编译工具：PyCharm

有些编译工具在绘图的时候不需要写plt.show()或者是print就可以显示绘图结果或者是显示打印结果，pycharm需要（matplotlib.pyplot）

特征选择

经过“数据清理”和“特征变换后的数据集，数据集中的数值基本可以使用。但是数据集的特征数量、质量也会影响运行速度和最终模型的预测效果。

先把数据集划分为训练集和测试集，并且对训练集和测试集分别实现特征标准化。

特征选择的三种方法：封装器法、过滤法、嵌入法。

数据集下载(不想下载数据集在文章末尾给出。)：
https://archive.ics.uci.edu/dataset/109/wine

下载过来的数据集需要修改一下，再wine_data里面最上面加上一行，分别表示列名，列名再wine.name里面有。

然后我们更改一下文件名称为wine_data.csv

import pandas as pd
df_wine = pd.read_csv("./data/wine_data.csv")
print(df_wine)

嵌入法特征选择

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

df_wine = pd.read_csv("./data/wine_data.csv")
# print(df_wine)
# 切分数据集:该数据集的第一列是标签，将标签和样本分离
# X是除了第一列的所有行数据（样本值）
# y是只有第一列的所有行数据（标签）
X,y = df_wine.iloc[:,1:],df_wine.iloc[:,0].values
# X_train训练集的样本值，y_train训练集的标签值
# X_test测试值的样本值,y_test测试值的标签值
# test_size=0.3:切分采用3/7分，test测试值占30%,训练集占70%
# random_state=0种子为0，stratify=y要求尽可能符合y的分布情况
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0,stratify=y)
# 数据预处理: 归一化(样本)
std=StandardScaler()
X_train_std=std.fit_transform(X_train) # 会在X_train中找到均值和标准差
X_test_std=std.fit_transform(X_test) # 会在X_test中找到均值和标准差
# 定义回归算法
# penalty:惩罚项为l1，使用损失函数优化算法是liblinear，不能用默认函数，因为其不支持l1
lr=LogisticRegression(penalty='l1',C=1.0,solver='liblinear')
# 训练模型
lr.fit(X_test,y_test)
# 打印模型参数
print(lr.coef_)

打印出来的模型参数如下：
可以看到，有一些权重为0，模型内部自动的把这些列过滤掉了，因为该参数对训练结果没有影响。

所以，我们可以对这些没用的维度进行舍弃，降低计算量。
实现：可以对权重由大到小排序，然后认为设计阈值，小于阈值的参数项舍弃，这样就可以实现特征选择了。
嵌入法特征选择：将特征选择过程与模型训练融为一体，在模型训练过程中自动进行特征选择。

from sklearn.feature_selection import SelectFromModel
# threshold:特征选择的阈值，prefix默认为False，如果模型经过了训练，应设置为True
model = SelectFromModel(lr,threshold='median',prefit=True)
# 模型已经经过训练，直接使用transform即可
X_new=model.transform(X_train_std)
# 如果模型没有训练过，及prefix为False，需要先训练后在对X（样本）进行特征选择
# model = SelectFromModel(lr,threshold='median')
# X_new = model.fit_transform(X_train_std,y_train)  # 训练除了X(样本)还有y(标签)
print("特征选择后:")
print(X_new.shape)
print("特征选择前:")
print(X_train_std.shape)

练习：糖尿病预测选取特征值，使用XGBoost中模型作为嵌入法特征选择的函数

在pycharm下载xgboost可能会遇到小问题，可以试试选择清华大学提供的pip程序进行下载。
file–>settings–>Python Interpreter，点击“+”号
输入
-i https://pypi.tuna.tsinghua.edu.cn/simple xgboost
然后install package

数据集链接：https://aistudio.baidu.com/datasetdetail/33810
该数据集包含数据集中共包含768个样本(entries)，每个样本8种特征。其中Outcome是样本的标签（即类别），0表示没有糖尿病，1表示患有糖尿病。

数据观察

print("-----------------糖尿病预测----------------")
# 小练习: 糖尿病预测
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns # 数据可视化
diabetes_data = pd.read_csv("./data/diabetes.csv")
print(diabetes_data.shape)
# 通过describe可以观察到数据的数量、平均值、标准差】最小最大值
print(diabetes_data.describe())
# 查看标签(Outcome)分布
print(diabetes_data.Outcome.value_counts())
# 可视化显示标签信息bar:柱状图显示
diabetes_data.Outcome.value_counts().plot(kind="bar")
plt.show()
# 绘制关系图: pairplot用于显示属性两两之间的关系
sns.pairplot(diabetes_data)
plt.show()
# 绘制热力图: 表示两个变量的相关系数，annot默认为False，True则在格子上显示数字
sns.heatmap(diabetes_data.corr(),annot=True)
plt.show()

标签信息柱状图

数据两两之间的关系图

数据热力图：图中相关系数越大，相关性越大

查看特征的重要度，进行特征选择

# 选出样本和标签
# X 样本集:所有行，"Age"之前的列，包含"Age"
X = diabetes_data.loc[:,:"Age"]
# y 标签集:所有行，最后一列(最后一列名:"Outcome")
y = diabetes_data.loc[:,"Outcome":]
# 引入XGBoost
from xgboost import XGBClassifier
from xgboost import plot_importance
model=XGBClassifier()
model.fit(X,y)
# 查看参数的默认值
print(model)
# 输出各个特征的重要程度
print(model.feature_importances_)
# 特征重要程度进行可视化，然后进行特征选择
plot_importance((model))
plt.show()

查看参数默认值以及各个特征的重要程度

特征重要度可视化结果：

根据特征重要程度，选取特征

print("# 根据特征重要程度，选取特征")
from sklearn.feature_selection import SelectFromModel
selection = SelectFromModel(
    model,
    threshold='median', #采用中位数
    prefit=True # 模型已经训练过
)
X_new=selection.transform(X)
print("特征选择前: ")
print(X.shape)
print("特征选择后: ")
print(X_new.shape)

wine_data.csv数据集

Class_label,Alcohol,Malic acid,Ash,Alcalinity of ash,Magnesium,Total phenols,Flavanoids,Nonflavanoid phenols,Proanthocyanins,Color intensity,Hue,OD280/OD315 of diluted wines,Proline
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
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