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08_xgb_lr.py
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import xgboost as xgb
from sklearn.datasets import load_svmlight_file
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.externals import joblib
import numpy as np
from scipy.sparse import hstack
from util import load_data
import pickle
def xgb_feature_encode(train_data_file, test_data_file, xgb_feature_file):
all_data = load_data(train_data_file, test_data_file)
# 训练/测试数据分割
X_train, X_test, y_train, y_test = train_test_split(all_data['X_train'], all_data['y_train'], test_size=0.1,
random_state=1)
# 定义模型
xgboost = xgb.XGBClassifier(learning_rate=0.05,
n_estimators=50,
max_depth=3,
gamma=0,
subsample=0.7,
colsample_bytree=0.7)
# 训练学习
xgboost.fit(X_train, y_train,
eval_set=[(X_test, y_test)],
eval_metric="mlogloss",
early_stopping_rounds=10,
verbose=True)
# 预测及AUC评测
y_pred_test = xgboost.predict(X_test)
num = 0
for i in range(0, len(y_pred_test)):
if y_test[i] == y_pred_test[i]:
num += 1
print("prediction accuracy is " + str((num) / len(y_pred_test)))
# xgboost编码原有特征
X_train_leaves = xgboost.apply(X_train)
X_test_leaves = xgboost.apply(X_test)
# 训练样本个数
train_rows = X_train_leaves.shape[0]
# 合并编码后的训练数据和测试数据
X_leaves = np.concatenate((X_train_leaves, X_test_leaves), axis=0)
X_leaves = X_leaves.astype(np.int32)
(rows, cols) = X_leaves.shape
# 记录每棵树的编码区间
cum_count = np.zeros((1, cols), dtype=np.int32)
for j in range(cols):
if j == 0:
cum_count[0][j] = len(np.unique(X_leaves[:, j]))
else:
cum_count[0][j] = len(np.unique(X_leaves[:, j])) + cum_count[0][j - 1]
print('Transform features genenrated by xgboost...')
# 对所有特征进行ont-hot编码
for j in range(cols):
keyMapDict = {}
if j == 0:
initial_index = 1
else:
initial_index = cum_count[0][j - 1] + 1
for i in range(rows):
if X_leaves[i, j] not in keyMapDict:
keyMapDict[X_leaves[i, j]] = initial_index
X_leaves[i, j] = initial_index
initial_index = initial_index + 1
else:
X_leaves[i, j] = keyMapDict[X_leaves[i, j]]
# 基于编码后的特征，将特征处理为libsvm格式且写入文件
print('Write xgboost learned features to file ...')
with open(xgb_feature_file, 'wb') as out_data:
xgb_feature = [X_leaves, all_data['y_train']]
pickle.dump(xgb_feature, out_data)
def xgboost_lr_train(train_data_file, test_data_file, xgb_feature_file):
# load 原始样本数据
all_data = load_data(train_data_file, test_data_file)
X_train_origin, X_test_origin, y_train_origin, y_test_origin = train_test_split(all_data['X_train'],
all_data['y_train'],
test_size=0.3,
random_state=42)
# load xgboost特征编码后的样本数据
with open(xgb_feature_file, 'rb') as in_data:
X_xg_all, y_xg_all = pickle.load(in_data)
X_train, X_test, y_train, y_test = train_test_split(X_xg_all, y_xg_all, test_size=0.3, random_state=42)
# lr对原始特征样本模型训练
lr = LogisticRegression(n_jobs=-1, C=0.1, penalty='l1')
lr.fit(X_train_origin, y_train_origin)
joblib.dump(lr, 'model/lr_orgin.m')
# 预测及AUC评测
y_pred_test = lr.predict(X_test_origin)
num = 0
for i in range(0, len(y_pred_test)):
if y_test_origin[i] == y_pred_test[i]:
num += 1
print("prediction accuracy is " + str((num) / len(y_pred_test)))
# lr对load xgboost特征编码后的样本模型训练
lr = LogisticRegression(n_jobs=-1, C=0.1, penalty='l1')
lr.fit(X_train, y_train)
joblib.dump(lr, 'model/lr_xgb.m')
# 预测及AUC评测
y_pred_test = lr.predict(X_test)
num = 0
for i in range(0, len(y_pred_test)):
if y_test_origin[i] == y_pred_test[i]:
num += 1
print("prediction accuracy is " + str((num) / len(y_pred_test)))
# 基于原始特征组合xgboost编码后的特征
X_train_ext = hstack([X_train_origin, X_train])
del (X_train)
del (X_train_origin)
X_test_ext = hstack([X_test_origin, X_test])
del (X_test)
del (X_test_origin)
# lr对组合后的新特征的样本进行模型训练
lr = LogisticRegression(n_jobs=-1, C=0.1, penalty='l1')
lr.fit(X_train_ext, y_train)
joblib.dump(lr, 'model/lr_ext.m')
# 预测及AUC评测
y_pred_test = lr.predict(X_test_ext)
num = 0
for i in range(0, len(y_pred_test)):
if y_test_origin[i] == y_pred_test[i]:
num += 1
print("prediction accuracy is " + str((num) / len(y_pred_test)))
if __name__ == '__main__':
# 加载数据集
train_data_file = 'data/new_data/train_set.csv'
test_data_file = 'data/new_data/train_set.csv'
xgb_feature_file = 'model/xgb_feature_libsvm'
xgb_feature_encode(train_data_file, test_data_file, xgb_feature_file)
xgboost_lr_train(train_data_file, test_data_file, xgb_feature_file)