模型选择与调优 交叉验证：为了让被评估的模型更加准确可信
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from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split,GridSearchCV from sklearn.preprocessing import StandardScaler import pandas as pd def knncls(): # k-近邻预测用户签到位置 # 1，读取数据 data = pd.read_csv("train.csv") # print(data.head(10)) #,2，处理数据 # 缩小数据,查询数据筛选 data = data.query("x > 1.0 & x <1.25 & y >2.5 & y < 2.75") # 处理时间数据 time_value = pd.to_datetime(data["time"],unit="s") # print(time_value) # 把日期格式转换成字典格式 time_value = pd.DatetimeIndex(time_value) # 3，构造一些特征 data["day"] = time_value.day data["hour"] = time_value.hour data["weekday"] = time_value.weekday # 把时间戳特征删除 data = data.drop(["time"],axis=1) # sklearn中1表示列和pandas不一样 # print(data) #把签到数量少于n个目标位置删除 place_count = data.groupby("place_id").count() tf = place_count[place_count.row_id > 3].reset_index() data = data[data["place_id"].isin(tf.place_id)] data = data.drop(["row_id"],axis=1) print(data) # 取出数据当中的特征值和目标值 y = data["place_id"] x = data.drop(["place_id"],axis=1) # 进行数据的分割 训练集和测试集 x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25) # 特征工程（标准化） std = StandardScaler() # 对测试集和训练集的特征值进行标准化 x_train = std.fit_transform(x_train) x_test = std.transform(x_test) # 进行算法流程 # 超参数 knn = KNeighborsClassifier() # # fit,predict,score # knn.fit(x_train,y_train) # # 得出预测结果 # y_predict = knn.predict(x_test) # # print("预测的目标签到位置为:",y_predict) # # # 得出准确率 # print("预测的准确率:",knn.score(x_test,y_test)) # 进行网格搜索 # 构造一些参数的值进行搜索 param = {"n_neighbors":[3,5,10]} gc = GridSearchCV(knn,param_grid=param,cv=10) gc.fit(x_train,y_train) # 预测准确率 gc.score(x_test,y_test) print("在测试集上的准确率:",gc.score(x_test,y_test)) print("在交叉验证中最好的结果:",gc.best_score_) print("最好的模型是:",gc.best_estimator_) print("每个超参数每次交叉验证的结果:",gc.cv_results_) return None if __name__=="__main__": knncls()