GridSearchCV on XGBoost model gives error

Question

I made a XGBoost classifier in python. I tried to do GridSearch to find optimal parameters like this

grid_search = GridSearchCV(model, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold)
grid_result = grid_search.fit(X, Y)

print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))

When running the Search I get an error like this

[Errno 28] No space left on device

I used a slightly big size dataset. Where, X.shape = (38932, 1002) Y.shape= (38932,)

What is the issue.? How to solve this.?

Is this because the dataset is too huge for my machine.? If so what can I do to preform GridSearch on this dataset.?

please include a description of the dataset either by providing a sample and shape or a link to the data — sgDysregulation
Is this a similar issue you are experiencing: stackoverflow.com/a/6999259/1577947 — Jarad

sgDysregulation sgDysregulation · Accepted Answer · 2017-10-15T17:36:28

The error indicates that the shared memory is running out, It's likely that increasing the number of kfolds and/or adjusting the number of threads used i.e. n_jobs will resolve this issue .Here is a working example using xgboost

import xgboost as xgb
from sklearn.model_selection import GridSearchCV
from sklearn import datasets

clf = xgb.XGBClassifier()
parameters = {
    'n_estimators': [100, 250, 500],
    'max_depth': [6, 9, 12],
    'subsample': [0.9, 1.0],
    'colsample_bytree': [0.9, 1.0],
}
bsn = datasets.load_iris()
X, Y = bsn.data, bsn.target
grid = GridSearchCV(clf,
                    parameters, n_jobs=4,
                    scoring="neg_log_loss",
                    cv=3)

grid.fit(X, Y)
print("Best: %f using %s" % (grid.best_score_, grid.best_params_))

means = grid.cv_results_['mean_test_score']
stds = grid.cv_results_['std_test_score']
params = grid.cv_results_['params']

for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))

The outputs is

Best: -0.121569 using {'colsample_bytree': 0.9, 'max_depth': 6, 'n_estimators': 100, 'subsample': 1.0}
-0.126334 (0.080193) with: {'colsample_bytree': 0.9, 'max_depth': 6, 'n_estimators': 100, 'subsample': 0.9}
-0.121569 (0.081561) with: {'colsample_bytree': 0.9, 'max_depth': 6, 'n_estimators': 100, 'subsample': 1.0}
-0.139359 (0.075462) with: {'colsample_bytree': 0.9, 'max_depth': 6, 'n_estimators': 250, 'subsample': 0.9}
-0.131887 (0.076174) with: {'colsample_bytree': 0.9, 'max_depth': 6, 'n_estimators': 250, 'subsample': 1.0}
-0.148302 (0.074890) with: {'colsample_bytree': 0.9, 'max_depth': 6, 'n_estimators': 500, 'subsample': 0.9}
-0.135973 (0.076167) with: {'colsample_bytree': 0.9, 'max_depth': 6, 'n_estimators': 500, 'subsample': 1.0}
-0.126334 (0.080193) with: {'colsample_bytree': 0.9, 'max_depth': 9, 'n_estimators': 100, 'subsample': 0.9}
-0.121569 (0.081561) with: {'colsample_bytree': 0.9, 'max_depth': 9, 'n_estimators': 100, 'subsample': 1.0}
-0.139359 (0.075462) with: {'colsample_bytree': 0.9, 'max_depth': 9, 'n_estimators': 250, 'subsample': 0.9}
-0.131887 (0.076174) with: {'colsample_bytree': 0.9, 'max_depth': 9, 'n_estimators': 250, 'subsample': 1.0}
-0.148302 (0.074890) with: {'colsample_bytree': 0.9, 'max_depth': 9, 'n_estimators': 500, 'subsample': 0.9}
-0.135973 (0.076167) with: {'colsample_bytree': 0.9, 'max_depth': 9, 'n_estimators': 500, 'subsample': 1.0}
-0.126334 (0.080193) with: {'colsample_bytree': 0.9, 'max_depth': 12, 'n_estimators': 100, 'subsample': 0.9}
-0.121569 (0.081561) with: {'colsample_bytree': 0.9, 'max_depth': 12, 'n_estimators': 100, 'subsample': 1.0}
-0.139359 (0.075462) with: {'colsample_bytree': 0.9, 'max_depth': 12, 'n_estimators': 250, 'subsample': 0.9}
-0.131887 (0.076174) with: {'colsample_bytree': 0.9, 'max_depth': 12, 'n_estimators': 250, 'subsample': 1.0}
-0.148302 (0.074890) with: {'colsample_bytree': 0.9, 'max_depth': 12, 'n_estimators': 500, 'subsample': 0.9}
-0.135973 (0.076167) with: {'colsample_bytree': 0.9, 'max_depth': 12, 'n_estimators': 500, 'subsample': 1.0}
-0.132745 (0.080433) with: {'colsample_bytree': 1.0, 'max_depth': 6, 'n_estimators': 100, 'subsample': 0.9}
-0.127030 (0.077692) with: {'colsample_bytree': 1.0, 'max_depth': 6, 'n_estimators': 100, 'subsample': 1.0}
-0.146143 (0.077623) with: {'colsample_bytree': 1.0, 'max_depth': 6, 'n_estimators': 250, 'subsample': 0.9}
-0.140400 (0.074645) with: {'colsample_bytree': 1.0, 'max_depth': 6, 'n_estimators': 250, 'subsample': 1.0}
-0.153624 (0.077594) with: {'colsample_bytree': 1.0, 'max_depth': 6, 'n_estimators': 500, 'subsample': 0.9}
-0.143833 (0.073645) with: {'colsample_bytree': 1.0, 'max_depth': 6, 'n_estimators': 500, 'subsample': 1.0}
-0.132745 (0.080433) with: {'colsample_bytree': 1.0, 'max_depth': 9, ...

GridSearchCV on XGBoost model gives error

1 Answers