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'

数据预处理

train = pd.read_csv('train.csv')

train['log_loss'] = np.log(train['loss'])

数据分红连续和离散特征html

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))

Categorical features: 116
Numerical features: 14

And use a label encoder for categorical features:python

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)

Xtrain: (188318, 130)
ytrain: (188318,)

简单的XGBoost 模型

首先，咱们训练一个基本的xgboost模型，而后进行参数调节经过交叉验证来观察结果的变换，使用平均绝对偏差来衡量算法

mean_absolute_error(np.exp(y), np.exp(yhat))。app

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，从而提升以后每次迭代的效率ide

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':损失降低多少才进行分裂
'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。若是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, 如同窗习率
'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性能

%%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'])

CV score: 1220.1099446666667
Wall time: 29.7 s

咱们获得了第一个基准结果：MAE＝1218.9 学习

plt.figure()
bst_cv1[['train-mae-mean', 'test-mae-mean']].plot()

咱们的第一个基础模型：

没有发生过拟合
只创建了50个树模型

%%time
#创建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'])

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，如今还没多大事优化

咱们获得了新的纪录 MAE = 1171.77 比第一次的要好 (1218.9). 接下来咱们要改变其余参数了。this

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 下降模型过拟合风险.

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)

test-mae-mean     1219.014551
test-mae-std         8.931061
train-mae-mean    1210.682813
train-mae-std        2.798608
Name: 49, dtype: float64

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，所以，他们应该调整第一。咱们简要地概述它们：spa

max_depth: 树的最大深度。增长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. 若是树分区中的实例权重小于定义的总和，则中止树构建过程。

xgb_param_grid = {'max_depth': list(range(4,9)), 'min_child_weight': list((1,3,6))}
xgb_param_grid['max_depth']

[4, 5, 6, 7, 8]

%%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)

Wall time: 29min 48s

grid.grid_scores_, grid.best_params_, grid.best_score_

([mean: -1243.19015, std: 6.70264, params: {'max_depth': 4, 'min_child_weight': 1},
  mean: -1243.30647, std: 6.82365, params: {'max_depth': 4, 'min_child_weight': 3},
  mean: -1243.50752, std: 6.60994, params: {'max_depth': 4, 'min_child_weight': 6},
  mean: -1219.60926, std: 7.09979, params: {'max_depth': 5, 'min_child_weight': 1},
  mean: -1218.72940, std: 6.82721, params: {'max_depth': 5, 'min_child_weight': 3},
  mean: -1219.25033, std: 6.89855, params: {'max_depth': 5, 'min_child_weight': 6},
  mean: -1204.68929, std: 6.28730, params: {'max_depth': 6, 'min_child_weight': 1},
  mean: -1203.44649, std: 7.19550, params: {'max_depth': 6, 'min_child_weight': 3},
  mean: -1203.76522, std: 7.13140, params: {'max_depth': 6, 'min_child_weight': 6},
  mean: -1195.35465, std: 6.38664, params: {'max_depth': 7, 'min_child_weight': 1},
  mean: -1194.02729, std: 6.69778, params: {'max_depth': 7, 'min_child_weight': 3},
  mean: -1193.51933, std: 6.73645, params: {'max_depth': 7, 'min_child_weight': 6},
  mean: -1189.10977, std: 6.18540, params: {'max_depth': 8, 'min_child_weight': 1},
  mean: -1188.21520, std: 6.15132, params: {'max_depth': 8, 'min_child_weight': 3},
  mean: -1187.95975, std: 6.71340, params: {'max_depth': 8, 'min_child_weight': 6}],
 {'max_depth': 8, 'min_child_weight': 6},
 -1187.9597499123447)

网格搜索发现的最佳结果:

{'max_depth': 8, 'min_child_weight': 6}, -1187.9597499123447)

设置成负的值是由于要找大的值

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()

咱们看到，从网格搜索的结果，分数的提升主要是基于max_depth增长. min_child_weight稍有影响的成绩，可是，咱们看到，min_child_weight = 6会更好一些。

Step 3: 调节 gamma去下降过拟合风险

%%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)

Wall time: 13min 45s

grid.grid_scores_, grid.best_params_, grid.best_score_

([mean: -1187.95975, std: 6.71340, params: {'gamma': 0.0},
  mean: -1187.67788, std: 6.44332, params: {'gamma': 0.1},
  mean: -1187.66616, std: 6.75004, params: {'gamma': 0.2},
  mean: -1187.21835, std: 7.06771, params: {'gamma': 0.30000000000000004},
  mean: -1188.35004, std: 6.50057, params: {'gamma': 0.4}],
 {'gamma': 0.30000000000000004},
 -1187.2183540791846)

