位号列预测

client/client.py

+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
 import grpc
 from protobuf import classify_pb2
 from protobuf import classify_pb2_grpc
-import pandas as pd
 import json
+import pandas as pd
+
 def get_test_data():
-    df=pd.read_excel(r'C:\Users\ICHUNT\Desktop\bomlist\DZ0901_V1.4_BOM.xlsx',header=None)
-    df.fillna(' ', inplace=True)
+    df = pd.read_excel(r'C:\Users\ICHUNT\Desktop\bomlist\150套标准.xlsx', header=None)
+    df.fillna('?', inplace=True)
     dic_dft = df.to_dict(orient='list')
+    print(dic_dft)
+    a=r'{"1":["?","10uF ±10% 16V","0603 ±20% 10V","3.9nF ±10% "],"2":["AC0201FR-0710KL","?","?","?"],"3":["?","?","?","0402"],"4":[2500,3000,1800,3000],"5":["?","?","?","?"],"6":["?","?","?","?"]}'
     return json.dumps(dic_dft)
 
+
 def run():
     # 连接 rpc 服务器
+    # channel = grpc.insecure_channel('192.168.2.232:50051')
     channel = grpc.insecure_channel('localhost:50051')
-    # 调用 rpc 服务
+    # 调用 rpc 服务C:\Users\ICHUNT\Pictures\Saved Pictures\1.png
     stub = classify_pb2_grpc.classifyStub(channel)
     test = get_test_data()
     response = stub.Classify(classify_pb2.ClassifyRequest(keyword=test))
     print("Classify client received: " + response.message)
 
+
 if __name__ == '__main__':
     run()
-
-
+# !/usr/bin/env python
+# -*- coding:utf-8 -*-
+#     import requests
+#     d={"1":["LIEXIN001","STM32L4R9ZIY6TR"],"2":[100,200],"3":["?","?"],"4":["?","?"],"5":["?","?"],"6":["?","?"]}
+#     js = json.dumps(d)
+#     # b=requests.post('http://192.168.2.232:50052/dict', data=js).text
+#     b=requests.post('http://127.0.0.1:50052/dict', data=js).text
+#     b=json.loads(b)
+#     print(b)

predict/base_handler.py

@@ -12,7 +12,7 @@ RIGHT_LEVEL = 0.7
 SEQ_LEVEL = 0.5
 CATE_LEVEL = 0.5
 MULTI_SAME_LEVEL = 3
-
+REF_LEVEL = 0.5
 
 class BasePredictor:
     '''
@@ -164,6 +164,16 @@ class BasePredictor:
                 comprehensive_res[col] = std_name
         return comprehensive_res
 
-
+    @classmethod
+    def is_ref(self, data):
+        """
+        位号列预测
+        """
+        pat = re.compile(r'[CJRULX][1-9]{1,3}')
+        count = 0
+        for i in data:
+            if pat.findall(str(i)):
+                count += 1
+        return round(count / len(data), 3) >= REF_LEVEL or False
 if __name__ == "__main__":
     print(BasePredictor.is_num([3400.0, 5920.0, 4849.0, 2544.0, 3270.0, 52751.0, 2031.0, 5302.0, 726.0, 1247.0, 2472.0, 689.0, 6049.0, 26796.0, 6164.0, 1605.0, 4346.0, 640.0, 960.0, 960.0, 320.0, 160.0, 860.0, 160.0, 320.0, 3183.0, 10151.0, 640.0, 130.0, 1237.0, 800.0, 960.0, 3740.0, 17701.0, 2146.0, 1280.0, 160.0, 1120.0, 160.0, 480.0, 960.0, 480.0, 160.0, 4717.0, 160.0, 160.0, 160.0, 640.0, 160.0, 320.0, 160.0, 160.0, 800.0, 800.0, 480.0, 1600.0, 155.0, 960.0, 320.0, 944.0, 160.0, 160.0, 1280.0, 1852.0, 7680.0, 7680.0, 2880.0, 160.0, 224.0, 480.0, 480.0, 640.0, 160.0, 640.0, 320.0, 1760.0, 640.0, 480.0, 960.0, 160.0, 160.0, 160.0, 160.0, 1920.0, 160.0, 5600.0, 480.0, 2560.0, 160.0, 160.0, 160.0, 160.0, 160.0, 1280.0, 160.0, 160.0, 160.0, 160.0, 160.0, 320.0, 0.0, 160.0, 160.0]))

predict/dict_predict.py

@@ -97,6 +97,9 @@ class DicPredict(BasePredictor):
             if self.is_num(no_null_v):
                 temp_pre_model_res[k] = '数量'
                 continue
+            if self.is_ref(no_null_v):
+                ab_result[k] = '位号'
+                continue
 
             if bol:
                 prob_columns.append(k)