social_model_stanford = CorrectSocialModelStanford() if user in network: social_model_ours = SimpleSocialModel(datasets, network[user], user) train = combination['train'] test = combination['test'] #--------------------------------------------------------------------------- model = StanfordModel() model.train(train, number_of_iterations = 10) if model.parameters != None: correct = 0 for check_in in test: real_venue = check_in["venue_id"] time = check_in["date"] predicted_venue = model.predict(time, train + test) if real_venue == predicted_venue: correct += 1 results_stanford.append(float(correct)/len(test)) #------------ if user in network: social_model_stanford.fit_model(model, network[user], datasets) correct = 0 for check_in in test: real_venue = check_in["venue_id"] date = check_in["date"] latitude = check_in["latitude"] longitude = check_in["longitude"] spatio_temporal_p = model.predict(time, train + test, True) social_p = social_model_stanford.get_probabilities(network[user], datasets, date, train + test)
combinations = Utils.break_dataset_in_folds(dataset, 5)
for combination in combinations:
train = combination['train']
test = combination['test']
#---------------------------------------------------------------------------
model = StanfordModel()
model.train(train, number_of_iterations = 10)
if model.parameters != None:
correct = 0
for check_in in test:
real_venue = check_in["venue_id"]
time = check_in["date"]
predicted_venue = model.predict(time, train + test)
if real_venue == predicted_venue:
correct += 1
results_stanford[day].append(float(correct)/len(test))
#---------------------------------------------------------------------------
all_check_ins = train + test
all_venues = [x["venue_id"] for x in all_check_ins]
n_values = Counter(all_venues)
coordinates = {}
for venue in all_venues:
latitude = np.median([x["latitude"] for x in all_check_ins if x["venue_id"] == venue])
longitude = np.median([x["longitude"] for x in all_check_ins if x["venue_id"] == venue])
coordinates[venue] = (latitude, longitude)
model = NCGModel(n_values, coordinates)
model.train(train, number_of_iterations = 10)
if user in network:
social_model_ours = SimpleSocialModel(datasets, network[user],
user)
train = combination['train']
test = combination['test']
#---------------------------------------------------------------------------
model = StanfordModel()
model.train(train, number_of_iterations=10)
if model.parameters != None:
correct = 0
for check_in in test:
real_venue = check_in["venue_id"]
time = check_in["date"]
predicted_venue = model.predict(time, train + test)
if real_venue == predicted_venue:
correct += 1
results_stanford.append(float(correct) / len(test))
#------------
if user in network:
social_model_stanford.fit_model(model, network[user],
datasets)
correct = 0
for check_in in test:
real_venue = check_in["venue_id"]
date = check_in["date"]
latitude = check_in["latitude"]
longitude = check_in["longitude"]
spatio_temporal_p = model.predict(
combinations = Utils.break_dataset_in_folds(dataset, 5)
for combination in combinations:
train = combination["train"]
test = combination["test"]
# ---------------------------------------------------------------------------
model = StanfordModel()
model.train(train, number_of_iterations=10)
if model.parameters != None:
correct = 0
for check_in in test:
real_venue = check_in["venue_id"]
time = check_in["date"]
predicted_venue = model.predict(time, train + test)
if real_venue == predicted_venue:
correct += 1
results_stanford[day].append(float(correct) / len(test))
# ---------------------------------------------------------------------------
all_check_ins = train + test
all_venues = [x["venue_id"] for x in all_check_ins]
n_values = Counter(all_venues)
coordinates = {}
for venue in all_venues:
latitude = np.median([x["latitude"] for x in all_check_ins if x["venue_id"] == venue])
longitude = np.median([x["longitude"] for x in all_check_ins if x["venue_id"] == venue])
coordinates[venue] = (latitude, longitude)
model = NCGModel(n_values, coordinates)
model.train(train, number_of_iterations=10)
