实现功能：
python机器学习-建立随机森林预测模型并特征分析。
实现效果：
# 导入需要的库
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
from sklearn.model_selection import train_test_split
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics import roc_curve, auc
import numpy as np
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
# =============读取数据===========
def Read_data(file):
dt = pd.read_csv(file)
dt.columns = ['age', 'sex', 'chest_pain_type', 'resting_blood_pressure', 'cholesterol','fasting_blood_sugar', 'rest_ecg', 'max_heart_rate_achieved','exercise_induced_angina','st_depression', 'st_slope', 'num_major_vessels', 'thalassemia', 'target']
data =dt
return data
# ===========数据清洗==============
def data_clean(data):
# 重复值处理
print('存在' if any(data.duplicated()) else '不存在', '重复观测值')
data.drop_duplicates()
# 缺失值处理
print('不存在' if any(data.isnull()) else '存在', '缺失值')
data.dropna() # 直接删除记录
data.fillna(method='ffill') # 前向填充
data.fillna(method='bfill') # 后向填充
data.fillna(value=2) # 值填充
data.fillna(value={'resting_blood_pressure': data['resting_blood_pressure'].mean()}) # 统计值填充
# 异常值处理
data1 = data['resting_blood_pressure']
# 标准差监测
xmean = data1.mean()
xstd = data1.std()
print('存在' if any(data1 > xmean + 2 * xstd) else '不存在', '上限异常值')
print('存在' if any(data1 < xmean - 2 * xstd) else '不存在', '下限异常值')
# 箱线图监测
q1 = data1.quantile(0.25)
q3 = data1.quantile(0.75)
up = q3 + 1.5 * (q3 - q1)
dw = q1 - 1.5 * (q3 - q1)
print('存在' if any(data1 > up) else '不存在', '上限异常值')
print('存在' if any(data1 < dw) else '不存在', '下限异常值')
data1[data1 > up] = data1[data1 < up].max()
data1[data1 < dw] = data1[data1 > dw].min()
return data
#==============数据编码=============
def data_encoding(data):
data = data[["age", 'sex', "chest_pain_type", "resting_blood_pressure", "cholesterol","fasting_blood_sugar", "rest_ecg","max_heart_rate_achieved", "exercise_induced_angina","st_depression", "st_slope", "num_major_vessels","thalassemia","target"]]
Discretefeature=['sex',"chest_pain_type", "fasting_blood_sugar", "rest_ecg","exercise_induced_angina", "st_slope", "thalassemia"]
Continuousfeature=["age", "resting_blood_pressure", "cholesterol","max_heart_rate_achieved","st_depression","num_major_vessels"]
df = pd.get_dummies(data,columns=Discretefeature)
df[Continuousfeature]=(df[Continuousfeature]-df[Continuousfeature].mean())/(df[Continuousfeature].std())
df["target"]=data[["target"]]
return df
#=============数据集划分==============
def data_partition(data):
# 1.4查看样本是否平衡
print(data["target"].value_counts())
# X提取变量特征；Y提取目标变量
X = data.drop('target', axis=1)
y = data['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2,random_state=10)
feature=list(X.columns)
return X_train, y_train, X_test, y_test,feature
#===========绘制ROC曲线================
def Draw_ROC(list1,list2):
fpr_model,tpr_model,thresholds=roc_curve(list1,list2,pos_label=1)
roc_auc_model=auc(fpr_model,tpr_model)
font = {'family': 'Times New Roman','size': 12,}
sns.set(font_scale=1.2)
plt.rc('font',family='Times New Roman')
plt.plot(fpr_model,tpr_model,'blue',label='AUC = %0.2f'% roc_auc_model)
plt.legend(loc='lower right',fontsize = 12)
plt.plot([0,1],[0,1],'r--')
plt.ylabel('True Positive Rate',fontsize = 14)
plt.xlabel('Flase Positive Rate',fontsize = 14)
plt.show()
return
#==============随机森林=================
def RF(X_train, y_train, X_test, y_test,feature):
rf1 = RandomForestClassifier(max_depth=5, n_estimators=100, random_state=0)
rf1.fit(X_train, y_train)
print("\nFinally results of RF fitting:")
print("Accuracy on training set: {:.3f}".format(rf1.score(X_train, y_train)))
print("Accuracy on test set: {:.3f}".format(rf1.score(X_test, y_test)))
print("Feature importance:\n{}".format(rf1.feature_importances_))
predict_target=rf1.predict(X_test)
predict_target_prob=rf1.predict_proba(X_test) # 输出分类概率
predict_target_prob_rf=predict_target_prob[:,1]
df = pd.DataFrame({'prob':predict_target_prob_rf,'target':predict_target,'labels':list(y_test)})
print('预测正确的数量:')
print(sum(predict_target==y_test))
print('RF测试集:')
print(metrics.classification_report(y_test,predict_target))
print(metrics.confusion_matrix(y_test, predict_target))
print('RF训练集:')
predict_Target=rf1.predict(X_train)
print(metrics.classification_report(y_train,predict_Target))
print(metrics.confusion_matrix(y_train, predict_Target))
id=np.argwhere(rf1.feature_importances_>=0)
id=[i for item in id for i in item]
dic={}
for i in id:
dic.update({feature[i]:rf1.feature_importances_[i]})
df=pd.DataFrame.from_dict(dic,orient='index',columns=['权重'])
df=df.reset_index().rename(columns={'index':'特征'})
df=df.sort_values(by='权重',ascending=False)
data_hight=df['权重'].values.tolist()
data_x=df['特征'].values.tolist()
font = {'family': 'Times New Roman','size': 7}
sns.set(font_scale=1.2)
plt.rc('font',family='Times New Roman')
plt.figure(figsize=(8,8))
plt.barh(range(len(data_x)), data_hight, color='#6699CC')
plt.yticks(range(len(data_x)),data_x,fontsize=12)
plt.tick_params(labelsize=12)
plt.xlabel('Feature importance',fontsize=14)
plt.title("GB feature importance analysis",fontsize = 14)
plt.show()
return list(y_test), list(predict_target_prob_rf)
if __name__=="__main__":
data1=Read_data("F:\数据杂坛\\0504\heartdisease\Heart-Disease-Data-Set-main\\UCI Heart Disease Dataset.csv")
data1=data_clean(data1)
data2=data_encoding(data1)
X_train, y_train, X_test, y_test,feature= data_partition(data2)
y_test,predict_target_prob_rf=RF(X_train, y_train, X_test, y_test,feature)
Draw_ROC(y_test,predict_target_prob_rf)
实现效果：
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