Column names are replaced by index

[sdv-dev]

Column names are replaced by index

[csala]

Thanks for reporting this @upura

The problem seems to be here: https://github.com/DAI-Lab/TGAN/blob/f5b9a9cbd9e4bc2f0755bdcf24daef537594cd72/tgan/data.py#L322

The fix would be to avoid replacing the column names, and rather use an enumerate on the subsequent loop to get the right i value without having to alter the data object.

[ManuelAlvarezC]

Hi @upura and thanks for your question.

the trouble is that the function fit_transform is called several times

I haven't been able in which case that can occur, could you please provide a snippet of code that reproduce your issue?

Thanks.

[upura]

Hello @ManuelAlvarezC, thank you for your reply.

Here is the notebook I used. Sorry for the Japanese comment. After fitting TGAN, it looks that column names are replaced (at cell [16]).
https://github.com/upura/upura.hatenablog/blob/master/books_sites/tgan/tgan-titanic.ipynb

Now I rechecked the codes and I've found that what I said is wrong. But I still can't see why column names are replaced.

the trouble is that the function fit_transform is called several times

Best.

[hasinaattaullah]

@csala can you please bit elaborate how exactly we will do it?
"The fix would be to avoid replacing the column names, and rather use an enumerate on the subsequent loop to get the right i value without having to alter the data object."
As I am also working on it and I am getting error ,
ValueError Traceback (most recent call last)
in
----> 1 tgan.fit(data)

~\Anaconda3\lib\site-packages\tgan\model.py in fit(self, data)
678 """
679 self.preprocessor = Preprocessor(continuous_columns=self.continuous_columns)
--> 680 data = self.preprocessor.fit_transform(data)
681 self.metadata = self.preprocessor.metadata
682 dataflow = TGANDataFlow(data, self.metadata)

~\Anaconda3\lib\site-packages\tgan\data.py in fit_transform(self, data, fitting)
328 if i in self.continuous_columns:
329 column_data = data[i].values.reshape([-1, 1])
--> 330 features, probs, means, stds = self.continous_transformer.transform(column_data)
331 transformed_data['f%02d' % i] = np.concatenate((features, probs), axis=1)
332

~\Anaconda3\lib\site-packages\tgan\data.py in decorated(self, data, *args, **kwargs)
61 raise ValueError('The argument data must be a numpy.ndarray with shape (n, 1).')
62
---> 63 return function(self, data, *args, **kwargs)
64
65 decorated.doc = function.doc

~\Anaconda3\lib\site-packages\tgan\data.py in transform(self, data)
238 """
239 model = GaussianMixture(self.num_modes)
--> 240 model.fit(data)
241
242 means = model.means_.reshape((1, self.num_modes))

~\Anaconda3\lib\site-packages\sklearn\mixture\base.py in fit(self, X, y)
192 self
193 """
--> 194 self.fit_predict(X, y)
195 return self
196

~\Anaconda3\lib\site-packages\sklearn\mixture\base.py in fit_predict(self, X, y)
218 Component labels.
219 """
--> 220 X = _check_X(X, self.n_components, ensure_min_samples=2)
221 self._check_initial_parameters(X)
222

~\Anaconda3\lib\site-packages\sklearn\mixture\base.py in _check_X(X, n_components, n_features, ensure_min_samples)
53 """
54 X = check_array(X, dtype=[np.float64, np.float32],
---> 55 ensure_min_samples=ensure_min_samples)
56 if n_components is not None and X.shape[0] < n_components:
57 raise ValueError('Expected n_samples >= n_components '

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py 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, warn_on_dtype, estimator)
571 if force_all_finite:
572 _assert_all_finite(array,
--> 573 allow_nan=force_all_finite == 'allow-nan')
574
575 shape_repr = _shape_repr(array.shape)

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X, allow_nan)
54 not allow_nan and not np.isfinite(X).all()):
55 type_err = 'infinity' if allow_nan else 'NaN, infinity'
---> 56 raise ValueError(msg_err.format(type_err, X.dtype))
57
58

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').