def fit(self, X, y):
from xgboost import XGBRegressor
if not KAGGLE:
from OptimizedOffsetRegressor import DigitizedOptimizedOffsetRegressor
self.xgb = XGBRegressor(objective=self.objective,
learning_rate=self.learning_rate,
min_child_weight=self.min_child_weight,
subsample=self.subsample,
colsample_bytree=self.colsample_bytree,
max_depth=self.max_depth,
n_estimators=self.n_estimators,
nthread=self.nthread,
missing=0.0,
seed=self.seed)
from OptimizedOffsetRegressor import FullDigitizedOptimizedOffsetRegressor
self.off = FullDigitizedOptimizedOffsetRegressor(
n_buckets=self.n_buckets,
# basinhopping=True,
initial_params=self.initial_params,
minimizer=self.minimizer,
scoring=self.scoring)
self.xgb.fit(X, y)
tr_y_hat = self.xgb.predict(
X, ntree_limit=self.xgb.booster().best_iteration)
print('Train score is:', -self.scoring(tr_y_hat, y))
self.off.fit(tr_y_hat, y)
print("Offsets:", self.off.params)
return self
def __call__(self, i, te_y_hat, ytest):
print('XGB[{}] Test score is:'.format(i + 1),
-self.scoring(te_y_hat, ytest))
from OptimizedOffsetRegressor import FullDigitizedOptimizedOffsetRegressor
off = FullDigitizedOptimizedOffsetRegressor(
n_buckets=self.n_buckets,
basinhopping=True,
initial_params=self.initial_params,
minimizer=self.minimizer,
scoring=self.scoring)
off.fit(te_y_hat, ytest)
print("Offsets[{}]:".format(i + 1), off.params)
return off
