update show

Modules/static_dbscan.py

@@ -1,8 +1,7 @@
 from sklearn.cluster import DBSCAN
 from sklearn import metrics
-import pandas as pd
 import numpy as np
-from pprint import pprint
+import matplotlib.pyplot as plt
 
 
 def my_db(eps, min_sample, metric, corpus_embeddings):
@@ -24,27 +23,32 @@ def my_db(eps, min_sample, metric, corpus_embeddings):
     return db
 
 
-def show_db(label_data, db, corpus, corpus_embeddings, corpus_file=None, label_file=None):
-    if label_file is not None:
-        # 读取真实标签数据
-        label_data = pd.read_csv(label_file)
-    labels_true = label_data.flag.to_list()
-    labels = db.labels_
+def show_db(labels_true, db, corpus, corpus_embeddings, show=False):
+    """
+    呈现函数结果
+    :param labels_true: 聚类数据的真实标签
+    :param db: 训练得到模型
+    :param corpus: 原始文本数据的list
+    :param corpus_embeddings:  待聚类的表征数据
+    :param show:bool值，是否输出聚类的图形效果；默认为false
+    :return:
+    """
+    labels = db.labels_   # 获取预测标签数据
+    # 获取聚类数量
     n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
+    # 获取噪音点信息，在DBSCAN聚类中，噪音点用-1标记
     n_noise_ = list(labels).count(-1)
-
+    # 生成存放聚类数据的容器[[],[],[],[],[]...]
     clustered_sentences = [[] for i in range(n_clusters_)]
-    if corpus_file is not None:
-        # 读取原始文本
-        corpus = pd.read_csv(corpus_file).content.to_list()
+    # 将同一类文本放到一个list中
     for sentence_id, cluster_id in enumerate(labels):
         clustered_sentences[cluster_id].append(corpus[sentence_id])
-
+    # 输出聚类结果
     for i, cluster in enumerate(clustered_sentences):
         print("Cluster ", i + 1)
         print(cluster)
         print("")
-
+    # 数据聚类评价信息
     print('Estimated number of clusters: %d' % n_clusters_)
     print('Estimated number of noise points: %d' % n_noise_)
     print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
@@ -59,28 +63,23 @@ def show_db(label_data, db, corpus, corpus_embeddings, corpus_file=None, label_f
 
     # #############################################################################
     # Plot result
-    import matplotlib.pyplot as plt
-
-    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
-    core_samples_mask[db.core_sample_indices_] = True
-    # Black removed and is used for noise instead.
-    unique_labels = set(labels)
-    colors = [plt.cm.Spectral(each)
-              for each in np.linspace(0, 1, len(unique_labels))]
-    for k, col in zip(unique_labels, colors):
-        if k == -1:
-            # Black used for noise.
-            col = [0, 0, 0, 1]
-
-        class_member_mask = (labels == k)
-
-        xy = corpus_embeddings[class_member_mask & core_samples_mask]
-        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
-                 markeredgecolor='k', markersize=14)
-
-        xy = corpus_embeddings[class_member_mask & ~core_samples_mask]
-        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
-                 markeredgecolor='k', markersize=6)
-
-    plt.title('Estimated number of clusters: %d' % n_clusters_)
-    plt.show()
+    if show:
+        # 获取聚类核心点信息
+        core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
+        core_samples_mask[db.core_sample_indices_] = True
+        # Black removed and is used for noise instead.
+        unique_labels = set(labels)
+        colors = [plt.cm.Spectral(each)
+                  for each in np.linspace(0, 1, len(unique_labels))]
+        for k, col in zip(unique_labels, colors):
+            if k == -1:
+                col = [0, 0, 0, 1]  # Black used for noise.
+            class_member_mask = (labels == k)
+            xy = corpus_embeddings[class_member_mask & core_samples_mask]
+            plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
+                     markeredgecolor='k', markersize=14)
+            xy = corpus_embeddings[class_member_mask & ~core_samples_mask]
+            plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
+                     markeredgecolor='k', markersize=6)
+        plt.title('Estimated number of clusters: %d' % n_clusters_)
+        plt.show()