def PPR():
todayDate = graphUtils.getTodayDateFolder()
lastRecommendationnDate = graphUtils.loadSettings(
graphConstants.LAST_GRAPH_RECOMM_DONE)
#lastRecommendationnDate = None
if todayDate == lastRecommendationnDate:
graphUtils.logger.info(
"Simple Graph recommendation PPR done for today ")
return
graphUtils.logger.info("Simple graph recommendation PPR last done for =" +
str(lastRecommendationnDate))
#Get the current version of stored graphs
G = None
graph_path = os.path.join(graphConstants.ROOT_FOLDER,
graphConstants.GRAPH_DIR,
graphConstants.GRAPH_DIR,
graphConstants.TYPE_MAIN)
graph_file = os.path.join(graph_path, graphConstants.GRAPH_FILE)
G = nx.read_gexf(graph_file)
list_nodes = {x: i for i, x in enumerate(G.nodes())}
R = get_init_R(G, list_nodes)
#Normalize edge transition weights
M = normalize_edge_Weights(list_nodes, G)
S, list_seednode_names = graphSeedNodes.findSeedNodes(G, list_nodes)
for idx, node in enumerate(list_seednode_names):
graphUtils.logger.info(
str(idx) + " seed node for simple graph today = " + node)
newR = personalizedPageRank(R, M, S)
printGraphRecommendedDocs(G, list_nodes, newR)
writeNewR(G, list_nodes, newR, graph_file)
graphUtils.saveSettings(graphConstants.LAST_GRAPH_RECOMM_DONE, todayDate)
pass
def Relevance():
todayDate = graphUtils.getTodayDateFolder()
lastRelevanceDate = graphUtils.loadSettings(
graphConstants.LAST_GRAPH_RELEVANCE_DIR)
lastSuggRelevanceDate = graphUtils.loadSettings(
graphConstants.LAST_GRAPH_SUGG_RELEVANCE_DIR)
if lastRelevanceDate:
graphUtils.logger.info("Graph Relevance done last for =" +
lastRelevanceDate)
else:
graphUtils.logger.info("Graph Relevance done last for None")
if lastSuggRelevanceDate:
graphUtils.logger.info("GraphSugg Relevance done last for =" +
lastSuggRelevanceDate)
else:
graphUtils.logger.info("GraphSugg Relevance done last for None")
if todayDate == lastRelevanceDate and todayDate == lastSuggRelevanceDate:
graphUtils.logger.info(
"Graph Relevance signal already done for today :" + todayDate)
return True
graph_path = os.path.join(graphConstants.ROOT_FOLDER,
graphConstants.GRAPH_DIR,
graphConstants.GRAPH_DIR,
graphConstants.TYPE_MAIN)
graph_file = os.path.join(graph_path, graphConstants.GRAPH_FILE)
write_graph_file = os.path.join(graph_path, graphConstants.GRAPH_FILE)
if not os.path.exists(graph_path):
os.makedirs(graph_path)
G = nx.read_gexf(graph_file)
trainFiles, trainFileNames = graphUtils.findRecommTrainGraphFiles()
trainCorpus = graphUtils.findCorpus(trainFiles)
all_tokens = sum(trainCorpus, [])
tokens_once = set(word for word in set(all_tokens)
if all_tokens.count(word) == 1)
texts = [[word for word in text if word not in tokens_once]
for text in trainCorpus]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
tfidf = models.TfidfModel(corpus=corpus,
id2word=dictionary,
normalize=True)
index = similarities.SparseMatrixSimilarity(tfidf[corpus],
num_features=len(dictionary))
if todayDate != lastRelevanceDate:
testFiles, testFileName = graphUtils.findRecommFiles()
testCorpus = graphUtils.findCorpus(testFiles)
mini = 100
maxi = -1
count = 0
for idx, text in enumerate(testCorpus):
#Add this recommendation node
recomm_nodename = testFileName[idx]
if recomm_nodename not in G.nodes():
G.add_node(recomm_nodename)
G.node[recomm_nodename]['type'] = graphConstants.TYPE_GOOGLE
vec = dictionary.doc2bow(text)
sims = index[tfidf[vec]]
for idxsim, prob in enumerate(sims):
if prob < 0.1:
continue
trainNode = trainFileNames[idxsim]
