xgb, lgb, Keras, LR(二分类、多分类代码)

preprocess

# 通用的预处理框架
 
import pandas as pd
import numpy as np
import scipy as sp
 
# 文件读取
def read_csv_file(f, logging=False):
    print("==========读取数据=========")
    data =  pd.read_csv(f)
    if logging:
        print(data.head(5))
        print(f, "包含以下列")
        print(data.columns.values)
        print(data.describe())
        print(data.info())
    return data

Logistic Regression

# 通用的LogisticRegression框架
 
import pandas as pd
import numpy as np
from scipy import sparse
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
 
# 1. load data
df_train = pd.DataFrame()
df_test  = pd.DataFrame()
y_train = df_train['label'].values
 
# 2. process data
ss = StandardScaler()
 
 
# 3. feature engineering/encoding
# 3.1 For Labeled Feature
enc = OneHotEncoder()
feats = ["creativeID", "adID", "campaignID"]
for i, feat in enumerate(feats):
    x_train = enc.fit_transform(df_train[feat].values.reshape(-1, 1))
    x_test = enc.fit_transform(df_test[feat].values.reshape(-1, 1))
    if i == 0:
        X_train, X_test = x_train, x_test
    else:
        X_train, X_test = sparse.hstack((X_train, x_train)), sparse.hstack((X_test, x_test))
 
# 3.2 For Numerical Feature
# It must be a 2-D Data for StandardScalar, otherwise reshape(-1, len(feats)) is required
feats = ["price", "age"]
x_train = ss.fit_transform(df_train[feats].values)
x_test  = ss.fit_transform(df_test[feats].values)
X_train, X_test = sparse.hstack((X_train, x_train)), sparse.hstack((X_test, x_test))
 
# model training
lr = LogisticRegression()
lr.fit(X_train, y_train)
proba_test = lr.predict_proba(X_test)[:, 1]

LightGBM

1. 二分类

import lightgbm as lgb
import pandas as pd
import numpy as np
import pickle
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
 
print("Loading Data ... ")
 
# 导入数据
train_x, train_y, test_x = load_data()
 
# 用sklearn.cross_validation进行训练数据集划分，这里训练集和交叉验证集比例为7：3，可以自己根据需要设置
X, val_X, y, val_y = train_test_split(
    train_x,
    train_y,
    test_size=0.05,
    random_state=1,
    stratify=train_y ## 这里保证分割后y的比例分布与原数据一致
)
 
X_train = X
y_train = y
X_test = val_X
y_test = val_y
 
 
# create dataset for lightgbm
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
# specify your configurations as a dict
params = {
    'boosting_type': 'gbdt',
    'objective': 'binary',
    'metric': {'binary_logloss', 'auc'},
    'num_leaves': 5,
    'max_depth': 6,
    'min_data_in_leaf': 450,
    'learning_rate': 0.1,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.95,
    'bagging_freq': 5,
    'lambda_l1': 1,  
    'lambda_l2': 0.001,  # 越小l2正则程度越高
    'min_gain_to_split': 0.2,
    'verbose': 5,
    'is_unbalance': True
}
 
# train
print('Start training...')
gbm = lgb.train(params,
                lgb_train,
                num_boost_round=10000,
                valid_sets=lgb_eval,
                early_stopping_rounds=500)
 
print('Start predicting...')
 
preds = gbm.predict(test_x, num_iteration=gbm.best_iteration)  # 输出的是概率结果
 
# 导出结果
threshold = 0.5
for pred in preds:
    result = 1 if pred > threshold else 0
 
# 导出特征重要性
importance = gbm.feature_importance()
names = gbm.feature_name()
with open('./feature_importance.txt', 'w+') as file:
    for index, im in enumerate(importance):
        string = names[index] + ', ' + str(im) + '
'
        file.write(string)

2.多分类

import lightgbm as lgb
import pandas as pd
import numpy as np
import pickle
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
 
print("Loading Data ... ")
 
# 导入数据
train_x, train_y, test_x = load_data()
 
# 用sklearn.cross_validation进行训练数据集划分，这里训练集和交叉验证集比例为7：3，可以自己根据需要设置
X, val_X, y, val_y = train_test_split(
    train_x,
    train_y,
    test_size=0.05,
    random_state=1,
    stratify=train_y ## 这里保证分割后y的比例分布与原数据一致
)
 
X_train = X
y_train = y
X_test = val_X
y_test = val_y
 
 
# create dataset for lightgbm
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
# specify your configurations as a dict
params = {
    'boosting_type': 'gbdt',
    'objective': 'multiclass',
    'num_class': 9,
    'metric': 'multi_error',
    'num_leaves': 300,
    'min_data_in_leaf': 100,
    'learning_rate': 0.01,
    'feature_fraction': 0.8,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'lambda_l1': 0.4,
    'lambda_l2': 0.5,
    'min_gain_to_split': 0.2,
    'verbose': 5,
    'is_unbalance': True
}
 
