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

eda/feededa/src/main/scala/com/gmei/EsmmData.scala

@@ -226,7 +226,6 @@ object EsmmData {
 
 
 
-
 object EsmmPredData {
 
   Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
@@ -425,6 +424,77 @@ object EsmmPredData {
 
 
 
+      sc.stop()
+
+    }
+  }
+
+}
+
+
+object GetPortrait {
+
+  Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
+  Logger.getLogger("org.apache.eclipse.jetty.server").setLevel(Level.OFF)
+
+  case class Params(env: String = "dev",
+                    date: String = GmeiConfig.getMinusNDate(1)
+                   ) extends AbstractParams[Params] with Serializable
+
+  val defaultParams = Params()
+
+  val parser = new OptionParser[Params]("Feed_EDA") {
+    head("EsmmData")
+    opt[String]("env")
+      .text(s"the databases environment you used")
+      .action((x, c) => c.copy(env = x))
+    opt[String]("date")
+      .text(s"the date you used")
+      .action((x,c) => c.copy(date = x))
+    note(
+      """
+        |For example, the following command runs this app on a tidb dataset:
+        |
+        | spark-submit --class com.gmei.EsmmData ./target/scala-2.11/feededa-assembly-0.1.jar \
+      """.stripMargin +
+        s"|   --env ${defaultParams.env}"
+    )
+  }
+
+
+
+  def main(args: Array[String]): Unit = {
+    parser.parse(args, defaultParams).map { param =>
+      GmeiConfig.setup(param.env)
+      val spark_env = GmeiConfig.getSparkSession()
+      val sc = spark_env._2
+
+      val ti = new TiContext(sc)
+      ti.tidbMapTable(dbName = "jerry_prod",tableName = "data_feed_click")
+
+      val diary_tag = sc.sql(
+        s"""
+           |select d.diary_id,
+           |(case when d.tag_type = '1' then d.level1_ids else "" end) level1_ids,
+           |(case when d.tag_type = '2' then d.level2_ids else "" end) level2_ids,
+           |(case when d.tag_type = '3' then d.level3_ids else "" end) level3_ids from
+           |	(select c.diary_id,c.tag_type,
+           |		concat_ws(c.level1_id) as level1_ids
+           |		concat_ws(c.level2_id) as level2_ids
+           |		concat_ws(c.level3_id) as level3_ids from
+           |	(select a.diary_id,a.tag_id,b.tag_type,b.level1_id,b.level2_id,b.level3_id
+           |		from tl_hdfs_diary_tags_view a
+           |		left join bl_tag_hierarchy_detail b
+           |		on a.tag_id = b.id
+           |		where a.partition_date = '20181218'
+           |		and b.partition_date = '20181218') c
+           |	group by c.diary_id,c.tag_type) d
+           |group by d.diary_id
+         """.stripMargin
+      )
+      diary_tag.show()
+
+
       sc.stop()
 
     }

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 +152,26 @@ 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["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)
     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):
@@ -193,28 +199,34 @@ 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["top"] = df["top"].astype("str")
     df["y"] = df["y"].astype("str")
     df["z"] = df["z"].astype("str")
     df["label"] = df["label"].astype("str")
     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 +259,9 @@ 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)