add time

tensnsorflow/ffm.py

@@ -142,7 +142,7 @@ def get_data():
     validate_date = con_sql(db, sql)[0].values.tolist()[0]
     print("validate_date:" + validate_date)
     temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
-    start = (temp - datetime.timedelta(days=18)).strftime("%Y-%m-%d")
+    start = (temp - datetime.timedelta(days=30)).strftime("%Y-%m-%d")
     print(start)
     db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select e.y,e.z,e.stat_date,e.ucity_id,e.clevel1_id,e.ccity_name," \
@@ -152,20 +152,25 @@ def get_data():
           "where e.stat_date >= '{}'".format(start)
     df = con_sql(db, sql)
     print(df.shape)
-    df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id",4: "clevel1_id", 5: "ccity_name"})
+    df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id",4: "clevel1_id", 5: "ccity_name",
+                            6:"device_type",7:"manufacturer",8:"channel",9:"top",10:"time"})
     print("esmm data ok")
     print(df.head(2))
-    ucity_id = list(set(df["ucity_id"].values.tolist()))
+
     df["clevel1_id"] = df["clevel1_id"].astype("str")
     df["y"] = df["y"].astype("str")
     df["z"] = df["z"].astype("str")
     df["y"] = df["stat_date"].str.cat([df["y"].values.tolist(),df["z"].values.tolist()], sep=",")
     df = df.drop(["z","stat_date"], axis=1).fillna(0.0)
     print(df.head(2))
-    features = len(df["ucity_id"].unique())
+    features = 0
+    for i in ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel"]:
+        features = features + len(df[i].unique())
     print("fields:{}".format(df.shape[1]-1))
     print("features:{}".format(features))
-    return df,validate_date,ucity_id
+    ccity_name = list(set(df["ccity_name"].values.tolist()))
+    ucity_id = list(set(df["ucity_id"].values.tolist()))
+    return df,validate_date,ucity_id,ccity_name
 
 
 def transform(a,validate_date):
@@ -193,20 +198,25 @@ def transform(a,validate_date):
     return model
 
 
-def get_predict_set(ucity_id,model):
+def get_predict_set(ucity_id,model,ccity_name):
     db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select e.y,e.z,e.label,e.ucity_id,e.clevel1_id,e.ccity_name," \
-          "u.device_type,u.manufacturer,u.channel,c.top,cid_time.time " \
+          "u.device_type,u.manufacturer,u.channel,c.top,cid_time.time,e.device_id,e.cid_id " \
           "from esmm_pre_data e left join user_feature u on e.device_id = u.device_id " \
-          "left join cid_type_top c on e.device_id = c.device_id left join cid_time on e.cid = cid_time.cid_id"
-
+          "left join cid_type_top c on e.device_id = c.device_id left join cid_time on e.cid_id = cid_time.cid_id"
     df = con_sql(db, sql)
-    df = df.rename(columns={0: "y", 1: "z", 2: "label", 3: "ucity_id", 4: "clevel1_id", 5: "ccity_name"})
+    df = df.rename(columns={0: "y", 1: "z", 2: "label", 3: "ucity_id", 4: "clevel1_id", 5: "ccity_name",
+                            6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "time",
+                            11:"device_id",12:"cid_id"})
     print("before filter:")
     print(df.shape)
     df = df[df["ucity_id"].isin(ucity_id)]
     print("after ucity filter:")
     print(df.shape)
+    df = df[df["ccity_name"].isin(ccity_name)]
+    print("after ccity_name filter:")
+    print(df.shape)
+    df["cid_id"] = df["cid_id"].astype("str")
     df["clevel1_id"] = df["clevel1_id"].astype("str")
     df["y"] = df["y"].astype("str")
     df["z"] = df["z"].astype("str")
@@ -214,7 +224,7 @@ def get_predict_set(ucity_id,model):
     df["y"] = df["label"].str.cat(
         [df["device_id"].values.tolist(), df["ucity_id"].values.tolist(), df["cid_id"].values.tolist(),
          df["y"].values.tolist(), df["z"].values.tolist()], sep=",")
-    df = df.drop(["z","label","device_id"], axis=1).fillna(0.0)
+    df = df.drop(["z","label","device_id","cid_id"], axis=1).fillna(0.0)
     print(df.head(2))
     df = model.transform(df,n=160000, processes=22)
     df = pd.DataFrame(df)
@@ -247,9 +257,9 @@ def get_predict_set(ucity_id,model):
 if __name__ == "__main__":
     path = "/home/gmuser/ffm/"
     a = time.time()
-    df, validate_date, ucity_id = get_data()
+    df, validate_date, ucity_id,ccity_name = get_data()
     model = transform(df, validate_date)
-    # get_predict_set(ucity_id,model)
+    get_predict_set(ucity_id,model,ccity_name)
     b = time.time()
     print("cost(分钟)")
     print((b-a)/60)