example code:

compileOptions = {
'optimizer': tf.keras.optimizers.Adam(learning_rate=5e-4),
'loss': condor.CondorOrdinalCrossEntropy(),
'metrics': [
            condor.OrdinalEarthMoversDistance(name='condorErrOrdinalMoversDist'),
            condor.OrdinalMeanAbsoluteError(name='ordinalMAbsErr')
            ]
'weighted_metrics': [
            condor.OrdinalEarthMoversDistance(name='condorErrOrdinalMoversDist'),
            condor.OrdinalMeanAbsoluteError(name='ordinalMAbsErr')
            ]
}

model.compile(**compileOptions)
model.fit(x=X_train,y=Y_train,batch_size=32,epochs=100,validation_data=(x_val, y_val, val_sample_weights), sample_weight=sampleweight_train)

would generate the following error:

    File "/usr/local/lib/python3.7/dist-packages/condor_tensorflow/metrics.py", line 24, in update_state  *
        if sample_weight:

    ValueError: condition of if statement expected to be `tf.bool` scalar, got Tensor("ExpandDims_1:0", shape=(None, 1), dtype=float32); to use as boolean Tensor, use `tf.cast`; to check for None, use `is not None`

If I don't provide weighted_metrics in model.compile option but remain to use sample_weight=sampleweight_train argument in model.fit, no errors would show up.

Thank you!

While I want to do a distributed training including training on Google Colab TPU, errors as shown below would occurs:

/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
    528     self._self_setattr_tracking = False  # pylint: disable=protected-access
    529     try:
--> 530       result = method(self, *args, **kwargs)
    531     finally:
    532       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)
    434           targets=self._targets,
    435           skip_target_masks=self._prepare_skip_target_masks(),
--> 436           masks=self._prepare_output_masks())
    437 
    438       # Prepare sample weight modes. List with the same length as model outputs.

/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py in _handle_metrics(self, outputs, targets, skip_target_masks, sample_weights, masks, return_weighted_metrics, return_weighted_and_unweighted_metrics)
   1962           metric_results.extend(
   1963               self._handle_per_output_metrics(self._per_output_metrics[i],
-> 1964                                               target, output, output_mask))
   1965         if return_weighted_and_unweighted_metrics or return_weighted_metrics:
   1966           metric_results.extend(

/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py in _handle_per_output_metrics(self, metrics_dict, y_true, y_pred, mask, weights)
   1913       with backend.name_scope(metric_name):
   1914         metric_result = training_utils_v1.call_metric_function(
-> 1915             metric_fn, y_true, y_pred, weights=weights, mask=mask)
   1916         metric_results.append(metric_result)
   1917     return metric_results

/usr/local/lib/python3.7/dist-packages/keras/engine/training_utils_v1.py in call_metric_function(metric_fn, y_true, y_pred, weights, mask)
   1175 
   1176   if y_pred is not None:
-> 1177     return metric_fn(y_true, y_pred, sample_weight=weights)
   1178   # `Mean` metric only takes a single value.
   1179   return metric_fn(y_true, sample_weight=weights)

/usr/local/lib/python3.7/dist-packages/keras/metrics.py in __call__(self, *args, **kwargs)
    235     from keras.distribute import distributed_training_utils  # pylint:disable=g-import-not-at-top
    236     return distributed_training_utils.call_replica_local_fn(
--> 237         replica_local_fn, *args, **kwargs)
    238 
    239   def __str__(self):

/usr/local/lib/python3.7/dist-packages/keras/distribute/distributed_training_utils.py in call_replica_local_fn(fn, *args, **kwargs)
     58     with strategy.scope():
     59       return strategy.extended.call_for_each_replica(fn, args, kwargs)
---> 60   return fn(*args, **kwargs)
     61 
     62 

/usr/local/lib/python3.7/dist-packages/keras/metrics.py in replica_local_fn(*args, **kwargs)
    215         update_op = None
    216       else:
--> 217         update_op = self.update_state(*args, **kwargs)  # pylint: disable=not-callable
    218       update_ops = []
    219       if update_op is not None:

/usr/local/lib/python3.7/dist-packages/keras/utils/metrics_utils.py in decorated(metric_obj, *args, **kwargs)
     71 
     72     with tf_utils.graph_context_for_symbolic_tensors(*args, **kwargs):
---> 73       update_op = update_state_fn(*args, **kwargs)
     74     if update_op is not None:  # update_op will be None in eager execution.
     75       metric_obj.add_update(update_op)

/usr/local/lib/python3.7/dist-packages/keras/metrics.py in update_state_fn(*args, **kwargs)
    175         control_status = tf.__internal__.autograph.control_status_ctx()
    176         ag_update_state = tf.__internal__.autograph.tf_convert(obj_update_state, control_status)
--> 177         return ag_update_state(*args, **kwargs)
    178     else:
    179       if isinstance(obj.update_state, tf.__internal__.function.Function):

/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    694       try:
    695         with conversion_ctx:
--> 696           return converted_call(f, args, kwargs, options=options)
    697       except Exception as e:  # pylint:disable=broad-except
    698         if hasattr(e, 'ag_error_metadata'):

/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, args, kwargs, caller_fn_scope, options)
    381 
    382   if not options.user_requested and conversion.is_allowlisted(f):
--> 383     return _call_unconverted(f, args, kwargs, options)
    384 
    385   # internal_convert_user_code is for example turned off when issuing a dynamic

