Question

我要从Microsoft Academic Knowledge API中提取数据，然后使用json响应作为字典来提取我需要的信息。在执行此操作时，我将信息添加到numpy数组中，最后将其更改为要导出的熊猫数据框。该程序可以正常运行，但是需要花费大量时间才能运行。不过，它似乎在运行时会变慢，因为在循环的前几次中，它只需要几秒钟，但随后需要几分钟。

我已经尽我所能简化了if else语句，这虽然有所帮助，但不足以起到很大作用。我也尽可能地减少了查询API的次数。每个查询只能返回1000个结果，但是我需要大约35000个结果。

rel_info = np.array([("Title", "Author_Name", "Jornal_Published_In", "Date")])

for l in range(0, loops):                        # loops is defined above to be 35
    offset = 1000 * l
    # keep track of progress
    print("Progress:" + str(round((offset/total_res)*100, 2)) + "%")
    # get data with request to MAK. 1000 is the max count
    url = "https://api.labs.cognitive.microsoft.com/academic/v1.0/evaluate?expr=And(Composite(AA.AfN=='brigham young university'),Y>=1908)&model=latest&count=1000&offset="+str(offset)+"&attributes=Ti,D,AA.DAfN,AA.DAuN,J.JN"
    response = req.get(url + '&subscription-key={key}')

    data = response.json()

    for i in range(0, len(data["entities"])):
        new_data = data["entities"][i]
        # get new data
        new_title = new_data["Ti"]                 # get title

        if 'J' not in new_data:                    # get journal account for if keys are not in dictionaries
            new_journ = ""
        else:
            new_journ = new_data["J"]["JN"] or ""

        new_date = new_data["D"]                   # get date

        new_auth = ""                              # get authors only affiliated with BYU account for if keys are not in dictionary
        for j in range(0, len(new_data["AA"])):
            if 'DAfN' not in new_data["AA"][j]:
                new_auth = new_auth + ""
            else:
                if new_data["AA"][j]["DAfN"] == "Brigham Young University" and new_auth == "":     # posibly combine conditionals to make less complex
                    new_auth = new_data["AA"][j]["DAuN"]
                elif new_data["AA"][j]["DAfN"] == "Brigham Young University" and new_auth != "":
                    new_auth = new_auth +", "+ new_data["AA"][j]["DAuN"]
        # keep adding new data to whole dataframe
        new_info = np.array([(new_title, new_auth, new_journ, new_date)])
        rel_info = np.vstack((rel_info, new_info))

Answer 1

尝试使用concurrent.futures在工作线程池中获取结果，如下所示：

import concurrent.futures
import urllib.request

URLS = ['http://www.foxnews.com/',
        'http://www.cnn.com/',
        'http://europe.wsj.com/',
        'http://www.bbc.co.uk/',
        'http://some-made-up-domain.com/']

# Retrieve a single page and report the URL and contents
def load_url(url, timeout):
    with urllib.request.urlopen(url, timeout=timeout) as conn:
        return conn.read()

# We can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.ThreadPoolExecutor() as executor:
    # Start the load operations and mark each future with its URL
    future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
    for future in concurrent.futures.as_completed(future_to_url):
        url = future_to_url[future]
        try:
            data = future.result()
        except Exception as exc:
            print('%r generated an exception: %s' % (url, exc))
        else:
            print('%r page is %d bytes' % (url, len(data)))

https://docs.python.org/3/library/concurrent.futures.html

Answer 2

最终，我通过更改添加到收集的大量数据中的方式来解决此问题。我没有在每次迭代中添加一行数据，而是构建了一个临时数组来容纳1000行数据，然后将这个临时数组添加到完整的数据中。与之前的43分钟相比，这将运行时间减少到大约一分钟。

rel_info = np.array([("Title", "Author_Name", "Jornal_Published_In", "Date")])

for req_num in range(0, loops):
offset = 1000 * req_num
# keep track of progress
print("Progress:" + str(round((offset/total_res)*100, 2)) + "%")
# get data with request to MAK. 1000 is the max count
url = "https://api.labs.cognitive.microsoft.com/academic/v1.0/evaluate?expr=And(Composite(AA.AfN=='brigham young university'),Y>=1908)&model=latest&count=1000&offset="+str(offset)+"&attributes=Ti,D,AA.DAfN,AA.DAuN,J.JN"
response = req.get(url + '&subscription-key={key}')

data = response.json()

for i in range(0, len(data["entities"])):
    new_data = data["entities"][i]
    # get new data
    new_title = new_data["Ti"]                 # get title

    if 'J' not in new_data:                    # get journal account for if keys are not in dictionaries
        new_journ = ""
    else:
        new_journ = new_data["J"]["JN"] or ""

    new_date = new_data["D"]                   # get date

    new_auth = ""                              # get authors only affiliated with BYU account for if keys are not in dictionary
    for j in range(0, len(new_data["AA"])):
        if 'DAfN' not in new_data["AA"][j]:
            new_auth = new_auth + ""
        else:
            if new_data["AA"][j]["DAfN"] == "Brigham Young University" and new_auth == "":     # posibly combine conditionals to make less complex
                new_auth = new_data["AA"][j]["DAuN"]
            elif new_data["AA"][j]["DAfN"] == "Brigham Young University" and new_auth != "":
                new_auth = new_auth +", "+ new_data["AA"][j]["DAuN"]

    # here are the changes
    # keep adding to a temporary array for 1000 entities
    new_info = np.array([(new_title, new_auth, new_journ, new_date)])
    if (i == 0): work_stack = new_info
    else: work_stack = np.vstack((work_stack, new_info))
# add temporary array to whole array (this is to speed up the program)
rel_info = np.vstack((rel_info, work_stack))

我的程序可以运行，但是非常慢，运行时也会变慢

2 个答案: