Question

在此函数中，因子k返回字典a中的值，然后将该值保存在列表中，然后通过Fit_transit将fid_t转换为缩放器来返回列表，但是存在错误。有什么问题吗？

def determineRank2(t, target, bid_t, k):
    encode = LabelEncoder()
    scaler = MinMaxScaler()
    # x = np.concatenate((t,n,bid_t,w,h,k),axis = 1).reshape(1,6,1)
    t = categorize_time(t)
    bid_t = scaler.fit_transform(bid_t)
    a = {'a':0, 'b':1, 'c':2, 'd':3, 'e':4, 'f':5, 'g':6, 'h':7, 'i':8,
     'j':9, 'k':10, 'l':11, 'm':12, 'n':13, 'o':14, 'p':15, 'q':16, 'r':17,'w':18,
     'x':19, 'y':20, 'z':21, 'ab':22, 'cd':23, 'ef':24, 'gh':25, 'qw':26, 'er':27,
     'dz':28, 'df':29}
    new_list = []
    new_list = [t,target,bid_t,k]
    new_list = np.array(new_list)
    new_list = new_list.reshape(1, 1, 4)
    rank = model.predict(new_list)
    rank = round(rank.item(0))
    return rank

当我输入这样的值

determineRank2("21:30:04", 3620,2 , "a")

这样的错误显示

<ipython-input-76-cea3690890e8> in determineRank2(t, target, bid_t, k)
 11     # x = np.concatenate((t,n,bid_t,w,h,k),axis = 1).reshape(1,6,1)
 12     t = categorize_time(t)
---> 13     bid_t = scaler.fit_transform(bid_t)


/usr/local/lib/python3.6/dist-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
551         if y is None:
552             # fit method of arity 1 (unsupervised transformation)
--> 553             return self.fit(X, **fit_params).transform(X)
554         else:
555             # fit method of arity 2 (supervised transformation)

/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py in fit(self, X, y)
323         # Reset internal state before fitting
324         self._reset()
--> 325         return self.partial_fit(X, y)
326 
327     def partial_fit(self, X, y=None):

/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py in partial_fit(self, X, y)
351         X = check_array(X, copy=self.copy,
352                         estimator=self, dtype=FLOAT_DTYPES,
--> 353                         force_all_finite="allow-nan")
354 
355         data_min = np.nanmin(X, axis=0)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
512                     "Reshape your data either using array.reshape(-1, 1) if "
513                     "your data has a single feature or array.reshape(1, -1) "
--> 514                     "if it contains a single sample.".format(array))
515             # If input is 1D raise error
516             if array.ndim == 1:

ValueError: Expected 2D array, got scalar array instead:
array=2.0.
Reshape your data either using array.reshape(-1, 1) if your data has a 
single feature or array.reshape(1, -1) if it contains a single sample.

我该如何解决？

Answer 1

如错误消息所述，您应该重塑bid_t的值。

来自sklearn的{{3}}：

参数：X：形状为[n_samples，n_features]的numpy数组

您的bid_t甚至不是数组。因此，您必须使其看起来像一个数组：

determineRank2("21:30:04", 3620, np.array([[2]]) , "a")

我在自己的函数中遇到一些值错误

1 个答案: