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社区首页 >问答首页 >使用Python计算合同桥接分数的elasticsearch json对象的元素

问使用Python计算合同桥接分数的elasticsearch json对象的元素
EN

Stack Overflow用户
提问于 2018-12-25 02:03:10
回答 2查看 277关注 0票数 7

数据在这里：

代码语言：javascript
复制
{'took': 0, 'timed_out': False, '_shards': {'total': 5, 'successful': 5, 'skipped': 0, 'failed': 0}, 'hits': {'total': 16, 'max_score': 1.0, 'hits': [{'_index': 'matchpoints', '_type': 'score', '_id': '6PKYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '1', 'nsp': '4', 'ewp': '11', 'contract': '3NT', 'by': 'N', 'tricks': '11', 'nsscore': '460', 'ewscore ': '0'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '7_KYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '2', 'nsp': '3', 'ewp': '10', 'contract': '3C', 'by': 'E', 'tricks': '10', 'nsscore': '-130', 'ewscore ': '130'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '6fKYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '1', 'nsp': '5', 'ewp': '12', 'contract': '3NT', 'by': 'S', 'tricks': '10', 'nsscore': '400', 'ewscore ': '0'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '8_KYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '2', 'nsp': '7', 'ewp': '14', 'contract': '3C', 'by': 'E', 'tricks': '10', 'nsscore': '-130', 'ewscore ': '130'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '9PKYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '2', 'nsp': '8', 'ewp': '15', 'contract': '3C', 'by': 'E', 'tricks': '11', 'nsscore': '-150', 'ewscore ': '150'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '5fKYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '1', 'nsp': '1', 'ewp': '16', 'contract': '3NT', 'by': 'N', 'tricks': '10', 'nsscore': '430', 'ewscore ': '0'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '6vKYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '1', 'nsp': '6', 'ewp': '13', 'contract': '4S', 'by': 'S', 'tricks': '11', 'nsscore': '480', 'ewscore ': '0'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '6_KYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '1', 'nsp': '7', 'ewp': '14', 'contract': '3NT', 'by': 'S', 'tricks': '8', 'nsscore': '-50', 'ewscore ': '50'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '7fKYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '2', 'nsp': '1', 'ewp': '16', 'contract': '6S', 'by': 'N', 'tricks': '12', 'nsscore': '1430', 'ewscore ': '0'}}, {'_index': 'matchpoints', '_type': 'score', '_id': '7vKYGGgBjpp4O0gQgUu5', '_score': 1.0, '_source': {'board_number': '2', 'nsp': '2', 'ewp': '9', 'contract': '3C', 'by': 'E', 'tricks': '10', 'nsscore': '-130', 'ewscore ': '130'}}]}}

包含最新更改的Python代码如下所示。作为我的中间尝试，不会尝试在不同的板之间循环。这些数据是由search all查询生成的。

代码语言：javascript
复制
@application.route('/', methods=['GET', 'POST'])
def index():
    search = {"query": {"match_all": {}}}
    resp = es.search(index="matchpoints", doc_type="score", body = search)
    rows = extract_rows(resp)
    for board in rows:
        scores = score_board(board)
        report(scores)
        print(report(scores))
    return 'ok'

def extract_rows(resp):                                                                                                          
    """Extract the rows for the board from the query response."""                                                                
    # Based on the data structure provided by the OP.                                                          
    rows = [row["_source"] for row in resp["hits"]["hits"]]
    # We want to return the group the data by board number
    # so that we can score each board.                                                                       
    keyfunc = lambda row: int(row['board_number'])                                                                               
    rows.sort(key=keyfunc)                                                                                                       
    for _, group in itertools.groupby(rows, keyfunc):                                                                            
        yield list(group)

def compute_mp(scores, score):
    """Compute the match point score for a pair."""
    mp_score = sum(v for k, v in scores.items() if score > k) * 2
    # The pair's own score will always compare equal - remove it.
    mp_score += sum(v for k, v in scores.items() if score == k) - 1
    return mp_score

