分类： Python/Ruby
2021-06-09 17:07:35
#-*- encoding=utf-8 -*-
import os
import time
import pickle
import numpy as np
import xgboost
import sklearn.metrics as metrics
from ray import tune
from ray.tune.suggest.bohb import TuneBOHB
from ray.tune.schedulers import HyperBandForBOHB
def get_auc_ks(scores, labels):
"""
计算KS,AUC值
:param scores: list-like, model scores;
:param labels: list-like, labels;
:return: tuple(float, float), auc & ks ;
"""
flg = False
if isinstance(labels, xgboost.DMatrix):
flg = True
labels = labels.get_label()
fpr, tpr, thresholds = metrics.roc_curve(labels, scores, pos_label=1)
auc = metrics.auc(fpr, tpr)
ks = np.max(np.abs(tpr - fpr))
if flg:
return [('my_auc', auc), ('KS', ks)]
else:
return auc, ks
def metric_ks(pred, dtrain):
"""
ks metric
:param estimator: 模型
:param X: 特征
:param y: label
"""
scores = pred
y = dtrain.get_label()
fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=1)
ks = np.max(np.abs(tpr - fpr))
return 'ks', ks
def custom_metric(pred, dtrain):
labels = dtrain.get_label()
scores = pred
fpr, tpr, thresholds = metrics.roc_curve(labels, scores, pos_label=1)
auc = metrics.auc(fpr, tpr)
ks = np.max(np.abs(tpr - fpr))
return [('auc', auc), ('KS', ks)]
def objective_function(config, checkpoint_dir=None, path=None):
"""
需要优化的目标函数
:config: 优化对象，超参范围
:path: (训练集，OOT文件路径)
"""
train_path, oot_path = path
train_mat = xgboost.DMatrix(train_path)
param = config.copy()
param["max_depth"] = int(param["max_depth"])
n_estimators = int(param.pop("n_estimators"))
result = {}
cv_results = xgboost.cv(param, dtrain=train_mat, num_boost_round=n_estimators,
nfold=5, metrics='logloss', feval=custom_metric, maximize=True,
callbacks=[record_evaluation(result, oot_path)])
test_score = (result["detail_metrics"]["my_oot"]["auc"][-1], result["detail_metrics"]["my_oot"]["KS"][-1])
valid_score = (result["detail_metrics"]["my_valid"]["auc"][-1], result["detail_metrics"]["my_valid"]["KS"][-1])
train_score = (result["detail_metrics"]["my_train"]["auc"][-1], result["detail_metrics"]["my_train"]["KS"][-1])
nfold = len(valid_score[0])
monitor_metric = sum(valid_score[0]) / nfold
with tune.checkpoint_dir(step=1) as checkpoint_dir:
path = os.path.join(checkpoint_dir, "cv_result")
with open(path, 'wb') as f:
pickle.dump(cv_results, f)
return tune.report(valid_auc=monitor_metric,
test_score=test_score,
valid_score=valid_score,
train_score=train_score,
done=True)
def record_evaluation(eval_result, oot_path):
"""
callback记录xgboost.cv的指标结果，包含train, valid, oot
:eval_result: dict A dictionary to store the evaluation results.
:oot_path: OOT Data file path
"""
if not isinstance(eval_result, dict):
raise TypeError('eval_result has to be a dictionary')
eval_result.clear()
oot_mat = xgboost.DMatrix(oot_path)
def init(env):
"""internal function"""
for item in env.evaluation_result_list:
k = item[0]
pos = k.index('-')
key = k[:pos]
metric = k[pos + 1:]
if key not in eval_result:
eval_result[key] = {}
if metric not in eval_result[key]:
eval_result[key][metric] = []
if 'detail_metrics' not in eval_result:
eval_result['detail_metrics'] = {"my_train": {}, "my_valid": {}, "my_oot": {}}
def callback(env):
"""internal function"""
if not eval_result:
init(env)
for item in env.evaluation_result_list:
k, v = item[0], item[1]
pos = k.index('-')
key = k[:pos]
metric = k[pos + 1:]
eval_result[key][metric].append(v)
tmp = {"my_train": {}, "my_valid": {}, "my_oot": {}}
for cvpack in env.cvfolds:
bst = cvpack.bst
pred_train = bst.predict(cvpack.dtrain)
pred_valid = bst.predict(cvpack.dtest)
pred_oot = bst.predict(oot_mat)
metrics_result_train = dict(custom_metric(pred_train, cvpack.dtrain))
metrics_result_valid = dict(custom_metric(pred_valid, cvpack.dtest))
metrics_result_oot = dict(custom_metric(pred_oot, oot_mat))
for k in metrics_result_oot:
tmp["my_train"][k] = tmp["my_train"].get(k, [])+ [metrics_result_train[k]]
tmp["my_valid"][k] = tmp["my_valid"].get(k, [])+ [metrics_result_valid[k]]
tmp["my_oot"][k] = tmp["my_oot"].get(k, [])+ [metrics_result_oot[k]]
for k1 in tmp:
for k2 in tmp[k1]:
eval_result["detail_metrics"][k1].setdefault(k2, []).append(tmp[k1][k2])
return callback
def hyperopt(param_space, trainpath, testpath, num_eval, name, obj_funcs, log_path='~/ray_results'):
"""
贝叶斯自动寻参数
:param_space: 参数范围，组合范围
:X_train: 训练集特征
:y_train: 寻链接标签
:X_test: 测试集特征
:y_test: 测试集标签
:num_eval: 寻参次数
:log_path: log文件存储路径
"""
start = time.time()
path = (trainpath, testpath)
opt = TuneBOHB(max_concurrent=2)
bohb = HyperBandForBOHB(time_attr="training_iteration",
max_t=num_eval)
analysis = tune.run(tune.with_parameters(obj_funcs, path=path),
config=param_space, num_samples=num_eval, local_dir=log_path,
metric='valid_auc', mode='max', search_alg=opt, scheduler=bohb,
resources_per_trial={"cpu": 5}, name=name)
best_params = analysis.get_best_config(metric="valid_auc", mode="max")
best_params["max_depth"] = int(best_params["max_depth"])
n_estimators = int(best_params.pop("n_estimators"))
train_mat = xgboost.DMatrix(trainpath)
test_mat = xgboost.DMatrix(testpath)
model = xgboost.train(best_params, train_mat, n_estimators)
pred_test = model.predict(test_mat)
pred_train = model.predict(train_mat)
print("-----Results-----")
print("Best model & parameters: {}".format(best_params))
print("Train Score: {}".format(get_auc_ks(pred_train, train_mat.get_label())))
print("Test Score: {}".format(get_auc_ks(pred_test, test_mat.get_label())))
print("Time elapsed: {}".format(time.time() - start))
print("Parameter combinations evaluated: {}".format(num_eval))
return None
if __name__ == "__main__":
trainfile_path = "./train.buffer"
testfile_path = "./oot.buffer"
name = 'ppdnew_V2'
control_overfitting = False
param = {
'booster': "gbtree",
'eta': tune.uniform(0.01, 1),
'seed': 1,
'max_depth': tune.uniform(3, 5),
'n_estimators': tune.uniform(50, 500),
'min_child_weight': tune.uniform(1, 300),
'colsample_bytree': tune.uniform(0.6, 1.0),
'subsample': tune.uniform(0.5, 1),
'lambda': tune.uniform(0.0, 100),
'alpha': tune.uniform(0.0, 100),
'scale_pos_weight': tune.uniform(1, 5),
'n_jobs': 5
}
print("begin tuning")
hyperopt(param, trainfile_path, testfile_path, 100, name, obj_funcs=objective_function)
