python - TypeError：不支持的类型对于结构化数据适配器

Question

谁能帮我解决上述错误？

### using trasnformers 
from sklearn.compose import ColumnTransformer
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler

column_trans = ColumnTransformer(
         [
          ('CompanyName_bow', TfidfVectorizer(), 'CompanyName'),
          ('state_category', OneHotEncoder(), ['state']),
          ('Termination_Reason_Desc_bow', TfidfVectorizer(), 'Termination_Reason_Desc'),
          ('TermType_category', OneHotEncoder(), ['TermType'])
         ],
         remainder=MinMaxScaler()
        )
X = column_trans.fit_transform(X.head(100))

from sklearn.preprocessing import LabelEncoder
y = LabelEncoder().fit_transform(y.head(100))

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=5)

X_train.shape  #(80, 92)
X_test.shape   #(20, 92)
y_train.shape  #(80,)
X_train.todense()
matrix([[0.        , 0.        , 0.        , ..., 0.26921709, 1.        ,
         0.        ],
        [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
         1.        ],
        [0.        , 0.        , 0.        , ..., 0.46148896, 1.        ,
         0.        ],
        ...,
        [0.        , 0.        , 0.        , ..., 0.46148896, 1.        ,
         0.        ],
        [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
         1.        ],
        [0.        , 0.        , 0.        , ..., 0.46148896, 1.        ,
         0.        ]])

type(X_train)
--> scipy.sparse.csr.csr_matrix

print(y_train)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
 
type(y_train)
numpy.ndarray

# use autokeras to find a model for the sonar dataset
from numpy import asarray
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from autokeras import StructuredDataClassifier

print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
# define the search
search = StructuredDataClassifier(max_trials=15)
# perform the search
search.fit(x=(X_train), y=y_train, verbose=0)
# evaluate the model
loss, acc = search.evaluate(X_test, y_test, verbose=0)
print('Accuracy: %.3f' % acc)

错误

(80, 92) (20, 92) (80,) (20,)
INFO:tensorflow:Reloading Oracle from existing project .\structured_data_classifier\oracle.json
INFO:tensorflow:Reloading Tuner from .\structured_data_classifier\tuner0.json
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-106-94708e5d279d> in <module>
     10 search = StructuredDataClassifier(max_trials=15)
     11 # perform the search
---> 12 search.fit(x=(X_train), y=y_train, verbose=0)
     13 # evaluate the model
     14 loss, acc = search.evaluate(X_test, y_test, verbose=0)

~\anaconda3\lib\site-packages\autokeras\tasks\structured_data.py in fit(self, x, y, epochs, callbacks, validation_split, validation_data, **kwargs)
    313                 [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
    314         """
--> 315         super().fit(
    316             x=x,
    317             y=y,

~\anaconda3\lib\site-packages\autokeras\tasks\structured_data.py in fit(self, x, y, epochs, callbacks, validation_split, validation_data, **kwargs)
    132         self.check_in_fit(x)
    133 
--> 134         super().fit(
    135             x=x,
    136             y=y,

~\anaconda3\lib\site-packages\autokeras\auto_model.py in fit(self, x, y, batch_size, epochs, callbacks, validation_split, validation_data, **kwargs)
    259             validation_split = 0
    260 
--> 261         dataset, validation_data = self._convert_to_dataset(
    262             x=x, y=y, validation_data=validation_data, batch_size=batch_size
    263         )

~\anaconda3\lib\site-packages\autokeras\auto_model.py in _convert_to_dataset(self, x, y, validation_data, batch_size)
    373             x = dataset.map(lambda x, y: x)
    374             y = dataset.map(lambda x, y: y)
--> 375         x = self._adapt(x, self.inputs, batch_size)
    376         y = self._adapt(y, self._heads, batch_size)
    377         dataset = tf.data.Dataset.zip((x, y))

~\anaconda3\lib\site-packages\autokeras\auto_model.py in _adapt(self, dataset, hms, batch_size)
    287         adapted = []
    288         for source, hm in zip(sources, hms):
--> 289             source = hm.get_adapter().adapt(source, batch_size)
    290             adapted.append(source)
    291         if len(adapted) == 1:

~\anaconda3\lib\site-packages\autokeras\engine\adapter.py in adapt(self, dataset, batch_size)
     65             tf.data.Dataset. The converted dataset.
     66         """
---> 67         self.check(dataset)
     68         dataset = self.convert_to_dataset(dataset, batch_size)
     69         return dataset

~\anaconda3\lib\site-packages\autokeras\adapters\input_adapters.py in check(self, x)
     63     def check(self, x):
     64         if not isinstance(x, (pd.DataFrame, np.ndarray, tf.data.Dataset)):
---> 65             raise TypeError(
     66                 "Unsupported type {type} for "
     67                 "{name}.".format(type=type(x), name=self.__class__.__name__)

TypeError: Unsupported type <class 'scipy.sparse.csr.csr_matrix'> for StructuredDataAdapter.

标签： pythonmachine-learningdeep-learningauto-keras

Answer 1

正如您在与此线程并行打开的Github 问题中所注意到的，AutoKeras （当前）不支持稀疏矩阵，建议将它们转换为密集的 Numpy 数组。事实上，从AutoKeras的文档来看，相应方法中StructuredDataClassifier的训练数据预计为：x.fit

字符串、numpy.ndarray、pandas.DataFrame 或 tensorflow.Dataset

而不是 SciPy 稀疏矩阵。

鉴于这里你X_train真的很小：

X_train.shape  
# (80, 92)

您绝对没有理由使用稀疏矩阵。尽管在这里您似乎试图转换X_train为密集的，但您没有重新分配它，结果是它仍然是稀疏的；从上面你自己的代码：

X_train.todense()
# ...
type(X_train)
# scipy.sparse.csr.csr_matrix

您需要做的只是将其重新分配给密集数组：

from scipy.sparse import csr_matrix
X_train = X_train.toarray()

这是一个简短的演示，它适用于虚拟数据：

import numpy as np
from scipy.sparse import csr_matrix
X_train = csr_matrix((3, 4), dtype=np.float)

type(X_train)
# scipy.sparse.csr.csr_matrix

# this will not work:
X_train.todense()
type(X_train)
# scipy.sparse.csr.csr_matrix # still sparse

# this will work:
X_train = X_train.toarray()
type(X_train)
# numpy.ndarray

X_test您应该对您的数据遵循类似的程序（您的y_train并且y_test似乎已经是密集的 Numpy 数组）。