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xgboost GridSearchCV take too long or does not goes to the next step

0

$begingroup$

just strange

%%time
xgb = xgb.XGBRegressor(n_estimators=500, learning_rate=0.07, gamma=0, subsample=0.75, colsample_bytree=1,
 max_depth=7, tree_method='gpu_exact')

this code takes around Wall time: 866 ms.

but when I do the gridsearchCV it does not goes to the next step

even though I gave only one parameter

 %%time
 xgb = XGBClassifier(tree_method='gpu_exact',verbose_eval=True, silence=False)
 kfold = StratifiedKFold(n_splits=10, random_state=0)
 xgb_param_grid = 
 'learning_rate': [0.08,0.09],
 'random_state': [0],
 'max_depth': [8,9],
 'n_estimators': [400,500]
 
 xgbGrid = gsRFC = GridSearchCV(xgb,param_grid = xgb_param_grid, cv=5, scoring="neg_mean_squared_error", n_jobs= 10, verbose = 1)
 xgbGrid.fit(X,y)
 xgb_best = xgbGrid.best_estimator_

for my understanding, this should not take that long.<br/>
it d

does not go to the next step I do not sure this even working or not

it stop with

Fitting 5 folds for each of 8 candidates, totalling 40 fits
[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent
workers.

data set size is (15035, 22)
am I doing something wrong?

machine-learning xgboost kaggle grid-search gridsearchcv

share

asked 3 mins ago

monkmonk

164

$endgroup$

add a comment | 

0

$begingroup$

just strange

%%time
xgb = xgb.XGBRegressor(n_estimators=500, learning_rate=0.07, gamma=0, subsample=0.75, colsample_bytree=1,
 max_depth=7, tree_method='gpu_exact')

this code takes around Wall time: 866 ms.

but when I do the gridsearchCV it does not goes to the next step

even though I gave only one parameter

 %%time
 xgb = XGBClassifier(tree_method='gpu_exact',verbose_eval=True, silence=False)
 kfold = StratifiedKFold(n_splits=10, random_state=0)
 xgb_param_grid = 
 'learning_rate': [0.08,0.09],
 'random_state': [0],
 'max_depth': [8,9],
 'n_estimators': [400,500]
 
 xgbGrid = gsRFC = GridSearchCV(xgb,param_grid = xgb_param_grid, cv=5, scoring="neg_mean_squared_error", n_jobs= 10, verbose = 1)
 xgbGrid.fit(X,y)
 xgb_best = xgbGrid.best_estimator_

for my understanding, this should not take that long.<br/>
it d

does not go to the next step I do not sure this even working or not

it stop with

Fitting 5 folds for each of 8 candidates, totalling 40 fits
[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent
workers.

data set size is (15035, 22)
am I doing something wrong?

machine-learning xgboost kaggle grid-search gridsearchcv

share

asked 3 mins ago

monkmonk

164

$endgroup$

add a comment | 

$begingroup$

just strange

%%time
xgb = xgb.XGBRegressor(n_estimators=500, learning_rate=0.07, gamma=0, subsample=0.75, colsample_bytree=1,
 max_depth=7, tree_method='gpu_exact')

this code takes around Wall time: 866 ms.

but when I do the gridsearchCV it does not goes to the next step

even though I gave only one parameter

 %%time
 xgb = XGBClassifier(tree_method='gpu_exact',verbose_eval=True, silence=False)
 kfold = StratifiedKFold(n_splits=10, random_state=0)
 xgb_param_grid = 
 'learning_rate': [0.08,0.09],
 'random_state': [0],
 'max_depth': [8,9],
 'n_estimators': [400,500]
 
 xgbGrid = gsRFC = GridSearchCV(xgb,param_grid = xgb_param_grid, cv=5, scoring="neg_mean_squared_error", n_jobs= 10, verbose = 1)
 xgbGrid.fit(X,y)
 xgb_best = xgbGrid.best_estimator_

for my understanding, this should not take that long.<br/>
it d

does not go to the next step I do not sure this even working or not

it stop with

Fitting 5 folds for each of 8 candidates, totalling 40 fits
[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent
workers.

data set size is (15035, 22)
am I doing something wrong?

machine-learning xgboost kaggle grid-search gridsearchcv

share

asked 3 mins ago

monkmonk

164

$endgroup$

%%time
xgb = xgb.XGBRegressor(n_estimators=500, learning_rate=0.07, gamma=0, subsample=0.75, colsample_bytree=1,
 max_depth=7, tree_method='gpu_exact')

 %%time
 xgb = XGBClassifier(tree_method='gpu_exact',verbose_eval=True, silence=False)
 kfold = StratifiedKFold(n_splits=10, random_state=0)
 xgb_param_grid = 
 'learning_rate': [0.08,0.09],
 'random_state': [0],
 'max_depth': [8,9],
 'n_estimators': [400,500]
 
 xgbGrid = gsRFC = GridSearchCV(xgb,param_grid = xgb_param_grid, cv=5, scoring="neg_mean_squared_error", n_jobs= 10, verbose = 1)
 xgbGrid.fit(X,y)
 xgb_best = xgbGrid.best_estimator_

for my understanding, this should not take that long.<br/>
it d

share

asked 3 mins ago

monkmonk

164

share

asked 3 mins ago

monkmonk

164

asked 3 mins ago

monkmonk

164

asked 3 mins ago

monkmonk

164