xgboost保险赔偿预测
XGBoost解决xgboost保险赔偿预测
import xgboost as xgb import pandas as pd import numpy as np import pickle import sys import matplotlib.pyplot as plt from sklearn.metrics import mean_absolute_error, make_scorer from sklearn.preprocessing import StandardScaler from sklearn.model_selection import GridSearchCV,KFold, train_test_split from scipy.sparse import csr_matrix, hstack from xgboost import XGBRegressor import warnings warnings.filterwarnings('ignore') %matplotlib inline # This may raise an exception in earlier versions of Jupyter %config InlineBackend.figure_format = 'retina'
这部分主要内容就是Xgboost啦
数据预处理
train = pd.read_csv('train.csv')
做对数转换
train['log_loss'] = np.log(train['loss'])
数据分成连续和离散特征
features = [x for x in train.columns if x not in ['id','loss', 'log_loss']] cat_features = [x for x in train.select_dtypes( include=['object']).columns if x not in ['id','loss', 'log_loss']] num_features = [x for x in train.select_dtypes( exclude=['object']).columns if x not in ['id','loss', 'log_loss']] print ("Categorical features:", len(cat_features)) print ("Numerical features:", len(num_features))
And use a label encoder for categorical features:
ntrain = train.shape[0] train_x = train[features] train_y = train['log_loss'] for c in range(len(cat_features)): train_x[cat_features[c]] = train_x[cat_features[c]].astype('category').cat.codes print ("Xtrain:", train_x.shape) print ("ytrain:", train_y.shape)
简单的XGBoost 模型
首先，我们训练一个基本的xgboost模型，然后进行参数调节通过交叉验证来观察结果的变换，使用平均绝对误差来衡量
mean_absolute_error(np.exp(y), np.exp(yhat))。
xgboost 自定义了一个数据矩阵类 DMatrix，会在训练开始时进行一遍预处理，从而提高之后每次迭代的效率
def xg_eval_mae(yhat, dtrain): y = dtrain.get_label() return 'mae', mean_absolute_error(np.exp(y), np.exp(yhat))
Model
# 将数据进行转化成xgboost支持的数据格式(效率问题) dtrain = xgb.DMatrix(train_x, train['log_loss'])
Xgboost参数
- 'booster':'gbtree',
- 'objective': 'multi:softmax', 多分类的问题
- 'num_class':10, 类别数，与 multisoftmax 并用
- 'gamma':损失下降多少才进行分裂
- 'max_depth':12, 构建树的深度，越大越容易过拟合
- 'lambda':2, 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':0.7, 生成树时进行的列采样
- 'min_child_weight':3, 孩子节点中最小的样本权重和。如果一个叶子节点的样本权重和小于min_child_weight则拆分过程结束
- 'silent':0 ,设置成1则没有运行信息输出，最好是设置为0.
- 'eta': 0.007, 如同学习率
- 'seed':1000,随即种子
- 'nthread':7, cpu 线程数
xgb_params = { 'seed': 0, 'eta': 0.1, 'colsample_bytree': 0.5, 'silent': 1, 'subsample': 0.5, 'objective': 'reg:linear', 'max_depth': 5, 'min_child_weight': 3 }
使用交叉验证 xgb.cv
%%time bst_cv1 = xgb.cv(xgb_params, dtrain, num_boost_round=50, nfold=3, seed=0, feval=xg_eval_mae, maximize=False, early_stopping_rounds=10) print ('CV score:', bst_cv1.iloc[-1,:]['test-mae-mean'])
我们得到了第一个基准结果：MAE＝1218.9
plt.figure() bst_cv1[['train-mae-mean', 'test-mae-mean']].plot()
<matplotlib.figure.Figure at 0x287d3418b38>
我们的第一个基础模型：
- 没有发生过拟合
- 只建立了50个树模型
%%time #建立100个树模型 bst_cv2 = xgb.cv(xgb_params, dtrain, num_boost_round=100, nfold=3, seed=0, feval=xg_eval_mae, maximize=False, early_stopping_rounds=10) print ('CV score:', bst_cv2.iloc[-1,:]['test-mae-mean'])
CV score: 1172.059570333333
Wall time: 55.7 s
fig, (ax1, ax2) = plt.subplots(1,2) fig.set_size_inches(16,4) ax1.set_title('100 rounds of training') ax1.set_xlabel('Rounds') ax1.set_ylabel('Loss') ax1.grid(True) ax1.plot(bst_cv2[['train-mae-mean', 'test-mae-mean']]) ax1.legend(['Training Loss', 'Test Loss']) ax2.set_title('60 last rounds of training') ax2.set_xlabel('Rounds') ax2.set_ylabel('Loss') ax2.grid(True) ax2.plot(bst_cv2.iloc[40:][['train-mae-mean', 'test-mae-mean']]) ax2.legend(['Training Loss', 'Test Loss'])
有那么一丢丢过拟合，现在还没多大事
我们得到了新的纪录 MAE = 1171.77 比第一次的要好 (1218.9). 接下来我们要改变其他参数了。
XGBoost 参数调节
- Step 1: 选择一组初始参数
- Step 2: 改变
max_depth和min_child_weight.
