基於sklearn實現LogisticRegression演算法(python)
阿新 • • 發佈:2019-02-15
本文使用的資料型別是數值型,每一個樣本6個特徵表示,所用的資料如圖所示:
圖中A,B,C,D,E,F列表示六個特徵,G表示樣本標籤。每一行資料即為一個樣本的六個特徵和標籤。
實現LogisticRegression演算法的程式碼如下:
from sklearn.linear_model import LogisticRegression import csv from sklearn.cross_validation import train_test_split from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report data=[] traffic_feature=[] traffic_target=[] csv_file = csv.reader(open('packSize_all.csv')) for content in csv_file: content=list(map(float,content)) if len(content)!=0: data.append(content) traffic_feature.append(content[0;6])//存放資料集特徵 traffic_target.append(content[-1])//存放資料集標籤 print('data=',data) print('traffic_feature=',traffic_feature) print('traffic_target=',traffic_target) feature_train, feature_test, target_train, target_test = train_test_split(traffic_feature, traffic_target, test_size=0.3,random_state=0) LR = LogisticRegression() LR.fit(feature_train,target_train) predict_results=LR.predict(feature_test) print(accuracy_score(predict_results, target_test)) conf_mat = confusion_matrix(target_test, predict_results) print(conf_mat) print(classification_report(target_test, predict_results))
執行結果如圖所示: