part1_data_discovery

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.cross_validation 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'

在这一部分，我们做一个简短的数据探索，看看我们有什么样的数据集，以及我们是否能找到其中的任何模式。

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

先来瞅瞅数据长啥样

train.shape

输出

(188318, 132)

188k训练实例，132列 数据量还可以。

print ('First 20 columns:', list(train.columns[:20]))

print ('Last 20 columns:', list(train.columns[-20:]))

输出

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.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

绝大多数情况下，我们都需要对缺失值进行处理

pd.isnull(train).values.any()

输出

False

竟然木有缺失值，可以愉快的玩耍了

Continuous vs caterogical features

Another way to see the division to categorical and continuous features is to run pd.DataFrame.info method:

train.info()

输出

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 188318 entries, 0 to 188317
Columns: 132 entries, id to loss
dtypes: float64(15), int64(1), object(116)
memory usage: 189.7+ MB

In here, float64(15), int64(1) are our continuous features (the one with int64 is probably id) while object(116) are categorical features. We may confirm this:

cat_features = list(train.select_dtypes(include=['object']).columns)
print "Categorical: {} features".format(len(cat_features))

Continuous: 14 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))

输出

A column of int64: ['id']

类别值中属性的个数

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()

fig, (ax1, ax2) = plt.subplots(1,2)
fig.set_size_inches(16,5)
ax1.hist(uniq_values_in_categories.unique_values, bins=50)
ax1.set_title('Amount of categorical features with X distinct values')
ax1.set_xlabel('Distinct values in a feature')
ax1.set_ylabel('Features')
ax1.annotate('A feature with 326 vals', xy=(322, 2), xytext=(200, 38), arrowprops=dict(facecolor='black'))

ax2.set_xlim(2,30)
ax2.set_title('Zooming in the [0,30] part of left histogram')
ax2.set_xlabel('Distinct values in a feature')
ax2.set_ylabel('Features')
ax2.grid(True)
ax2.hist(uniq_values_in_categories[uniq_values_in_categories.unique_values <= 30].unique_values, bins=30)
ax2.annotate('Binary features', xy=(3, 71), xytext=(7, 71), arrowprops=dict(facecolor='black'))

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
#输出 array(3.7949281496777445)

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

输出

array(0.0929738049841997)

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()

连续值特征

One thing we can do is to plot histogram of the numerical features and analyze their distributions:

train[cont_features].hist(bins=50, figsize=(16,12))

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)

我们看到几个特征之间有很高的相关性

part2_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 sklearn.grid_search import GridSearchCV
from scipy.sparse import csr_matrix, hstack
from sklearn.cross_validation import KFold, train_test_split
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))

输出

Categorical features: 116
Numerical features: 14

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)

输出

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

Simple XGBoost Model

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['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,
• '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'])

输出

CV score: 1218.92834467
Wall time: 1min 6s

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()

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'])

输出

CV score: 1171.13663733
Wall time: 1min 57s

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。

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)

输出

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，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']

输出

[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_

输出

([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)

Wall time: 13min 45s

grid.grid_scores_, grid.best_params_, grid.best_score_

输出

([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)

Wall time: 28min 26s

grid.grid_scores_, grid.best_params_, grid.best_score_

输出

([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
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_

输出

([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
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)

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_

输出

([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)

输出

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

在增加树的个数呢？

grid.grid_scores_, grid.best_params_, grid.best_score_

输出

([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)

输出

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

cv

输出

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