- Step 3: 调节
gamma降低模型过拟合风险.
- Step 4: 调节
subsample和colsample_bytree改变数据采样策略.
- Step 5: 调节学习率
eta.
class XGBoostRegressor(object): def __init__(self, **kwargs): self.params = kwargs if 'num_boost_round' in self.params: self.num_boost_round = self.params['num_boost_round'] self.params.update({'silent': 1, 'objective': 'reg:linear', 'seed': 0}) def fit(self, x_train, y_train): dtrain = xgb.DMatrix(x_train, y_train) self.bst = xgb.train(params=self.params, dtrain=dtrain, num_boost_round=self.num_boost_round, feval=xg_eval_mae, maximize=False) def predict(self, x_pred): dpred = xgb.DMatrix(x_pred) return self.bst.predict(dpred) def kfold(self, x_train, y_train, nfold=5): dtrain = xgb.DMatrix(x_train, y_train) cv_rounds = xgb.cv(params=self.params, dtrain=dtrain, num_boost_round=self.num_boost_round, nfold=nfold, feval=xg_eval_mae, maximize=False, early_stopping_rounds=10) return cv_rounds.iloc[-1,:] def plot_feature_importances(self): feat_imp = pd.Series(self.bst.get_fscore()).sort_values(ascending=False) feat_imp.plot(title='Feature Importances') plt.ylabel('Feature Importance Score') def get_params(self, deep=True): return self.params def set_params(self, **params): self.params.update(params) return self
def mae_score(y_true, y_pred): return mean_absolute_error(np.exp(y_true), np.exp(y_pred)) mae_scorer = make_scorer(mae_score, greater_is_better=False)
bst = XGBoostRegressor(eta=0.1, colsample_bytree=0.5, subsample=0.5,
max_depth=5, min_child_weight=3, num_boost_round=50)
bst.kfold(train_x, train_y, nfold=5)
Out[22]:
Step 1: 学习率与树个数
Step 2: 树的深度与节点权重
这些参数对xgboost性能影响最大，因此，他们应该调整第一。我们简要地概述它们：
-
max_depth: 树的最大深度。增加这个值会使模型更加复杂，也容易出现过拟合，深度3-10是合理的。 -
min_child_weight: 正则化参数. 如果树分区中的实例权重小于定义的总和，则停止树构建过程。
xgb_param_grid = {'max_depth': list(range(4,9)), 'min_child_weight': list((1,3,6))}
xgb_param_grid['max_depth']
Out[29]:
%%time grid = GridSearchCV(XGBoostRegressor(eta=0.1, num_boost_round=50, colsample_bytree=0.5, subsample=0.5), param_grid=xgb_param_grid, cv=5, scoring=mae_scorer) grid.fit(train_x, train_y.values)
grid.grid_scores_, grid.best_params_, grid.best_score_
网格搜索发现的最佳结果:
{'max_depth': 8, 'min_child_weight': 6}, -1187.9597499123447)
设置成负的值是因为要找大的值
def convert_grid_scores(scores): _params = [] _params_mae = [] for i in scores: _params.append(i[0].values()) _params_mae.append(i[1]) params = np.array(_params) grid_res = np.column_stack((_params,_params_mae)) return [grid_res[:,i] for i in range(grid_res.shape[1])]
_,scores = convert_grid_scores(grid.grid_scores_)
scores = scores.reshape(5,3)
plt.figure(figsize=(10,5)) cp = plt.contourf(xgb_param_grid['min_child_weight'], xgb_param_grid['max_depth'], scores, cmap='BrBG') plt.colorbar(cp) plt.title('Depth / min_child_weight optimization') plt.annotate('We use this', xy=(5.95, 7.95), xytext=(4, 7.5), arrowprops=dict(facecolor='white'), color='white') plt.annotate('Good for depth=7', xy=(5.98, 7.05), xytext=(4, 6.5), arrowprops=dict(facecolor='white'), color='white') plt.xlabel('min_child_weight') plt.ylabel('max_depth') plt.grid(True) plt.show()
我们看到，从网格搜索的结果，分数的提高主要是基于max_depth增加. min_child_weight稍有影响的成绩，但是，我们看到，min_child_weight = 6会更好一些。
Step 3: 调节 gamma去降低过拟合风险
%%time xgb_param_grid = {'gamma':[ 0.1 * i for i in range(0,5)]} grid = GridSearchCV(XGBoostRegressor(eta=0.1, num_boost_round=50, max_depth=8, min_child_weight=6, colsample_bytree=0.5, subsample=0.5), param_grid=xgb_param_grid, cv=5, scoring=mae_scorer) grid.fit(train_x, train_y.values)
grid.grid_scores_, grid.best_params_, grid.best_score_
Out[39]:
我们选择使用偏小一些的 gamma.
