【实战 01】心脏病二分类数据集

（注：每一章节可以为一个py文件，4、5、6、7写在同一个文件中，最好用jupyter notebook）

1. 获取数据集

下面两种方式：UCI、Kaggle

UCI Machine Learning Repository: Heart Disease Data Sethttps://archive.ics.uci.edu/ml/datasets/heart+disease

得到的csv文件为：

2. 数据集介绍

数据集有1025行，14列。每行表示一个病人。13列表示特征，1列表示标签(是否患心脏病)

3. 数据预处理

首先要区分好：定类、定序、定距、定比、四种数据的特征

① 我们需要将定类特征由整数转为实际对应的字符串，还原为真实含义。

② 将定类数据扩展为特征

③ 导出预处理后的数据

import pandas as pd
df = pd.read_csv('dataset/heart.csv')

# 将定类特征由整数编码转为实际对应的字符串，还原为真实含义
df['sex'][df['sex'] == 0] = 'female'
df['sex'][df['sex'] == 1] = 'male'
df['cp'][df['cp'] == 0] = 'typical angina'
df['cp'][df['cp'] == 1] = 'atypical angina'
df['cp'][df['cp'] == 2] = 'non-anginal pain'
df['cp'][df['cp'] == 3] = 'asymptomatic'

df['fbs'][df['fbs'] == 0] = 'lower than 120mg/ml'
df['fbs'][df['fbs'] == 1] = 'greater than 120mg ml'

df['restecg'][df['restecg'] == 0] = 'normal'
df['restecg'][df['restecg'] == 1] = 'ST-T wave abnormality'
df['restecg'][df['restecg'] == 1] = 'left ventricular hyper trophy'

df['exang'][df['exang'] == 0] = 'no'
df['exang'][df['exang'] == 1] = 'yes'

df['slope'][df['slope'] == 0] = 'upsloping'
df['slope'][df['slope'] == 1] = 'flat'
df['slope'][df['slope'] == 1] = 'downsloping'

df['thal'][df['thal'] == 0] = 'unknown'
df['thal'][df['thal'] == 1] = 'normal'
df['thal'][df['thal'] == 1] = 'fixed defect'
df['thal'][df['thal'] == 1] = 'reversable defect'


# 将离散的定类和定序特征列转为One-Hot独热编码
# 将定类数据扩展为特征
df = pd.get_dummies(df)

# 导出预处理后的数据
df.to_csv('process_heart.csv',index=False)

4. 构建随机森林分类模型

第一，要拿数据与处理后的文件。

import numpy as np
import pandas as pd
import matplotlib.pyplot as p1t
%matplotlib inline
# 忽略警告
import warnings
warnings.filterwarnings("ignore")
df = pd.read_csv('process_heart.csv')

第二，将数据分为输入和输出

# 去掉这一列  矩阵用X表示  input
X = df.drop('target',axis=1)

# y向量
y = df['target']

第三，数据划分为测试集和训练集

# 将数据划分为训练集和测试集,20%作为测试集，随机数种子
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=10)

第四，构建随机森林分类模型，在训练集上训练模型

# 构建随机森林分类模型，在训练集上训练模型
from sklearn.ensemble import RandomForestClassifier
# 最大深度为5，决策树为100，随机种子数为5
model = RandomForestClassifier(max_depth=5,n_estimators=100,random_state=5)
# fit 拟合
model.fit(X_train,y_train)
# 可以查看第7个决策树
estimator = model.estimators_[7]

第五，将决策树可视化

# 将输出特征值转为字符串
feature_names = X_train.columns
y_train_str = y_train.astype('str')
y_train_str[y_train=='0'] = 'no disease'
y_train_str[y_train=='1'] = 'disease'
y_train_str = y_train_str.values

# 将决策树可视化
from sklearn.tree import export_graphviz
export_graphviz(estimator,out_file='tree.dot',
                feature_names = feature_names,
                class_names = y_train_str, 
                rounded = True,proportion = True,
                label='root',
                precision = 2,filled = True)

from subprocess import call
call(['dot','-Tpng','tree.dot','-o','tree.png','-Gdpi=600'],shell=True)
from IPython.display import Image
Image(filename = 'tree.png')

5. 预测测试集数据

#在训练集上训练得到随机森林模型之后，就可以对测试集上的数据进行预测，也可以对新未知数据进行预测。将预测结果与测试集真正的标签相比#较，可以定量评估模型指标，绘制混淆矩阵，计算Precision、Recall、F1-Score等评估指标，并绘制ROC曲线。混淆矩阵、ROC曲线、F1-#Score
第一，模型准备


# 忽略警告
import warnings
warnings.filterwarnings("ignore")

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
#导入数据集,划分特征和标签
df = pd.read_csv('process_heart.csv')
X = df.drop('target',axis=1)
y = df['target']
#划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2,random_state=10)

#构建随机森林模型
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(max_depth=5,n_estimators=100)
model.fit(X_train, y_train)

第二，将其中一个测试样本转成数组的形式


## 对数据进行位置索引，从而在数据表中提取出相应的数据。
X_test.iloc
# 筛选出未知样本
test_sample = X_test.iloc[2]
# 变成二维
test_sample = np.array(test_sample).reshape(1,-1)

第三，预测单个未知样本

#  二分类定性分类结果
model.predict(test_sample)
# 二分类定量分类结果
model.predict_proba(test_sample)

第四，预测整个测试样本

y_pred = model.predict(X_test)
# 得到患心脏病和不患心脏病的置信度
y_pred_proba = model.predict_proba(X_test)
# 切片操作 只获得患心脏病的置信度
model.predict_proba(X_test)[:,1]

6. 构建混淆矩阵

# 混淆矩阵
from sklearn.metrics import confusion_matrix
confusion_matrix_model = confusion_matrix(y_test, y_pred)
# 将混淆矩阵绘制出来
import itertools
def cnf_matrix_plotter(cm,classes):
    '''
    传入混淆矩阵和标签名称列表，绘制混淆矩阵
    '''
    # plt.imshow (cm, interpolation='nearest', cmap=plt.cm.Greens)
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Oranges)
    plt.title('Confusion Matrix')
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks,classes,rotation=45)
    plt.yticks(tick_marks,classes)
    
    threshold = cm.max() / 2.
    for i, j in itertools.product(range(cm. shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i,j],
        horizontalalignment="center",
        color="white" if cm[i,j] > threshold else "black",fontsize=25)
    plt.tight_layout()
    plt.ylabel('True Label')
    plt.xlabel(' Predicted Label')
    plt.show()
  cnf_matrix_plotter(confusion_matrix_model,['Healthy','Disease'])

7. 计算查全率、召回率、调和平均值

# 计算查全率、召回率、调和平均值
from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred,target_names=['Healthy', 'Disease']))

8. ROC曲线、AUC曲线

# ROC曲线
y_pred_quant = model.predict_proba(X_test)[:,1]
from sklearn. metrics import roc_curve,auc
fpr,tpr,thresholds = roc_curve(y_test,y_pred_quant)
plt.plot(fpr,tpr)
plt.plot([0,1],[0,1],ls="--",c=".3")
plt.xlim([0.0,1.0])
plt.ylim([0.0,1.0])
plt.rcParams['font.size'] = 12
plt.title('ROC curve')
plt.xlabel('False Positive Rate (1 - Specificity)')
plt.ylabel('True Positive Rate (Sensitivity)')
plt.grid(True)

# 计算AUC曲线
auc(fpr,tpr)

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