kaggle上边这个签到的项目有同学做过没有，为啥用KNN全量跑只有10%多一点的准确率呀[流泪]
Facebook V：Predicting Check ins

全量3000w差不多，反而只拿两万条跑还有30%准确率，下面是代码：

import pandas as pd
import numpy as np
import datetime
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
import gc #做内存释放

1、获取数据

data = pd.read_csv(r'E:\kaggle dataset\train.csv')

2、基本的数据处理

1）缩小数据范围

data = data.query("x<2.5 & x>2 & y<1.5 & y>1.0") #83197 rows × 6 columns

data.head()

2)处理时间特征

time_value = pd.to_datetime(data["time"], unit="s") #Name: time, Length: 83197
date = pd.DatetimeIndex(time_value)
data["day"] = date.day
data["weekday"] = date.weekday
data["hour"] = date.hour
data.head()

3)过滤签到次数少的地点

place_count = data.groupby("place_id").count()["row_id"] #2514 rows × 8 columns
place_count[place_count > 3].head()
data_final = data[data["place_id"].isin(place_count[place_count>3].index.values)]
data_final.head() #80910 rows × 9 columns

筛选特征值和目标值

x = data_final[["x", "y", "accuracy", "day", "weekday", "hour"]]
y = data_final["place_id"]

数据集划分

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=22)

3、特征工程：标准化

transfer = StandardScaler()
x_train = transfer.fit_transform(x_train) # 训练集标准化
x_test = transfer.transform(x_test) # 测试集标准化
x_train.shape

a = datetime.datetime.now()

4、KNN算法预估器

1) 单次训练

estimator = KNeighborsClassifier(n_neighbors=1) #单次训练预先设置参数

estimator.fit(x_train, y_train) #开始训练

2) 加入网格搜索与交叉验证

参数准备

estimator = KNeighborsClassifier() #超参数搜索不需提前设置参数
param_dict = {"n_neighbors": [1,2,3,4,5,7,8,9,10]}
estimator = GridSearchCV(estimator, param_grid=param_dict, cv=2) # 10折，数据量不大，可以多折
estimator.fit(x_train, y_train) #开始训练

计算运行时间

b= datetime.datetime.now()
print(a.strftime('%Y-%m-%d %H:%M:%S'),'\n',b.strftime('%Y-%m-%d %H:%M:%S'),'\n')

a = datetime.datetime.now()

5、模型评估

方法1：直接比对真实值和预测值

%time y_predict = estimator.predict(x_test)

print("y_predict:\n", y_predict)

%time print("直接必读真实值和预测值：\n", y_test == y_predict) # 直接比对

方法2：计算准确率

%time score = estimator.score(x_test, y_test) # 测试集的特征值，测试集的目标值
print("准确率：", score)

计算运行时间

b= datetime.datetime.now()
print(a.strftime('%Y-%m-%d %H:%M:%S'),'\n',b.strftime('%Y-%m-%d %H:%M:%S'),'\n')