LDA service almost done

Docker/Dockerfile

@@ -80,7 +80,7 @@ RUN mkdir /home/top/pay/ \
 
 
 
-ENV DAEMON_VERSION 0.1.7
+ENV DAEMON_VERSION 1.0.0
 
 RUN wget -O snet-daemon.tar.gz https://github.com/singnet/snet-daemon/releases/download/v$DAEMON_VERSION/snet-daemon-v$DAEMON_VERSION-linux-amd64.tar.gz && \
     tar  --strip-components=1 -xzf snet-daemon.tar.gz && \

lda-service/lda_wrapper.py

@@ -0,0 +1,336 @@
+__author__ = 'eyob'
+# Tested on python3.6
+
+
+import psutil
+print('===================ram used at program start:',float(list(psutil.virtual_memory())[3])/1073741824.0,'GB')
+
+import os
+import sys
+import pathlib
+import csv
+import random
+import datetime
+import time
+import json
+import logging
+
+import re
+import numpy as np
+import pandas as pd
+from pprint import pprint
+
+import gensim
+import gensim.corpora as corpora
+from gensim.utils import simple_preprocess
+from gensim.models import CoherenceModel
+
+# Enable logging for gensim - optional
+import logging
+logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)
+
+import warnings
+warnings.filterwarnings("ignore",category=DeprecationWarning)
+
+# sys.path.append(str(pathlib.Path(os.path.abspath('')).parents[0])+'/plsa-service/plsa')
+sys.path.append(str(pathlib.Path(os.path.abspath('')).parents[0])+'/plsa-service/preprocessing')
+# sys.path.append(str(pathlib.Path(os.path.abspath('')).parents[1])+'/plsa-service/plsa')
+sys.path.append(str(pathlib.Path(os.path.abspath('')).parents[1])+'/topic-analysis/plsa-service/preprocessing')
+
+# import example_plsa as pplsa
+import cleansing as pclean
+import porter_dictionary
+
+class LDA_wrapper:
+
+    def __init__(self, docs,local=False):
+
+        self.docs = docs
+        if not local:
+            self.root_path = str(pathlib.Path(os.path.abspath('')).parents[0]) + '/appData/lda/'
+        else:
+            self.root_path = str(pathlib.Path(os.path.abspath('')).parents[1]) + '/appData/lda/'
+        print('>>>>>>>>>>>>>self.root_path>>>>>>>>>>>')
+        print(self.root_path)
+        self.extracted_folder = self.root_path + 'extracted/'
+        self.file_dict = self.root_path + 'dict/'
+        self.source_texts = self.root_path + 'extracted/'
+        self.output_dir = self.root_path + 'cleaned/'
+        print(self.output_dir)
+        self.folder = self.root_path + 'cleaned/'
+        self.dict_path = self.root_path + 'dict/'
+        self.lda_parameters_path = self.root_path + 'lda-parameters/'
+        self.LDA_PARAMETERS_PATH = ''
+
+        # self.messages
+        self.unique_folder_naming = None
+        self.num_topics = None
+        self.topic_divider = None
+        self.max_iter = None
+
+    def __del__(self):
+
+        # Close db connections
+        pass
+
+
+
+    def write_to_json(self):
+
+
+
+        # self.unique_folder_naming = str(datetime.datetime.now()).replace(':','-').replace('.','-') + '^' + str(random.randint(100000000000, 999999999999)) + '/'
+        print(self.unique_folder_naming)
+
+        os.mkdir(self.extracted_folder+self.unique_folder_naming)
+
+        contents_dict = {}
+
+        file = self.extracted_folder + self.unique_folder_naming + 'extracted' + '.json'
+
+        for i in range(len(self.docs)):
+            contents_dict[str(i)] = self.docs[i]
+
+        with open(file, "w") as f:
+            json.dump(contents_dict, f, indent=4)
+
+        print("len(contents_dict):",len(contents_dict))
+
+
+
+    def generate_topics_gensim(self,num_topics, passes, chunksize,
+                               update_every=0, alpha='auto', eta='auto', decay=0.5, offset=1.0, eval_every=1,
+                               iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None,
+                               minimum_phi_value=0.01, per_word_topics=True, callbacks=None):
+
+        start_time_1 = time.time()
+
+        pclean.file_dict = self.file_dict + self.unique_folder_naming[:-1] + '_dict'
+        pclean.source_texts = self.source_texts + self.unique_folder_naming + 'extracted.json'
+        pclean.output_dir = self.output_dir + self.unique_folder_naming
+
+        os.mkdir(pclean.output_dir)
+
+        # Do cleansing on the data and turing it to bad-of-words model
+
+        with open(self.lda_parameters_path + self.unique_folder_naming + 'status.txt', 'w') as f:
+            f.write('Preprocessing started.')
+
+        pclean.pre_pro()
+
+        with open(self.lda_parameters_path + self.unique_folder_naming + 'status.txt', 'w') as f:
