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chat_function__analysis.py

+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
+import matplotlib.pyplot as plt
+from sklearn.manifold import MDS
+from sklearn.metrics.pairwise import cosine_similarity
+import random
+from matplotlib.font_manager import FontProperties
+from sklearn.cluster import KMeans
+from sklearn import metrics
+from collections import Counter
+from scipy.cluster.hierarchy import ward, dendrogram
+import bottom_function.normalization as normalization
+import bottom_function.m_SQL as qb
+import json
+from flask import Flask
+from flask import request
+from flask_cors import CORS
+
+
+class Culter:
+
+    def __init__(self, start_time, end_time):
+        self.start_time = start_time
+        self.end_time = end_time
+
+        csv_data = pd.DataFrame()
+        self.chat_data = pd.DataFrame()
+        tablename = "semantic_data_table"
+        db = qb.Schema(host="localhost", user="560193", password="jay560193", mysqlName="semantic_data_schema",
+                       port="3306")
+        csv_data = db.getData(tableName=tablename, startTime=start_time, endTime=end_time)
+        self.chat_data = csv_data[(csv_data['domain'] == 'chat') & (csv_data['code'] == 0)]
+
+        self.chat_data.drop_duplicates(subset=['query'], inplace=True)
+        self.chat_data.dropna(subset=['query'], inplace=True)
+
+        self.out_data = ''  # 写入分析结果
+        self.feature_names = []
+        self.f_sse = []
+        self.feature_matrix = np.matrix([])
+
+    def build_feature_matrix(self, documents, feature_type, ngram_range, min_df, max_df):
+
+        feature_type = feature_type.lower().strip()
+        if feature_type == 'binary':
+            vectorizer = CountVectorizer(binary=True,
+                                         max_df=max_df, ngram_range=ngram_range)
+        elif feature_type == 'frequency':
+            vectorizer = CountVectorizer(binary=False, min_df=min_df,
+                                         max_df=max_df, ngram_range=ngram_range)
+        elif feature_type == 'tfidf':
+            vectorizer = TfidfVectorizer(token_pattern=r"(?u)\b\w+\b", max_df=max_df)
+        else:
+            raise Exception("Wrong feature type entered. Possible values: 'binary', 'frequency', 'tfidf'")
+
+        feature_matrix = vectorizer.fit_transform(documents).astype(float)
+
+        return vectorizer, feature_matrix
+
+    def feature_extraction_data(self):
+
+        chat_one = self.chat_data['query'].tolist()
+
+        norm_chat_one = normalization.normalize_corpus(chat_one, pos=False)
+
+        # 提取 tf-idf 特征
+        vectorizer, self.feature_matrix = self.build_feature_matrix(norm_chat_one, feature_type='tfidf', min_df=0.2,
+                                                                    max_df=0.90,
+                                                                    ngram_range=(1, 2))
+
+        # 查看特征数量)
+        self.out_data = '聚类分析结果：\n' + '**' * 30
+        self.out_data = self.out_data + '\n特征数量：\n' + str(self.feature_matrix.shape)
+
+        # 获取特征名字
+        self.feature_names = vectorizer.get_feature_names()
+
+        # 打印某些特征
+        self.out_data = self.out_data + '\n部分特征：\n' + ', '.join(self.feature_names[:5])
+
+    def get_cluster_data(self, clustering_obj, m_data, feature_names, num_clusters, topn_features):
+        cluster_data = {}
+
+        # 获取cluster的center
+        ordered_centroids = clustering_obj.cluster_centers_.argsort()[:, ::-1]
+        # 获取每个cluster的关键特征
+        # 获取每个cluster的query
+        for cluster_num in range(num_clusters):
+            cluster_data[cluster_num] = {}
+            cluster_data[cluster_num]['cluster_num'] = cluster_num
+            key_features = [feature_names[index]
+                            for index
+                            in ordered_centroids[cluster_num, :topn_features]]
+            cluster_data[cluster_num]['key_features'] = key_features
+
+            c_datas = m_data[m_data['Cluster'] == cluster_num]['query'].values.tolist()
+            cluster_data[cluster_num]['query'] = c_datas
+
+        return cluster_data
+
+    def print_cluster_data(self, cluster_data):
+        self.out_data = self.out_data + '\n\n聚类详细数据：\n'
+
+        for cluster_num, cluster_details in cluster_data.items():
+            self.out_data = self.out_data + '\nCluster {} details:\n'.format(cluster_num)
+
+            self.out_data = self.out_data + '-' * 20
+            self.out_data = self.out_data + '\nKey features:\n'
+            self.out_data = self.out_data + ', '.join(cluster_details['key_features'])
+
