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basic_xgb.py
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# -*- coding: utf-8 -*-
# Author: XuMing <[email protected]>
# Brief:
import pickle
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.metrics import classification_report
class XGB(object):
"""
xgboost model for text classification
"""
def __init__(self, xgb_model_name):
self.xgb_model_name = xgb_model_name
self.init = False
def train_model(self, train_data):
"""
use Feature vector
:param train_x:
:param train_y:
:return:
"""
data_np = np.array(train_data, dtype=float)
data_pd = pd.DataFrame(data=data_np)
train = data_pd.iloc[:, 1:].values
labels = data_pd.iloc[:, :1].values
# Using 10000 rows for early stopping.
offset = 500 # 训练集中数据5000，划分4500用作训练，500用作验证
# 划分训练集与验证集
xgtrain = xgb.DMatrix(train[:offset, :], label=labels[:offset])
xgval = xgb.DMatrix(train[offset:, :], label=labels[offset:])
params = {
'objective': 'binary:logistic',
'gamma': 0.05, # 在树的叶子节点下一个分区的最小损失，越大算法模型越保守 。[0:]
'max_depth': 12, # 构建树的深度 [1:]
'subsample': 0.8, # 采样训练数据，设置为0.5，随机选择一般的数据实例 (0:1]
'colsample_bytree': 0.8, # 构建树树时的采样比率 (0:1]
'silent': 1,
'eta': 0.01, # 如同学习率
'seed': 10,
}
plst = list(params.items())
num_rounds = 100 # 迭代你次数
# return 训练和验证的错误率
watchlist = [(xgtrain, 'train'), (xgval, 'val')]
# training model
# early_stopping_rounds 当设置的迭代次数较大时，early_stopping_rounds 可在一定的迭代次数内准确率没有提升就停止训练
self.clf = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50)
with open(self.xgb_model_name, 'wb')as f:
pickle.dump(self.clf, f, True)
def load_model(self, xgb_model_name):
with open(xgb_model_name, 'rb') as f:
self.clf = pickle.load(f)
self.init = True
def test_model(self, test_data):
if not self.init:
print("not init xgb model. load ...")
self.load_model(self.xgb_model_name)
print("load xgb model done.")
data_np = np.array(test_data, dtype=float)
data_pd = pd.DataFrame(data=data_np)
test = data_pd.iloc[:, 1:].values
labels = data_pd.iloc[:, :1].values
xgtest = xgb.DMatrix(test)
preds = self.clf.predict(xgtest)
print('pred:', preds[:10])
total_count = int(len(preds))
right_count = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) == labels[i])
out_preds = []
for i in range(len(preds)):
if float(preds[i]) > 0.5:
out_preds.append(1)
else:
out_preds.append(0)
target_names = ['risk', 'norisk']
report = classification_report(labels, out_preds, target_names=target_names)
print(report)
print('Xgb test: total_count, right_count, pred:', total_count, right_count, right_count / total_count)