def txt_ltn(self, step, req_n): with open("./data/Text_Only_Ascii_Coll_MWI_NoSem", "r") as f: start = time.time() parser = Parser() parser.parse(f) print("time execution parsing {}".format(time.time() - start)) start = time.time() parser.createVector() print("time execution create vector {}".format(time.time() - start)) start = time.time() search = {2009011: ["olive", "oil", "health", "benefit"]} search2 = {2009036: ["notting", "hill", "film", "actors"]} search3 = { 2009067: ["probabilistic", "models", "in", "information", "retrieval"] } search4 = {2009073: ["web", "link", "network", "analysis"]} search5 = {2009074: ["web", "ranking", "scoring", "algorithm"]} search6 = { 2009078: ["supervised", "machine", "learning", "algorithm"] } search7 = { 2009085: ["operating", "system", "+mutual", "exclusion"] } parser.scoreAndGenerate("GuillaumeBenoitGauthierTheo", step, req_n, "ltn", "articles", "test", search, search2, search3, search4, search5, search6, search7) print("time execution generate runs {}".format(time.time() - start))
def run2(re): with open("../src/data/Text_Only_Ascii_Coll_MWI_NoSem", "r") as f: parser = Parser() parser.parse(f) #print(parser.corpusW) #[print(i,j) for i,j in parser.corpusW.items()] parser.createVector() search = {2009011: ["olive", "oil", "health", "benefit"]} search2 = {2009036: ["notting", "hill", "film", "actors"]} search3 = {2009067: ["probabilistic", "models", "in", "information", "retrieval"]} search4 = {2009073: ["web", "link", "network", "analysis"]} search5 = {2009074: ["web", "ranking", "scoring", "algorithm"]} search6 = {2009078: ["supervised", "machine", "learning", "algorithm"]} search7 = {2009085: ["operating", "system", "+mutual", "exclusion"]} parser.scoreAndGenerate("GuillaumeBenoitGauthierTheo", "02", re, "ltn", "articles", "sem_test", search, search2, search3, search4, search5, search6, search7)