[add]-upload LDA module

jiagu/lda

@@ -0,0 +1,146 @@
+# -*- coding: utf-8 -*-
+
+import glob
+import jiagu
+import numpy as np
+from random import random
+
+
+def normalize(vec):
+    total = sum(vec)
+    assert(abs(total) > 1e-6)
+    for i in range(len(vec)):
+        assert(vec[i] >= 0)
+        vec[i] = float(vec[i]) / total
+
+
+def get_prob(vec, prob):
+    assert (len(vec) == len(prob))
+    # 归一化分布
+    normalize(prob)
+    r = random()
+    index = -1
+    while r > 0:
+        index = index + 1
+        r = r - prob[index]
+    return vec[index]
+
+
+class Document(object):
+    def __init__(self, filename):
+        self.doc_name = filename[:-4]
+        self.__load_document(filename)
+
+    def __load_document(self, filename):
+        """
+            读取一篇文章，默认一个file里面包含一篇文章
+        :param   filename: filename 为 *.txt
+        :return: self.document    文章
+                 self.words_list  文章中所有的词
+        """
+        try:
+            doc_file = open(filename, "r", encoding="utf-8")
+            self.document = ""
+            self.words_list = []
+            for line in doc_file:
+                if line:
+                    line = line.strip().replace("\t", "")
+                    self.document += line
+                    self.words_list.extend(jiagu.seg(line))
+        except Exception as e:
+            print("无法加载文件，错误信息 : {}".format(e))
+
+
+class Corpus(object):
+    def __init__(self, filepath):
+        self.Documents = []
+        self.filepath = filepath
+        self._build_corpus()
+
+    def _build_corpus(self):
+        """
+            把所有的文章加载进来
+        :return:
+        """
+        vocabulary = set()
+        files = glob.glob(self.filepath + "/*.txt")
+        if len(files) > 0:
+            for each in files:
+                target = Document(each)
+                self.Documents.append(target)
+                for word in target.words_list:
+                    vocabulary.add(word)
+            self.vocabulary = list(vocabulary)
+            return True
+        else:
+            print("目标文件夹下没有文件！！！")
+            return False
+
+
+class LdaModel(object):
+    def __init__(self, filepath, number_of_topics, alpha=50, beta=0.1, iteration=3):
+        self.alpha = alpha
+        self.beta = beta
+        self.iteration = iteration
+        self.corpus = Corpus(filepath)
+        self.number_of_topics = number_of_topics
+        self.__initialize_all()
+
+    def __initialize_all(self):
+        print("LDA Initializing... \nnumber of topics : {}, iteration : {}".format(self.number_of_topics, self.iteration))
+        self.number_of_documents = len(self.corpus.Documents)
+        assert(self.number_of_documents > self.number_of_topics)
+        self.document_topic_counts = np.zeros([self.number_of_documents, self.number_of_topics], dtype=np.int)
+        self.topic_word_counts = np.zeros([self.number_of_topics, len(self.corpus.vocabulary)], dtype=np.int)
+        self.current_word_topic_assignments = []
+        self.topic_counts = np.zeros(self.number_of_topics)
+        self.doc_name = dict()
+        for d_index, document in enumerate(self.corpus.Documents):
+            self.doc_name.setdefault(d_index, document.doc_name)
+            word_topic_assignments = []
+            for word in document.words_list:
+                if word in self.corpus.vocabulary:
+                    w_index = self.corpus.vocabulary.index(word)
+                    starting_topic_index = np.random.randint(self.number_of_topics)
+                    word_topic_assignments.append(starting_topic_index)
+                    self.document_topic_counts[d_index, starting_topic_index] += 1
+                    self.topic_word_counts[starting_topic_index, w_index] += 1
+                    self.topic_counts[starting_topic_index] += 1
+            self.current_word_topic_assignments.append(np.array(word_topic_assignments))
+
+        for iteration in range(self.iteration):
+            print("Iteration #" + str(iteration + 1) + "...")
+            for d_index, document in enumerate(self.corpus.Documents):
+                for w, word in enumerate(document.words_list):
+                    if word in self.corpus.vocabulary:
+                        w_index = self.corpus.vocabulary.index(word)
+                        current_topic_index = self.current_word_topic_assignments[d_index][w]
+                        self.document_topic_counts[d_index, current_topic_index] -= 1
+                        self.topic_word_counts[current_topic_index, w_index] -= 1
+                        self.topic_counts[current_topic_index] -= 1
+                        topic_distribution = (self.topic_word_counts[:, w_index] + self.beta) * \
+                            (self.document_topic_counts[d_index] + self.alpha) / \
+                            (self.topic_counts + self.beta)
+                        new_topic_index = get_prob(range(self.number_of_topics), topic_distribution)
+                        self.current_word_topic_assignments[d_index][w] = new_topic_index
+                        self.document_topic_counts[d_index, new_topic_index] += 1
+                        self.topic_word_counts[new_topic_index, w_index] += 1
+                        self.topic_counts[new_topic_index] += 1
+        print("LDA Initializing finished !\n")
+
+    def get_document_topic(self):
+        for d_index, topic in enumerate(np.argmax(self.document_topic_counts, axis=1)):
+            print("this is file {}, topic : #{}".format(self.doc_name.get(d_index), topic))
+
+    def get_word_topic(self, topN=10):
+        for row in (self.topic_word_counts.argsort(axis=1)[:, -topN:]):
+            print(list(map(lambda x: self.corpus.vocabulary[x], row)))
+
+
+if __name__ == "__main__":
+    filepath = "documents"
+    number_of_topics = 3
+    test = LdaModel(filepath, number_of_topics)
+    test.get_document_topic()
+    test.get_word_topic()
+