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XGBoost hyper parameter optimization using bayes_opt
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from bayes_opt import BayesianOptimization | |
from sklearn.cross_validation import KFold | |
import xgboost as xgb | |
def xgbCv(train, features, numRounds, eta, gamma, maxDepth, minChildWeight, subsample, colSample): | |
# prepare xgb parameters | |
params = { | |
"objective": "reg:linear", | |
"booster" : "gbtree", | |
"eval_metric": "mae", | |
"tree_method": 'auto', | |
"silent": 1, | |
"eta": eta, | |
"max_depth": int(maxDepth), | |
"min_child_weight" : minChildWeight, | |
"subsample": subsample, | |
"colsample_bytree": colSample, | |
"gamma": gamma | |
} | |
cvScore = kFoldValidation(train, features, params, int(numRounds), nFolds = 3) | |
print('CV score: {:.6f}'.format(cvScore)) | |
return -1.0 * cvScore # invert the cv score to let bayopt maximize | |
def bayesOpt(train, features): | |
ranges = { | |
'numRounds': (1000, 5000), | |
'eta': (0.001, 0.3), | |
'gamma': (0, 25), | |
'maxDepth': (1, 10), | |
'minChildWeight': (0, 10), | |
'subsample': (0, 1), | |
'colSample': (0, 1) | |
} | |
# proxy through a lambda to be able to pass train and features | |
optFunc = lambda numRounds, eta, gamma, maxDepth, minChildWeight, subsample, colSample: xgbCv(train, features, numRounds, eta, gamma, maxDepth, minChildWeight, subsample, colSample) | |
bo = BayesianOptimization(optFunc, ranges) | |
bo.maximize(init_points = 50, n_iter = 5, kappa = 2, acq = "ei", xi = 0.0) | |
bestMAE = round((-1.0 * bo.res['max']['max_val']), 6) | |
print("\n Best MAE found: %f" % bestMAE) | |
print("\n Parameters: %s" % bo.res['max']['max_params']) | |
def kFoldValidation(train, features, xgbParams, numRounds, nFolds, target='loss'): | |
kf = KFold(len(train), n_folds = nFolds, shuffle = True) | |
fold_score=[] | |
for train_index, cv_index in kf: | |
# split train/validation | |
X_train, X_valid = train[features].as_matrix()[train_index], train[features].as_matrix()[cv_index] | |
y_train, y_valid = (train[target].as_matrix()[train_index]), (train[target].as_matrix()[cv_index]) | |
dtrain = xgb.DMatrix(X_train, y_train) | |
dvalid = xgb.DMatrix(X_valid, y_valid) | |
watchlist = [(dtrain, 'train'), (dvalid, 'eval')] | |
gbm = xgb.train(xgbParams, dtrain, numRounds, evals = watchlist, early_stopping_rounds = 100) | |
score = gbm.best_score | |
fold_score.append(score) | |
return np.mean(fold_score) |
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