5

我有一些工作代码可以正确地将 csv 文件中的数据加载到 PyBrain 数据集中：

def old_get_dataset():

    reader = csv.reader(open('test.csv', 'rb'))

    header = reader.next()
    fields = dict(zip(header, range(len(header))))
    print header

    # assume last field in csv is single target variable
    # and all other fields are input variables
    dataset = SupervisedDataSet(len(fields) - 1, 1)

    for row in reader:
        #print row[:-1]
        #print row[-1]
        dataset.addSample(row[:-1], row[-1])

    return dataset

现在我正在尝试重写此代码以改用 numpy 的 loadtxt 函数。我相信 addSample 可以采用 numpy 数组，而不必一次添加一行数据。

假设我加载的 numpy 数组是 mxn 维的，我如何传入第一组 mx (n-1) 数据作为第一个参数，最后一列数据作为第二个参数？这就是我正在尝试的：

def get_dataset():

    array = numpy.loadtxt('test.csv', delimiter=',', skiprows=1)

    # assume last field in csv is single target variable
    # and all other fields are input variables
    number_of_columns = array.shape[1]
    dataset = SupervisedDataSet(number_of_columns - 1, 1)

    #print array[0]
    #print array[:,:-1]
    #print array[:,-1]
    dataset.addSample(array[:,:-1], array[:,-1])

    return dataset

但我收到以下错误：

Traceback (most recent call last):
  File "C:\test.py", line 109, in <module>
    (d, n, t) = main()
  File "C:\test.py", line 87, in main
    ds = get_dataset()
  File "C:\test.py", line 45, in get_dataset
    dataset.addSample(array[:,:-1], array[:,-1])
  File "C:\Python27\lib\site-packages\pybrain\datasets\supervised.py",
       line 45, in addSample self.appendLinked(inp, target)
  File "C:\Python27\lib\site-packages\pybrain\datasets\dataset.py",
       line 215, in appendLinked self._appendUnlinked(l, args[i])
  File "C:\Python27\lib\site-packages\pybrain\datasets\dataset.py",
       line 197, in _appendUnlinked self.data[label][self.endmarker[label], :] = row
ValueError: output operand requires a reduction, but reduction is not enabled

我怎样才能解决这个问题？

4

2 回答 2

8

经过大量试验和重新阅读数据集文档后，以下运行没有错误：

def get_dataset():

    array = numpy.loadtxt('test.csv', delimiter=',', skiprows=1)

    # assume last field in csv is single target variable
    # and all other fields are input variables
    number_of_columns = array.shape[1]
    dataset = SupervisedDataSet(number_of_columns - 1, 1)

    print array[0]
    #print array[:,:-1]
    #print array[:,-1]
    #dataset.addSample(array[:,:-1], array[:,-1])
    #dataset.addSample(array[:,:-1], array[:,-2:-1])
    dataset.setField('input', array[:,:-1])
    dataset.setField('target', array[:,-1:])

    return dataset

我必须仔细检查它是否在做正确的事情。

于 2012-04-13T02:23:32.263 回答
0

我写了一个小函数来做到这一点

def load_csv(filename, cols, sep = ',', skip = 0):
    from numpy import loadtxt
    data = loadtxt(filename, delimiter = sep, usecols = cols, skiprows = skip)
    return data
于 2013-12-04T18:28:04.147 回答