日记候选集从top2000改为3000

diaryCandidateSet.py

@@ -16,17 +16,17 @@ def filter_cid(df):
     return df
 
 
-def get_allCitiesDiaryTop2000():
-    # 获取全国点击量TOP2000日记
+def get_allCitiesDiaryTop3000():
+    # 获取全国点击量TOP3000日记
 
     sql = "select city_id,cid from data_feed_click " \
-          "where cid_type = 'diary' group by cid order by max(click_count_choice) desc limit 2000"
-    allCitiesTop2000 = con_sql(sql)
-    allCitiesTop2000 = allCitiesTop2000.rename(columns={0: "city_id", 1: "cid"})
-    allCitiesTop2000 = filter_cid(allCitiesTop2000)
-    allCitiesTop2000.to_csv(DIRECTORY_PATH + "diaryTestSet/allCitiesDiaryTop2000.csv",index=False)
-    print("成功获取全国日记点击量TOP2000")
-    return allCitiesTop2000
+          "where cid_type = 'diary' group by cid order by max(click_count_choice) desc limit 3000"
+    allCitiesTop3000 = con_sql(sql)
+    allCitiesTop3000 = allCitiesTop3000.rename(columns={0: "city_id", 1: "cid"})
+    allCitiesTop3000 = filter_cid(allCitiesTop3000)
+    allCitiesTop3000.to_csv(DIRECTORY_PATH + "diaryTestSet/allCitiesDiaryTop3000.csv",index=False)
+    print("成功获取全国日记点击量TOP3000")
+    return allCitiesTop3000
 
 
 def get_cityList():
@@ -40,28 +40,28 @@ def get_cityList():
     return cityList
 
 
-def get_eachCityDiaryTop2000():
-    # 获取每个城市点击量TOP2000日记，如果数量小于2000，用全国点击量TOP2000日记补充
+def get_eachCityDiaryTop3000():
+    # 获取每个城市点击量TOP3000日记，如果数量小于3000，用全国点击量TOP3000日记补充
     cityList = get_cityList()
-    allCitiesTop2000 = get_allCitiesDiaryTop2000()
+    allCitiesTop3000 = get_allCitiesDiaryTop3000()
     for i in cityList:
         sql = "select city_id,cid from data_feed_click " \
               "where cid_type = 'diary' and city_id = '{0}' group by cid " \
-              "order by max(click_count_choice) desc limit 2000".format(i)
+              "order by max(click_count_choice) desc limit 3000".format(i)
         data = con_sql(sql)
         data = data.rename(columns={0: "city_id", 1: "cid"})
         data = filter_cid(data)
-        if data.shape[0] < 2000:
-            n = 2000 - data.shape[0]
-            # 全国点击量TOP2000日记中去除该城市的日记
-            temp = allCitiesTop2000[allCitiesTop2000["city_id"] != i].loc[:n - 1]
+        if data.shape[0] < 3000:
+            n = 3000 - data.shape[0]
+            # 全国点击量TOP3000日记中去除该城市的日记
+            temp = allCitiesTop3000[allCitiesTop3000["city_id"] != i].loc[:n - 1]
             data = data.append(temp)
         else:
             pass
 
-        file_name = DIRECTORY_PATH + "diaryTestSet/{0}DiaryTop2000.csv".format(i)
+        file_name = DIRECTORY_PATH + "diaryTestSet/{0}DiaryTop3000.csv".format(i)
         data.to_csv(file_name,index=False)
 
 
 if __name__ == "__main__":
-    get_eachCityDiaryTop2000()
+    get_eachCityDiaryTop3000()

predictDiary.py

@@ -11,7 +11,7 @@ from userProfile import fetch_user_profile
 
 # 将device_id、city_id拼接到对应的城市热门日记表。注意：下面预测集特征顺序要与训练集保持一致
 def feature_en(user_profile):
-    file_name = DIRECTORY_PATH + "diaryTestSet/{0}DiaryTop2000.csv".format(user_profile['city_id'])
+    file_name = DIRECTORY_PATH + "diaryTestSet/{0}DiaryTop3000.csv".format(user_profile['city_id'])
     data = pd.read_csv(file_name)
 
     data["device_id"] = user_profile['device_id']
@@ -24,7 +24,7 @@ def feature_en(user_profile):
     data.loc[data["minute"] == 0, ["minute"]] = 60
     data["hour"] = data["hour"].astype("category")
     data["minute"] = data["minute"].astype("category")
-
+    # 虽然预测y，但ffm转化需要y，并不影响预测结果
     data["y"] = 0
     data = data.drop("city_id", axis=1)
     print(data.head(2))

utils.py

@@ -71,7 +71,7 @@ class FFMFormatPandas:
     def transform(self, df):
         t = df.dtypes.to_dict()
         return pd.Series({idx: self.transform_row_(row, t) for idx, row in df.iterrows()})
-
+    # 下面这个方法不是这个类原有的方法，是新增的。目的是用来判断这个用户是不是在训练数据集中存在
     def is_feature_index_exist(self, name):
         if name in self.feature_index_:
             return True