我在尝试从一个 TSV 文件中读取数据以用于 Hyperas，但无论我怎么做，似乎都得到同样的错误：

Traceback (most recent call last):  File "/path/to/cnn_search.py", line 233, in <module>    trials=trials)  File "~/miniconda3/lib/python3.6/site-packages/hyperas/optim.py", line 67, in minimize    verbose=verbose)  File "~/miniconda3/lib/python3.6/site-packages/hyperas/optim.py", line 133, in base_minimizer    return_argmin=True),  File "~/miniconda3/lib/python3.6/site-packages/hyperopt/fmin.py", line 312, in fmin    return_argmin=return_argmin,  File "~/miniconda3/lib/python3.6/site-packages/hyperopt/base.py", line 635, in fmin    return_argmin=return_argmin)  File "~/miniconda3/lib/python3.6/site-packages/hyperopt/fmin.py", line 325, in fmin    rval.exhaust()  File "~/miniconda3/lib/python3.6/site-packages/hyperopt/fmin.py", line 204, in exhaust    self.run(self.max_evals - n_done, block_until_done=self.async)  File "~/miniconda3/lib/python3.6/site-packages/hyperopt/fmin.py", line 178, in run    self.serial_evaluate()  File "~/miniconda3/lib/python3.6/site-packages/hyperopt/fmin.py", line 97, in serial_evaluate    result = self.domain.evaluate(spec, ctrl)  File "~/miniconda3/lib/python3.6/site-packages/hyperopt/base.py", line 840, in evaluate    rval = self.fn(pyll_rval)  File "~/temp_model.py", line 218, in keras_fmin_fnctAttributeError: 'list' object has no attribute 'shape'

根据我看到的其他问题，这个错误是由于使用了普通数组而不是 NumPy 数组引起的。因此，我尝试在每一步都将我读取的 TSV 转换为 NumPy 数组：

from hyperas import optim...import numpy as npimport csvdef data():    dataPath="/path/to/fm.labeled.10m.txt"    X = []    Y = []    with open(dataPath) as dP:            reader = csv.reader(dP, delimiter="\t")            for row in reader:                    #跳过前两列，最后一列是标签                    X.append(np.array(row[2:-1]))                    #标签                    Y.append(row[-1])    encoder = LabelBinarizer()    Y_categorical = encoder.fit_transform(Y)    #将数据分成测试和训练集     X_train, X_test, Y_train, Y_test = train_test_split(X, Y_categorical, test_size=0.25)    X_train_np = np.array(X_train)    X_test_np = np.array(X_test)    Y_train_np = np.array([np.array(y) for y in Y_train])    Y_test_np = np.array([np.array(y) for y in Y_test])    return X_train_np, Y_train_np, X_test_np, Y_test_np...trials = Trials()best_run, best_model = optim.minimize(model=model_name,                                      data=data,                                      algo=tpe.suggest,                                      max_evals=numRuns,                                      trials=trials)

我还认为，有一种更有效的方法来做这件事，不需要创建这么多中间数组——这将是很好的，因为我将要读取数百万行的数据。

我做错了什么？

编辑：Hyperopt 维基描述了 Trials。

回答：

你有没有考虑使用 np.genfromtxt('your_file.tsv')？它在读取 csv 和 tsv 数据方面效果很好，我最近有很好的体验。另外，如果你需要更详细的回答，你应该提供更多关于你具体问题的信息（数据类型、布局等）。