+            self.out_data = self.out_data + '\ndata in this cluster:\n'
+            self.out_data = self.out_data + ', '.join(cluster_details['query'])
+            self.out_data = self.out_data + '\n' + '=' * 40
+
+    def plot_clusters(self, feature_matrix, cluster_data, m_data, plot_size):
+        def generate_random_color():  # generate random color for clusters
+            color = '#%06x' % random.randint(0, 0xFFFFFF)
+            return color
+
+        # define markers for clusters
+        markers = ['o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd']
+        # build cosine distance matrix
+        cosine_distance = 1 - cosine_similarity(feature_matrix)
+        # dimensionality reduction using MDS
+        mds = MDS(n_components=2, dissimilarity="precomputed",
+                  random_state=1)
+        # get coordinates of clusters in new low-dimensional space
+        plot_positions = mds.fit_transform(cosine_distance)
+        x_pos, y_pos = plot_positions[:, 0], plot_positions[:, 1]
+        # build cluster plotting data
+        cluster_color_map = {}
+        cluster_name_map = {}
+        # print(cluster_data)
+        for cluster_num, cluster_details in cluster_data.items():
+            # assign cluster features to unique label
+            cluster_color_map[cluster_num] = generate_random_color()
+            cluster_name_map[cluster_num] = ', '.join(cluster_details['key_features'][:5]).strip()
+        # map each unique cluster label with its coordinates and books
+        cluster_plot_frame = pd.DataFrame({'x': x_pos,
+                                           'y': y_pos,
+                                           'label': m_data['Cluster'].values.tolist(),
+                                           'query': m_data['query'].values.tolist()
+                                           })
+        grouped_plot_frame = cluster_plot_frame.groupby('label')
+        # set plot figure size and axes
+
+        plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
+        plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
+        fig, ax = plt.subplots(figsize=plot_size)
+        ax.margins(0.05)
+        # plot each cluster using co-ordinates and  titles
+        for cluster_num, cluster_frame in grouped_plot_frame:
+            marker = markers[cluster_num] if cluster_num < len(markers) \
+                else np.random.choice(markers, size=1)[0]
+            ax.plot(cluster_frame['x'], cluster_frame['y'],
+                    marker=marker, linestyle='', ms=12,
+                    label=cluster_name_map[cluster_num],
+                    color=cluster_color_map[cluster_num], mec='none')
+            ax.set_aspect('auto')
+            ax.tick_params(axis='x', which='both', bottom=False, top=False,
+                           labelbottom='off')
+            ax.tick_params(axis='y', which='both', left=False, top=False,
+                           labelleft=False)
+        fontP = FontProperties()
+        fontP.set_size(23)
+        ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), fancybox=True,
+                  shadow=True, ncol=5, numpoints=1, prop=fontP)
+        # add labels as the film titles
+        for index in range(len(cluster_plot_frame)):
+            ax.text(cluster_plot_frame.ix[index]['x'], cluster_plot_frame.ix[index]['y'],
+                    cluster_plot_frame.ix[index]['query'], size=20)
+            # show the plot
+        plt.title(self.start_time + ' to ' + self.end_time + 'chat data cluster point set')
+        path = '/roobo/soft/phpmyadmin/cluster_point.jpg'
+        plt.savefig(path)
+        return path
+
+    def k_means(self, feature_matrix):
+        f_sse = []
+        num_clusters = []
+        for i in range(2, 21):
+            km = KMeans(n_clusters=i, max_iter=10000)
+            km.fit(feature_matrix)
+            clusters = km.labels_
+            num_matrix = feature_matrix.todense()
+            sse = metrics.calinski_harabaz_score(num_matrix, clusters)
+            num_clusters.append(i)
+            f_sse.append(sse)
+
+        pd_see = pd.Series(f_sse, index=num_clusters)
+        pct_see = pd_see.pct_change()
+
+        fig, ax = plt.subplots()
+        ax.plot(num_clusters, f_sse, 'o-', c='orangered', label='clustering quality')
+        plt.legend(loc=2)
+        plt.xticks(num_clusters)
+        ax.set_xlabel("cluster number")
+
+        ax.set_ylabel("coefficient")
+
+        ax1 = ax.twinx()
+        ax1.plot(pct_see.values, 'o-', c='blue', label='gradient change')
+        ax1.set_ylabel("gradient")
+        plt.legend(loc=1)
+
+        plt.title(self.start_time + " to " + self.end_time + " the analysis of clusters with different numbers")
