–– 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: 特色
: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):

"""
须要优化的指标函数
:config: 优化对象，超参范畴
:path: (训练集，OOT 文件门路)
"""
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 的指标后果 [Skrill 下载](https://www.gendan5.com/wallet/Skrill.html)，蕴含 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’):

"""
贝叶斯主动寻参数
:param_space: 参数范畴，组合范畴
:X_train: 训练集特色
:y_train: 寻链接标签
:X_test: 测试集特色
:y_test: 测试集标签
:num_eval: 寻参次数
:log_path: log 文件存储门路
"""
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)