I am trying to tune my XGBClassifier model. But I am failing to do so. Please find the code below and please help me clean and edit the code.
import csv
from hyperopt import STATUS_OK
from timeit import default_timer as timer
MAX_EVALS = 200
N_FOLDS = 10
def objective(params, n_folds = N_FOLDS):
"""Objective function for Gradient Boosting Machine Hyperparameter Optimization"""
# Keep track of evals
global ITERATION
ITERATION += 1
# Retrieve the subsample if present otherwise set to 1.0
subsample = params['boosting_type'].get('subsample', 1.0)
# Extract the boosting type
params['boosting_type'] = params['boosting_type']['boosting_type']
params['subsample'] = subsample
# Make sure parameters that need to be integers are integers
for parameter_name in ['num_leaves', 'subsample_for_bin',
'min_child_samples']:
params[parameter_name] = int(params[parameter_name])
start = timer()
# Perform n_folds cross validation
cv_results = lgb.cv(params, train_set, num_boost_round = 10000,
nfold = n_folds, early_stopping_rounds = 100,
metrics = 'auc', seed = 50)
run_time = timer() - start
# Extract the best score
best_score = np.max(cv_results['auc-mean'])
# Loss must be minimized
loss = 1 - best_score
# Boosting rounds that returned the highest cv score
n_estimators = int(np.argmax(cv_results['auc-mean']) + 1)
# Write to the csv file ('a' means append)
of_connection = open(out_file, 'a')
writer = csv.writer(of_connection)
writer.writerow([loss, params, ITERATION, n_estimators,
run_time])
# Dictionary with information for evaluation
return {'loss': loss, 'params': params, 'iteration': ITERATION,
'estimators': n_estimators, 'train_time': run_time,
'status': STATUS_OK}
I believe I am doing something wrong in the objective function, as I am trying to edit the objective function of LightGBM.
Please help me.