if trainNode in G.nodes():
if prob < mini:
mini = prob
if prob > maxi:
maxi = prob
G.add_edge(recomm_nodename, trainNode, weight=prob)
G.add_edge(trainNode, recomm_nodename, weight=prob)
count = count + 1
text = readFromFile(testFiles[idx])
#NERFunc(text,G, recomm_nodename)
graphUtils.logger.info(
"Simple graph relevance completed for today. Stats follow")
graphUtils.logger.info("mini =" + str(mini))
graphUtils.logger.info("maxi =" + str(maxi))
graphUtils.logger.info("Relevance count =" + str(count))
nx.write_gexf(G, write_graph_file)
graphUtils.saveSettings(graphConstants.LAST_GRAPH_RELEVANCE_DIR,
todayDate)
if todayDate != lastRelevanceDate:
testFiles, testFileName = graphUtils.findSuggRecommFiles()
testCorpus = graphUtils.findCorpus(testFiles)
mini = 100
maxi = -1
count = 0
for idx, text in enumerate(testCorpus):
#Add this recommendation node
recomm_nodename = testFileName[idx]
if recomm_nodename not in G.nodes():
G.add_node(recomm_nodename)
G.node[recomm_nodename]['type'] = graphConstants.TYPE_SUGG
vec = dictionary.doc2bow(text)
sims = index[tfidf[vec]]
for idxsim, prob in enumerate(sims):
if prob < 0.1:
continue
trainNode = trainFileNames[idxsim]
if trainNode in G.nodes():
if prob < mini:
mini = prob
if prob > maxi:
maxi = prob
G.add_edge(recomm_nodename, trainNode, weight=prob)
G.add_edge(trainNode, recomm_nodename, weight=prob)
count = count + 1
text = readFromFile(testFiles[idx])
#NERFunc(text,G, recomm_nodename)
graphUtils.logger.info(
"Simple graph relevance completed for suggestGoogle today. Stats follow"
)
graphUtils.logger.info("mini =" + str(mini))
graphUtils.logger.info("maxi =" + str(maxi))
graphUtils.logger.info("Relevance count =" + str(count))
nx.write_gexf(G, write_graph_file)
graphUtils.saveSettings(graphConstants.LAST_GRAPH_SUGG_RELEVANCE_DIR,
todayDate)
pass
def buildGraph():
#Load up the current graph we may have
#Last Graph date done
date_LAST_GRAPH_DONE = graphUtils.loadSettings(graphConstants.date_LAST_GRAPH_DONE)
date_LAST_TEXTCORPUS_DONE = graphUtils.loadSettings(graphConstants.date_LAST_TEXTCORPUS_DONE)
date_LAST_LDA_DONE = graphUtils.loadSettings(graphConstants.date_LAST_LDA_DONE)
yesterdayFolder = graphUtils.getYesterdayDateFolder()
if date_LAST_GRAPH_DONE == yesterdayFolder:
graphUtils.logger.info("simple Graph already built till yesterday")
return
graphFiles, graphFileNames = graphUtils.findGraphFiles()
graphCorpus = graphUtils.findCorpus(graphFiles)
all_tokens = sum(graphCorpus, [])
tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) == 1)
new_texts = [[word for word in text if word not in tokens_once]
for text in graphCorpus]
pass
#Retrieve text corpus
txtcorpus_path = os.path.join(graphConstants.ROOT_FOLDER,graphConstants.GRAPH_DIR, graphConstants.TEXTCORPUS_DIR, graphConstants.TYPE_MAIN)
txtcorpus_file = os.path.join(txtcorpus_path,graphConstants.TEXTCORPUS_FILE)
if not os.path.exists(txtcorpus_path):
os.makedirs(txtcorpus_path)
objtxt_corpus = None
if date_LAST_TEXTCORPUS_DONE == None:
objtxt_corpus = MyCorpus(new_texts)
objtxt_corpus.save(txtcorpus_file)
graphUtils.saveSettings(graphConstants.date_LAST_TEXTCORPUS_DONE, yesterdayFolder)
elif date_LAST_TEXTCORPUS_DONE != yesterdayFolder:
objtxt_corpus = MyCorpus.load(txtcorpus_file)
objtxt_corpus.update_corpus(new_texts)
objtxt_corpus.save(txtcorpus_file)
graphUtils.saveSettings(graphConstants.date_LAST_TEXTCORPUS_DONE, yesterdayFolder)
else:
objtxt_corpus = MyCorpus.load(txtcorpus_file)