def score_board(tables):
    """Build the scores for each pair."""
    scores = []
    top = 2 * (len(tables) - 1)
    # Store the scores for each N-S partnership.
    ns_scores = collections.Counter(int(table["nsscore"]) for table in tables)
    # Build the output for each pair.
    for table in tables:
        output = {
            "board": table["board_number"],
            "nsp": table["nsp"],
            "ewp": table["ewp"],
        }
        ns_score = int(table["nsscore"])
        ns_mp_score = compute_mp(ns_scores, ns_score)
        output["ns_mp_score"] = ns_mp_score
        ew_mp_score = top - ns_mp_score
        output["ew_mp_score"] = ew_mp_score
        scores.append(output)
    return scores

# Replace this function with one that adds the rows to
# the new search index
def report(scores):
    """Print the scores."""
    for row in scores:
        print(row)

它像以前一样产生正确的字典，其中评分是正确的，但是有重复的结果和太多的行。另外，有两个"None“的例子，我不知道这是从哪里来的。：

代码语言：javascript
复制
{'board': '1', 'nsp': '4', 'ewp': '11', 'ns_mp_score': 6, 'ew_mp_score': 2}
{'board': '1', 'nsp': '5', 'ewp': '12', 'ns_mp_score': 2, 'ew_mp_score': 6}
{'board': '1', 'nsp': '1', 'ewp': '16', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '1', 'nsp': '6', 'ewp': '13', 'ns_mp_score': 8, 'ew_mp_score': 0}
{'board': '1', 'nsp': '7', 'ewp': '14', 'ns_mp_score': 0, 'ew_mp_score': 8}
{'board': '1', 'nsp': '4', 'ewp': '11', 'ns_mp_score': 6, 'ew_mp_score': 2}
{'board': '1', 'nsp': '5', 'ewp': '12', 'ns_mp_score': 2, 'ew_mp_score': 6}
{'board': '1', 'nsp': '1', 'ewp': '16', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '1', 'nsp': '6', 'ewp': '13', 'ns_mp_score': 8, 'ew_mp_score': 0}
{'board': '1', 'nsp': '7', 'ewp': '14', 'ns_mp_score': 0, 'ew_mp_score': 8}
None
{'board': '2', 'nsp': '3', 'ewp': '10', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '2', 'nsp': '7', 'ewp': '14', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '2', 'nsp': '8', 'ewp': '15', 'ns_mp_score': 0, 'ew_mp_score': 8}
{'board': '2', 'nsp': '1', 'ewp': '16', 'ns_mp_score': 8, 'ew_mp_score': 0}
{'board': '2', 'nsp': '2', 'ewp': '9', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '2', 'nsp': '3', 'ewp': '10', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '2', 'nsp': '7', 'ewp': '14', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '2', 'nsp': '8', 'ewp': '15', 'ns_mp_score': 0, 'ew_mp_score': 8}
{'board': '2', 'nsp': '1', 'ewp': '16', 'ns_mp_score': 8, 'ew_mp_score': 0}
{'board': '2', 'nsp': '2', 'ewp': '9', 'ns_mp_score': 4, 'ew_mp_score': 4}
None

评分是正确的，但同样也存在相同配对结果重复的多个情况。

python
elasticsearch
EN

回答 2

Stack Overflow用户

回答已采纳

发布于 2019-01-03 00:53:21

这段代码将计算分数。代码相当简单。

不是迭代输入字典来计算每一对的分数，而是将北南分数存储在一个collections.Counter实例中，该实例保存每个分数对的数量的计数。这使得计算每一对的赛点分数变得更容易-我们只需将较低分数的数量增加一倍，然后将相等分数的数量减去1来计算当前合作伙伴的分数。

