sys.path.insert(0, os.getcwd())
import workdir.classes.config
from qml.cv import QCV
from qml.models import QXgb, QXgb2
from workdir.classes.models import qm
if __name__ == "__main__":
CV_SCORE_TO_STOP = 0.5417
DATAS = [27]
EVALS_ROUNDS = 100000
rounds = EVALS_ROUNDS
cv = QCV(qm)
counter = 0
def fn(params):
global counter
counter += 1
data_id = params['data_id']
del params['data_id']
params['num_boost_rounds'] = int(1.3**params['num_boost_rounds'])
params['eta'] = round(1 / (1.3**params['eta']), 4)
params['lr_decay'] = round(1 / (2**params['lr_decay']), 4)
params['subsample'] = params['subsample'] / 10
params['colsample_bytree'] = params['colsample_bytree'] / 10
params['colsample_bylevel'] = params['colsample_bylevel'] / 10
import datetime
import numpy as np
from hyperopt import hp, fmin, tpe
import os
import sys
sys.path.insert(0, os.getcwd())
import workdir.classes.config
from qml.cv import QCV
from qml.models import QXgb, QAvg, QAvgOneModelData
from workdir.classes.models import qm
cv = QCV(qm)
# model_id = qm.add_by_params(
# QXgb(
# ** {"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.8, "eta": 0.004, "eval_metric": "logloss",
# "gamma": 0.2, "max_depth": 4, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}
# ),
# 'hyperopt xgb', level=-1
# )
model_id = qm.add_by_params(QAvgOneModelData(416, 2), level=-2)
cv.features_sel_del(
model_id,
23,
early_stop_cv=lambda x: x < 0.557, # minmax
log_file='workdir/logs/data23_sub_cols3.txt',
exclude=[
from hyperopt import hp, fmin, tpe
import os
import sys
sys.path.insert(0, os.getcwd())
import workdir.classes.config
from qml.cv import QCV
from qml.helpers import get_engine
from qml.models import QXgb, QAvg, QRankedAvg, QRankedByLineAvg, QStackModel
from workdir.classes.models import qm
if __name__ == "__main__":
_, conn = get_engine()
cv = QCV(qm)
CV_SCORE_TO_SELECT = 0.56
CV_SCORE_TO_STOP = 0.5416
ROUNDS = 20000
res = conn.execute(
"""
select data_id, cls, descr,
substring_index(group_concat(model_id order by cv_score), ',', 5) as models
from qml_results r
inner join qml_models m using(model_id)
where m.level=1 and cv_score < {} and data_id in (66, 69, 266, 269, 47 )
import datetime
import numpy as np
from hyperopt import hp, fmin, tpe
import os
import sys
sys.path.insert(0, os.getcwd())
import workdir.classes.config
from qml.cv import QCV
from qml.models import QXgb, QAvg, QAvgOneModelData
from workdir.classes.models import qm
cv = QCV(qm)
# model_id = qm.add_by_params(
# QXgb(
# ** {"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.8, "eta": 0.004, "eval_metric": "logloss",
# "gamma": 0.2, "max_depth": 4, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}
# ),
# 'hyperopt xgb', level=-1
# )
model_id = qm.add_by_params(QAvgOneModelData(416, 2), level=-2)
cv.features_sel_del(
model_id,
13,
early_stop_cv=lambda x: x < 0.53, # minmax
log_file='workdir/logs/data13_sub_cols2.txt',
exclude=[],
import datetime
import numpy as np
from hyperopt import hp, fmin, tpe
import os
import sys
sys.path.insert(0, os.getcwd())
import workdir.classes.config
from qml.cv import QCV
from qml.models import QXgb, QAvg, QAvgOneModelData
from workdir.classes.models import qm
cv = QCV(qm)
model_id = qm.add_by_params(
QXgb(
** {"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.8, "eta": 0.004, "eval_metric": "logloss",
"gamma": 0.2, "max_depth": 4, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}
),
'hyperopt xgb', level=-1
)
model_id =qm.add_by_params(QAvgOneModelData(model_id, 3), level=-2)
cv.features_sel_del(model_id, 66, early_stop_cv=lambda x: x>0.5414, log_file='workdir/logs/feat19.txt', exclude=[])
import datetime
import numpy as np
from hyperopt import hp, fmin, tpe
import os
import sys
sys.path.insert(0, os.getcwd())
import workdir.classes.config
from qml.cv import QCV
from qml.models import QXgb, QAvg, QAvgOneModelData
from workdir.classes.models import qm
cv = QCV(qm)
# model_id = qm.add_by_params(
# QXgb(
# ** {"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.8, "eta": 0.004, "eval_metric": "logloss",
# "gamma": 0.2, "max_depth": 4, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}
# ),
# 'hyperopt xgb', level=-1
# )
model_id =qm.add_by_params(QAvgOneModelData(416, 2), level=-2)
cv.features_sel_del(model_id, 3, early_stop_cv=lambda x: x>0.53, log_file='workdir/logs/feat19.txt', exclude=['category_2_82'])
import datetime
import numpy as np
import os
import sys
sys.path.insert(0, os.getcwd())
from hyperopt import hp, fmin, tpe
import workdir.classes.config
from qml.cv import QCV
from qml.models import QXgb, QAvg, QAvgOneModelData
from workdir.classes.models import qm
cv = QCV(qm)
model_id = qm.add_by_params(
QXgb(
**{
"alpha": 0.008,
"booster": "gbtree",
"colsample_bylevel": 0.9,
"colsample_bytree": 0.9,
"eta": 0.0024,
"eval_metric": "logloss",
"gamma": 0.04,
"max_depth": 4,
"num_boost_round": 2619,
"objective": "binary:logistic",
"subsample": 0.7,
"tree_method": "hist"
import datetime
import numpy as np
from hyperopt import hp, fmin, tpe
import os
import sys
sys.path.insert(0, os.getcwd())
import workdir.classes.config
from qml.cv import QCV
from qml.models import QXgb, QAvg, QAvgOneModelData
from workdir.classes.models import qm
cv = QCV(qm)
# model_id = qm.add_by_params(
# QXgb(
# ** {"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.8, "eta": 0.004, "eval_metric": "logloss",
# "gamma": 0.2, "max_depth": 4, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}
# ),
# 'hyperopt xgb', level=-1
# )
model_id =qm.add_by_params(QAvgOneModelData(416, 2), level=-2)
cv.features_sel_del(
model_id, 19,
early_stop_cv=lambda x: x<0.557, # minmax
log_file='workdir/logs/data19_sub_cols1.txt',
exclude=[