txt_dictionary = objtxt_corpus.dictionary
corpus = [txt_dictionary.doc2bow(text) for text in objtxt_corpus.corpus]
newtxt_corpus = [txt_dictionary.doc2bow(text) for text in new_texts]
tfidf = models.TfidfModel(corpus=corpus, id2word=txt_dictionary,normalize=True)
idf = models.tfidfmodel.precompute_idfs(tfidf.wglobal,txt_dictionary.dfs,len(corpus))
if date_LAST_LDA_DONE!= None:
graphUtils.logger.info("Simple graph nodes lda after ="+date_LAST_LDA_DONE+" starts")
else:
graphUtils.logger.info("Simple graph nodes lda after = none starts")
t0 = time()
#Do lda
#Retrieve text corpus
lda_path = os.path.join(graphConstants.ROOT_FOLDER,graphConstants.GRAPH_DIR, graphConstants.LDA_DIR, graphConstants.TYPE_MAIN)
lda_file = os.path.join(lda_path,graphConstants.LDA_FILE)
lda = None
if not os.path.exists(lda_path):
os.makedirs(lda_path)
date_LAST_LDA_DONE = None
if date_LAST_LDA_DONE == None:
lda = models.LdaModel(corpus=corpus, id2word=txt_dictionary, num_topics=50, \
update_every=1, chunksize=10000, passes=LDA_PASSES)
lda.save(lda_file)
graphUtils.saveSettings(graphConstants.date_LAST_LDA_DONE, yesterdayFolder)
elif date_LAST_LDA_DONE != yesterdayFolder:
lda = models.LdaModel.load(lda_file)
lda.update(newtxt_corpus)
lda.save(lda_file)
graphUtils.saveSettings(graphConstants.date_LAST_LDA_DONE, yesterdayFolder)
else:
lda = models.LdaModel.load(lda_file)
t1= time()
graphUtils.logger.info("Simple graph nodes lda time ="+str(t1-t0)+" seconds ends")
#Develop graph
G = None
graph_path = os.path.join(graphConstants.ROOT_FOLDER,graphConstants.GRAPH_DIR, graphConstants.GRAPH_DIR, graphConstants.TYPE_MAIN)
graph_file = os.path.join(graph_path,graphConstants.GRAPH_FILE)
if not os.path.exists(graph_path):
os.makedirs(graph_path)
if date_LAST_GRAPH_DONE == None:
G=nx.DiGraph()
elif date_LAST_GRAPH_DONE != yesterdayFolder:
G = nx.read_gexf(graph_file)
if date_LAST_GRAPH_DONE != None:
graphUtils.logger.info("Simple graph nodes addition after ="+date_LAST_GRAPH_DONE+" starts")
else:
graphUtils.logger.info("Simple graph nodes addition after = None")
#Add nodes and edges for current new corpus which is supposed to be added
if date_LAST_GRAPH_DONE != yesterdayFolder:
for topic in [50]:
for index,document in enumerate(newtxt_corpus):
node_name = graphFileNames[index]
G.add_node(node_name)
G.node[node_name]['type'] = graphConstants.TYPE_HISTORY
topics = lda[document]
#print "Document start"
for topicObj,topicProb in topics:
#Compare topicProb with some threshold value
if topicProb > 0.1:
topicid = topicObj
words = lda.show_topic(topicid, topn=10)
for wordProb, word in words:
wordId = txt_dictionary.doc2bow([word])[0][0]
idfWord = idf[wordId]
if idfWord > 3.0:
word = word.lower()
#If this topic doesn't exist as a node then add it
if word not in G.nodes():
G.add_node(word)
G.node[word]['type'] = graphConstants.TYPE_TOPICS
#If the edge between this doc and topic is already present or not
if G.has_edge(node_name,word) is False:
G.add_edge(node_name,word, weight = 1)
else:
G[node_name][word]["weight"] = G[node_name][word]["weight"] + 1
if G.has_edge(word,node_name) is False:
G.add_edge(word,node_name, weight = 1)
else:
G[word][node_name]["weight"] = G[word][node_name]["weight"] + 1
# print "word = "+word + " document ="+node_name
graphUtils.logger.info("word = "+word + " document ="+node_name)
#print "Word = "+word + " idf="+str(idfWord)
pass
#f=open('lda_topics_'+str(topic)+'_'+str(LDA_PASSES)+'_'+'.txt','w')