+        path = '/roobo/soft/phpmyadmin/choice_num.jpg'
+        plt.savefig(path)
+
+        # input_num = input('输入最优聚类数目：')
+        # best_num = int(input_num)
+
+        self.f_sse = f_sse
+        return path
+
+    def k_means_cluster(self, best_num):
+
+        self.out_data = self.out_data + '\n' + "=" * 20
+        self.out_data = self.out_data + "\n\n聚类效果分析：\n"
+        self.out_data = self.out_data + "\n聚类数目为：" + str(best_num)
+
+        f_sse = self.f_sse
+        sse = f_sse[best_num]
+        km = KMeans(n_clusters=best_num, max_iter=10000)
+        km.fit(self.feature_matrix)
+        clusters = km.labels_
+        self.chat_data['Cluster'] = clusters
+
+        # 获取每个cluster的数量
+        c = Counter(clusters)
+
+        sort_c = sorted(c.items(), key=lambda c: c[0], reverse=False)
+        c.clear()
+        for key, value in sort_c:
+            c[key] = value
+
+        self.out_data = self.out_data + '\nCalinski-Harabasz分数:' + str(sse)
+        self.out_data = self.out_data + '\n每个特征的数据量：\n'
+        self.out_data = self.out_data + (str(c.items()))
+        self.out_data = self.out_data + '\n' + "=" * 20
+        cluster_data = self.get_cluster_data(clustering_obj=km,
+                                             m_data=self.chat_data,
+                                             feature_names=self.feature_names,
+                                             num_clusters=best_num,
+                                             topn_features=5)
+
+        self.print_cluster_data(cluster_data)
+
+        path = self.plot_clusters(feature_matrix=self.feature_matrix, cluster_data=cluster_data, m_data=self.chat_data,
+                                  plot_size=(40, 25))
+        return path
+
+    def ward_hierarchical_clustering(self, feature_matrix):
+        cosine_distance = 1 - cosine_similarity(feature_matrix)
+        linkage_matrix = ward(cosine_distance)
+        return linkage_matrix
+
+    def plot_hierarchical_clusters(self, linkage_matrix, m_data, figure_size):
+        # set size
+        fig, ax = plt.subplots(figsize=figure_size)
+        m_titles = m_data['query'].values.tolist()
+
+        # plot dendrogram
+        ax = dendrogram(linkage_matrix, orientation="left", labels=m_titles)
+        plt.tick_params(axis='x',
+                        which='both',
+                        bottom=False,
+                        top=False,
+                        labelbottom=False)
+        plt.tight_layout()
+        plt.title(self.start_time + ' to ' + self.end_time + 'chat data ward hierachical clusters')
+        path = '/roobo/soft/phpmyadmin/hierachical_clusters.jpg'
+        plt.savefig(path)
+        return path
+
+
+app = Flask(__name__)
+CORS(app, supports_credentials=True)
+
+data_cluster = Culter(start_time="2018-12-01 00:00:00", end_time="2018-12-02 00:00:00")
+
+
+@app.route('/SPDAS/chat_function_analysis/choice1', methods=['POST'])
+def choice():
+    param = ({"time": "2018-12-01 00:00:00/2018-12-02 00:00:00"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/chat_function_analysis/choice2', methods=['POST'])
+def choice_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+
+    m_time = json_re['time']
+    str_time = str(m_time)
+    m_time = str_time.split('/')
+    starttime = m_time[0]
+    endtime = m_time[1]
+    data_cluster = Culter(start_time=starttime, end_time=endtime)
+    data_cluster.feature_extraction_data()
+    image_path = data_cluster.k_means(data_cluster.feature_matrix)
+    path = ({"num_image": image_path})
+    return json.JSONEncoder().encode(path)
+
+
+@app.route('/SPDAS/chat_function_analysis/chat1', methods=['POST'])
+def chat():
+    param = ({"best_num": "2"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/chat_function_analysis/chat2', methods=['POST'])
+def chat_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+    bestnum = json_re['best_num']
+    image_path1 = data_cluster.k_means_cluster(best_num=bestnum)
+
+    linkage_matrix = data_cluster.ward_hierarchical_clustering(data_cluster.feature_matrix)
+
+    image_path2 = data_cluster.plot_hierarchical_clusters(linkage_matrix=linkage_matrix, m_data=data_cluster.chat_data,
+                                                          figure_size=(16, 14))
+    with open("/roobo/soft/phpmyadmin/chat_function_data.txt", 'w') as file:
+        file.writelines(data_cluster.out_data)
+
+    path = ({"cluster_point": image_path1, "ward_image": image_path2})
+    return json.JSONEncoder().encode(path)
+
+
+if __name__ == '__main__':
+    app.run(debug=True, host='10.7.19.129', port=5000)