add check

tensnsorflow/ffm.py

@@ -141,16 +141,15 @@ def check():
     sql = "select max(stat_date) from esmm_train_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")
-    print(start)
+    # temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
+    # start = (temp - datetime.timedelta(days=90)).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," \
           "u.device_type,u.manufacturer,u.channel,c.top,df.level2_ids,e.device_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 diary_feat df on e.cid_id = df.diary_id " \
-          "where e.stat_date >= '{}'".format(start)
+          "left join diary_feat df on e.cid_id = df.diary_id"
     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",

tensnsorflow/test.py

@@ -29,6 +29,7 @@ def exp():
     db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_prod')
     sql = "select diary_id,level1_ids,level2_ids,level3_ids from diary_feat where diary_id in {}".format(native)
     df = con_sql(db,sql)
+    df.drop_duplicates
 
     n = df.shape[0]
     one = df[1].unique()
@@ -75,19 +76,8 @@ def get_cid():
 
 
 if __name__ == "__main__":
-    writer = tf.python_io.TFRecordWriter('csv_train.tfrecords')
 
-    for i in xrange(train_values.shape[0]):
-        image_raw = train_values[i].tostring()
 
-        # build example protobuf
-        example = tf.train.Example(
-            features=tf.train.Features(feature={
-                'image_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[image_raw])),
-                'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[train_labels[i]]))
-            }))
-        writer.write(record=example.SerializeToString())
 
-    writer.close()