为已操作)
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labels[pointId] = clusterId
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# 然后寻找种子点的eps邻域且没有被分类的点，将其放入种子集合
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neighbour = np.where((disMat[:, pointId] <= eps) & (labels == -1))[0]
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seeds = set(neighbour)
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# 通过种子点，开始生长，寻找密度可达的数据点，一直到种子集合为空，一个簇集寻找完毕
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while len(seeds) > 0:
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# 弹出一个新种子点
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newPoint = seeds.pop()
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# 将newPoint标记为当前类
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labels[newPoint] = clusterId
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# 寻找newPoint种子点eps邻域（包含自己）
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queryResults = np.where(disMat[:, newPoint] <= eps)[0]
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# 如果newPoint属于核心点，那么newPoint是可以扩展的，即密度是可以通过newPoint继续密度可达的
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if len(queryResults) >= minPts:
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# 将邻域内且没有被分类的点压入种子集合
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for resultPoint in queryResults:
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if labels[resultPoint] == -1:
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seeds.add(resultPoint)
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# 簇集生长完毕，寻找到一个类别
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clusterId = clusterId + 1
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return labels
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def plotFeature(md_keys_, data_, 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 = 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.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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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'
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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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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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data.append(md_values[1:4])
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if len(data) == 10000:
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break
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data = np.array(data, dtype=np.float32)
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labels = DBSCAN(data, 0.5, 30)
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output_path = outcome_path + rf'\{dataset_name}.png'
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plotFeature(md_keys, data, labels, output_path)
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