# Prints the topics.
#for top in lda.print_topics(num_words=1000,num_topics=topic):
# f.write(top+' \n')
#print 'Document topic printed for'+str(topic)
#G = NER.NERFunc(graphFiles, graphFileNames, G)
nx.write_gexf(G, graph_file)
graphUtils.saveSettings(graphConstants.date_LAST_GRAPH_DONE, yesterdayFolder)
graphUtils.logger.info("Simple graph nodes addition after ="+str(date_LAST_GRAPH_DONE)+" ends")
def Smoothness():
todayDate = graphUtils.getTodayDateFolder()
lastSmoothnessDate = graphUtils.loadSettings(
graphConstants.LAST_GRAPHNER_SMOOTHNESS_DIR)
lastSuggSmoothnessDate = graphUtils.loadSettings(
graphConstants.LAST_GRAPHNER_SUGG_SMOOTHNESS_DIR)
if lastSmoothnessDate:
graphUtils.logger.info("NERGraph Smoothness done last for =" +
lastSmoothnessDate)
else:
graphUtils.logger.info("NERGraph Smoothness done last for None")
if lastSuggSmoothnessDate:
graphUtils.logger.info("NERGraphSugg Smoothness done last for =" +
lastSuggSmoothnessDate)
else:
graphUtils.logger.info("NERGraphSugg Smoothness done last for None")
if todayDate == lastSmoothnessDate and todayDate == lastSuggSmoothnessDate:
graphUtils.logger.info(
"NERGraph Smoothness signal already done for today :" + todayDate)
return True
graph_path = os.path.join(graphConstants.ROOT_FOLDER,
graphConstants.GRAPH_DIR,
graphConstants.GRAPH_DIR,
graphConstants.TYPE_NER)
graph_file = os.path.join(graph_path, graphConstants.GRAPH_FILE)
write_graph_file = os.path.join(graph_path, graphConstants.GRAPH_FILE)
if not os.path.exists(graph_path):
os.makedirs(graph_path)
G = nx.read_gexf(graph_file)
trainFiles, trainFileNames = graphUtils.findRecommTrainGraphNerFiles()
trainCorpus = graphUtils.findCorpus(trainFiles)
bm25obj = Bm25(trainCorpus)
trainUniqueWords = []
for trainText in trainCorpus:
trainUniqueWords.append(set(trainText))
if todayDate != lastSmoothnessDate:
testFiles, testFileName = graphUtils.findRecommFiles()
testCorpus = graphUtils.findCorpus(testFiles)
testUniqueWords = []
mini = 100
maxi = -1
count = 0
smoothness = zeros((len(testCorpus), len(trainCorpus)))
for testText in testCorpus:
testUniqueWords.append(set(testText))
for testDoc in range(len(testCorpus)):
recomm_nodename = testFileName[testDoc]
uniqueTest = testUniqueWords[testDoc]
SminusDcontext = zeros(bm25obj.N)
DminusScontext = zeros(bm25obj.N)
for trainDoc in range(len(trainCorpus)):
uniqueTrain = trainUniqueWords[trainDoc]
SminusD = [
word for word in trainCorpus[trainDoc]
if word not in uniqueTest
]
DminusS = [
word for word in testCorpus[testDoc]
if word not in uniqueTrain
]
SminusDcontext = bm25obj.BM25Score(SminusD)
DminusScontext = bm25obj.BM25Score(DminusS)
smoothness[testDoc][trainDoc] = np.dot(SminusDcontext,
DminusScontext)
dict_arr = {
key: value
for (key, value) in enumerate(smoothness[testDoc])
}
sorted_x = sorted(dict_arr.items(), key=operator.itemgetter(1))
sorted_x.reverse()
sorted_x = sorted_x[:graphConstants.MAX_SMOOTHNESS_EDGE]
