“numpy.ndarray”对象在 x_test 中没有属性“iloc”

Question

这里我有四个输入，我试图预测未来的价值。在此之前，我将输入数据缩放为 0,1。然后我创建了 x_test 值。

然后在预测代码之前，我必须编写另一个代码来预测我每隔一小时的值。为此，我想提取到 x_test_n 值中的行。然后我使用了 il​​oc 代码。但不幸的是，由于 numpy 数组，它没有工作。然后我找到了代码并尝试了该代码，它也给了我一个错误。这是我尝试过的代码，

data10 = pd.read_csv('data.csv',"," )
data10 = data10.replace(np.nan, 0)
data10 = pd.DataFrame(data10,columns=['date','x1','x2','x3','x4'])
data10.set_index('date', inplace=True)

data10 = data10.values
X = 1
n_out = 1

 x,y=list(),list()
 start =0



for _ in range(len(data10)):
in_end = start+X
out_end= in_end + n_out
if out_end < len(data10):
    x_input = data10[start:in_end]
    x.append(x_input)
    y.append(data10[in_end:out_end,0])
start +=1
 x = np.asanyarray(x)
 y = np.asanyarray(y)



scaler_x = preprocessing.MinMaxScaler(feature_range =(0, 1))
x = np.array(x).reshape ((len(x),4 ))
x = scaler_x.fit_transform((x))
scaler_y = preprocessing.MinMaxScaler(feature_range =(0, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)

train_end = 150
x_test=x[train_end: ,]
y_test=y[train_end:] 
x_test,y_test = np.array(x_test),np.array(y_test)
x_test = np.reshape(x_test,(x_test.shape[0], x_test.shape[1],1))

那么我的 x_test 是这样的：

[[[0.0000000e+00 0.0000000e+00 1.4332613e-01 0.0000000e+00]
  [0.0000000e+00 0.0000000e+00 0.0000000e+00 6.8191981e-01]]

 [[0.0000000e+00 0.0000000e+00 0.0000000e+00 6.8191981e-01]
  [0.0000000e+00 1.4034396e-02 0.0000000e+00 0.0000000e+00]]

 [[0.0000000e+00 1.4034396e-02 0.0000000e+00 0.0000000e+00]
  [0.0000000e+00 0.0000000e+00 6.3639030e-02 0.0000000e+00]]

之后我想使用 iloc 提取 x_test_n 中的行

filtered_3 = x_test_n
new_df = pd.DataFrame(scaler_x.fit_transform(filtered_3), columns=filtered_3.columns, index=df.index)

然后出现错误：

ValueError                                Traceback (most recent call last)
<ipython-input-26-715b662d895d> in <module>()
    101 
    102 filtered_3 = x_test_n
--> 103 new_df = pd.DataFrame(scaler_x.fit_transform(filtered_3), columns=filtered_3.columns, index=df.index)
    104 # current_calorie = filtered_3.iloc[:,]
    105 # last_calorie_record = 0

~\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
    515         if y is None:
    516             # fit method of arity 1 (unsupervised transformation)
--> 517             return self.fit(X, **fit_params).transform(X)
    518         else:
    519             # fit method of arity 2 (supervised transformation)

~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit(self, X, y)
    306         # Reset internal state before fitting
    307         self._reset()
--> 308         return self.partial_fit(X, y)
    309 
    310     def partial_fit(self, X, y=None):

~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in partial_fit(self, X, y)
    332 
    333         X = check_array(X, copy=self.copy, warn_on_dtype=True,
--> 334                         estimator=self, dtype=FLOAT_DTYPES)
    335 
    336         data_min = np.min(X, axis=0)

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    449         if not allow_nd and array.ndim >= 3:
    450             raise ValueError("Found array with dim %d. %s expected <= 2."
--> 451                              % (array.ndim, estimator_name))
    452         if force_all_finite:
    453             _assert_all_finite(array)

ValueError: Found array with dim 3. MinMaxScaler expected <= 2.

谁能帮我解决这个错误？

Answer 1

如果“x_test.shape”是 3d，（结果中有 3 个值）您可以尝试下面的代码，否则我可以查看不同的方式。

nsamples, nx, ny = x_test.shape
x_test_reshape = x_test.reshape((nsamples,nx*ny))

如果可行，您可以使用相同的方式将数组转换为数据框。