MD聚类作图

draw/draw_md_cluster.py

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+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.spatial.distance import pdist
+from scipy.spatial.distance import squareform
+
+
+def DBSCAN(data_, eps, minPts):
+    # 获得距离矩阵
+    disMat = squareform(pdist(data_, metric='euclidean'))
+    # 获得数据的行和列(一共有n条数据)
+    n, m = data_.shape
+    # 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
+    core_points_index = np.where(np.sum(np.where(disMat <= eps, 1, 0), axis=1) >= minPts)[0]
+    # 初始化类别，-1代表未分类。
+    labels = np.full((n,), -1)
+    clusterId = 0
+    # 遍历所有的核心点
+    for pointId in core_points_index:
+        # 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
+        if labels[pointId] == -1:
+            # 首先将点pointId标记为当前类别(即标识为已操作)
+            labels[pointId] = clusterId
+            # 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
+            neighbour = np.where((disMat[:, pointId] <= eps) & (labels == -1))[0]
+            seeds = set(neighbour)
+            # 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
+            while len(seeds) > 0:
+                # 弹出一个新种子点
+                newPoint = seeds.pop()
+                # 将newPoint标记为当前类
+                labels[newPoint] = clusterId
+                # 寻找newPoint种子点eps邻域（包含自己）
+                queryResults = np.where(disMat[:, newPoint] <= eps)[0]
+                # 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
+                if len(queryResults) >= minPts:
+                    # 将邻域内且没有被分类的点压入种子集合
+                    for resultPoint in queryResults:
+                        if labels[resultPoint] == -1:
+                            seeds.add(resultPoint)
+            # 簇集生长完毕，寻找到一个类别
+            clusterId = clusterId + 1
+    return labels
+
+
+def plotFeature(data_, 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 = data_[np.where(labels_ == i)]
+        ax.scatter(subCluster[:, 0], subCluster[:, 1], subCluster[:, 2], c=colorStyle, s=12)
+    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'
+        with open(absolute_path, 'r') as f:
+            # 读取每一行的md，加入该文件的md列表
+            data = []
+            for line in f.readlines():
+                md_metadata = line.strip().split('\t')
+                md_tuple = eval(md_metadata[1])
+                md_values = list(md_tuple[0].values())
+                data.append(md_values[1:4])
+                if len(data) == 10000:
+                    break
+
+        data = np.array(data, dtype=np.float32)
+        labels = DBSCAN(data, 0.5, 30)
+        output_path = outcome_path + rf'\{dataset_name}.png'
+        plotFeature(data, labels, output_path)