total = sum([pair[1] for pair in sorted_x])
for (idxsim, val) in sorted_x:
prob = val / total
if recomm_nodename not in G.nodes():
G.add_node(recomm_nodename)
G.node[recomm_nodename][
'type'] = graphConstants.TYPE_GOOGLE
trainNode = trainFileNames[idxsim]
if trainNode in G.nodes():
if prob < mini:
mini = prob
if prob > maxi:
maxi = prob
if G.has_edge(recomm_nodename, trainNode) is False:
G.add_edge(recomm_nodename,
trainNode,
weight=prob *
graphConstants.SMOOTHNESS_EDGE_WEIGHT)
else:
G[recomm_nodename][trainNode][
'weight'] = G[recomm_nodename][trainNode][
'weight'] + prob * graphConstants.SMOOTHNESS_EDGE_WEIGHT
if G.has_edge(trainNode, recomm_nodename) is False:
G.add_edge(trainNode,
recomm_nodename,
weight=prob *
graphConstants.SMOOTHNESS_EDGE_WEIGHT)
else:
G[trainNode][recomm_nodename][
'weight'] = G[trainNode][recomm_nodename][
'weight'] + prob * graphConstants.SMOOTHNESS_EDGE_WEIGHT
count = count + 1
#print smoothness[testDoc]
graphUtils.logger.info(
" ner graph Smoothness completed for normalGoogle today. Stats follow"
)
graphUtils.logger.info("mini =" + str(mini))
graphUtils.logger.info("maxi =" + str(maxi))
graphUtils.logger.info("Smoothness edges count =" + str(count))
nx.write_gexf(G, write_graph_file)
graphUtils.saveSettings(graphConstants.LAST_GRAPHNER_SMOOTHNESS_DIR,
todayDate)
pass
if todayDate != lastSuggSmoothnessDate:
testFiles, testFileName = graphUtils.findSuggRecommFiles()
testCorpus = graphUtils.findCorpus(testFiles)
testUniqueWords = []
mini = 100
maxi = -1
count = 0
smoothness = zeros((len(testCorpus), len(trainCorpus)))
for testText in testCorpus:
testUniqueWords.append(set(testText))
for testDoc in range(len(testCorpus)):
recomm_nodename = testFileName[testDoc]
uniqueTest = testUniqueWords[testDoc]
SminusDcontext = zeros(bm25obj.N)
DminusScontext = zeros(bm25obj.N)
for trainDoc in range(len(trainCorpus)):
uniqueTrain = trainUniqueWords[trainDoc]
SminusD = [
word for word in trainCorpus[trainDoc]
if word not in uniqueTest
]
DminusS = [
word for word in testCorpus[testDoc]
if word not in uniqueTrain
]
SminusDcontext = bm25obj.BM25Score(SminusD)
DminusScontext = bm25obj.BM25Score(DminusS)
smoothness[testDoc][trainDoc] = np.dot(SminusDcontext,
DminusScontext)
dict_arr = {
key: value
for (key, value) in enumerate(smoothness[testDoc])
}
sorted_x = sorted(dict_arr.items(), key=operator.itemgetter(1))
sorted_x.reverse()
sorted_x = sorted_x[:graphConstants.MAX_SMOOTHNESS_EDGE]
total = sum([pair[1] for pair in sorted_x])
for (idxsim, val) in sorted_x:
prob = val / total
if recomm_nodename not in G.nodes():
G.add_node(recomm_nodename)
G.node[recomm_nodename]['type'] = graphConstants.TYPE_SUGG
trainNode = trainFileNames[idxsim]
if trainNode in G.nodes():
if prob < mini:
mini = prob
if prob > maxi:
maxi = prob
if G.has_edge(recomm_nodename, trainNode) is False:
G.add_edge(recomm_nodename,
trainNode,
weight=prob *
