def test_calculate_page_rank(self):
pagerank = PageRank(self.graph, 0.85, 0.0001)
pagerank_dict = pagerank.run()
for k, v in pagerank_dict.items():
print k, v
assert True
def test_pagerank(self):
M = mat([[0, 1 / 2, 0, 0], [1 / 3, 0, 0, 1 / 2], [1 / 3, 0, 1, 1 / 2],
[
1 / 3,
1 / 2,
0,
0,
]])
R = array([1 / 4, 1 / 4, 1 / 4, 1 / 4]).reshape(-1, 1)
pr = PageRank(M, R, damping=0.8, max_iter=100)
R = pr.fit()
R_true = array([15 / 148, 19 / 148, 95 / 148, 19 / 148]).reshape(-1, 1)
assert mean(R - R_true) < 1e-3
def calculateSolution(dswa, method, gt_solution):
# BaselineApproach
if method == 'pagerank':
pagerank_approach = PageRank(dswa)
print("Calculation solution...")
calculated_solution = pagerank_approach.returnSolution(5)
#UnsupevisedApproach
elif method == 'unsupervised':
unsupervisedApproach = Unsupervised_Approach(dswa)
print("Calculation solution...")
calculated_solution = unsupervisedApproach.returnSolution()
print('--- Solution ---')
print(calculated_solution)
return calculated_solution
def and_rank3(r1, r2):
t1s = map(lambda x: x[0], r1)
t2s = map(lambda x: x[0], r2)
intersec = set(t1s).intersection(set(t2s))
map1, map2 = {}, {}
for t1, fr1 in r1:
map1[t1] = fr1
for t2, fr2 in r2:
map2[t2] = fr2
r1_i, r2_i = [], []
for e in intersec:
r1_i.append((e,map1[e]))
r2_i.append((e,map2[e]))
r1_i.sort(snd_cmp)
r2_i.sort(snd_cmp)
sum_pos_rank = {}
for e in intersec:
sum_pos_rank[e] = 0.0
for (e, pr), pos in zip(r1_i, range(1,len(r1_i)+1)):
sum_pos_rank[e] += float(pos)
for (e, pr), pos in zip(r2_i, range(1,len(r2_i)+1)):
sum_pos_rank[e] += float(pos)
final = []
for e, val in sum_pos_rank.iteritems():
final.append((e,val))
final.sort(snd_cmp)
final.reverse()
return PageRank.normalize(final)
def or_rank(r1, r2):
# ret = not_rank(and_rank(not_rank(r1), not_rank(r2)))
# ret = ret.sort(snd_cmp, None, True)
# return ret
t1s = map(lambda x: x[0], r1)
t2s = map(lambda x: x[0], r2)
newr = []
for t1, fr1 in r1:
for t2, fr2 in r2:
if t1 == t2:
newr.append((t1, fr1 + fr2 - fr1 * fr2))
diff12 = set(t1s).difference(set(t2s))
diff21 = set(t2s).difference(set(t1s))
for ent1 in diff12:
for ent1_aux, float_rank in r1:
if ent1 == ent1_aux:
newr.append((ent1, float_rank))
for ent2 in diff21:
for ent2_aux, float_rank in r2:
if ent2 == ent2_aux:
newr.append((ent2, float_rank))
newr.sort(snd_cmp, None, True)
#print str(newr)
return PageRank.normalize(newr)
def and_rank5_gold2(tags, ranker):
# first compute offline rank
# OR tag formula
tag_form = TagBooleanFormula()
for tag in tags:
and1 = TagBooleanConjunction()
and1.addAtom(TagBooleanAtom(True,tag))
tag_form.addTagAnd(and1)
# filter graph by OR of tags
ranker.filter(tag_form)
if len(ranker.get_nodes()) > 0:
ranker.rank(10)
offline_rank = ranker.get_rank()
else:
offline_rank = []
# AND tag formula
and_nodes = set([])
for tag in set(tags):
ranker.filter_one_tag(tag)
if tag == list(set(tags))[0]:
and_nodes = ranker.get_nodes()
else:
and_nodes = and_nodes.intersection(ranker.get_nodes())
# now filter by intersection
ret = []
for name, pagerank in offline_rank:
if name in and_nodes:
ret.append((name, pagerank))
return PageRank.normalize(ret)
def rank_author():
ac_net = AuthorCitationNetwork()
ac_net_m, ac_net_list = ac_net.make_matrix()
print("Caculate pagerank...")