+            f.write('Preprocessing finished. Topic analysis started.')
+
+        with open(pclean.output_dir+'cleaned.json', "r") as read_file:
+            ret = json.load(read_file)
+
+        data_lemmatized = []
+
+        for k in ret:
+            data_lemmatized.append(ret[k].splitlines())
+
+        # Create Dictionary
+        id2word = corpora.Dictionary(data_lemmatized)
+
+        # Create Corpus
+        texts = data_lemmatized
+
+        # Term Document Frequency
+        corpus = [id2word.doc2bow(text) for text in texts]
+
+        # View
+        # print(corpus[0:1])
+        # print(id2word[1])
+
+        self.lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
+                                                    id2word=id2word,
+                                                    num_topics=num_topics,
+                                                    random_state=random_state,
+                                                    update_every=update_every,
+                                                    chunksize=chunksize,
+                                                    passes=passes,
+                                                    alpha=alpha,
+                                                    eta=eta,
+                                                    per_word_topics=per_word_topics,
+                                                    decay=decay,
+                                                    offset=offset,
+                                                    eval_every=eval_every,
+                                                    iterations=iterations,
+                                                    gamma_threshold=gamma_threshold,
+                                                    minimum_probability=minimum_probability,
+                                                    minimum_phi_value=minimum_phi_value,
+                                                    callbacks=callbacks)
+
+        port_dict = porter_dictionary.porter_dictionary()
+
+        topics = self.lda_model.show_topics(num_topics=num_topics,num_words=300,formatted=False)
+
+        extracted_topics = []
+
+        for topic in topics:
+            a_topic = []
+            for item in topic[1]:
+                a_topic.append(item[0])
+            extracted_topics.append(a_topic)
+
+        port_dict.load_dict(self.dict_path + self.unique_folder_naming[:-1] + '_dict')
+
+
+        self.topics_destemmed = []
+
+        for i in extracted_topics:
+            destemmed = []
+            for j in i:
+                try:
+                    destemmed.append(port_dict.dictionary[j][0])
+                except:
+                    logging.exception('message')
+            self.topics_destemmed.append(destemmed)
+
+        '''
+        Seems remaining code is to extract any produced parameters from the resulting lda model, like the weights. We need to define the proto formats of course
+        for all the returned parameters
+
+        also code that writes the final status that shows total running time that elapsed
+
+        in general, compare the outputs of plsa and as much as possible try to apply it to the results that are returned by lda
+        '''
+
+
+
+
+
+
+
+
+def run_lda():
+
+    docs = []
+    s = LDA_wrapper(docs, local=True)
+
+    # path = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/extracted_2.json'
+    # path = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/extracted_singnet_all.json'
+    # path = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/extracted_bio_all.json'
+    # path = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/extracted_hersheys_all.json'
+    # path = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/extracted_hr_all.json'
+    path = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/extracted_all.json'
+
+    docs = []
+
+
+    with open(path, "r") as read_file:
+        fileList = json.load(read_file)
+
+    for k in fileList:
+        docs.append(fileList[k])
+
+    s = LDA_wrapper(docs,local=True)
+    # s.topic_divider = 0
+    # s.num_topics = 2
+    # s.max_iter = 22
+    # s.beta = 1
+    s.unique_folder_naming = str(datetime.datetime.now()).replace(':','-').replace('.','-') + '^' + str(random.randint(100000000000, 999999999999)) + '/'
+    os.mkdir(str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/lda/lda-parameters/'+s.unique_folder_naming)
+    s.write_to_json()
+    # s.generate_topics_gensim(num_topics=3,passes=22,chunksize=200)
