change sql

tensnsorflow/ffm.py

@@ -167,7 +167,7 @@ def get_data():
 
 def transform(a,validate_date):
     model = multiFFMFormatPandas()
-    df = model.fit_transform(a, y="y", n=160000, processes=25)
+    df = model.fit_transform(a, y="y", n=160000, processes=22)
     df = pd.DataFrame(df)
     df["stat_date"] = df[0].apply(lambda x: x.split(",")[0])
     df["device_id"] = df[0].apply(lambda x: x.split(",")[1])
@@ -193,7 +193,7 @@ def transform(a,validate_date):
     return model
 
 def get_statistics():
-    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='eagle')
+    db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select device_id,total,精选,直播,鼻部,眼部,微整,牙齿,轮廓,美肤抗衰," \
           "吸脂,脂肪填充,隆胸,私密,毛发管理,公立,韩国 from home_tab_click"
     df = con_sql(db, sql)
@@ -229,7 +229,7 @@ def get_predict_set(ucity_id, cid,model):
     print("df ok")
     print(df.shape)
     print(df.head(2))
-    df = model.transform(df,n=160000, processes=25)
+    df = model.transform(df,n=160000, processes=22)
     df = pd.DataFrame(df)
     df["label"] = df[0].apply(lambda x: x.split(",")[0])
     df["device_id"] = df[0].apply(lambda x: x.split(",")[1])