add print

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=30)).strftime("%Y-%m-%d")
+    start = (temp - datetime.timedelta(days=6)).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," \
@@ -165,7 +165,7 @@ def get_data():
     df = df.drop(["z","stat_date"], axis=1).fillna(0.0)
     print(df.head(2))
     features = 0
-    for i in ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel"]:
+    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))
@@ -261,7 +261,7 @@ if __name__ == "__main__":
     a = time.time()
     df, validate_date, ucity_id,ccity_name = get_data()
     model = transform(df, validate_date)
-    get_predict_set(ucity_id,model,ccity_name)
+    # get_predict_set(ucity_id,model,ccity_name)
     b = time.time()
     print("cost(分钟)")
     print((b-a)/60)