Merge branch 'master' of git.wanmeizhensuo.com:ML/ffm-baseline

change , to ;

tensnsorflow/ffm.py

@@ -137,36 +137,48 @@ class multiFFMFormatPandas:
 
 
 def get_data():
-    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
-    sql = "select max(stat_date) from esmm_train_data"
-    validate_date = con_sql(db, sql)[0].values.tolist()[0]
+    # db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+    # sql = "select max(stat_date) from esmm_train_data"
+    # validate_date = con_sql(db, sql)[0].values.tolist()[0]
+    validate_date = "2018-12-19"
     print("validate_date:" + validate_date)
-    temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
-    start = (temp - datetime.timedelta(days=6)).strftime("%Y-%m-%d")
+    # temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
+    # start = (temp - datetime.timedelta(days=30)).strftime("%Y-%m-%d")
+    start = "2018-11-19"
     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," \
-          "u.device_type,u.manufacturer,u.channel,c.top,cid_time.time " \
-          "from esmm_train_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_id = cid_time.cid_id " \
+    # 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,f.level2_ids,e.ccity_name," \
+    #       "u.device_type,u.manufacturer,u.channel,c.top,cid_time.time,s.hospital_id,s.doctor_id " \
+    #       "from esmm_train_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_id = cid_time.cid_id " \
+    #       "left join service_hospital s on e.diary_service_id = s.id left join diary_feat f" \
+    #       "where e.stat_date >= '{}'".format(start)
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC')
+    sql = "select e.y,e.z,e.stat_date,e.ucity_id,f.level2_ids " \
+          "from jerry_test.esmm_train_data e left join jerry_prod.diary_feat f " \
           "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",
-                            6:"device_type",7:"manufacturer",8:"channel",9:"top",10:"time"})
+                            6:"device_type",7:"manufacturer",8:"channel",9:"top",10:"time",
+                            11:"hospital_id",12:"doctor_id"})
     print("esmm data ok")
-    print(df.head(2))
 
+    print(df.head(2))
+    features = 0
+    category = ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel","top","doctor_id",
+                "hospital_id"]
+    for i in category:
+        df[i] = df[i].fillna("na")
+        features = features + len(df[i].unique())
+    df["time"] = df["time"].fillna(0.0)
     df["clevel1_id"] = df["clevel1_id"].astype("str")
     df["y"] = df["y"].astype("str")
     df["z"] = df["z"].astype("str")
     df["top"] = df["top"].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)
+    df = df.drop(["z","stat_date"], axis=1)
     print(df.head(2))
-    features = 0
-    for i in ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel","top"]:
-        features = features + len(df[i].unique())
     print("fields:{}".format(df.shape[1]-1))
     print("features:{}".format(features))
     ccity_name = list(set(df["ccity_name"].values.tolist()))
@@ -217,6 +229,10 @@ def get_predict_set(ucity_id,model,ccity_name):
     df = df[df["ccity_name"].isin(ccity_name)]
     print("after ccity_name filter:")
     print(df.shape)
+    category = ["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer", "channel", "top"]
+    for i in category:
+        df[i] = df[i].fillna("na")
+    df["time"] = df["time"].fillna(0.0)
     df["cid_id"] = df["cid_id"].astype("str")
     df["clevel1_id"] = df["clevel1_id"].astype("str")
     df["top"] = df["top"].astype("str")
@@ -226,7 +242,7 @@ def get_predict_set(ucity_id,model,ccity_name):
     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","cid_id"], axis=1).fillna(0.0)
+    df = df.drop(["z","label","device_id","cid_id"], axis=1)
     print(df.head(2))
     df = model.transform(df,n=160000, processes=22)
     df = pd.DataFrame(df)
@@ -262,6 +278,7 @@ if __name__ == "__main__":
     df, validate_date, ucity_id,ccity_name = get_data()
     model = transform(df, validate_date)
     # get_predict_set(ucity_id,model,ccity_name)
+
     b = time.time()
     print("cost(分钟)")
     print((b-a)/60)