File "D:\pythonProject\信息检索系统实践\第三次实验\textrank.py", line 102, in <module> results.extend(future.result()) File "D:\python\lib\concurrent\futures\_base.py", line 451, in result return self.__get_result() File "D:\python\lib\concurrent\futures\_base.py", line 403, in __get_result raise self._exception File "D:\python\lib\concurrent\futures\thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) File "D:\pythonProject\信息检索系统实践\第三次实验\textrank.py", line 83, in process_chunk tr4w.analyze(chunk, lower=True, window=2) File "D:\python\lib\site-packages\textrank4zh\TextRank4Keyword.py", line 93, in analyze self.keywords = util.sort_words(_vertex_source, _edge_source, window = window, pagerank_config = pagerank_config) File "D:\python\lib\site-packages\textrank4zh\util.py", line 160, in sort_words nx_graph = nx.from_numpy_matrix(graph) AttributeError: module 'networkx' has no attribute 'from_numpy_matrix' 进程已结束，退出代码为 1

这个错误与你之前提到的 `networkx` 相关，可能是因为 `networkx` 版本太低，不支持 `from_numpy_matrix` 方法。

你可以尝试更新 `networkx` 到最新版本：

pip install --upgrade networkx

如果你已经安装了最新版本的 `networkx`，仍然出现这个错误，你可以尝试修改 `TextRank4Keyword.py` 文件中的代码，在导入 `networkx` 时使用以下语句：

import networkx as nx

然后在 `sort_words` 函数中的 `from_numpy_matrix` 方法改为：

nx_graph = nx.Graph(graph)

这应该可以解决你遇到的问题。

相关问题

Traceback (most recent call last): File "D:\pythonFiles\wikiextractor-master\wikiextractor-master\wikiextractor\WikiExtractor.py", line 648, in <module> main() File "D:\pythonFiles\wikiextractor-master\wikiextractor-master\wikiextractor\WikiExtractor.py", line 642, in main process_dump(input_file, args.templates, output_path, file_size, File "D:\pythonFiles\wikiextractor-master\wikiextractor-master\wikiextractor\WikiExtractor.py", line 428, in process_dump reduce.start() File "D:\软件\python\lib\multiprocessing\process.py", line 121, in start self._popen = self._Popen(self) File "D:\软件\python\lib\multiprocessing\context.py", line 327, in _Popen return Popen(process_obj) File "D:\软件\python\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__ reduction.dump(process_obj, to_child) File "D:\软件\python\lib\multiprocessing\reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj) TypeError: cannot pickle '_io.TextIOWrapper' object (venv) D:\pythonFiles\wikiextractor-master\wikiextractor-master>Traceback (most recent call last): File "<string>", line 1, in <module> File "D:\软件\python\lib\multiprocessing\spawn.py", line 102, in spawn_main source_process = _winapi.OpenProcess( OSError: [WinError 87] 参数错误。 怎么解决

这个错误通常是由于在使用pickle模块将对象序列化时，对象中存在不能被序列化的属性或方法所导致的。有可能是在使用多进程库时，进程间传递了一个不能被序列化的_io.TextIOWrapper对象，导致了这个错误。你可以尝试将输出路径参数output_path改为一个字符串类型而非一个文件对象类型。另外，你可以尝试将输出文件的写入操作放在主进程中，而非在子进程中进行。这样可以避免在子进程中对文件对象进行pickle序列化。以下是一个可能的解决方案：

def worker_process(input_queue, output_queue, output_path):
    # create output file in main process
    with open(output_path, 'w', encoding='utf-8') as output_file:
        while True:
            data = input_queue.get()
            if data is None:
                break
            # process data
            # ...
            output = process_data(data)
            # put result into output queue
            output_queue.put(output)
            # write result to output file
            output_file.write(output)

def process_dump(input_file, templates, output_path, file_size, process_count):
    # create input queue
    input_queue = Queue(maxsize=process_count)
    # create output queue
    output_queue = Queue(maxsize=process_count)
    # spawn processes
    processes = []
    for i in range(process_count):
        p = Process(target=worker_process, args=(input_queue, output_queue, output_path))
        processes.append(p)
        p.start()
    # read input file and put data into input queue
    with open(input_file, 'r', encoding='utf-8') as f:
        for line in f:
            input_queue.put(line)
    # stop worker processes
    for i in range(process_count):
        input_queue.put(None)
    for p in processes:
        p.join()
    # collect results from output queue and write to output file
    with open(output_path, 'w', encoding='utf-8') as output_file:
        while not output_queue.empty():
            output = output_queue.get()
            output_file.write(output)

在这个改进的代码中，我们将输出路径参数output_path改为一个字符串类型，并在主进程中打开输出文件，将输出文件的写入操作放在主进程中进行，避免在子进程中对文件对象进行pickle序列化。

Traceback (most recent call last): File "D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3460, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-dc57cfac1dc3>", line 1, in <module> runfile('D:\\Users\\Admin\\PycharmProjects\\pythonProject2\\用模型预测.py', wdir='D:\\Users\\Admin\\PycharmProjects\\pythonProject2') File "D:\PyCharm 2023.1.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "D:\PyCharm 2023.1.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "D:\Users\Admin\PycharmProjects\pythonProject2\用模型预测.py", line 20, in <module> print([round(x) for x in y_pred_proba]) File "D:\Users\Admin\PycharmProjects\pythonProject2\用模型预测.py", line 20, in <listcomp> print([round(x) for x in y_pred_proba]) TypeError: type numpy.ndarray doesn't define __round__ method

这个错误是因为`round`函数无法直接作用于numpy数组中的元素。你可以通过将numpy数组中的元素转换为Python内置的`float`类型来解决这个问题。比如，如果你想将`y_pred_proba`中的概率值四舍五入取整，可以使用如下代码：

# 使用模型进行预测
y_pred = np.argmax(model.predict(X), axis=-1)
y_pred_proba = model.predict(X)

# 过滤掉和值超过6或小于6的预测值，并去除重复的行
results = pd.DataFrame({'y_pred': y_pred, 'y_pred_proba': [round(float(x)) for x in y_pred_proba[:, 1]]})
results = results[(results['y_pred'] >= 0) & (results['y_pred'] <= 6)]
results.drop_duplicates(inplace=True)

# 将预测结果打印出来
print([round(x) for x in y_pred])
print([round(float(x)) for x in y_pred_proba])

这里使用列表推导式和`round`函数将概率值四舍五入取整，并将其转换为Python内置的`float`类型，然后将其添加到DataFrame中。