class Classifier(object):
def __init__(self, conf, task, train=None, test=None):
self.conf = conf
self.task = task
self.train_ = train
self.test_ = test
self.features = [
"hasWith", "hasIn", "simiBucket", "textPos", "hasOf", "hasAnd",
"startEntity", "distance", "hasFrom", "endEntity", "similarity",
"hasThan", "hasVerb"
]
self.labels = ["relation"]
self.num_round = 500
self.eval_set = list()
self.early_stopping_rounds = 20
self.classifier = XGBClassifier(max_depth=4,
learning_rate=0.1,
n_estimators=1000,
gamma=4,
verbosity=1,
objective='multi:softmax',
num_class=6,
booster='gbtree',
n_jobs=4,
seed=27)
def train(self):
train_X, test_X, train_y, test_y = train_test_split(
self.train_[self.features],
self.train_[self.labels],
test_size=0.4,
random_state=42)
self.eval_set = [(train_X.values, train_y.values),
(test_X.values, test_y.values)]
self.classifier.fit(train_X.values,
train_y.values,
eval_metric='merror',
eval_set=self.eval_set,
early_stopping_rounds=self.early_stopping_rounds,
verbose=True)
self.classifier.save_model(self.conf.model_path.format(self.task))
return 'Model has been saved!'
def test(self):
test_set = self.test_[self.features].values
self.classifier.load_model(self.conf.model_path.format(self.task))
self.classifier._le = LabelEncoder().fit([
'USAGE', 'TOPIC', 'MODEL-FEATURE', 'PART_WHOLE', 'RESULT',
'COMPARE'
])
pred = self.classifier.predict(test_set)
predictions = pd.concat([
self.test_[self.features],
pd.DataFrame(pred, columns=["relation"])
],
axis=1)
return predictions
start_status = True
count = 0
month_start_time = datetime.datetime.now()
xgb = XGBClassifier(learning_rate=0.1,
n_estimators=120,
max_depth=3,
min_child_weight=7,
gamma=0.7,
subsample=0.8,
colsample_bytree=0.8,
objective='binary:logistic',
nthread=4,
scale_pos_weight=1,
reg_alpha=0.01,
seed=27)
xgb.load_model('models/xgb.model')
conn = tidbReader.pool.connection()
engine = create_engine(
'mysql+pymysql://root:[email protected]:3306/match_result?charset=utf8'
)
mysql_conn = engine.connect()
order_inform, current_inform = [], None
recievers, teachers_unit, teachers_unit_spare = [], [], []
for _ in range(4):
recievers.append({'status': 0, 'orders': []})
while True:
if month_begin(past_start_time=month_start_time):
teachers_unit, teachers_unit_spare = teachers_unit_reset(
teachers_unit, teachers_unit_spare)
recievers = recievers_reset(recievers)
month_start_time = datetime.datetime.now()