ac_net_pgr = PageRank(ac_net_m)
ac_net_pr = ac_net_pgr.caculate("author_iter.txt", m_d=1e-5)
ac_net_results = list(zip(ac_net_list, ac_net_pr.tolist()))
ac_net_results.sort(key=itemgetter(1), reverse=True)
results = []
for result in ac_net_results:
results.append(str(result[0][1]) + " " + str(result[1][0]) + "\n")
# results.sort(key=itemgetter(2),reverse=True)
print("Writing results...")
f = open("2014/results/author.txt", 'w')
# f.writelines(str(ac_net_results))
f.writelines(str(results))
def rank_paper():
pp_net = PaperCitationNetwork()
pp_net_m, pp_net_list = pp_net.make_matrix()
print("Caculating pagerank...")
pp_net_pgr = PageRank(pp_net_m)
pp_net_pr = pp_net_pgr.caculate("paper_iter.txt", m_d=1e-7)
pp_net_results = list(zip(pp_net_list, pp_net_pr.tolist()))
pp_net_results.sort(key=itemgetter(1))
results = []
for result in pp_net_results:
results.append(str(result[0][1]) + " " + str(result[1][0]) + "\n")
# results.sort(key=lambda x:-1*x[2])
# printer=[]
# for r in results:
# printer.append(str(r[1])+" "+str(r[2])+"\n")
print("Writing results...")
f = open("2014/results/paper.txt", 'w')
f.writelines(str(results))
def __merge_rank_and_monolitic(self, tags):
users = self.__map_tag_users[tags[0]]
for tag in tags[1:]:
users = users.intersection(self.__map_tag_users[tag])
mono_rank = []
for name, pagerank, pos in self.__rank:
if name in users:
mono_rank.append((name,pagerank))
if self.__max_per_rank <= len(mono_rank):
break
mono_rank = PageRank.normalize(mono_rank)
return add_pos(mono_rank)
def and_rank2(r1, r2):
t1s = map(lambda x: x[0], r1)
t2s = map(lambda x: x[0], r2)
intersec = set(t1s).intersection(set(t2s))
map1, map2 = {}, {}
for t1, fr1 in r1:
map1[t1] = fr1
for t2, fr2 in r2:
map2[t2] = fr2
newr = []
r1_int, r2_int = [], []
for t in intersec:
r1_int.append((t,map1[t]))
r2_int.append((t,map2[t]))
r1_int = PageRank.normalize(r1_int)
r2_int = PageRank.normalize(r2_int)
return and_rank(r1_int, r2_int)
def rank_venue():
vn_net = VenueCitationNetwork()
vn_net_m, vn_net_list = vn_net.make_matrix()
print("Caculating pagerank...")
vn_net_pgr = PageRank(vn_net_m)
vn_net_pr = vn_net_pgr.caculate("venue_iter.txt", m_d=1e-7)
vn_list = []
for vn in vn_net_list:
vn_list.append(vn[0:2])
vn_net_results = list(zip(vn_list, vn_net_pr.tolist()))
vn_net_results.sort(key=itemgetter(1), reverse=True)
# ac_net_results.sort(key=lambda x:-1*x[1])
results = []
for result in vn_net_results:
results.append(
str(result[0][0]) + " " + str(result[0][1]) + " " +
str(result[1][0]) + "\n")
f = open("2014/results/venue.txt", 'w')
# f.writelines(str(vn_net_results))
f.writelines(results)
print("Writing results...")
