计算蟒蛇

Question

在2个字典值之间的平均绝对误差百分比我的位置的字典，然后属性值对，像这样：

{"Russia": 
    {"/location/statistical_region/size_of_armed_forces": 65700.0, 
    "/location/statistical_region/gni_per_capita_in_ppp_dollars": 42530.0, 
    "/location/statistical_region/gdp_nominal": 1736050505050.0, 
    "/location/statistical_region/foreign_direct_investment_net_inflows": 8683048195.0, 
    "/location/statistical_region/life_expectancy": 80.929, ... 

等等，对每一个国家。

，然后将含有单个阵列字典，数组中的每个值是3个键的字典：

{ 
    "sentences": [ 
     { 
      "location-value-pair": { 
       "Russia": 6.1 
      }, 
      "parsedSentence": "On Tuesday , the Federal State Statistics Service -LRB- Rosstat -RRB- reported that consumer price inflation in LOCATION_SLOT hit a historic post-Soviet period low of NUMBER_SLOT percent in 2011 , citing final data .", 
      "sentence": "On Tuesday , the Federal State Statistics Service -LRB- Rosstat -RRB- reported that consumer price inflation in Russia hit a historic post-Soviet period low of 6.1 percent in 2011 , citing final data ." 
     }, 
     { 
      "location-value-pair": { 
       "Russia": 8.8 
      }, 
      "parsedSentence": "In 2010 , annual inflation in LOCATION_SLOT hit NUMBER_SLOT percent due to the summer drought , exceeding forecasts and equalling the figure for 2009 , the year of the global financial meltdown .", 
      "sentence": "In 2010 , annual inflation in Russia hit 8.8 percent due to the summer drought , exceeding forecasts and equalling the figure for 2009 , the year of the global financial meltdown ." 
     }, ... 

我想要做的就是比较每个句子，每个位置和价值计算与第一个字典中的位置 - 值对匹配的最接近的匹配值，然后返回其对应的顶部统计属性，并将其添加为句子字典的新关键字。

例如：

句子1，我看到，我们正在寻找在俄罗斯和6.1的值。我想索引第一本字典，找到“俄罗斯”，并查看所有存在的值，例如65700.0,42530.0,1736050505050.0,8683048195.0。然后，我想找出每个属性的平均绝对误差，例如想着当

{ 
       "location-value-pair": { 
        "Russia": 6.1 
       }, 
       "predictedRegion": "/location/statistical_region/gni_in_ppp_dollars" 
       "meanabserror": 2% 
       "parsedSentence": "On Tuesday , the Federal State Statistics Service -LRB- Rosstat -RRB- reported that consumer price inflation in LOCATION_SLOT hit a historic post-Soviet period low of NUMBER_SLOT percent in 2011 , citing final data .", 
       "sentence": "On Tuesday , the Federal State Statistics Service -LRB- Rosstat -RRB- reported that consumer price inflation in Russia hit a historic post-Soviet period low of 6.1 percent in 2011 , citing final data ." 
      }, 

我的困惑：23％的size_of_armed_forces价值，为gni_per_capita财产等的话，我想找到10％的最小的一个假设，并将其添加为重点，以第二字典，所以写这只是如何访问另一个字典的键值作为另一个字典的条件。我现在的想法是：

def predictRegion(sentenceArray,trueDict): 

    absPercentageErrors = {} 

    for location, property2value in trueDict.items(): 
     print location 
     absPercentageErrors['location'] = {} 
     for property,trueValue in property2value.iteritems(): 
      print property 
      absError = abs(sentenceArray['sentences']['location-value-pair'].key() - trueValue) 
      absPercentageErrors['location']['property'] = absError/numpy.abs(trueValue) 

    for index, dataTriples in enumerate(sentenceArray["sentences"]): 
     for location, trueValue in dataTriples['location-value-pair'].items(): 
      print location 

但是很明显，我不能在此行中访问sentenceArray['sentences']['location-value-pair'].key()：absError = abs(sentenceArray['sentences']['location-value-pair'].key() - trueValue)因为它是循环之外。

我怎样才能获得从环指的是完全不同的变量如此重要呢？

Answer 1

今后请阅读如何制定一个很好的问题：https://stackoverflow.com/help/mcve

最小的，完整的和可验证的。

---

我想这就是你要找的。

countries = {'Canada': {'a': 10, 'b': 150, 'c': 1000}, 
      'Russia': {'d': 9, 'e': 5, 'f': 1e5}} 
sentences = [ 
     {"location-value-pair": {"Russia": 6.1}, 
     "parsedSentence": "bob loblaw", 
     "sentence": "lobs law bomb" 
     }, 
     {"location-value-pair": {"Russia": 8.8}, 
      "parsedSentence": "some sentence", 
      "sentence": "lorem ipsum test" 
     }] 


def absError(numer,denom): 
    return abs(numer-denom)/float(denom) 

def findMatch(target, country): 
    return min(country, key= lambda x: absError(target, country.get(x))) 

def update(sentence): 
    (c,target), = sentence.get("location-value-pair").items() 
    country = countries[c] 
    matched = findMatch(target,country) 
    error = absError(target, country.get(matched)) 
    res = sentence.copy() 
    res.update({'predictedRegion': matched, 'meanabserror': "{:.2f}%".format(100*error)}) 
    return res 

updated = [update(sentence) for sentence in sentences]  
updated 

输出：

[{'location-value-pair': {'Russia': 6.1}, 
    'meanabserror': '22.00%', 
    'parsedSentence': 'bob loblaw', 
    'predictedRegion': 'e', 
    'sentence': 'lobs law bomb'}, 
{'location-value-pair': {'Russia': 8.8}, 
    'meanabserror': '2.22%', 
    'parsedSentence': 'some sentence', 
    'predictedRegion': 'd', 
    'sentence': 'lorem ipsum test'}]