# train
print('Start training...')
gbm = lgb.train(params,
                lgb_train,
                num_boost_round=10000,
                valid_sets=lgb_eval,
                early_stopping_rounds=500)
 
print('Start predicting...')
 
preds = gbm.predict(test_x, num_iteration=gbm.best_iteration)  # 输出的是概率结果
 
# 导出结果
for pred in preds:
    result = prediction = int(np.argmax(pred))
 
# 导出特征重要性
importance = gbm.feature_importance()
names = gbm.feature_name()
with open('./feature_importance.txt', 'w+') as file:
    for index, im in enumerate(importance):
        string = names[index] + ', ' + str(im) + '
'
        file.write(string)

XGBoost

1. 二分类

import numpy as np
import pandas as pd
import xgboost as xgb
import time
from sklearn.model_selection import StratifiedKFold
 
 
from sklearn.model_selection import train_test_split
train_x, train_y, test_x = load_data()
 
# 构建特征
 
 
# 用sklearn.cross_validation进行训练数据集划分，这里训练集和交叉验证集比例为7：3，可以自己根据需要设置
X, val_X, y, val_y = train_test_split(
    train_x,
    train_y,
    test_size=0.01,
    random_state=1,
    stratify=train_y
)
 
# xgb矩阵赋值
xgb_val = xgb.DMatrix(val_X, label=val_y)
xgb_train = xgb.DMatrix(X, label=y)
xgb_test = xgb.DMatrix(test_x)
 
# xgboost模型 #####################
 
params = {
    'booster': 'gbtree',
    # 'objective': 'multi:softmax',  # 多分类的问题、
    # 'objective': 'multi:softprob',   # 多分类概率
    'objective': 'binary:logistic',
    'eval_metric': 'logloss',
    # 'num_class': 9,  # 类别数，与 multisoftmax 并用
    'gamma': 0.1,  # 用于控制是否后剪枝的参数,越大越保守，一般0.1、0.2这样子。
    'max_depth': 8,  # 构建树的深度，越大越容易过拟合
    'alpha': 0,   # L1正则化系数
    'lambda': 10,  # 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。
    'subsample': 0.7,  # 随机采样训练样本
    'colsample_bytree': 0.5,  # 生成树时进行的列采样
    'min_child_weight': 3,
    # 这个参数默认是 1，是每个叶子里面 h 的和至少是多少，对正负样本不均衡时的 0-1 分类而言
    # ，假设 h 在 0.01 附近，min_child_weight 为 1 意味着叶子节点中最少需要包含 100 个样本。
    # 这个参数非常影响结果，控制叶子节点中二阶导的和的最小值，该参数值越小，越容易 overfitting。
    'silent': 0,  # 设置成1则没有运行信息输出，最好是设置为0.
    'eta': 0.03,  # 如同学习率
    'seed': 1000,
    'nthread': -1,  # cpu 线程数
    'missing': 1,
    'scale_pos_weight': (np.sum(y==0)/np.sum(y==1))  # 用来处理正负样本不均衡的问题,通常取：sum(negative cases) / sum(positive cases)
    # 'eval_metric': 'auc'
}
plst = list(params.items())
num_rounds = 2000  # 迭代次数
watchlist = [(xgb_train, 'train'), (xgb_val, 'val')]
 
# 交叉验证
result = xgb.cv(plst, xgb_train, num_boost_round=200, nfold=4, early_stopping_rounds=200, verbose_eval=True, folds=StratifiedKFold(n_splits=4).split(X, y))
 
# 训练模型并保存
# 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
model = xgb.train(plst, xgb_train, num_rounds, watchlist, early_stopping_rounds=200)
model.save_model('../data/model/xgb.model')  # 用于存储训练出的模型
 
preds = model.predict(xgb_test)
 
# 导出结果
threshold = 0.5
for pred in preds:
    result = 1 if pred > threshold else 0

处理正负样本不均匀的案例

# 计算正负样本比例
positive_num = df_train[df_train['label']==1].values.shape[0]
negative_num = df_train[df_train['label']==0].values.shape[0]
print(float(positive_num)/float(negative_num))

主要思路

1. 手动调整正负样本比例
2. 过采样 Over-Sampling
   对训练集里面样本数量较少的类别（少数类）进行过采样，合成新的样本来缓解类不平衡，比如SMOTE算法
3. 欠采样 Under-Sampling
4. 将样本按比例一一组合进行训练，训练出多个弱分类器，最后进行集成