def main(args):
summarizer = {
'tfidf': TfIdf(),
'cluster': Cluster(),
'svd': SVD(),
'pagerank': PageRank()
}[args['alg']]
summarizer.initialize(args['tf'], args['df'])
summary = summarizer.summarize(args['doc'])
for s in summary:
print(s),
def sample_generation(args):
# Preprocessing Step
print("Numpy Version Check")
print(np.__version__)
print("Scipy Version Check")
print(scipy.__version__)
data_dicts = preprocessing(transition_matrix_path=args.transition_matrix,
doc_topics_path=args.document_topic,
user_topic_path=args.user_topic_interest,
query_topic_path=args.query_topic_relation,
search_relevance_path=args.search_relevance)
# GPR, PTSPR, QTSPR construction
if args.pagerank == "gpr":
pr = PageRank(trans_matrix=data_dicts['transition_matrix'],
dampening_factor=args.dampening_factor)
elif args.pagerank == "ptspr" or args.pagerank == "qtspr":
pr = TopicSensitivePageRank(
trans_matrix=data_dicts['transition_matrix'],
topic_matrix=data_dicts['doc_topic_matrix'],
dampening_factor=args.dampening_factor,
topic_factor=args.topic_factor)
pr.converge()
if args.pagerank == "gpr":
np.savetxt("GPR.txt", pr.ranked_vector, delimiter=" ")
elif args.pagerank == "ptspr":
topic_prob = data_dicts['user_topic_probs']["2-1"]
vector = (pr.ranked_matrix * topic_prob.reshape(12, 1)).view(
np.ndarray).squeeze()
np.savetxt("QTSPR-U2Q1.txt", vector, delimiter=" ")
elif args.pagerank == "qtspr":
topic_prob = data_dicts['query_topic_probs']["2-1"]
vector = (pr.ranked_matrix * topic_prob.reshape(12, 1)).view(
np.ndarray).squeeze()
np.savetxt("PTSPR-U2Q1.txt", vector, delimiter=" ")
print("===================== END =====================")
def main():
google_file = "web-Google.txt"
simple_test = "web-Matvii.txt"
sparse_matrix = create_sparse_matrix(simple_test)
pr = PageRank(sparse_matrix)
pr.init_weights()
print("Initial weights\n", pr.weights)
pr.calculate_page_rank(5)
print("Weights after 5 iterations\n", pr.weights)
def runStates():
"""run pagerank on stateborders.csv"""
f = open('stateborders.csv')
graph = PageRank()
for line in f:
columns = line.split(',')
left = columns[0].strip('"')
right = columns[2].strip('"')
graph.addEdge(left, right)
graph.printGraph()
iterations, ranks = graph.getPageRank()
print "Number of iterations:", iterations
print returnSorted(ranks)
def and_rank(r1, r2):
t1s = map(lambda x: x[0], r1)
t2s = map(lambda x: x[0], r2)
intersec = set(t1s).intersection(set(t2s))
map1, map2 = {}, {}
for t1, fr1 in r1:
map1[t1] = fr1
for t2, fr2 in r2:
map2[t2] = fr2
newr = []
for t in intersec:
newr.append((t,map1[t]*map2[t]))
# newr = []
# for t1, fr1 in r1:
# for t2, fr2 in r2:
# if t1 == t2:
# newr.append((t1, fr1 * fr2))
newr.sort(snd_cmp, None, True)
#print str(newr)
return PageRank.normalize(newr)
def __init__(self):
self.pagerank = PageRank()
self.ranking = None
self.res = None
lista = list(self.pagerank.autores)
lista.sort()
self.AutorList = lista
self.raiz = Tk()
self.raiz.geometry('950x500')
self.raiz.title('Buscador')
# Celda donde se muestran los resultados.
self.tinfo = scrolledtext.ScrolledText(self.raiz, width=50, height=30)
self.tinfo.grid(column = 0, row = 6)
# Celda donde se introduce la búsqueda.
self.tentry = Entry(self.raiz, width=40)
self.tentry.grid(column = 0, row = 5)
# Botón de buscar.
self.binfo = ttk.Button(self.raiz, text='Buscar', command=self.verinfo)
self.binfo.grid(column = 1, row =5)
# Botón de búsqueda personalizada.
self.bper = ttk.Button(self.raiz, text='Búsqueda personalizada', command=self.verper)
self.bper.grid(column = 1, row =3)
# Botón de mostrar Ranking inicial.
self.bpag = ttk.Button(self.raiz, text='Mostrar Ranking inicial', command=self.verpag)
self.bpag.grid(column = 2, row =3)
# Botón de salir.
self.bsalir = ttk.Button(self.raiz, text='Salir',
command=self.raiz.destroy)
self.bsalir.grid(column = 2, row = 5)
