谁能帮我解决上述错误？
### 使用来自 sklearn.compose 的变换器
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
# 使用 autokeras 寻找 sonar 数据集的模型
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)
# 定义搜索
search = StructuredDataClassifier(max_trials=15)
# 执行搜索
search.fit(x=(X_train), y=y_train, verbose=0)
# 评估模型
loss, acc = search.evaluate(X_test, y_test, verbose=0)
print('准确率: %.3f' % acc)错误
(80, 92) (20, 92) (80,) (20,)
INFO:tensorflow:从现有项目 .\structured_data_classifier\oracle.json 重新加载 Oracle
INFO:tensorflow:从 .\structured_data_classifier\tuner0.json 重新加载 Tuner
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-106-94708e5d279d> in <module>
10 search = StructuredDataClassifier(max_trials=15)
11 # 执行搜索
---> 12 search.fit(x=(X_train), y=y_train, verbose=0)
13 # 评估模型
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: 不支持的类型 <class 'scipy.sparse.csr.csr_matrix'> 用于 StructuredDataAdapter.回答：
正如您在与此线程并行打开的Github issue中所注意到的，稀疏矩阵在AutoKeras中（目前）不被支持，建议将其转换为密集的Numpy数组。实际上，从AutoKeras StructuredDataClassifier的文档中可以看到，相应的.fit方法中的训练数据x应为：
字符串，numpy.ndarray，pandas.DataFrame或tensorflow.Dataset
而不是SciPy稀疏矩阵。
考虑到这里您的X_train非常小：
X_train.shape # (80, 92)您完全没有理由使用稀疏矩阵。虽然您似乎尝试将X_train转换为密集矩阵，但您没有重新赋值，结果它仍然是稀疏矩阵；从您上面的代码中可以看到：
X_train.todense()
# ...
type(X_train)
# scipy.sparse.csr.csr_matrix您需要做的只是简单地重新赋值为密集数组：
from scipy.sparse import csr_matrix
X_train = X_train.toarray()这里有一个使用虚拟数据的简短演示，证明这是可行的：
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
# 这不会起作用：
X_train.todense()
type(X_train)
# scipy.sparse.csr.csr_matrix # 仍然是稀疏的
# 这会起作用：
X_train = X_train.toarray()
type(X_train)
# numpy.ndarray您应该对X_test数据执行类似的程序（您的y_train和y_test似乎已经是密集的Numpy数组）。