+    s.generate_topics_gensim(num_topics=70,passes=22,chunksize=20000)
+    # s.generate_topics_gensim(num_topics=2,passes=22,chunksize=200)
+    # s.generate_topics_gensim(num_topics=2,passes=100,chunksize=200,random_state=2)
+
+
+    # pprint(s.lda_model.print_topics(3,50))
+    # topics = s.lda_model.show_topics(2,5,formatted=False)
+    # print(topics)
+    print_two_d(s.topics_destemmed)
+
+
+    # topics_snet_all_plsa_file = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/topics/singnet_all_plsa_topics_2.txt'
+    # topics_snet_all_plsa_file = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/topics/hersheys_all_plsa_topics.txt'
+    # topics_snet_all_plsa_file = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/topics/bio_all_plsa_topics.txt'
+
+    # topics_snet_all_plsa_file = str(pathlib.Path(os.path.abspath('')).parents[1])+'/appData/misc/topics/hr_all_plsa_topics.txt'
+    # with open(topics_snet_all_plsa_file,'r') as f:
+    #     temp_list = f.readlines()
+    #     topics_snet_all_plsa = []
+    #     for l in temp_list:
+    #         topics_snet_all_plsa.append(l.split(','))
+    #
+    #     for i in range(len(topics_snet_all_plsa)):
+    #         for j in range(len(topics_snet_all_plsa[0])):
+    #             topics_snet_all_plsa[i][j] = topics_snet_all_plsa[i][j].strip()
+    #
+    # topics_snet_all_plsa_file_2 = str(pathlib.Path(os.path.abspath('')).parents[1]) + '/appData/misc/topics/hr_all_plsa_topics_2.txt'
+    # with open(topics_snet_all_plsa_file_2, 'r') as f:
+    #     temp_list = f.readlines()
+    #     topics_snet_all_plsa_2 = []
+    #     for l in temp_list:
+    #         topics_snet_all_plsa_2.append(l.split(','))
+    #
+    #     for i in range(len(topics_snet_all_plsa_2)):
+    #         for j in range(len(topics_snet_all_plsa_2[0])):
+    #             topics_snet_all_plsa_2[i][j] = topics_snet_all_plsa_2[i][j].strip()
+
+
+
+    # two topics
+    # print(dot_product(topics_snet_all_plsa[0],s.topics_destemmed[0],depth=30))
+    # print(dot_product(topics_snet_all_plsa[0],s.topics_destemmed[1],depth=30))
+    # print('=========================')
+    # print(dot_product(topics_snet_all_plsa[1],s.topics_destemmed[0],depth=30))
+    # print(dot_product(topics_snet_all_plsa[1],s.topics_destemmed[1],depth=30))
+    # two topics
+
+    # three topics
+    # print(dot_product(topics_snet_all_plsa[0],s.topics_destemmed[0],depth=30))
+    # print(dot_product(topics_snet_all_plsa[0],s.topics_destemmed[1],depth=30))
+    # print(dot_product(topics_snet_all_plsa[0],s.topics_destemmed[2],depth=30))
+    # print('=========================')
+    # print(dot_product(topics_snet_all_plsa[1], s.topics_destemmed[0], depth=30))
+    # print(dot_product(topics_snet_all_plsa[1], s.topics_destemmed[1], depth=30))
+    # print(dot_product(topics_snet_all_plsa[1], s.topics_destemmed[2], depth=30))
+    # print('=========================')
+    # print(dot_product(topics_snet_all_plsa[2], s.topics_destemmed[0], depth=30))
+    # print(dot_product(topics_snet_all_plsa[2], s.topics_destemmed[1], depth=30))
+    # print(dot_product(topics_snet_all_plsa[2], s.topics_destemmed[2], depth=30))
+    # print('=========================')
+    # three topics
+
+    # plsa self
+    # print(dot_product(topics_snet_all_plsa[0], topics_snet_all_plsa_2[0], depth=30))
+    # print(dot_product(topics_snet_all_plsa[0], topics_snet_all_plsa_2[1], depth=30))
+    # print(dot_product(topics_snet_all_plsa[0], topics_snet_all_plsa_2[2], depth=30))
+    # print('=========================')
+    # print(dot_product(topics_snet_all_plsa[1], topics_snet_all_plsa_2[0], depth=30))
+    # print(dot_product(topics_snet_all_plsa[1], topics_snet_all_plsa_2[1], depth=30))
+    # print(dot_product(topics_snet_all_plsa[1], topics_snet_all_plsa_2[2], depth=30))
+    # print('=========================')
+    # print(dot_product(topics_snet_all_plsa[2], topics_snet_all_plsa_2[0], depth=30))
+    # print(dot_product(topics_snet_all_plsa[2], topics_snet_all_plsa_2[1], depth=30))
+    # print(dot_product(topics_snet_all_plsa[2], topics_snet_all_plsa_2[2], depth=30))
+    # print('=========================')
+    # plsa self
+
+
+def dot_product(list_1,list_2,depth=30):
+
+    count = 0
+    for i in list_1[0:depth]:
+        if i in list_2[0:depth]:
+            count = count + 1
+    return count
+
+def print_two_d(two_d):
+    for i in two_d:
+        print(i)
+
+
+
+
+__end__ = '__end__'
+
+
+if __name__ == '__main__':
+
+    run_lda()
+
+    pass

