#we separe between categorical variables and date variables to preprocess it separately
categorical_val.remove('answer_creation_date')
categorical_val.remove('group_creation_date')
categorical_val.remove('request_creation_date')
categorical_val.remove('victim_of_violence_type')
date_columns = [
'answer_creation_date', 'group_creation_date', 'request_creation_date'
]
X_test, y_test = preprocess(requests_test, categorical_val, date_columns)
# Drop Nan value because otherwise there are memory errors
X_test['granted_number_of_nights'] = y_test
X_test = X_test.dropna()
y_test = X_test['granted_number_of_nights']
X_test = X_test.drop(columns=['granted_number_of_nights'])
#preprocess the datasets for TabNet
X_test_tab, y_test_tab = preprocess_for_tabnet(X_test, y_test)
# retrieve model
# Not working with version <3.7
PATH = '../model_zoo/TabNet_model.zip'
clf = TabNetClassifier()
clf.load_model(PATH)
#run inference
start = time.time()
preds = clf.predict_proba(X_test_tab)
end = time.time()
score = competition_scorer(y_test_tab, preds)
print('time per prediction:', (end - start) / len(X_test))
print('The competition score on test data', score)
class TabNetCv1(TabNetBase):
def act_init_ai(self, is_test=False):
if is_test is True:
_TPI(self, locals())
else:
MATCH = self.env.match_loader
self.ai = TabNetClassifier(n_steps=10,
input_dim=MATCH.count_cols *
MATCH.count_players,
cat_dims=self.cat_dims,
cat_emb_dim=self.cat_emb_dim,
cat_idxs=self.cat_idxs,
device_name='cuda')
def act_modify_data(self, is_test=False):
if is_test is True:
_TPI(self, locals())
else:
MATCH = self.env.match_loader
for i in range(len(self.y_train)):
result = MATCH.train_plus[i] - MATCH.train_minus[i]
if result < 0:
result = 0
elif result == 0:
result = 1
else:
result = 2
self.y_train[i] = result
for i in range(len(self.y_valid)):
result = MATCH.valid_plus[i] - MATCH.valid_minus[i]
if result < 0:
result = 0
elif result == 0:
result = 1
else:
result = 2
self.y_valid[i] = result
for i in range(len(self.y_valid)):
result = MATCH.test_plus[i] - MATCH.test_minus[i]
if result < 0:
result = 0
elif result == 0:
result = 1
else:
result = 2
self.y_test[i] = result
def load_model(self):
print("Loading Model")
print(self.save_name + ".zip")
self.ai.load_model(self.save_name + ".zip")
def get_result(self, my_team: list, your_team: list):
# tmp
'''
MATCH = self.env.match_loader
self.act_register_data(data=MATCH.act_get(is_flat=True))
self.act_modify_data()
self.ai = TabNetClassifier(
n_steps=10,
input_dim=MATCH.count_cols * MATCH.count_players,
cat_dims=self.cat_dims,
cat_emb_dim=self.cat_emb_dim,
cat_idxs=self.cat_idxs,
device_name='cuda'
)
self.ai.fit(
X_train=self.X_train, y_train=self.y_train,
X_valid=self.X_valid, y_valid=self.y_valid,
max_epochs=self.epochs,
patience=500,
batch_size=512,
drop_last=False
)
'''
try:
x = list()
for player in my_team:
x.append(0)
x.extend(player)
for player in your_team:
x.append(1)
x.extend(player)
x = np.array(x)
x = np.array([x])
print(x.shape)
# predictions = self.ai.predict(x)
probability = self.ai.predict_proba(x)
print(probability)
return probability[0][2]
except Exception as e:
print(e)