咱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)

grid.grid_scores_, grid.best_params_, grid.best_score_

([mean: -1185.67108, std: 5.40097, params: {'colsample_bytree': 0.6000000000000001, 'subsample': 0.6000000000000001},
  mean: -1184.90641, std: 5.61239, params: {'colsample_bytree': 0.6000000000000001, 'subsample': 0.7000000000000001},
  mean: -1183.73767, std: 6.15639, params: {'colsample_bytree': 0.6000000000000001, 'subsample': 0.8},
  mean: -1185.09329, std: 7.04215, params: {'colsample_bytree': 0.7000000000000001, 'subsample': 0.6000000000000001},
  mean: -1184.36149, std: 5.71298, params: {'colsample_bytree': 0.7000000000000001, 'subsample': 0.7000000000000001},
  mean: -1183.83446, std: 6.24654, params: {'colsample_bytree': 0.7000000000000001, 'subsample': 0.8},
  mean: -1184.43055, std: 6.68009, params: {'colsample_bytree': 0.8, 'subsample': 0.6000000000000001},
  mean: -1183.33878, std: 5.74989, params: {'colsample_bytree': 0.8, 'subsample': 0.7000000000000001},
  mean: -1182.93099, std: 5.75849, params: {'colsample_bytree': 0.8, 'subsample': 0.8}],
 {'colsample_bytree': 0.8, 'subsample': 0.8},
 -1182.9309918891634)

_, 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，我获得了下面的结果：

`{'colsample_bytree': 0.8, 'subsample': 0.8}, -1182.9309918891634)

Step 5: 减少学习率并增大树个数

参数优化的最后一步是下降学习速度，同时增长更多的估计量

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)

CPU times: user 6.69 ms, sys: 0 ns, total: 6.69 ms
Wall time: 6.55 ms

grid.grid_scores_, grid.best_params_, grid.best_score_

([mean: -1205.85372, std: 3.46146, params: {'eta': 0.5},
  mean: -1185.32847, std: 4.87321, params: {'eta': 0.4},
  mean: -1170.00284, std: 4.76399, params: {'eta': 0.3},
  mean: -1160.97363, std: 6.05830, params: {'eta': 0.2},
  mean: -1183.66720, std: 6.69439, params: {'eta': 0.1},
  mean: -1266.12628, std: 7.26130, params: {'eta': 0.075},
  mean: -1709.15130, std: 8.19994, params: {'eta': 0.05},
  mean: -2104.42708, std: 8.02827, params: {'eta': 0.04},
  mean: -2545.97334, std: 7.76440, params: {'eta': 0.03}],
 {'eta': 0.2},
 -1160.9736284869114)

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

如今咱们把树的个数增长到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)

CPU times: user 11.5 ms, sys: 0 ns, total: 11.5 ms
Wall time: 11.4 ms

grid.grid_scores_, grid.best_params_, grid.best_score_

([mean: -1231.04517, std: 5.41136, params: {'eta': 0.5},
  mean: -1201.31398, std: 4.75456, params: {'eta': 0.4},
  mean: -1177.86344, std: 3.67324, params: {'eta': 0.3},
  mean: -1160.48853, std: 5.65336, params: {'eta': 0.2},
  mean: -1152.24715, std: 5.85286, params: {'eta': 0.1},
  mean: -1156.75829, std: 5.30250, params: {'eta': 0.075},
  mean: -1184.88913, std: 6.08852, params: {'eta': 0.05},
  mean: -1243.60808, std: 7.40326, params: {'eta': 0.04},
  mean: -1467.04736, std: 8.70704, params: {'eta': 0.03}],
 {'eta': 0.1},
 -1152.2471498726127)

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)

CPU times: user 21.9 ms, sys: 34 µs, total: 22 ms
Wall time: 22 ms

在增长树的个数呢？

grid.grid_scores_, grid.best_params_, grid.best_score_

([mean: -1148.37246, std: 6.51203, params: {'eta': 0.09},
  mean: -1146.67343, std: 6.13261, params: {'eta': 0.08},
  mean: -1145.92359, std: 5.68531, params: {'eta': 0.07},
  mean: -1147.44050, std: 6.33336, params: {'eta': 0.06},
  mean: -1147.98062, std: 6.39481, params: {'eta': 0.05},
  mean: -1153.17886, std: 5.74059, params: {'eta': 0.04}],
 {'eta': 0.07},
 -1145.9235944370419)

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)

CPU times: user 1.26 ms, sys: 22 µs, total: 1.28 ms
Wall time: 1.07 ms

cv

test-mae-mean     1146.997852
test-mae-std         9.541592
train-mae-mean    1036.557251
train-mae-std        0.974437
Name: 199, dtype: float64

总结

能够看到200棵树最好的ETA是0.07。正如咱们所预料的那样，ETA和num_boost_round依赖关系不是线性的，可是有些关联。

花了至关长的一段时间优化xgboost. 从初始值: 1219.57. 通过调参以后达到 MAE=1171.77.

咱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做为kaggle和天池等各类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,

​

xgboost保险赔偿预测

XGBoost解决xgboost保险赔偿预测