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AutoML library for deep learning
http://autokeras.com/
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TypeError: Unsupported type <class 'scipy.sparse.csr.csr_matrix'> for StructuredDataAdapter. #1521

Closed Juned-Ansari closed 3 years ago

Juned-Ansari commented 3 years ago

Bug Description

Unsupported type <class 'scipy.sparse.csr.csr_matrix'> for StructuredDataAdapter. When I Convert Multiple Text column into Vectors it will convert into csr_matrix which is not supported by auto-keras.

Bug Reproduction

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. ,

  1. ], [0. , 0. , 0. , ..., 0. , 0. ,
  2. ], [0. , 0. , 0. , ..., 0.46148896, 1. ,
  3. ], ..., [0. , 0. , 0. , ..., 0.46148896, 1. ,
  4. ], [0. , 0. , 0. , ..., 0. , 0. ,
  5. ], [0. , 0. , 0. , ..., 0.46148896, 1. ,
  6. ]])

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)

Expected Behavior

(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)

in 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 for StructuredDataAdapter. ### Setup Details Include the details about the versions of: - OS type and version: - Python: - autokeras: - keras-tuner: - scikit-learn: - numpy: - pandas: - tensorflow: ### Additional context
haifeng-jin commented 3 years ago

I don't think we can support this type. Is it possible to convert it to numpy array?

desertnaut commented 3 years ago

Indeed, the solution was simply 

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

as pointed out in this (accepted) SO answer on the exact same issue: https://stackoverflow.com/questions/66495126/typeerror-unsupported-type-class-scipy-sparse-csr-csr-matrix-for-structured/66614090#66614090

We can safely close this now @haifeng-jin.