from sklearn.model_selection import train_test_split, GridSearchCV

from sklearn.neighbors import KNeighborsClassifier

from sklearn.preprocessing import StandardScaler

import pandas as pd

def knncls():

    """

    K-近邻预测用户签到位置

    :return:None

    """

    # 读取数据

    data = pd.read_csv("./data/FBlocation/train.csv")

    # print(data.head(10))

    # 处理数据

    # 1、缩小数据,查询数据晒讯

    data = data.query("x > 1.0 &  x < 1.25 & y > 2.5 & y < 2.75")

    # 处理时间的数据

    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)

    # 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)

    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)]

    # 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合测试集

    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))

    # 构造一些参数的值进行搜索

    param = {"n_neighbors": [3, 5, 10]}

    # 进行网格搜索

    gc = GridSearchCV(knn, param_grid=param, cv=2)

    gc.fit(x_train, y_train)

    # 预测准确率

    print("在测试集上准确率：", gc.score(x_test, y_test))

    print("在交叉验证当中最好的结果：", gc.best_score_)

    print("选择最好的模型是：", 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()