How mlxtend StackingRegressor with multiple cpu?
I would like to use mlxtend StackingRegressor to ensemble XGBoost,LGBM and Catboost .But I am not sure how much cpu I will use in this method.
For example:
In XGboost:
import xgboost as xgb
xgb_pars = {'nthread': -1}
xgb1=XGBRegressor(**xgb_pars)
Then I know I will use up all cpu core in this algorithm
But what if I try it with mlxtend StackingRegressor?
I guess this method will use cpu that I arranged to each algorithm.
Example:
XGBoost:2 LGBM:2 CatBoost:2 Meta regressor:1
So finally I am using 7 cores.
machine-learning xgboost lightgbm catboost mlxtend
asked Nov 22 at 16:46
Hq Li
34
add a comment |
I would like to use mlxtend StackingRegressor to ensemble XGBoost,LGBM and Catboost .But I am not sure how much cpu I will use in this method.
For example:
In XGboost:
import xgboost as xgb
xgb_pars = {'nthread': -1}
xgb1=XGBRegressor(**xgb_pars)
Then I know I will use up all cpu core in this algorithm
But what if I try it with mlxtend StackingRegressor?
I guess this method will use cpu that I arranged to each algorithm.
Example:
XGBoost:2 LGBM:2 CatBoost:2 Meta regressor:1
So finally I am using 7 cores.
machine-learning xgboost lightgbm catboost mlxtend
asked Nov 22 at 16:46
Hq Li
34
add a comment |
I would like to use mlxtend StackingRegressor to ensemble XGBoost,LGBM and Catboost .But I am not sure how much cpu I will use in this method.
For example:
In XGboost:
import xgboost as xgb
xgb_pars = {'nthread': -1}
xgb1=XGBRegressor(**xgb_pars)
Then I know I will use up all cpu core in this algorithm
But what if I try it with mlxtend StackingRegressor?
I guess this method will use cpu that I arranged to each algorithm.
Example:
XGBoost:2 LGBM:2 CatBoost:2 Meta regressor:1
So finally I am using 7 cores.
machine-learning xgboost lightgbm catboost mlxtend
asked Nov 22 at 16:46
Hq Li
34
I would like to use mlxtend StackingRegressor to ensemble XGBoost,LGBM and Catboost .But I am not sure how much cpu I will use in this method.
For example:
In XGboost:
import xgboost as xgb
xgb_pars = {'nthread': -1}
xgb1=XGBRegressor(**xgb_pars)
Then I know I will use up all cpu core in this algorithm
But what if I try it with mlxtend StackingRegressor?
I guess this method will use cpu that I arranged to each algorithm.
Example:
XGBoost:2 LGBM:2 CatBoost:2 Meta regressor:1
So finally I am using 7 cores.
machine-learning xgboost lightgbm catboost mlxtend
machine-learning xgboost lightgbm catboost mlxtend
asked Nov 22 at 16:46
Hq Li
34
asked Nov 22 at 16:46
Hq Li
34
asked Nov 22 at 16:46
Hq Li
34
asked Nov 22 at 16:46
Hq Li
34
asked Nov 22 at 16:46
Hq Li
34
34
add a comment |
add a comment |
1 Answer
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Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.
BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples
answered Dec 1 at 15:35
Mykhailo Lisovyi
887110
add a comment |
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Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.
BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples
answered Dec 1 at 15:35
Mykhailo Lisovyi
887110
add a comment |
Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.
BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples
answered Dec 1 at 15:35
Mykhailo Lisovyi
887110
add a comment |
Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.
BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples
answered Dec 1 at 15:35
Mykhailo Lisovyi
887110
Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.
BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples
answered Dec 1 at 15:35
Mykhailo Lisovyi
887110
answered Dec 1 at 15:35
Mykhailo Lisovyi
887110
answered Dec 1 at 15:35
Mykhailo Lisovyi
887110
answered Dec 1 at 15:35
Mykhailo Lisovyi
887110
887110
add a comment |
add a comment |
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