add test file

local/precitDIaryLocal.py

 import pickle
 import xlearn as xl
-from utils import *
+import pandas as pd
+import pymysql
+from datetime import datetime
+import utils
 
 
 # 本地测试脚本
@@ -21,9 +24,14 @@ def test_con_sql(device_id):
         nearby_queue = df.loc[0, "nearby_queue"].split(",")
         nation_queue = df.loc[0, "nation_queue"].split(",")
         megacity_queue = df.loc[0, "megacity_queue"].split(",")
-
-    db.close()
-    return native_queue, nearby_queue, nation_queue, megacity_queue
+        db.close()
+        print(native_queue)
+        print(nearby_queue)
+        print(nation_queue)
+        print(megacity_queue)
+        return native_queue, nearby_queue, nation_queue, megacity_queue
+    else:
+        print("该用户对应的日记队列为空")
 
 
 # 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
@@ -40,6 +48,8 @@ def feature_en(x_list, device_id):
     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.to_csv("/Users/mac/utils/result/data.csv")
+    print(data)
     return data
 
 
@@ -57,7 +67,7 @@ def transform_ffm_format(df, device_id):
 
 # 将模型加载，预测，把预测日记的概率值按照降序排序，存到一个表里
 def predict(queue_name, x_list, device_id):
-    data = feature_en(x_list)
+    data = feature_en(x_list,device_id)
     data_file_path = transform_ffm_format(data, device_id)
 
     ffm_model = xl.create_ffm()
@@ -156,12 +166,14 @@ def get_update_time():
 if __name__ == "__main__":
     # 数据库没有更新时间字段，下面的代码不能使用
     # sql_update_time_start = get_update_time()
-    native_queue_list, nearby_queue_list, nation_queue_list, megacity_queue_list = test_con_sql("device_id")
+    device_id = "358035085192742"
+    native_queue_list, nearby_queue_list, nation_queue_list, megacity_queue_list = test_con_sql(device_id)
     name_dict = {"native_queue": native_queue_list, "nearby_queue": nearby_queue_list,
                  "nation_queue": nation_queue_list, "megacity_queue": megacity_queue_list}
     for key in name_dict.keys():
-        diary_id = predict(key, name_dict[key], "devcie_id")
-        sql_update_time_end = get_update_time()
+        diary_id = predict(key, name_dict[key], device_id)
+        # sql_update_time_end = get_update_time()
         # 数据库没有更新时间字段，下面的代码不能使用
         # if sql_update_time_start == sql_update_time_end:
-        update_sql_dairy_queue(key, diary_id, "device_id")
+        update_sql_dairy_queue(key, diary_id, device_id)
+

local/testCases.py

-from utils import *
-import datetime
+import utils 
 import pickle
-import time
-
-
-
-
-if __name__ == '__main__':
-    df = pd.read_csv("/Users/mac/PycharmProjects/nvwa/ffm-baseline/data/test-data/大数据.csv")
-    for i in range(500,10000,500):
+import pandas as pd
+DIRECTORY_PATH = '/data2/models/'
+with open(DIRECTORY_PATH + "ffm.pkl", "rb") as f:
+    ffm_format_pandas = pickle.load(f)
+    df = pd.read_csv("/home/zhangyanzhao/data.csv")
+    data = ffm_format_pandas.transform(df)
+    data.to_csv("/home/zhangyanzhao/ffm.csv", index=False, header=None)
\ No newline at end of file
-
-        start = time.time()
-        ffm = multiFFMFormatPandas()
-        data = ffm.fit_transform(df, y="y",n=i,processes=3)
-        end = time.time()
-        print("分割单位{}耗时{}".format(i,end-start))