Step 4: 调节样本采样方式 subsample 和 colsample_bytree
%%time xgb_param_grid = {'subsample':[ 0.1 * i for i in range(6,9)], 'colsample_bytree':[ 0.1 * i for i in range(6,9)]} grid = GridSearchCV(XGBoostRegressor(eta=0.1, gamma=0.2, num_boost_round=50, max_depth=8, min_child_weight=6), param_grid=xgb_param_grid, cv=5, scoring=mae_scorer) grid.fit(train_x, train_y.values)
Wall time: 28min 26s
grid.grid_scores_, grid.best_params_, grid.best_score_
Out[41]:
_, scores = convert_grid_scores(grid.grid_scores_) scores = scores.reshape(3,3) plt.figure(figsize=(10,5)) cp = plt.contourf(xgb_param_grid['subsample'], xgb_param_grid['colsample_bytree'], scores, cmap='BrBG') plt.colorbar(cp) plt.title('Subsampling params tuning') plt.annotate('Optimum', xy=(0.895, 0.6), xytext=(0.8, 0.695), arrowprops=dict(facecolor='black')) plt.xlabel('subsample') plt.ylabel('colsample_bytree') plt.grid(True) plt.show()
在当前的预训练模式的具体案例，我得到了下面的结果：
`{'colsample_bytree': 0.8, 'subsample': 0.8}, -1182.9309918891634)
Step 5: 减小学习率并增大树个数
参数优化的最后一步是降低学习速度，同时增加更多的估计量
First, we plot different learning rates for a simpler model (50 trees):
%%time xgb_param_grid = {'eta':[0.5,0.4,0.3,0.2,0.1,0.075,0.05,0.04,0.03]} grid = GridSearchCV(XGBoostRegressor(num_boost_round=50, gamma=0.2, max_depth=8, min_child_weight=6, colsample_bytree=0.6, subsample=0.9), param_grid=xgb_param_grid, cv=5, scoring=mae_scorer) grid.fit(train_x, train_y.values)
grid.grid_scores_, grid.best_params_, grid.best_score_
Out[30]:
eta, y = convert_grid_scores(grid.grid_scores_) plt.figure(figsize=(10,4)) plt.title('MAE and ETA, 50 trees') plt.xlabel('eta') plt.ylabel('score') plt.plot(eta, -y) plt.grid(True) plt.show()
{'eta': 0.2}, -1160.9736284869114 是目前最好的结果
现在我们把树的个数增加到100
xgb_param_grid = {'eta':[0.5,0.4,0.3,0.2,0.1,0.075,0.05,0.04,0.03]}
grid = GridSearchCV(XGBoostRegressor(num_boost_round=100, gamma=0.2, max_depth=8, min_child_weight=6,
colsample_bytree=0.6, subsample=0.9),
param_grid=xgb_param_grid, cv=5, scoring=mae_scorer)
grid.fit(train_x, train_y.values)
grid.grid_scores_, grid.best_params_, grid.best_score_
Out[33]:
eta, y = convert_grid_scores(grid.grid_scores_) plt.figure(figsize=(10,4)) plt.title('MAE and ETA, 100 trees') plt.xlabel('eta') plt.ylabel('score') plt.plot(eta, -y) plt.grid(True) plt.show()
学习率低一些的效果更好
%%time xgb_param_grid = {'eta':[0.09,0.08,0.07,0.06,0.05,0.04]} grid = GridSearchCV(XGBoostRegressor(num_boost_round=200, gamma=0.2, max_depth=8, min_child_weight=6, colsample_bytree=0.6, subsample=0.9), param_grid=xgb_param_grid, cv=5, scoring=mae_scorer) grid.fit(train_x, train_y.values)
在增加树的个数呢？
grid.grid_scores_, grid.best_params_, grid.best_score_
Out[36]:
eta, y = convert_grid_scores(grid.grid_scores_) plt.figure(figsize=(10,4)) plt.title('MAE and ETA, 200 trees') plt.xlabel('eta') plt.ylabel('score') plt.plot(eta, -y) plt.grid(True) plt.show()
%%time # Final XGBoost model bst = XGBoostRegressor(num_boost_round=200, eta=0.07, gamma=0.2, max_depth=8, min_child_weight=6, colsample_bytree=0.6, subsample=0.9) cv = bst.kfold(train_x, train_y, nfold=5)
cv
Out[39]:
总结
可以看到200棵树最好的ETA是0.07。正如我们所预料的那样，ETA和num_boost_round依赖关系不是线性的，但是有些关联。
花了相当长的一段时间优化xgboost. 从初始值: 1219.57. 经过调参之后达到 MAE=1171.77.
我们还发现参数之间的关系ETA和num_boost_round：
- 100 trees,
eta=0.1: MAE=1152.247 - 200 trees,
eta=0.07: MAE=1145.92
`XGBoostRegressor(num_boost_round=200, gamma=0.2, max_depth=8, min_child_weight=6,
colsample_bytree=0.6, subsample=0.9, eta=0.07).
xgboost作为kaggle和天池等各种数据比赛最受欢迎的算法之一,从项目中可见调参也是一件很容易的事情,并不复杂,好用精确率高,叫谁谁不用,
