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pos機(jī)交易代碼dt
實(shí)現(xiàn)功能：
python機(jī)器學(xué)習(xí)案例-支持向量機(jī)建模及評估。
實(shí)現(xiàn)代碼：
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# 導(dǎo)入需要的庫
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from warnings import simplefilter
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simplefilter(action='ignore', category=FutureWarning)
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn import metrics
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from sklearn.metrics import roc_curve, auc
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from sklearn.svm import SVC
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# =============讀取數(shù)據(jù)===========
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def Read_data(file):
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dt = pd.read_csv(file)
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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']
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data =dt
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return data
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# ===========數(shù)據(jù)清洗==============
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def data_clean(data):
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# 重復(fù)值處理
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print('存在' if any(data.duplicated()) else '不存在', '重復(fù)觀測值')
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data.drop_duplicates()
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# 缺失值處理
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print('不存在' if any(data.isnull()) else '存在', '缺失值')
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data.dropna() # 直接刪除記錄
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data.fillna(method='ffill') # 前向填充
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data.fillna(method='bfill') # 后向填充
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data.fillna(value=2) # 值填充
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data.fillna(value={'resting_blood_pressure': data['resting_blood_pressure'].mean()}) # 統(tǒng)計(jì)值填充
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# 異常值處理
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data1 = data['resting_blood_pressure']
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# 標(biāo)準(zhǔn)差監(jiān)測
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xmean = data1.mean()
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xstd = data1.std()
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print('存在' if any(data1 > xmean + 2 * xstd) else '不存在', '上限異常值')
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print('存在' if any(data1 < xmean - 2 * xstd) else '不存在', '下限異常值')
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# 箱線圖監(jiān)測
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q1 = data1.quantile(0.25)
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q3 = data1.quantile(0.75)
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up = q3 + 1.5 * (q3 - q1)
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dw = q1 - 1.5 * (q3 - q1)
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print('存在' if any(data1 > up) else '不存在', '上限異常值')
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print('存在' if any(data1 < dw) else '不存在', '下限異常值')
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data1[data1 > up] = data1[data1 < up].max()
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data1[data1 < dw] = data1[data1 > dw].min()
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return data
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#==============數(shù)據(jù)編碼=============
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def data_encoding(data):
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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"]]
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Discretefeature=['sex',"chest_pain_type", "fasting_blood_sugar", "rest_ecg","exercise_induced_angina", "st_slope", "thalassemia"]
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Continuousfeature=["age", "resting_blood_pressure", "cholesterol","max_heart_rate_achieved","st_depression","num_major_vessels"]
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df = pd.get_dummies(data,columns=Discretefeature)
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df[Continuousfeature]=(df[Continuousfeature]-df[Continuousfeature].mean())/(df[Continuousfeature].std())
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df["target"]=data[["target"]]
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return df
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#=============數(shù)據(jù)集劃分==============
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def data_partition(data):
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# 1.4查看樣本是否平衡
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print(data["target"].value_counts())
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# X提取變量特征；Y提取目標(biāo)變量
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X = data.drop('target', axis=1)
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y = data['target']
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2,random_state=10)
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feature=list(X.columns)
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return X_train, y_train, X_test, y_test,feature
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#===========繪制ROC曲線================
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def Draw_ROC(list1,list2):
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fpr_model,tpr_model,thresholds=roc_curve(list1,list2,pos_label=1)
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roc_auc_model=auc(fpr_model,tpr_model)
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font = {'family': 'Times New Roman','size': 12,}
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sns.set(font_scale=1.2)
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plt.rc('font',family='Times New Roman')
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plt.plot(fpr_model,tpr_model,'blue',label='AUC = %0.2f'% roc_auc_model)
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plt.legend(loc='lower right',fontsize = 12)
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plt.plot([0,1],[0,1],'r--')
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plt.ylabel('True Positive Rate',fontsize = 14)
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plt.xlabel('Flase Positive Rate',fontsize = 14)
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plt.show()
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return
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#============支持向量機(jī)===================
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def SVM(X_train, y_train, X_test, y_test,feature):
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svc = SVC(probability = True)
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svc.fit(X_train, y_train)
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print("\Finally results of SVM fitting:")
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print("Accuracy on training set: {:.3f}".format(svc.score(X_train, y_train)))
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print("Accuracy on test set: {:.3f}".format(svc.score(X_test, y_test)))
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predict_target=svc.predict(X_test)
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predict_target_prob=svc.predict_proba(X_test) # 輸出分類概率
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predict_target_prob_svc=predict_target_prob[:,1]
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df = pd.DataFrame({'prob':predict_target_prob_svc,'target':predict_target,'labels':list(y_test)})
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print('正確預(yù)測數(shù)量：')
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print(sum(predict_target==y_test))
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print('SVM驗(yàn)證集：')
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print(metrics.classification_report(y_test,predict_target))
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print(metrics.confusion_matrix(y_test, predict_target))
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print('SVM訓(xùn)練集：')
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predict_Target=svc.predict(X_train)
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print(metrics.classification_report(y_train,predict_Target))
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print(metrics.confusion_matrix(y_train, predict_Target))
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return list(y_test), list(predict_target_prob_svc)
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#============主函數(shù)==============
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if __name__=="__main__":
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data1=Read_data("F:\\數(shù)據(jù)雜壇\\\\0504\\heartdisease\\Heart-Disease-Data-Set-main\\\\UCI Heart Disease Dataset.csv")
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data1=data_clean(data1)
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data2=data_encoding(data1)
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X_train, y_train, X_test, y_test,feature= data_partition(data2)
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y_test,predict_target_prob_svc=SVM(X_train, y_train, X_test, y_test,feature)
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Draw_ROC(y_test,predict_target_prob_svc)
實(shí)現(xiàn)效果：
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