local/utils.py

+# encoding = "utf-8"
+from datetime import datetime
+from datetime import timedelta
+import pymysql
+import numpy as np
+import redis
+import pandas as pd
+from sklearn import metrics
+from sklearn.metrics import auc
+from multiprocessing import Pool
+
+
+def get_date():
+    now = datetime.now()
+    year = now.year
+    month = now.month
+    day = now.day
+    date = datetime(year,month,day)
+    data_start_date = (date - timedelta(days=31)).strftime("%Y-%m-%d")
+    data_end_date = (date - timedelta(days=1)).strftime("%Y-%m-%d")
+    validation_date = (date - timedelta(days=2)).strftime("%Y-%m-%d")
+    # 验证集和测试集的日期必须相差一天，否则切割数据集时会报错
+    test_date = data_end_date
+    print("data_start_date,data_end_date,validation_date,test_date：")
+    print(data_start_date,data_end_date,validation_date,test_date)
+    return data_start_date,data_end_date,validation_date,test_date
+
+
+def get_roc_curve(y, pred, pos_label):
+    """
+    计算二分类问题的roc和auc
+    """
+    fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label)
+    AUC = metrics.auc(fpr, tpr)
+    print(AUC)
+
+
+# 从Tidb数据库的表里获取数据，并转化成df格式
+def con_sql(sql):
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    df = pd.DataFrame(list(result)).dropna()
+    db.close()
+    return df
+
+
+# 把数据写到redis里
+# TODO 生产环境的redis地址没有提供，下面的地址是测试环境的，需要改成生产环境地址
+def add_data_to_redis(key, val):
+    r = redis.StrictRedis(host='10.30.50.58', port=6379, db=12)
+    r.set(key, val)
+    # 设置key的过期时间，36小时后过期
+    r.expire(key, 36 * 60 * 60)
+
+
+# 多线程ffm转化类：
+class multiFFMFormatPandas:
+    def __init__(self):
+        self.field_index_ = None
+        self.feature_index_ = None
+        self.y = None
+
+    def fit(self, df, y=None):
+        self.y = y
+        df_ffm = df[df.columns.difference([self.y])]
+        if self.field_index_ is None:
+            self.field_index_ = {col: i for i, col in enumerate(df_ffm)}
+
+        if self.feature_index_ is not None:
+            last_idx = max(list(self.feature_index_.values()))
+
+        if self.feature_index_ is None:
+            self.feature_index_ = dict()
+            last_idx = 0
+
+        for col in df.columns:
+            vals = df[col].unique()
+            for val in vals:
+                if pd.isnull(val):
+                    continue
+                name = '{}_{}'.format(col, val)
+                if name not in self.feature_index_:
+                    self.feature_index_[name] = last_idx
+                    last_idx += 1
+            self.feature_index_[col] = last_idx
+            last_idx += 1
+
+        return self
+
+    def fit_transform(self, df, y=None,n=50000,processes=4):
+        # n是每个线程运行最大的数据条数,processes是线程数
+        self.fit(df, y)
+        n = n
+        processes = processes
+        return self.transform(df,n,processes)
+
+    def transform_row_(self, row, t):
+        ffm = []
+        if self.y is not None:
+            ffm.append(str(row.loc[row.index == self.y][0]))
+        if self.y is None:
+            ffm.append(str(0))
+
+        for col, val in row.loc[row.index != self.y].to_dict().items():
+            col_type = t[col]
+            name = '{}_{}'.format(col, val)
+            if col_type.kind == 'O':
+                ffm.append('{}:{}:1'.format(self.field_index_[col], self.feature_index_[name]))
+            elif col_type.kind == 'i':
+                ffm.append('{}:{}:{}'.format(self.field_index_[col], self.feature_index_[col], val))
+        return ' '.join(ffm)
+
+    def transform(self, df,n=1500,processes=2):
+        # n是每个线程运行最大的数据条数,processes是线程数
+        t = df.dtypes.to_dict()
+        data_list = self.data_split_line(df,n)
+
+        # 设置进程的数量
+        pool = Pool(processes)
+        print("总进度: " + str(len(data_list)))
+        for i in range(len(data_list)):
+            data_list[i] = pool.apply_async(self.pool_function, (data_list[i], t,))
+
+        result_map = {}
+        for i in data_list:
+            result_map.update(i.get())
+        pool.close()
+        pool.join()
+
+        return pd.Series(result_map)
+
+    # 多进程计算方法
+    def pool_function(self, df, t):
+        return {idx: self.transform_row_(row, t) for idx, row in df.iterrows()}
+
+    # 切分数据方法，传人dataframe和切分条数的步长,返回dataframe的集合，每个dataframe中含有若干条数据
+    def data_split_line(self, data, step):
+        data_list = []
+        x = 0
+        while True:
+            if x + step < data.__len__():
+                data_list.append(data.iloc[x:x + step])
+                x = x + step + 1
+            else:
+                data_list.append(data.iloc[x:data.__len__()])
+                break
+
+        '''
+        # 返回生成器方法，但是本地测试效率不高
+        x = 0
+        while True:
+            if x + step < data.__len__():
+                yield data.iloc[x:x + step]
+                x = x + step + 1
+            else:
+                yield data.iloc[x:data.__len__()]
+                break
+        '''
+
+        return data_list
+
+
+    # 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
+        else:
+            return False
+
+# ffm 格式转换函数、类
+# class FFMFormatPandas:
+#     def __init__(self):
+#         self.field_index_ = None
+#         self.feature_index_ = None
+#         self.y = None
+#
+#     def fit(self, df, y=None):
+#         self.y = y
+#         df_ffm = df[df.columns.difference([self.y])]
+#         if self.field_index_ is None:
+#             self.field_index_ = {col: i for i, col in enumerate(df_ffm)}
+#
+#         if self.feature_index_ is not None:
+#             last_idx = max(list(self.feature_index_.values()))
+#
+#         if self.feature_index_ is None:
+#             self.feature_index_ = dict()
+#             last_idx = 0
+#
+#         for col in df.columns:
+#             vals = df[col].unique()
+#             for val in vals:
+#                 if pd.isnull(val):
+#                     continue
+#                 name = '{}_{}'.format(col, val)
+#                 if name not in self.feature_index_:
+#                     self.feature_index_[name] = last_idx
+#                     last_idx += 1
+#             self.feature_index_[col] = last_idx
+#             last_idx += 1
+#         return self
+#
+#     def fit_transform(self, df, y=None):
+#         self.fit(df, y)
+#         return self.transform(df)
+#
+#     def transform_row_(self, row, t):
+#         ffm = []
+#         if self.y is not None:
+#             ffm.append(str(row.loc[row.index == self.y][0]))
+#         if self.y is None:
+#             ffm.append(str(0))
+#
+#         for col, val in row.loc[row.index != self.y].to_dict().items():
+#             col_type = t[col]
+#             name = '{}_{}'.format(col, val)
+#             if col_type.kind == 'O':
+#                 ffm.append('{}:{}:1'.format(self.field_index_[col], self.feature_index_[name]))
+#             elif col_type.kind == 'i':
+#                 ffm.append('{}:{}:{}'.format(self.field_index_[col], self.feature_index_[col], val))
+#         return ' '.join(ffm)
+#
+#     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
+#         else:
+#             return False