# Desplegable para elegir el sitio de búsqueda.
self.variable = tk.StringVar(self.raiz)
self.variable.set(self.OptionList[0])
opt = tk.OptionMenu(self.raiz, self.variable, *self.OptionList)
opt.config(width=30, font=('Helvetica', 12))
opt.grid(column = 0, row = 1)
# Desplegable para elegir el autor.
self.variable2 = tk.StringVar(self.raiz)
self.variable2.set(self.AutorList[0])
opt2 = ttk.Combobox(self.raiz, textvariable = self.variable2, values = self.AutorList)
opt2.config(width=30, font=('Helvetica', 12))
opt2.grid(column = 0, row = 3)
# Desplegable para elegir el modelo.
self.variable3 = tk.StringVar(self.raiz)
self.variable3.set(self.OptionMod[0])
opt3 = tk.OptionMenu(self.raiz, self.variable3, *self.OptionMod)
opt3.config(width=30, font=('Helvetica', 12))
opt3.grid(column = 2, row = 1)
# Desplegable para elegir si mostrar los pesos.
self.variable4 = tk.StringVar(self.raiz)
self.variable4.set(self.OptionRank[0])
opt3 = tk.OptionMenu(self.raiz, self.variable4, *self.OptionRank)
opt3.config(width=30, font=('Helvetica', 12))
opt3.grid(column = 1, row = 1)
# Muestra la opción del desplegable del sitio de búsqueda.
self.labelTest = tk.Label(text="", font=('Helvetica', 12), fg='red')
self.labelTest.grid(column = 0, row = 2)
self.variable.trace("w", self.callback)
# Muestra la opción del desplegable del autor.
self.labelTest2 = tk.Label(text="", font=('Helvetica', 12), fg='red')
self.labelTest2.grid(column = 0, row = 4)
self.variable2.trace("w", self.callback2)
# Muestra la opción del desplegable del modelo.
self.labelTest3 = tk.Label(text="", font=('Helvetica', 12), fg='red')
self.labelTest3.grid(column = 2, row = 2)
self.variable3.trace("w", self.callback3)
# Muestra la opción elegida de mostrar los pesos.
self.labelTest4 = tk.Label(text="", font=('Helvetica', 12), fg='red')
self.labelTest4.grid(column = 1, row = 2)
self.variable4.trace("w", self.callback4)
self.tentry.focus_set()
self.raiz.mainloop()
def rank(self, iterations=50, damping_factor=0.85, accurate=False):
# pagerank = PageRankNumarray(list(self.__nodes), self.__edges)
# self.__pagerank = pagerank.rank()
use_native = len(self.__edges) > 0
pagerank = PageRank(list(self.__nodes), self.__edges, use_native, damping_factor)
self.__pagerank = pagerank.ranking(-1, iterations)
def not_rank(rank):
notr = []
for thing, float_rank in rank:
notr.append((thing, 1-float_rank))
notr.reverse()
return PageRank.normalize(notr)
def __init__(self):
util.log("Loading data set...")
self.newsgroup_data = fetch_20newsgroups(remove=('headers', 'footers'))
# print(data_set.target.shape) # categories per document
# print(data_set.filenames.shape) # filenames per document
for doc in self.newsgroup_data.data:
if len(doc) < 5:
self.newsgroup_data.data.remove(doc)
self.newsgroup_frame = pd.DataFrame.from_dict(
{'text': self.newsgroup_data.data})
#f = self.newsgroup_frame.text.str.contains('National Rifle Association')
#ids = np.arange(len(self.newsgroup_frame))[f]
#self.list_docs(ids)
#return
self.tfidf_matrix = TfIdfMatrix.from_data_set(self.newsgroup_data.data)
self.inverted_index = InvertedIndex.from_tf_idf_matrix(
self.tfidf_matrix)
util.log("Clustering...")
self.kmeans = KMeans(tfidf=self.tfidf_matrix.get_matrix(),
k=100,
max_iterations=30,
random_initial=False)
try:
self.kmeans.load_cluster_vector('cluster_vector.pkl')
except FileNotFoundError:
self.kmeans.do_magic()
self.kmeans.store_cluster_vector('cluster_vector.pkl')
util.log("Finished.")