代码语言：javascript
复制
import collections                                                                                                               
import itertools                                                                                                                                                                                                                                    


def extract_rows(resp):                                                                                                          
    """Extract the rows for the board from the query response."""                                                                
    # Based on the data structure provided by the OP.                                                          
    rows = [row["_source"] for row in resp["hits"]["hits"]]
    # We want to return the group the data by board number
    # so that we can score each board.                                                                       
    keyfunc = lambda row: int(row['board_number'])                                                                               
    rows.sort(key=keyfunc)                                                                                                       
    for _, group in itertools.groupby(rows, keyfunc):                                                                            
        yield list(group)


def compute_mp(scores, score):
    """Compute the match point score for a pair."""
    mp_score = sum(v for k, v in scores.items() if score > k) * 2
    # The pair's own score will always compare equal - remove it.
    mp_score += sum(v for k, v in scores.items() if score == k) - 1
    return mp_score


def score_board(tables):
    """Build the scores for each pair."""
    scores = []

    # Store the scores for each N-S partnership.
    ns_scores = collections.Counter(int(table["nsscore"]) for table in tables)
    # The top score is (2 * number of tables) - 2, then reduced by one for each 
    # equal top score.
    top = 2 * (len(tables) - 1) - (ns_scores[max(ns_scores)] - 1)
    # Build the output for each pair.
    for table in tables:
        output = {
            "board": table["board_number"],
            "nsp": table["nsp"],
            "ewp": table["ewp"],
        }
        ns_score = int(table["nsscore"])
        ns_mp_score = compute_mp(ns_scores, ns_score)
        output["ns_mp_score"] = ns_mp_score
        ew_mp_score = top - ns_mp_score
        output["ew_mp_score"] = ew_mp_score
        scores.append(output)
    return scores

# Replace this function with one that adds the rows to
# the new search index
def report(scores):
    """Print the scores."""
    for row in scores:
        print(row)

运行代码：

代码语言：javascript
复制
rows = extract_rows(resp)
scores = [score for rows in extract_rows(resp) for score in score_board(rows)]
report(scores)

生成以下输出：

代码语言：javascript
复制
{'board': '1', 'nsp': '4', 'ewp': '11', 'ns_mp_score': 6, 'ew_mp_score': 2}
{'board': '1', 'nsp': '5', 'ewp': '12', 'ns_mp_score': 2, 'ew_mp_score': 6}
{'board': '1', 'nsp': '1', 'ewp': '16', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '1', 'nsp': '6', 'ewp': '13', 'ns_mp_score': 8, 'ew_mp_score': 0}
{'board': '1', 'nsp': '7', 'ewp': '14', 'ns_mp_score': 0, 'ew_mp_score': 8}
{'board': '2', 'nsp': '3', 'ewp': '10', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '2', 'nsp': '7', 'ewp': '14', 'ns_mp_score': 4, 'ew_mp_score': 4}
{'board': '2', 'nsp': '8', 'ewp': '15', 'ns_mp_score': 0, 'ew_mp_score': 8}
{'board': '2', 'nsp': '1', 'ewp': '16', 'ns_mp_score': 8, 'ew_mp_score': 0}
{'board': '2', 'nsp': '2', 'ewp': '9', 'ns_mp_score': 4, 'ew_mp_score': 4}
票数 3
EN

Stack Overflow用户

发布于 2019-01-02 23:30:16

这不是我的工作，这是“房车”的工作，但由于这是我正在寻找的答案，我将它张贴在这里，以便可以帮助其他人。

代码语言：javascript
复制
scores = {}
for row in arr["hits"]["hits"]:
  nsp = row["_source"]["nsp"]
  nsscore = row["_source"]["nsscore"]
  scores[nsp] = nsscore

input_scores = {}

def calculate_score(pair, scores):
    score = 0
    for p in scores:
        if p == pair:
            continue
        if scores[p] < scores[pair]:
            score += 2  # win
        elif scores[p] == scores[pair]:
            score += 1
    return score


board_num = arr["hits"]["total"]
top = (board_num - 1) * 2
result_score = {}
for row in arr["hits"]["hits"]:
  nsp = row["_source"]["nsp"]
  ewp = row["_source"]["ewp"]
  res = calculate_score(nsp, scores)
  ew_mp_score = top - res
  result_score.update({'nsp':nsp, 'ns_mp_score': res, 'ewp': ewp, 'ew_mp_score': ew_mp_score})
  print(result_score)

谢谢。

票数 0
EN
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：

https://stackoverflow.com/questions/53916503

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