graphConstants.SMOOTHNESS_EDGE_WEIGHT)
else:
G[recomm_nodename][trainNode][
'weight'] = G[recomm_nodename][trainNode][
'weight'] + prob * graphConstants.SMOOTHNESS_EDGE_WEIGHT
if G.has_edge(trainNode, recomm_nodename) is False:
G.add_edge(trainNode,
recomm_nodename,
weight=prob *
graphConstants.SMOOTHNESS_EDGE_WEIGHT)
else:
G[trainNode][recomm_nodename][
'weight'] = G[trainNode][recomm_nodename][
'weight'] + prob * graphConstants.SMOOTHNESS_EDGE_WEIGHT
count = count + 1
#print smoothness[testDoc]
graphUtils.logger.info(
" ner graph Smoothness completed for suggestGoogle today. Stats follow"
)
graphUtils.logger.info("mini =" + str(mini))
graphUtils.logger.info("maxi =" + str(maxi))
graphUtils.logger.info("Smoothness edges count =" + str(count))
nx.write_gexf(G, write_graph_file)
graphUtils.saveSettings(
graphConstants.LAST_GRAPHNER_SUGG_SMOOTHNESS_DIR, todayDate)
pass
def ConnectionClarity():
todayDate = graphUtils.getYesterdayDateFolder()
lastClarityDate = graphUtils.loadSettings(
graphConstants.LAST_GRAPH_CLARITY_DIR)
lastSuggClarityDate = graphUtils.loadSettings(
graphConstants.LAST_GRAPH_SUGG_CLARITY_DIR)
if lastClarityDate:
graphUtils.logger.info("Graph Google Clarity done last for =" +
lastClarityDate)
else:
graphUtils.logger.info("Graph Google Clarity done last for none")
if lastSuggClarityDate:
graphUtils.logger.info("Graph Sugg Clarity done last for =" +
lastSuggClarityDate)
else:
graphUtils.logger.info("Graph Sugg Clarity done last for none")
if todayDate == lastClarityDate and todayDate == lastSuggClarityDate:
graphUtils.logger.info("graph Clarity signal done for today =" +
todayDate)
return True
graph_path = os.path.join(graphConstants.ROOT_FOLDER,
graphConstants.GRAPH_DIR,
graphConstants.GRAPH_DIR,
graphConstants.TYPE_MAIN)
graph_file = os.path.join(graph_path, graphConstants.GRAPH_FILE)
write_graph_file = os.path.join(graph_path, graphConstants.GRAPH_FILE)
if not os.path.exists(graph_path):
os.makedirs(graph_path)
G = nx.read_gexf(graph_file)
trainFiles, trainFileNames = graphUtils.findRecommTrainGraphFiles()
trainCorpus = graphUtils.findCorpus(trainFiles)
if todayDate != lastClarityDate:
testFiles, testFileName = graphUtils.findRecommFiles()
testCorpus = graphUtils.findCorpus(testFiles)
clarityobj = Clarity(trainCorpus, testCorpus)
clarityScore = clarityobj.ClarityScore()
mini = 100
maxi = -1
count = 0
for testidx, text in enumerate(testCorpus):
recomm_nodename = testFileName[testidx]
dict_arr = {
key: value
for (key, value) in enumerate(clarityScore[testidx])
}
sorted_x = sorted(dict_arr.items(), key=operator.itemgetter(1))
sorted_x.reverse()
sorted_x = sorted_x[:graphConstants.MAX_CLARITY_EDGE]
total = sum([pair[1] for pair in sorted_x])
for (idxsim, val) in sorted_x:
prob = val / total
if prob < 0.0:
break
if recomm_nodename not in G.nodes():
G.add_node(recomm_nodename)
G.node[recomm_nodename][
'type'] = graphConstants.TYPE_GOOGLE
