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@parulnith
Forked from jphall663/xgb_grid.py
Created April 7, 2022 14:25
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Manual XGBoost grid search (Python)
iter_ = 0
best_error = 0
best_iter = 0
best_model = None
col_sample_rates = [0.1, 0.5, 0.9]
subsamples = [0.1, 0.5, 0.9]
etas = [0.01, 0.001]
max_depths = [3, 6, 12, 15, 18]
reg_alphas = [0.01, 0.001]
reg_lambdas = [0.01, 0.001]
ntrees = [200, 400]
total_models = len(col_sample_rates)*len(subsamples)*len(etas)*len(max_depths)*len(reg_alphas)*len(reg_lambdas)*len(ntrees)
# determine mean y value in training
y_mean = train[y].mean()
for col_sample_rate in col_sample_rates:
for subsample in subsamples:
for eta in etas:
for max_depth in max_depths:
for reg_alpha in reg_alphas:
for reg_lambda in reg_lambdas:
for ntree in ntrees:
tic = time.time()
print('---------- ---------')
print('Training model %d of %d ...' % (iter_ + 1, total_models))
print('col_sample_rate =', col_sample_rate)
print('subsample =', subsample)
print('eta =', eta)
print('max_depth =', max_depth)
print('reg_alpha =', reg_alpha)
print('reg_lambda =', reg_lambda)
print('ntree =', ntree)
params = {
'base_score': y_mean,
'booster': 'gbtree',
'colsample_bytree': col_sample_rate,
'eta': eta,
'eval_metric': 'auc',
'max_depth': max_depth,
'nthread': 4,
'objective': 'binary:logistic',
'reg_alpha': reg_alpha,
'reg_lambda': reg_lambda,
'monotone_constraints': mono_constraints,
'seed': 12345,
'silent': 0,
'subsample': subsample}
watchlist = [(dtrain, 'train'), (dtest, 'eval')]
model = xgb.train(
params,
dtrain,
ntree,
early_stopping_rounds=100,
evals=watchlist,
verbose_eval=False)
print('Model %d trained in %.2f s.' % (iter_, time.time()-tic))
print('Model %d best score = %.4f' % (iter_, model.best_score))
if model.best_score > best_error:
best_error = model.best_score
best_iter = iter_
best_model = model
print('Best so far!!!')
print('Best error =', best_error)
iter_ += 1
print('Best model found at iteration: %d, with error: %.4f.' % (best_iter + 1, best_error))
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