NO.89——应用Xgboost进行保险赔偿预测
文章目录
- 1 数据分析
- 1.1 先瞅瞅数据长啥样
- 1.2 连续变量和分类变量
- 1.3 分类变量中属性的个数
- 1.4 赔偿值
- 1.5 连续变量特征
- 1.6 特征之间的相关性
- 2 Xgboost
- 2.1 数据预处理
- 2.2 简单的Xgboost模型
- 2.3 第一个基础模型
- 2.4 Xgboost参数调节
- 3 总结
我的github地址.
1 数据分析
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score as AUC
from sklearn.metrics import mean_absolute_error
from sklearn.decomposition import PCA
from sklearn.preprocessing import LabelEncoder, LabelBinarizer
from sklearn.model_selection import cross_val_score
from scipy import stats
import seaborn as sns
from copy import deepcopy
%matplotlib inline
# This may raise an exception in earlier versions of Jupyter
%config InlineBackend.figure_format = 'retina'
1.1 先瞅瞅数据长啥样
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
train.shape
(188318, 132)
print ('First 20 columns:', list(train.columns[:20]))
print ('Last 20 columns:', list(train.columns[-20:]))
First 20 columns: [‘id’, ‘cat1’, ‘cat2’, ‘cat3’, ‘cat4’, ‘cat5’, ‘cat6’, ‘cat7’, ‘cat8’, ‘cat9’, ‘cat10’, ‘cat11’, ‘cat12’, ‘cat13’, ‘cat14’, ‘cat15’, ‘cat16’, ‘cat17’, ‘cat18’, ‘cat19’]
Last 20 columns: [‘cat112’, ‘cat113’, ‘cat114’, ‘cat115’, ‘cat116’, ‘cont1’, ‘cont2’, ‘cont3’, ‘cont4’, ‘cont5’, ‘cont6’, ‘cont7’, ‘cont8’, ‘cont9’, ‘cont10’, ‘cont11’, ‘cont12’, ‘cont13’, ‘cont14’, ‘loss’]
我们看到，大概有116个种类属性（如它们的名字所示）和14个连续（数字）属性。 此外，还有ID和赔偿。总计为132列。
train.head(5)
train.describe()
正如我们看到的，所有的连续的功能已被缩放到[0,1]区间，均值基本为0.5。其实数据已经被预处理了，我们拿到的是特征数据。
查看缺失值
pd.isnull(train).values.any()
False
1.2 连续变量和分类变量
train.info()
RangeIndex: 188318 entries, 0 to 188317
Columns: 132 entries, id to loss
dtypes: float64(15), int64(1), object(116)
memory usage: 189.7+ MB
在这里，float64(15)是14个连续变量+loss值；int64(1)是ID；
object(116)是分类变量。
cat_features = list(train.select_dtypes(include=['object']).columns)
print ("Categorical: {} features".format(len(cat_features)))
Categorical: 116 features
cont_features = [cont for cont in list(train.select_dtypes(
include=['float64', 'int64']).columns) if cont not in ['loss', 'id']]
print ("Continuous: {} features".format(len(cont_features)))
Continuous: 14 features
id_col = list(train.select_dtypes(include=['int64']).columns)
print ("A column of int64: {}".format(id_col))
A column of int64: [‘id’]
1.3 分类变量中属性的个数
cat_uniques = []
for cat in cat_features:
cat_uniques.append(len(train[cat].unique()))
uniq_values_in_categories = pd.DataFrame.from_items([('cat_name', cat_features), ('unique_values', cat_uniques)])
uniq_values_in_categories.head()
1.4 赔偿值
plt.figure(figsize=(16,8))
plt.plot(train['id'], train['loss'])
plt.title('Loss values per id')
plt.xlabel('id')
plt.ylabel('loss')
plt.legend()
plt.show()
损失值中有几个显著的峰值表示严重事故。这样的数据分布，使得这个功能非常扭曲导致的回归表现不佳。
基本上，偏度度量了实值随机变量的均值分布的不对称性。让我们计算损失的偏度：
stats.mstats.skew(train['loss']).data
array(3.79492815)
偏度大于1，表示数据确实是倾斜的
对数据进行对数变换通常可以改善倾斜，可以使用np.log
stats.mstats.skew(np.log(train['loss'])).data
array(0.0929738)
fig, (ax1, ax2) = plt.subplots(1,2)
fig.set_size_inches(16,5)
ax1.hist(train['loss'], bins=50)
ax1.set_title('Train Loss target histogram')
ax1.grid(True)
ax2.hist(np.log(train['loss']), bins=50, color='g')
ax2.set_title('Train Log Loss target histogram')
ax2.grid(True)
plt.show()
1.5 连续变量特征
train[cont_features].hist(bins=50, figsize=(16,12))
1.6 特征之间的相关性
plt.subplots(figsize=(16,9))
correlation_mat = train[cont_features].corr()
sns.heatmap(correlation_mat, annot=True)
2 Xgboost
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 scipy.sparse import csr_matrix, hstack
from sklearn.model_selection import KFold, train_test_split, GridSearchCV
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'
2.1 数据预处理
train = pd.read_csv('train.csv')
做对数变换
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列 ,categorical特征
cat_features = [x for x in train.select_dtypes(
include=['object']).columns if x not in ['id','loss', 'log_loss']]
#cont列 ，数字特征
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
给分类变量进行编码
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 #.cat.codes将分类变量用数字进行编码
print ("Xtrain:", train_x.shape)
print ("ytrain:", train_y.shape)
Xtrain: (188318, 130)
ytrain: (188318,)
2.2 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
dtrain = xgb.DMatrix(train_x, train_y)
Xgboost参数
- ‘booster’:‘gbtree’。用梯度提升的决策树进行节点分裂
- ‘objective’: ‘multi:softmax’, 多分类的问题。损失函数，有分类，有回归
- ‘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（对样本采样）
- ‘colsample_bytree’:0.7, 生成树时进行的列采样（对特征采样）
- ‘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，串行添加树，新加进来的树贡献占比多少。经验上讲增大树的数量，降低学习率
- ‘seed’:1000,
- ‘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
%%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']) #[-1,:]取最后一个元素
CV score: 1220.054769
CPU times: user 2min 24s, sys: 1.54 s, total: 2min 25s
Wall time: 2min 26s
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()
2.3 第一个基础模型
在上边的树模型中：
- 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: 1171.2875569999999
CPU times: user 4min 47s, sys: 2.05 s, total: 4min 49s
Wall time: 4min 51s
fig, (ax1, ax2) = plt.subplots(1,2)
fig.set_size_inches(16,4)
#100颗树
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'])
#后60颗树
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'])
有那么一丁丁过拟合，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。
2.4 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})
#训练模型
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
#评估指标
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) #make_scorer
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 1218.528027
test-mae-std 10.423910
test-rmse-mean 0.562570
test-rmse-std 0.002914
train-mae-mean 1209.757422
train-mae-std 2.306814
train-rmse-mean 0.558842
train-rmse-std 0.000475
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，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']
%%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.cv_results_, 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
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_
([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)
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)
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。
1、首先我们迭代50颗树
%%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_
([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
2、现在我们将树增大到100个
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_
([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
3、继续增加树的个数到200个
%%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)
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()
3 总结
%%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
test-mae-mean 1146.997852
test-mae-std 9.541592
train-mae-mean 1036.557251
train-mae-std 0.974437
Name: 199, dtype: float64
我们看到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。
们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).