r = self.kmeans.vector.ravel()
u = np.unique(self.kmeans.vector)
print(u)
try:
self.adjacency_matrix = pkl.load(open('adjacency_matrix.pkl',
"rb"))
except FileNotFoundError:
self.adjacency_matrix = AdjacencyMatrix.from_cluster_and_tf_idf_matrix(
r, self.tfidf_matrix)
with open('adjacency_matrix.pkl', 'wb') as f:
pkl.dump(self.adjacency_matrix, f)
try:
pr = PageRank(pickle='pr.pkl')
except FileNotFoundError:
util.log("No precomputed PageRank...")
util.log("Calculating PR...")
pr = PageRank(adjacency_matrix=self.adjacency_matrix.get_matrix(),
alpha=0.85,
converge=0.00001)
util.log("Finished PR")
pr.store_rank_vector('pr.pkl')
self.pr_vector = pr.get_pagerank(normalized=True)
def search_init(self):
jieba.initialize()
self.pagerank = PageRank()
def main(args):
# Preprocessing Step
data_dicts = preprocessing(transition_matrix_path=args.transition_matrix,
doc_topics_path=args.document_topic,
user_topic_path=args.user_topic_interest,
query_topic_path=args.query_topic_relation,
search_relevance_path=args.search_relevance)
# GPR, PTSPR, QTSPR construction
if args.pagerank == "gpr":
pr = PageRank(trans_matrix=data_dicts['transition_matrix'],
dampening_factor=args.dampening_factor)
elif args.pagerank == "ptspr" or args.pagerank == "qtspr":
pr = TopicSensitivePageRank(
trans_matrix=data_dicts['transition_matrix'],
topic_matrix=data_dicts['doc_topic_matrix'],
dampening_factor=args.dampening_factor,
topic_factor=args.topic_factor)
pr_start = time.time()
pr.converge()
pr_end = time.time()
print("Power iteration - {} required time: {:.3f}seconds".format(
args.pagerank, pr_end - pr_start))
pr_result = []
for query_ID in data_dicts['search_relevance_score'].keys():
candidate_indices, retrieval_scores = data_dicts[
'search_relevance_score'][query_ID]
user_topic_prob = data_dicts['user_topic_probs'][query_ID]
query_topic_prob = data_dicts['query_topic_probs'][query_ID]
if args.pagerank == "gpr":
pr_indices, pr_scores = pr.ranking(candidate_indices,
retrieval_scores,
criterion=args.criterion)
elif args.pagerank == "ptspr":
pr_indices, pr_scores = pr.ranking(candidate_indices,
retrieval_scores,
user_topic_prob,
criterion=args.criterion)
elif args.pagerank == "qtspr":
pr_indices, pr_scores = pr.ranking(candidate_indices,
retrieval_scores,
query_topic_prob,
criterion=args.criterion)
for idx in range(len(candidate_indices)):
# Print function
temp = [[]]
temp[0].append(query_ID)
temp[0].append("Q0")
temp[0].append(str(pr_indices[idx] + 1))
temp[0].append(str(idx + 1))
temp[0].append(str(pr_scores[idx]))
temp[0].append(args.cfg)
pr_str = " ".join(temp[0])
pr_result.append(pr_str)
pr_result_text = "\n".join(pr_result)
with open(args.pagerank + "_" + args.cfg + ".txt", "w") as f:
f.write(pr_result_text)
pr_end = time.time()
print("total {} required time : {:.3f}seconds".format(
args.pagerank, pr_end - pr_start))
print("===================== END =====================")
class SQLDB:
def __init__(self, path, clean, commit_rate=50):
self.db = None
self.logger = logging.getLogger('SQLDB')
if clean:
if os.path.exists(path):
os.remove(path)
self.logger.info('Previous database has been deleted.')
rebuild = not os.path.exists(path)
try:
self.db = sqlite3.connect(path)
except:
self.logger.error('Error while opening database.')
exit(1)
else:
self.logger.info('Database opened successfully.')
cursor = self.db.cursor()
if rebuild:
cursor.execute(
'CREATE TABLE pages (id int primary key, journal text, title text, content text, keys text)'
)
cursor.execute('CREATE TABLE dicts (word text, id int)')
self.index = 0
else:
self.index = cursor.execute(
'SELECT max(id) from pages').fetchone()[0]
self.logger.info('The size of database if {}'.format(self.index))
self.commit_rate = commit_rate
self.commit_count = 0
def search_init(self):
jieba.initialize()
self.pagerank = PageRank()
def commit(self, show=True):
self.db.commit()
if show:
self.logger.info('Database commit finished.')
