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
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
class PrudentialRegressorFO(BaseEstimator, RegressorMixin): def __init__(self, objective='reg:linear', learning_rate=0.045, min_child_weight=50, subsample=0.8, colsample_bytree=0.7, max_depth=7, n_estimators=700, nthread=-1, seed=0, n_buckets=8, initial_params=[-1.5, -2.6, -3.6, -1.2, -0.8, 0.04, 0.7, 3.6, #1., 2., 3., 4., 5., 6., 7. ], minimizer='BFGS', scoring=NegQWKappaScorer): self.objective = objective self.learning_rate = learning_rate self.min_child_weight = min_child_weight self.subsample = subsample self.colsample_bytree = colsample_bytree self.max_depth = max_depth self.n_estimators = n_estimators self.nthread = nthread self.seed = seed self.n_buckets = n_buckets self.initial_params = initial_params self.minimizer = minimizer self.scoring = scoring return 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 predict(self, X): from numpy import clip te_y_hat = self.xgb.predict(X, ntree_limit=self.xgb.booster().best_iteration) return clip(self.off.predict(te_y_hat), 1, 8) pass
class PrudentialRegressorFO(BaseEstimator, RegressorMixin): def __init__( self, objective='reg:linear', learning_rate=0.045, min_child_weight=50, subsample=0.8, colsample_bytree=0.7, max_depth=7, n_estimators=700, nthread=-1, seed=0, n_buckets=8, initial_params=[ -1.5, -2.6, -3.6, -1.2, -0.8, 0.04, 0.7, 3.6, #1., 2., 3., 4., 5., 6., 7. ], minimizer='BFGS', scoring=NegQWKappaScorer): self.objective = objective self.learning_rate = learning_rate self.min_child_weight = min_child_weight self.subsample = subsample self.colsample_bytree = colsample_bytree self.max_depth = max_depth self.n_estimators = n_estimators self.nthread = nthread self.seed = seed self.n_buckets = n_buckets self.initial_params = initial_params self.minimizer = minimizer self.scoring = scoring return 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 predict(self, X): from numpy import clip te_y_hat = self.xgb.predict( X, ntree_limit=self.xgb.booster().best_iteration) return clip(self.off.predict(te_y_hat), 1, 8) pass