trainNode = trainFileNames[idxsim]
if trainNode in G.nodes():
if prob < mini:
mini = prob
if prob > maxi:
maxi = prob
if G.has_edge(recomm_nodename, trainNode) is False:
G.add_edge(recomm_nodename,
trainNode,
weight=prob *
graphConstants.CLARITY_EDGE_WEIGHT)
else:
G[recomm_nodename][trainNode][
'weight'] = G[recomm_nodename][trainNode][
'weight'] + prob * graphConstants.CLARITY_EDGE_WEIGHT
if G.has_edge(trainNode, recomm_nodename) is False:
G.add_edge(trainNode,
recomm_nodename,
weight=prob *
graphConstants.CLARITY_EDGE_WEIGHT)
else:
G[trainNode][recomm_nodename][
'weight'] = G[trainNode][recomm_nodename][
'weight'] + prob * graphConstants.CLARITY_EDGE_WEIGHT
count = count + 1
graphUtils.logger.info(
"Simple graph clarity completed for googlenews today. Stats follow"
)
graphUtils.logger.info("mini =" + str(mini))
graphUtils.logger.info("maxi =" + str(maxi))
graphUtils.logger.info("clarity edges count =" + str(count))
nx.write_gexf(G, write_graph_file)
graphUtils.saveSettings(graphConstants.LAST_GRAPH_CLARITY_DIR,
todayDate)
pass
if todayDate != lastSuggClarityDate:
testFiles, testFileName = graphUtils.findSuggRecommFiles()
testCorpus = graphUtils.findCorpus(testFiles)
clarityobj = Clarity(trainCorpus, testCorpus)
clarityScore = clarityobj.ClarityScore()
mini = 100
maxi = -1
count = 0
for testidx, text in enumerate(testCorpus):
recomm_nodename = testFileName[testidx]
dict_arr = {
key: value
for (key, value) in enumerate(clarityScore[testidx])
}
sorted_x = sorted(dict_arr.items(), key=operator.itemgetter(1))
sorted_x.reverse()
sorted_x = sorted_x[:graphConstants.MAX_CLARITY_EDGE]
total = sum([pair[1] for pair in sorted_x])
for (idxsim, val) in sorted_x:
prob = val / total
if prob < 0.0:
break
if recomm_nodename not in G.nodes():
G.add_node(recomm_nodename)
G.node[recomm_nodename]['type'] = graphConstants.TYPE_SUGG
trainNode = trainFileNames[idxsim]
if trainNode in G.nodes():
if prob < mini:
mini = prob
if prob > maxi:
maxi = prob
if G.has_edge(recomm_nodename, trainNode) is False:
G.add_edge(recomm_nodename,
trainNode,
weight=prob *
graphConstants.CLARITY_EDGE_WEIGHT)
else:
G[recomm_nodename][trainNode][
'weight'] = G[recomm_nodename][trainNode][
'weight'] + prob * graphConstants.CLARITY_EDGE_WEIGHT
if G.has_edge(trainNode, recomm_nodename) is False:
G.add_edge(trainNode,
recomm_nodename,
weight=prob *
graphConstants.CLARITY_EDGE_WEIGHT)
else:
G[trainNode][recomm_nodename][
'weight'] = G[trainNode][recomm_nodename][
'weight'] + prob * graphConstants.CLARITY_EDGE_WEIGHT
count = count + 1
graphUtils.logger.info(
"Simple graph clarity completed for SuggestGoogle today. Stats follow"
)
graphUtils.logger.info("mini =" + str(mini))
graphUtils.logger.info("maxi =" + str(maxi))
graphUtils.logger.info("clarity edges count =" + str(count))
nx.write_gexf(G, write_graph_file)
graphUtils.saveSettings(graphConstants.LAST_GRAPH_SUGG_CLARITY_DIR,
todayDate)
pass