def flush(self):
self.db.commit()
self.db.close()
self.logger.info('Database flushed.')
def save(self, journal, title, content, words, keys, commit=False):
self.index += 1
cursor = self.db.cursor()
cursor.execute("INSERT INTO pages VALUES (?, ?, ?, ?, ?)",
[self.index, journal, title, content, "+".join(keys)])
for word in words:
cursor.execute("INSERT INTO dicts VALUES (?, ?)",
[word, self.index])
info = '{} (Journal {})'.format(title, journal)
self.logger.info('Saved {}, index will be {}.'.format(
info, self.index))
self.commit_count += 1
if commit or self.commit_count % self.commit_rate == 0:
self.commit()
def search(self, keyword, sort=True):
# search
keys = re.sub('[\s+\.\!\/_,$%^*(+\"\']+|[+——！，。？、~@#￥%……&*·（）：；【】“”]+',
'', keyword)
self.logger.info('Get request for {}.'.format(keys))
keys = list(jieba.cut_for_search(keys))
cursor = self.db.cursor()
cursor.execute(
'SELECT * from dicts WHERE word in ({})'.format(', '.join(
'?' for _ in keys)), keys)
arts = set()
for art in cursor.fetchall():
arts.add(art[1])
arts = list(arts)
cursor.execute(
'SELECT * from pages WHERE id in ({})'.format(', '.join(
'?' for _ in arts)), arts)
arts = cursor.fetchall()
results = self.pagerank.sort(keys, arts)
for art in results:
print('Title: {}'.format(art[2]))
return
# pagerank
if sort:
return self.pagerank.sort(keys, arts)
return arts
class Aplicacion():
OptionList = [ "Todos los campos", "Título", "Abstract", "Palabras clave"]
OptionMod = ["Vectorial", "Booleano"]
OptionRank = ["No mostrar Ranking", "Mostrar Ranking"]
def __init__(self):
self.pagerank = PageRank()
self.ranking = None
self.res = None
lista = list(self.pagerank.autores)
lista.sort()
self.AutorList = lista
self.raiz = Tk()
self.raiz.geometry('950x500')
self.raiz.title('Buscador')
# Celda donde se muestran los resultados.
self.tinfo = scrolledtext.ScrolledText(self.raiz, width=50, height=30)
self.tinfo.grid(column = 0, row = 6)
# Celda donde se introduce la búsqueda.
self.tentry = Entry(self.raiz, width=40)
self.tentry.grid(column = 0, row = 5)
# Botón de buscar.
self.binfo = ttk.Button(self.raiz, text='Buscar', command=self.verinfo)
self.binfo.grid(column = 1, row =5)
# Botón de búsqueda personalizada.
self.bper = ttk.Button(self.raiz, text='Búsqueda personalizada', command=self.verper)
self.bper.grid(column = 1, row =3)
# Botón de mostrar Ranking inicial.
self.bpag = ttk.Button(self.raiz, text='Mostrar Ranking inicial', command=self.verpag)
self.bpag.grid(column = 2, row =3)
# Botón de salir.
self.bsalir = ttk.Button(self.raiz, text='Salir',
command=self.raiz.destroy)
self.bsalir.grid(column = 2, row = 5)
# Desplegable para elegir el sitio de búsqueda.
self.variable = tk.StringVar(self.raiz)
self.variable.set(self.OptionList[0])
opt = tk.OptionMenu(self.raiz, self.variable, *self.OptionList)
opt.config(width=30, font=('Helvetica', 12))
opt.grid(column = 0, row = 1)
# Desplegable para elegir el autor.
self.variable2 = tk.StringVar(self.raiz)
self.variable2.set(self.AutorList[0])
opt2 = ttk.Combobox(self.raiz, textvariable = self.variable2, values = self.AutorList)
opt2.config(width=30, font=('Helvetica', 12))
opt2.grid(column = 0, row = 3)
