增加类别

predict/base_handler.py

@@ -6,8 +6,8 @@ from collections import Counter
 from utils.log_manager import get_logger
 from utils.robots import dd_send_msg
 import pandas as pd
+from static_config import *
 
-CATEGORY = ['二极管']
 RIGHT_LEVEL = 0.7
 SEQ_LEVEL = 0.5
 CATE_LEVEL = 0.5

predict/dict_predict.py

@@ -112,8 +112,8 @@ class DicPredict(BasePredictor):
             if self.is_catecol(v):
                 temp_pre_model_res[k] = '类别'
                 continue
-        temp_dic_data = {k: list(filter(lambda x: x != self.PLACEHOLDER, dic_data[k])) for k in prob_columns}
-        for k, v in temp_dic_data.items():
+        not_null_dic_data = {k: list(filter(lambda x: x != self.PLACEHOLDER, dic_data[k])) for k in prob_columns}
+        for k, v in not_null_dic_data.items():
             li_single_pred_res = []
             for string in v:
                 single_pred_res, probdic = self.get_single_predict(string)
@@ -125,8 +125,9 @@ class DicPredict(BasePredictor):
         prob_param_and_gn_cols = [i for i in temp_pre_model_res if
                                   temp_pre_model_res[i] == '参数' or temp_pre_model_res[i] == '型号']
         for col in prob_param_and_gn_cols:
-            if self.is_multi_same(temp_dic_data[col]):
+            if self.is_multi_same(not_null_dic_data[col]):
                 temp_pre_model_res.pop(col)
+
         model_id_res = {
             'std_result': temp_pre_model_res,
             'ab_result': ab_result,
@@ -148,7 +149,7 @@ class DicPredict(BasePredictor):
             if comprehensive_res:
                 res = {
                     'std_result': comprehensive_res,
-                    'ab_result': pre_id_res['ab_result'] or model_std_result['ab_result'],
+                    'ab_result': pre_id_res['ab_result'],
                 }
                 return res
 

predict/list_predict.py

-#!/usr/bin/env python
-# -*- coding:utf-8 -*-
-import re
-from predict.base_handler import BasePredictor
-
-
-RIGHT_LEVEL = 0.7
-REPEAT_TIMES = 3
-
-
-class LiPredict(BasePredictor):
-
-    def predict(self, key):
-        # if len(key) > 0:
-        #     self.order_predict(key[1])
-        for ix in key:
-            self.num_predict(key[ix])
-        pass
-
-    """
-    序列预测
-    """
-    def order_predict(self, data):
-        collect_order = [int(kw) for kw in data if isinstance(kw, float) or isinstance(kw, int)]
-        judge = self.isIncrease(collect_order, len(collect_order)) if len(collect_order) > 0 else False
-        print('judge: ' + str(judge))
-        return judge
-
-    """
-    数量预测
-    """
-    def num_predict(self, data):
-        collect_num = [kw for kw in data if isinstance(kw, int) or self.isNumberCol(kw)]
-        rate = round(len(collect_num) / len(data), 3)
-        return True if rate >= RIGHT_LEVEL else False
-
-    """
-    判断列表元素是否递增
-    """
-    def isIncrease(self, arr, size):
-        if size == 1:
-            return True
-        return (arr[size - 1] >= arr[size - 2]) and self.isIncrease(arr, size - 1)
-
-    """
-    判断是否数量列
-    """
-    def isNumberCol(self, kw):
-        if isinstance(kw, str):
-            return re.match(r'(\d+)((K)|([\u4E00-\u9FA5]{1,3}))$', kw, re.M | re.I)
-        else:
-            return False
-
-    def isRepeat(self, data):
-        repeat_dict = {}
-        for kw in data:
-            if repeat_dict.get(kw):
-                repeat_dict[kw] += 1
-            else:
-                repeat_dict[kw] = 1
-        print(repeat_dict)
-
-
-    #
-    # """
-    # 判断是否重复列
-    # """
-    # def vailed(self, data):
-
-
-
-