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 printGraphRecommendedDocs(G, list_nodes, R):
todayDateFolder = graphUtils.getTodayDateFolder()
jsonData = readLinksJson(todayDateFolder)
if jsonData is None:
return False
result = False
jsonData['GoogleNews'][Constants.NERGRAPH] = []
recommInfo = {}
graphDocs = {}
googleLinks = jsonData['GoogleNews'][Constants.GOOGLE]
for linkObj in googleLinks:
download = linkObj['download']
htmlFile = graphConstants.TYPE_GOOGLE + "_" + linkObj[
'id'] + "_" + todayDateFolder
if download == "yes" and htmlFile in list_nodes:
recommInfo[htmlFile] = linkObj
htmlFile_idx = list_nodes[htmlFile]
graphDocs[htmlFile] = R[htmlFile_idx]
try:
sorted_x = sorted(graphDocs.items(), key=operator.itemgetter(1))
sorted_x.reverse()
write_directory = os.path.join(graphConstants.ROOT_FOLDER,
graphConstants.FINAL_DIR,
todayDateFolder)
if not os.path.exists(write_directory):
os.makedirs(write_directory)
outfile = open(
os.path.join(write_directory, graphConstants.ULTIMATE_FILE), 'w')
json_write = {}
count = 1
for (key, val) in sorted_x:
if key in recommInfo:
linkObj = recommInfo[key]
linkObj['rank'] = -1
jsonData['GoogleNews'][Constants.NERGRAPH].append(linkObj)
count = count + 1
if count >= graphConstants.RECOMMENDED_LINKS:
break
else:
graphUtils.logger.error(
"NER Graph normalGoogle key not found = " + key)
json.dump(jsonData, outfile)
outfile.close()
result = True
except Exception, e:
graphUtils.logger.error("Exception = %s" % e)
graphUtils.logger.error(
"Exception at writing final Graph Recommendation docs for data : %s"
% write_directory)
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 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
