Can't pass strings as inputs

I have a net that does some string processing as it’s first step, that works with the deprecated wrapper, but not with this (very nice looking) library

model = Sequential(
  [
      layers.Input(shape=(1,), dtype=tf.string),
      text_vectorizer,
      layers.Embedding(max_features + 1, 50 * multiplier),
      layers.Dropout(.1),
      layers.GlobalAveragePooling1D(),
      layers.Dropout(.1),
      layers.Dense(20, kernel_initializer=initializers.random_uniform, activation=activations.swish),
   ]
)

Traceback

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File ~/Library/Caches/pypoetry/virtualenvs/tensorflow-part-2-JIKxCiSF-py3.10/lib/python3.10/site-packages/sklearn/utils/validation.py:787, 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, estimator)
    786 try:
--> 787     array = array.astype(np.float64)
    788 except ValueError as e:
ValueError: could not convert string to float: ‘two fires american indians civil war laurence hauptman reveals several hundred thousand indians affected civil war twenty thousand indians enlisted sides attempt gain legitimacy autonomy simply land’
The above exception was the direct cause of the following exception:
ValueError                                Traceback (most recent call last)
Input In [28], in <module>
9 classifier = KerasClassifier(X=feature.values.astype(np.str_), y=label.values, model=make_model, batch_size=-1, validation_split=.2, verbose=1, sample_weight=1, )
10 grid = GridSearchCV(
11     estimator=classifier,
12     param_grid={},
13     verbose=1,
14 )
—> 15 grid_result = grid.fit(feature.values.astype(np.str_), label.values, callbacks=[callback], verbose=1)
16 history = grid_result.history_
18 grid_result
File ~/Library/Caches/pypoetry/virtualenvs/tensorflow-part-2-JIKxCiSF-py3.10/lib/python3.10/site-packages/sklearn/model_selection/search.py:926, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
924 refit_start_time = time.time()
925 if y is not None:
–> 926     self.best_estimator.fit(X, y, **fit_params)
927 else:
928     self.best_estimator_.fit(X, **fit_params)
File ~/Library/Caches/pypoetry/virtualenvs/tensorflow-part-2-JIKxCiSF-py3.10/lib/python3.10/site-packages/scikeras/wrappers.py:523, in BaseWrapper.fit(self, X, y, sample_weight, warm_start, **kwargs)
520 else:
521     # No warm start requested
522     reset = True
–> 523 X, y = self._validate_data(X=X, y=y, reset=reset)
525 if sample_weight is not None:
526     sample_weight = _check_sample_weight(
527         sample_weight, X, dtype=[“float64”, “int”]
528     )
File ~/Library/Caches/pypoetry/virtualenvs/tensorflow-part-2-JIKxCiSF-py3.10/lib/python3.10/site-packages/scikeras/wrappers.py:383, in BaseWrapper._validate_data(self, X, y, reset)
363 “”“Validate input data and set or check the n_features_in_ attribute.
364 Parameters
365 ----------
(…)
380     The validated input. A tuple is returned if y is not None.
381 “””
382 if y is not None:
–> 383     X, y = check_X_y(
384         X,
385         y,
386         allow_nd=True,  # allow X to have more than 2 dimensions
387         multi_output=True,  # allow y to be 2D
388     )
389 X = check_array(X, allow_nd=True, dtype=[“float64”, “int”])
391 n_features = X.shape[1]
File ~/Library/Caches/pypoetry/virtualenvs/tensorflow-part-2-JIKxCiSF-py3.10/lib/python3.10/site-packages/sklearn/utils/validation.py:964, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
961 if y is None:
962     raise ValueError(“y cannot be None”)
–> 964 X = check_array(
965     X,
966     accept_sparse=accept_sparse,
967     accept_large_sparse=accept_large_sparse,
968     dtype=dtype,
969     order=order,
970     copy=copy,
971     force_all_finite=force_all_finite,
972     ensure_2d=ensure_2d,
973     allow_nd=allow_nd,
974     ensure_min_samples=ensure_min_samples,
975     ensure_min_features=ensure_min_features,
976     estimator=estimator,
977 )
979 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric)
981 check_consistent_length(X, y)
File ~/Library/Caches/pypoetry/virtualenvs/tensorflow-part-2-JIKxCiSF-py3.10/lib/python3.10/site-packages/sklearn/utils/validation.py:789, 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, estimator)
787         array = array.astype(np.float64)
788     except ValueError as e:
–> 789         raise ValueError(
790             "Unable to convert array of bytes/strings "
791             “into decimal numbers with dtype=‘numeric’”
792         ) from e
793 if not allow_nd and array.ndim >= 3:
794     raise ValueError(
795         “Found array with dim %d. %s expected <= 2.”
796         % (array.ndim, estimator_name)
797     )
ValueError: Unable to convert array of bytes/strings into decimal numbers with dtype=‘numeric’

There’s also a bunch of related warnings

/Users/billie/Library/Caches/pypoetry/virtualenvs/tensorflow-part-2-JIKxCiSF-py3.10/lib/python3.10/site-packages/sklearn/utils/validation.py:964: FutureWarning: Arrays of bytes/strings is being converted to decimal numbers if dtype='numeric'. This behavior is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Please convert your data to numeric values explicitly instead.
  X = check_array(
/Users/billie/Library/Caches/pypoetry/virtualenvs/tensorflow-part-2-JIKxCiSF-py3.10/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: 
5 fits failed out of a total of 5.

Version

scikeras 0.6.0 Scikit-Learn API wrapper for Keras.

[adriangb]

Yeah fair enough, I’ll keep this issue open until I make that change!

[adriangb]

We do some preprocessing that the old wrappers don’t do, namely calling sklearn’s check{X,y} functions that mirrors what other sklearn estimators do internally and increases compatibility. But it looks like one of these doesn’t like your string input, I’m just not sure where things are going wrong since there’s a lot of layers to work with here (sklearn, scikeras, Keras, tensorflow).

Would you mind providing a minimally reproducible example, ideally with 1 row of input data so that I can debug locally and figure out where things are going wrong?