plsa-service/plsa/plsa_wrapper.py

@@ -145,7 +145,7 @@ def generate_topics_json(self):
 
 def run_plsa():
 
-    path = str(pathlib.Path(os.path.abspath('')).parents[2])+'/appData/misc/extracted.json'
+    path = str(pathlib.Path(os.path.abspath('')).parents[2])+'/appData/misc/extracted_2.json'
 
     docs = []
 
@@ -157,6 +157,12 @@ def run_plsa():
         docs.append(fileList[k])
 
     s = PLSA_wrapper(docs,local=True)
+    s.topic_divider = 0
+    s.num_topics = 2
+    s.max_iter = 22
+    s.beta = 1
+    s.unique_folder_naming = str(datetime.datetime.now()).replace(':','-').replace('.','-') + '^' + str(random.randint(100000000000, 999999999999)) + '/'
+    os.mkdir(str(pathlib.Path(os.path.abspath('')).parents[2])+'/appData/plsa/plsa-parameters/'+s.unique_folder_naming)
     s.write_to_json()
     s.generate_topics_json()
 

topic_analysis_grpc.py

@@ -24,6 +24,7 @@
 
 import plsa_wrapper
 import threading
+import multiprocessing as mp
 
 from service_spec import topic_analysis_pb2
 from service_spec import topic_analysis_pb2_grpc
@@ -96,8 +97,10 @@ def PLSA(self,request,context):
 
             unique_folder_naming = str(datetime.datetime.now()).replace(':', '-').replace('.', '-') + '^' + str(random.randint(100000000000, 999999999999)) + '/'
 
-            thread1 = threading.Thread(target=generate_topics_plsa, args=(docs,unique_folder_naming,num_topics,topic_divider,maxiter,beta))
-            thread1.start()
+            # thread1 = threading.Thread(target=generate_topics_plsa, args=(docs,unique_folder_naming,num_topics,topic_divider,maxiter,beta))
+            p1 = mp.Process(target=generate_topics_plsa, args=(docs,unique_folder_naming,num_topics,topic_divider,maxiter,beta))
+
+            p1.start()
 
             resp = topic_analysis_pb2.PLSAResponse(status=True, message='success', handle=unique_folder_naming[:-1].replace('-','e').replace(' ','d').replace('^','y'))
 
@@ -157,7 +160,7 @@ def generate_topics_plsa(docs,unique_folder_naming,num_topics,topic_divider,maxi
 
 def serve():
 
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
     topic_analysis_pb2_grpc.add_TopicAnalysisServicer_to_server(TopicAnalysis(), server)
     print('Starting server. Listening on port 5000.')
     server.add_insecure_port('127.0.0.1:5000')