I used my own data to run your code. My model is regression. I followed your code and it is okay for catboost, but for deeplearning part, I got the following error messages:
KeyError Traceback (most recent call last)
in
52 shuffle=True)
53
---> 54 history = model.fit(train_batch,
55 # validation_data=(tb.transform(X.iloc[test_idx]), y[test_idx]),
56 validation_data=test_batch,
~/.virtualenvs/tf24/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1048 training_utils.RespectCompiledTrainableState(self):
1049 # Creates a tf.data.Dataset and handles batch and epoch iteration.
-> 1050 data_handler = data_adapter.DataHandler(
1051 x=x,
1052 y=y,
~/.virtualenvs/tf24/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in init(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
1098
1099 adapter_cls = select_data_adapter(x, y)
-> 1100 self._adapter = adapter_cls(
1101 x,
1102 y,
~/.virtualenvs/tf24/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in init(self, x, y, sample_weights, shuffle, workers, use_multiprocessing, max_queue_size, model, **kwargs)
900 self._keras_sequence = x
901 self._enqueuer = None
--> 902 super(KerasSequenceAdapter, self).init(
903 x,
904 shuffle=False, # Shuffle is handed in the _make_callable override.
~/.virtualenvs/tf24/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in init(self, x, y, sample_weights, workers, use_multiprocessing, max_queue_size, model, **kwargs)
777 # Since we have to know the dtype of the python generator when we build the
778 # dataset, we have to look at a batch to infer the structure.
--> 779 peek, x = self._peek_and_restore(x)
780 peek = self._standardize_batch(peek)
781 peek = _process_tensorlike(peek)
~/.virtualenvs/tf24/lib/python3.8/site-packages/tensorflow/python/keras/engine/data_adapter.py in _peek_and_restore(x)
911 @staticmethod
912 def _peek_and_restore(x):
--> 913 return x[0], x
914
915 def _handle_multiprocessing(self, x, workers, use_multiprocessing,
~/projects/ifp85/tabular.py in getitem(self, index)
348 def getitem(self, index):
349 indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
--> 350 samples, labels = self.__data_generation(indexes)
351 return samples, labels
352
~/projects/ifp85/tabular.py in __data_generation(self, selection)
342 return dct, self.y[selection]
343 else:
--> 344 return self.tbt.transform(self.X.iloc[selection, :]), self.y[selection]
345 else:
346 return self.X.iloc[selection, :], self.y[selection]
~/.virtualenvs/tf24/lib/python3.8/site-packages/pandas/core/series.py in getitem(self, key)
904 return self._get_values(key)
905
--> 906 return self._get_with(key)
907
908 def _get_with(self, key):
~/.virtualenvs/tf24/lib/python3.8/site-packages/pandas/core/series.py in _get_with(self, key)
939 # (i.e. self.iloc) or label-based (i.e. self.loc)
940 if not self.index._should_fallback_to_positional():
--> 941 return self.loc[key]
942 else:
943 return self.iloc[key]
~/.virtualenvs/tf24/lib/python3.8/site-packages/pandas/core/indexing.py in getitem(self, key)
877
878 maybe_callable = com.apply_if_callable(key, self.obj)
--> 879 return self._getitem_axis(maybe_callable, axis=axis)
880
881 def _is_scalar_access(self, key: Tuple):
~/.virtualenvs/tf24/lib/python3.8/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1097 raise ValueError("Cannot index with multidimensional key")
1098
-> 1099 return self._getitem_iterable(key, axis=axis)
1100
1101 # nested tuple slicing
~/.virtualenvs/tf24/lib/python3.8/site-packages/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
1035
1036 # A collection of keys
-> 1037 keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
1038 return self.obj._reindex_with_indexers(
1039 {axis: [keyarr, indexer]}, copy=True, allow_dups=True
~/.virtualenvs/tf24/lib/python3.8/site-packages/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1252 keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
1253
-> 1254 self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
1255 return keyarr, indexer
1256
~/.virtualenvs/tf24/lib/python3.8/site-packages/pandas/core/indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
1313
1314 with option_context("display.max_seq_items", 10, "display.width", 80):
-> 1315 raise KeyError(
1316 "Passing list-likes to .loc or [] with any missing labels "
1317 "is no longer supported. "
KeyError: "Passing list-likes to .loc or [] with any missing labels is no longer supported. The following labels were missing: Int64Index([ 963, 26089, 37285, 32796, 21419,\n ...\n 7514, 35430, 5619, 9022, 40319],\n dtype='int64', length=253). See https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike"
I couldn't know how to solve this.
By the way, I don't fully understand the meaning of variables sizes and categorical_levels
tb = TabularTransformer(numeric = numeric_variables,
ordinal = [],
lowcat = [],
highcat = categorical_variables)
tb.fit(X.iloc[train_idx])
sizes = tb.shape(X.iloc[train_idx])
categorical_levels = dict(zip(categorical_variables, sizes[1:]))
print(f"Input array sizes: {sizes}")
print(f"Categorical levels: {categorical_levels}\n")
Thank you very much!
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