# Desplegable para elegir el modelo.
self.variable3 = tk.StringVar(self.raiz)
self.variable3.set(self.OptionMod[0])
opt3 = tk.OptionMenu(self.raiz, self.variable3, *self.OptionMod)
opt3.config(width=30, font=('Helvetica', 12))
opt3.grid(column = 2, row = 1)
# Desplegable para elegir si mostrar los pesos.
self.variable4 = tk.StringVar(self.raiz)
self.variable4.set(self.OptionRank[0])
opt3 = tk.OptionMenu(self.raiz, self.variable4, *self.OptionRank)
opt3.config(width=30, font=('Helvetica', 12))
opt3.grid(column = 1, row = 1)
# Muestra la opción del desplegable del sitio de búsqueda.
self.labelTest = tk.Label(text="", font=('Helvetica', 12), fg='red')
self.labelTest.grid(column = 0, row = 2)
self.variable.trace("w", self.callback)
# Muestra la opción del desplegable del autor.
self.labelTest2 = tk.Label(text="", font=('Helvetica', 12), fg='red')
self.labelTest2.grid(column = 0, row = 4)
self.variable2.trace("w", self.callback2)
# Muestra la opción del desplegable del modelo.
self.labelTest3 = tk.Label(text="", font=('Helvetica', 12), fg='red')
self.labelTest3.grid(column = 2, row = 2)
self.variable3.trace("w", self.callback3)
# Muestra la opción elegida de mostrar los pesos.
self.labelTest4 = tk.Label(text="", font=('Helvetica', 12), fg='red')
self.labelTest4.grid(column = 1, row = 2)
self.variable4.trace("w", self.callback4)
self.tentry.focus_set()
self.raiz.mainloop()
# Función que cambia el desplegable, cambia el sitio de búsqueda.
def callback(self, *args):
self.labelTest.configure(text="Has seleccionado {}".format(self.variable.get()))
# Función que cambia el desplegable, cambia el autor.
def callback2(self, *args):
self.labelTest2.configure(text="Has seleccionado {}".format(self.variable2.get()))
# Función que cambia el desplegable, cambia el modelo.
def callback3(self, *args):
self.labelTest3.configure(text="Has seleccionado {}".format(self.variable3.get()))
# Función que cambia el desplegable, cambia la elección de mostrar los pesos.
def callback4(self, *args):
self.labelTest4.configure(text="Has seleccionado {}".format(self.variable4.get()))
self.tinfo.delete("1.0", END)
texto_info = ""
if(not self.res):
texto_info = "No hay articulos que coincidan con su búsqueda \n"
else:
if(isinstance(self.res, list)):
if(self.variable4.get() == "No mostrar Ranking"):
for r in self.res:
texto_info += "- " + r.titulo + "\n"
else:
for i in range(len(self.res)):
texto_info += "- " + self.res[i].titulo + " - "+ str(self.ranking[i]) +"\n"
else:
texto_info = self.res
self.tinfo.insert("1.0", texto_info)
# Función llamada por el boton "Buscar". Realiza la búsqueda de la consulta
# introducida en el modelo fijado.
def verinfo(self):
self.tinfo.delete("1.0", END)
palabra = self.tentry.get()
if(self.variable3.get() == "Vectorial"):
self.res, self.ranking = self.pagerank.busquedapersonalizada(palabra, self.variable2.get(), False)
else:
self.res, self.ranking = self.pagerank.filtrar(palabra, self.variable.get())
texto_info = ""
if(not self.res):
texto_info = "No hay articulos que coincidan con su búsqueda \n"
else:
if(isinstance(self.res, list)):
if(self.variable4.get() == "No mostrar Ranking"):
for r in self.res:
texto_info += "- " + r.titulo + "\n"
else:
for i in range(len(self.res)):
texto_info += "- " + self.res[i].titulo + " - "+ str(self.ranking[i]) +"\n"
else:
texto_info = self.res
self.tinfo.insert("1.0", texto_info)