/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs, options, update_cache)
    462 
    463   if kwargs is not None:
--> 464     return f(*args, **kwargs)
    465   return f(*args)
    466 

/usr/local/lib/python3.7/dist-packages/keras/metrics.py in update_state(self, y_true, y_pred, sample_weight)
    723 
    724     ag_fn = tf.__internal__.autograph.tf_convert(self._fn, tf.__internal__.autograph.control_status_ctx())
--> 725     matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
    726     return super(MeanMetricWrapper, self).update_state(
    727         matches, sample_weight=sample_weight)

/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    694       try:
    695         with conversion_ctx:
--> 696           return converted_call(f, args, kwargs, options=options)
    697       except Exception as e:  # pylint:disable=broad-except
    698         if hasattr(e, 'ag_error_metadata'):

/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in converted_call(f, args, kwargs, caller_fn_scope, options)
    381 
    382   if not options.user_requested and conversion.is_allowlisted(f):
--> 383     return _call_unconverted(f, args, kwargs, options)
    384 
    385   # internal_convert_user_code is for example turned off when issuing a dynamic

/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs, options, update_cache)
    462 
    463   if kwargs is not None:
--> 464     return f(*args, **kwargs)
    465   return f(*args)
    466 

/usr/local/lib/python3.7/dist-packages/keras/losses.py in __call__(self, y_true, y_pred, sample_weight)
    141       losses = call_fn(y_true, y_pred)
    142       return losses_utils.compute_weighted_loss(
--> 143           losses, sample_weight, reduction=self._get_reduction())
    144 
    145   @classmethod

/usr/local/lib/python3.7/dist-packages/keras/losses.py in _get_reduction(self)
    182          self.reduction == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE)):
    183       raise ValueError(
--> 184           'Please use `tf.keras.losses.Reduction.SUM` or '
    185           '`tf.keras.losses.Reduction.NONE` for loss reduction when losses are '
    186           'used with `tf.distribute.Strategy` outside of the built-in training '

ValueError: Please use `tf.keras.losses.Reduction.SUM` or `tf.keras.losses.Reduction.NONE` for loss reduction when losses are used with `tf.distribute.Strategy` outside of the built-in training loops. You can implement `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` using global batch size like:

with strategy.scope():
    loss_obj = tf.keras.losses.CategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)
    loss = tf.reduce_sum(loss_obj(labels, predictions)) * (1. / global_batch_size)
Please see https://www.tensorflow.org/tutorials/distribute/custom_training for more details.

it seems that support of loss reduction has not been implemented. It may be a little tricky, but it would be nice if you can add this enhancement.

Thank you!

I had a question about the importance weights code below that was in one of the tutorial docs.

Importance weights customization
A quick example to show how the importance weights can be customized.
model = create_model(num_classes = NUM_CLASSES)
model.summary()
# We have num_classes - 1 outputs (cumulative logits), so there are 9 elements
# in the importance vector to customize.
importance_weights = [1., 1., 0.5, 0.5, 0.5, 1., 1., 0.1, 0.1]
loss_fn = condor.SparseCondorOrdinalCrossEntropy(importance_weights = importance_weights)
model.compile(tf.keras.optimizers.Adam(lr = learning_rate), loss = loss_fn)
history = model.fit(dataset, epochs = num_epochs)

My problem:

I have 5 classes, with underrepresentation of say the first and lass class. I want to use weights to assign higher importance to the underrepresented classes. In a dense layer with n(classes) == n(output_layers), the vector would look like.

[1,0.5,0.5,0.5,1]

With the CONDOR, using num_classes - 1 output layers, is it still possible to assign higher weights to underrepresented classes?

I don't understand how to relate the N-1 output layers weights to the original weights where n(classes) == n(output_layers).

Any feedback is appreciated.

Hello, I've a dataset in which the labels are like (0,1,2,3). It means the number of classes in Y is 4.

Method 1:

Using the condor.CondorOrdinalEncoder(nclasses=4).fit_transform(labels) to transform labels to an array in shape (n, 3). [ [0,0,1], 　[1,0,0] ] as model prediction objects. The last layer is tf.keras.layers.Dense(units=4-1), according to the readme, however by this design the default activation function of the last layer would be None/Linear( f(x) = x), and the output of the model would be simple logits. Should I keep the model outputs simple logits(no activation function)?

Method 2:

If I use tf.keras.layers.Dense(units=4-2, activation=condor.ordinal_softmax) as the last layer together with label data in shape (n, 3), would that be fine? (the condor.ordinal_softmax function would increase the number of dimension)

Method 3: Or I should use tf.keras.layers.Dense(units=4-1, activation=condor.ordinal_softmax) as the last layer together with label data in shape (n, 4)?

Which method is better? Thank you!

When fitting data with nclass=0:

1. self.feature_names_in_ would lose its functionality(the previous commit).
2. Also, using sklearn.compose.ColumnTransformer to transform multiple columns with CondorOrdinalEncoder at a time would cause self.nclass changing in every transformation and thus the transformation would fail, and therefore it is necessary to differentiate.

When I try to integrate sklearn.compose.ColumnTransformer, sklearn.pipeline with condor encoder, I find it difficult and errors happen due to lack of support. Therefore I add the support of get_feature_names_out method, which complies with the structure of sklearn.