fix: fix the error of pylint in demo directory

demo/predict-taxi-trip-duration/script/convert_data.py

@@ -14,16 +14,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""Module of covert data from system stdin"""
 import sys
-import time, datetime
+import time
+
 i = 0
 for line in sys.stdin:
     if i == 0:
-        i+=1
+        i += 1
         print(line.strip())
         continue
     arr = line.strip().split(",")
     arr[2] = str(int(time.mktime(time.strptime(arr[2], "%Y-%m-%d %H:%M:%S"))) * 1000)
     arr[3] = str(int(time.mktime(time.strptime(arr[3], "%Y-%m-%d %H:%M:%S"))) * 1000)
     print(",".join(arr))
-

demo/predict-taxi-trip-duration/script/predict.py

@@ -13,25 +13,20 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
+"""Module of request predict in script"""
 import requests
-import os
-import base64
-import random
-import time
-import hashlib
 
 url = "http://127.0.0.1:8887/predict"
-req ={"id":"id0376262",
-	"vendor_id":1,
-	"pickup_datetime":1467302350000,
-	"dropoff_datetime":1467304896000,
-	"passenger_count":2,
-	"pickup_longitude":-73.873093,
-	"pickup_latitude":40.774097,
-	"dropoff_longitude":-73.926704,
-	"dropoff_latitude":40.856739,
-	"store_and_fwd_flag":"N",
-	"trip_duration":1}
+req = {"id": "id0376262",
+       "vendor_id": 1,
+       "pickup_datetime": 1467302350000,
+       "dropoff_datetime": 1467304896000,
+       "passenger_count": 2,
+       "pickup_longitude": -73.873093,
+       "pickup_latitude": 40.774097,
+       "dropoff_longitude": -73.926704,
+       "dropoff_latitude": 40.856739,
+       "store_and_fwd_flag": "N",
+       "trip_duration": 1}
 r = requests.post(url, json=req)
 print(r.text)

demo/predict-taxi-trip-duration/script/predict_server.py

@@ -14,52 +14,57 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""Module of predict server"""
 import numpy as np
 import tornado.web
 import tornado.ioloop
 import json
 import lightgbm as lgb
-import sqlalchemy as db
 import requests
 import argparse
 
 bst = None
 
 table_schema = [
-	("id", "string"),
-	("vendor_id", "int"),
-	("pickup_datetime", "timestamp"),
-	("dropoff_datetime", "timestamp"),
-	("passenger_count", "int"),
-	("pickup_longitude", "double"),
-	("pickup_latitude", "double"),
-	("dropoff_longitude", "double"),
-	("dropoff_latitude", "double"),
-	("store_and_fwd_flag", "string"),
-	("trip_duration", "int"),
+    ("id", "string"),
+    ("vendor_id", "int"),
+    ("pickup_datetime", "timestamp"),
+    ("dropoff_datetime", "timestamp"),
+    ("passenger_count", "int"),
+    ("pickup_longitude", "double"),
+    ("pickup_latitude", "double"),
+    ("dropoff_longitude", "double"),
+    ("dropoff_latitude", "double"),
+    ("store_and_fwd_flag", "string"),
+    ("trip_duration", "int"),
 ]
 
 url = ""
 
+
 def get_schema():
-    dict_schema = {}
+    dict_schema_tmp = {}
     for i in table_schema:
-        dict_schema[i[0]] = i[1]
-    return dict_schema
+        dict_schema_tmp[i[0]] = i[1]
+    return dict_schema_tmp
+
 
 dict_schema = get_schema()
 json_schema = json.dumps(dict_schema)
 
+
 def build_feature(rs):
-    var_Y = [rs[0]]
-    var_X = [rs[1:12]]
-    return np.array(var_X)
+    var_x = [rs[1:12]]
+    return np.array(var_x)
+
 
 class SchemaHandler(tornado.web.RequestHandler):
     def get(self):
         self.write(json_schema)
 
+
 class PredictHandler(tornado.web.RequestHandler):
+    """Class of PredictHandler docstring."""
     def post(self):
         row = json.loads(self.request.body)
         data = {}
@@ -72,7 +77,8 @@ def post(self):
                 row_data.append(row.get(i[0], 0))
             else:
                 row_data.append(None)
-        data["input"].append(row_data)       
+
+        data["input"].append(row_data)
         rs = requests.post(url, json=data)
         result = json.loads(rs.text)
         for r in result["data"]["data"]:
@@ -81,25 +87,28 @@ def post(self):
             self.write(str(ins) + "\n")
             duration = bst.predict(ins)
             self.write("---------------predict trip_duration -------------\n")
-            self.write("%s s"%str(duration[0]))
+            self.write(f"{str(duration[0])} s")
+
 
 class MainHandler(tornado.web.RequestHandler):
     def get(self):
         self.write("real time execute sparksql demo")
 