# Función llamada por el boton "Búsqueda personalizada". Realiza la búsqueda
# de la consulta introducida con el método de realimentación de consultas.
def verper(self):
self.tinfo.delete("1.0", END)
palabra = self.tentry.get()
self.res, self.ranking = self.pagerank.busquedapersonalizada(palabra, self.variable2.get(), True)
texto_info = ""
if(not self.res):
texto_info = "No hay articulos que coincidan con su búsqueda \n"
else:
if(isinstance(self.res, list)):
if(self.variable4.get() == "No mostrar Ranking"):
for r in self.res:
texto_info += "- " + r.titulo + "\n"
else:
for i in range(len(self.res)):
texto_info += "- " + self.res[i].titulo + " - "+ str(self.ranking[i]) +"\n"
else:
texto_info = self.res
self.tinfo.insert("1.0", texto_info)
# Función llamada por el boton "Mostrar Ranking Inicial". Muestra
# los documentos ordenados por el PageRank.
def verpag(self):
self.tinfo.delete("1.0", END)
self.res, self.ranking = self.pagerank.ordenarresultados(self.pagerank.nodos.copy())
texto_info = ""
if(not self.res):
texto_info = "No hay articulos que coincidan con su búsqueda \n"
else:
if(isinstance(self.res, list)):
if(self.variable4.get() == "No mostrar Ranking"):
for r in self.res:
texto_info += "- " + r.titulo + "\n"
else:
for i in range(len(self.res)):
texto_info += "- " + self.res[i].titulo + " - "+ str(self.ranking[i]) +"\n"
else:
texto_info = self.res
self.tinfo.insert("1.0", texto_info)
seCur = "d"
indCur = -1
seSeed = seasons[0]
for line in seedsF:
gal = line.split("\t")
if seCur not in gal[0]:
if indCur != -1:
seasons[indCur] = deepcopy(seSeed)
seCur = gal[0]
indCur += 1
seSeed = deepcopy(seasons[indCur])
te = gal[2][0:3]
seSeed[te].seed = float(gal[1][1:3])
seasons[indCur] = deepcopy(seSeed)
# j = seasons[0]
pr = PageRank()
# m = pr.rank(j)
# new_dict = dict(zip(m.values(), m.keys()))
# sorted_arr = sorted(new_dict.keys())
# for n in reversed(sorted_arr):
# print names[new_dict[n]] + " " + str(n)
mad = open("madresults.txt", "r")
curseason = "0"
curInd = -1
correct = 0
wrong = 0
correctAvg = 0.0
overCorrect = 0
wrongAvg = 0.0
lowestWrong = 0.0
def runFootball():
"""run pagerank on NCAA_football.csv"""
f = open('NCAA_football.csv')
graph = PageRank()
for line in f:
columns = line.split(',')
team1 = columns[0].strip()
value1 = int(columns[1])
team2 = columns[2].strip()
value2 = int(columns[3])
if value1 > value2:
graph.addEdge(team2, team1)
elif value1 < value2:
graph.addEdge(team1, team2)
else:
graph.addEdge(team2, team1)
graph.addEdge(team1, team2)
graph.printGraph()
iterations, ranks = graph.getPageRank()
print "Number of iterations:", iterations
print returnSorted(ranks)
def __init__(self):
self.db = Datasource()
self.pagerank = PageRank()
from pagerank import PageRank
graph = {}
graph["A"] = set(["B", "C", "E"])
graph["B"] = set(["C", "E"])
graph["C"] = set(["D"])
graph["D"] = set([])
graph["E"] = set([])
pr = PageRank(graph, .25, "test")
print "Itterations"
pr.runPageRankI(100)
print "\nConverge"
pr.runPageRankE(.000001)
from copy import deepcopy
names = dict()
e = open('teamname.txt', 'r')
for name in e:
sp = name.split('\t')
names[sp[0]] = sp[1][0:len(sp[1])-1]
f = open('2014se.txt', 'r')
seasons = []
seas = dict() #team, score
for line in f:
gam = line.split('\t')
if gam[2] in seas:
team1 = seas[gam[2]]
else:
team1 = Team(names[gam[2]])
if gam[4] in seas:
team2 = seas[gam[4]]
else:
team2 = Team(names[gam[4]])
team1.addGame(Game(gam[4], gam[3], gam[5]))
team2.addGame(Game(gam[2], gam[5], gam[3]))
seas[gam[2]] = deepcopy(team1)
seas[gam[4]] = deepcopy(team2)
seasons.append(deepcopy(seas))
j = seas
pr = PageRank()
m = pr.rank(j)
for team in j.keys():
print names[team] + '\t' + str(m[team]*169.6 + j[team].getScores()[0]*0.0701)