+
 def make_app():
     return tornado.web.Application([
         (r"/", MainHandler),
         (r"/schema", SchemaHandler),
         (r"/predict", PredictHandler),
     ])
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("endpoint",  help="specify the endpoint of apiserver")
-    parser.add_argument("model_path",  help="specify the model path")
+    parser.add_argument("endpoint", help="specify the endpoint of apiserver")
+    parser.add_argument("model_path", help="specify the model path")
     args = parser.parse_args()
-    url = "http://%s/dbs/demo_db/deployments/demo" % args.endpoint
+    url = f"http://{args.endpoint}/dbs/demo_db/deployments/demo"
     bst = lgb.Booster(model_file=args.model_path)
     app = make_app()
     app.listen(8887)

demo/predict-taxi-trip-duration/script/train.py

@@ -14,30 +14,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""Module of train and save model"""
 import lightgbm as lgb
 import pandas as pd
-from sklearn.metrics import mean_squared_error
-from sklearn.model_selection import GridSearchCV
 from sklearn.model_selection import train_test_split
 import argparse
 import os
 
 parser = argparse.ArgumentParser()
-parser.add_argument("feature_path", help="specify the feature path")
-parser.add_argument("model_path", help="specify the model path")
+parser.add_argument('feature_path', help='specify the feature path')
+parser.add_argument('model_path', help='specify the model path')
 args = parser.parse_args()
 
 feature_path = args.feature_path
 # merge file
 if os.path.isdir(feature_path):
     path_list = os.listdir(feature_path)
-    new_file = "/tmp/merged_feature.csv"
-    with open(new_file, 'w') as wf:
+    new_file = '/tmp/merged_feature.csv'
+    with open(new_file, 'w', encoding='utf-8') as wf:
         has_write_header = False
         for filename in path_list:
-            if filename == "_SUCCESS" or filename.startswith('.'):
+            if filename == '_SUCCESS' or filename.startswith('.'):
                 continue
-            with open(os.path.join(feature_path, filename), 'r') as f:
+            with open(os.path.join(feature_path, filename), 'r', encoding='utf-8') as f:
                 first_line = True
                 for line in f.readlines():
                     if first_line is True:
@@ -50,7 +49,7 @@
     feature_path = new_file
 
 # run batch sql and get instances
-df = pd.read_csv(feature_path);
+df = pd.read_csv(feature_path)
 train_set, predict_set = train_test_split(df, test_size=0.2)
 y_train = train_set['trip_duration']
 x_train = train_set.drop(columns=['trip_duration'])
@@ -83,4 +82,4 @@
                 early_stopping_rounds=5)
 
 gbm.save_model(args.model_path)
-print("save model.txt done")
+print('save model.txt done')

demo/predict-taxi-trip-duration/test/import.py

@@ -14,15 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
-"""
+"""Module of insert data to table"""
 import sqlalchemy as db
 
-
-import sys
 import datetime
 
-ddl="""
+ddl = """
 create table t1(
 id string,
 vendor_id int,
@@ -42,26 +39,29 @@
 engine = db.create_engine('openmldb:///db_test?zk=127.0.0.1:2181&zkPath=/openmldb')
 connection = engine.connect()
 try:
-    connection.execute("create database db_test;");
+    connection.execute('create database db_test;')
 except Exception as e:
     print(e)
 try:
-    connection.execute(ddl);
+    connection.execute(ddl)
 except Exception as e:
     print(e)
 
+
 def insert_row(line):
     row = line.split(',')
-    row[2] = '%dl'%int(datetime.datetime.strptime(row[2], '%Y-%m-%d %H:%M:%S').timestamp() * 1000)
-    row[3] = '%dl'%int(datetime.datetime.strptime(row[3], '%Y-%m-%d %H:%M:%S').timestamp() * 1000)
-    insert = "insert into t1 values('%s', %s, %s, %s, %s, %s, %s, %s, %s, '%s', %s);"% tuple(row)
+    row[2] = f"{int(datetime.datetime.strptime(row[2], '%Y-%m-%d %H:%M:%S').timestamp() * 1000)}l"
+    row[3] = f"{int(datetime.datetime.strptime(row[3], '%Y-%m-%d %H:%M:%S').timestamp() * 1000)}l"
+    insert = f"insert into t1 values('{row[0]}', {row[1]}, {row[2]}, {row[3]}, {row[4]}, {row[5]}, " \
+             f"{row[6]}, {row[7]}, {row[8]}, '{row[9]}', {row[10]});"
     connection.execute(insert)
 
-with open('data/taxi_tour_table_train_simple.csv', 'r') as fd:
+
+with open('data/taxi_tour_table_train_simple.csv', 'r', encoding='utf-8') as fd:
     idx = 0
-    for line in fd:
+    for csv_line in fd:
         if idx == 0:
             idx = idx + 1
             continue
-        insert_row(line.replace('\n', ''))
+        insert_row(csv_line.replace('\n', ''))
         idx = idx + 1

demo/talkingdata-adtracking-fraud-detection/predict.py

@@ -13,7 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
+"""Module of request predict in talkingdata-adtracking-fraud-detection"""
 import requests
 
 url = "http://127.0.0.1:8881/predict"