can't use classical (1d) scores with joint_test (label_test and one statistical test)

[jasperalbers]

I am trying to use the kl_divergence score to compare distributions from two sets of spiking data from two simulators (NEST and PyNN). Since the model I am investigating has multiple distinct populations, I want to compare the distributions of these populations separately. For this, I am using the joint_test example together with a label_test that checks for spiketrains to be from the same population and a conventional test (e.g. firing rate, cv isi, ...). However, this is currently not possible, even though the joint_test only produces a single score and not multiple ones, while using wasserstein_distance works.
A first attempt at rectifying this was adding code that ensures that the layer_prediction and layer_observation are 1d (see below), but this did resulted in the same error which I append at the end.

Here is a reduced code example (sorry, still quite long), provided that one has some spiking data at hand that can be separated according to their annotation:

class fr_test(tests.firing_rate_test):
    name = 'FR'

class label_test(tests.two_sample_test):
    name = 'Label Test'
    params = {'label_key': 'source_population'}

    def generate_prediction(self, model, **kwargs):
        labels = self.get_prediction(model)
        if labels is None:
            if kwargs:
                self.params.update(kwargs)
            spiketrains = model.produce_spiketrains(**self.params)
            labels = np.array([st.annotations[self.params['label_key']]
                               for st in spiketrains])

            label_ids = np.zeros((len(labels)))
            if hasattr(self, "label_mapping"):
                max_id = max(self.label_mapping.values())
            else:
                self.label_mapping = {}
                max_id = -1

            for label in np.unique(labels):
                if label in self.label_mapping:
                    id = self.label_mapping[label]
                else:
                    id = max_id + 1
                    max_id += 1
                    self.label_mapping[label] = id

                label_ids[labels == label] = id

            self.set_prediction(model, label_ids)
        return label_ids


class joint_test(tests.TestM2M, tests.joint_test):
    score_type = scores.kl_divergence
    test_list = [fr_test,
                 label_test
                 ]
    test_params = [{},
                   {'label_key': 'source_population'}
                   ]

    def compute_score(self, observation, prediction):
        """Generates a score given the observations provided in the constructor
        and the prediction generated by generate_prediction.

        Must generate a score of score_type.
        No default implementation.
        """
        if not hasattr(self, 'score_type') or \
           not hasattr(self.score_type, 'compute'):
            raise NotImplementedError(("Test %s either implements no "
                                       "compute_score method or provides no "
                                       "score_type with a compute method.")
                                      % self.name)

        # separate predictions according to labels
        label_test_list = [i for i, t in enumerate(self.test_list)
                           if t.name == 'Label Test']
        if len(label_test_list) == 1:
            label_test_idx = label_test_list[0]
            pred_labels = prediction[label_test_idx]
            prediction = np.delete(prediction, label_test_idx, axis=0)
            obsv_labels = observation[label_test_idx]
            observation = np.delete(observation, label_test_idx, axis=0)

            # compute score for each label separately
            scores = []
            label_intersection = np.intersect1d(pred_labels, obsv_labels)

            for label in np.unique(label_intersection):
                layer_prediction = prediction[:,
                                              np.where(pred_labels == label)[0]]
                layer_observation = observation[:,
                                                np.where(obsv_labels == label)[0]]
                if layer_prediction.shape[0] == 1:
                    layer_prediction = layer_prediction[0]
                if layer_observation.shape[0] == 1:
                    layer_observation = layer_observation[0]
                scores.append(self.score_type.compute(layer_observation,
                                                      layer_prediction))

            # merge scores
            mean_score = np.mean([s.score for s in scores])
            score = self.score_type(mean_score)

            for attribute in scores[0].__dict__:
                if attribute != 'score':
                    score_attr = [getattr(s, attribute) for s in scores]
                    setattr(score, attribute, score_attr)

            mapping = self.test_inst[label_test_idx].label_mapping
            inverse_mapping = dict([(value, key)
                                    for key, value in mapping.items()])
            score.labels = [inverse_mapping[i]
                            for i in np.unique(label_intersection)]
        else:
            s.score

        return score

class LoadNest(models.spiketrain_data):
    file_path = ''
    params = {}

    def load(self, file_path, simulator, **kwargs):
        return list(pd.read_hdf(file_path))

class LoadPyNN(models.spiketrain_data):
    file_path = ''
    params = {}

    def load(self, file_path, simulator, **kwargs):
        return list(pd.read_hdf(file_path))

class NestSim(LoadNest):
    file_path = os.path.join(PyNEST_path, 'some_file.h5')
    params = copy(LoadNest.params)
    params.update(color='#ee7733', simulator='Nest')

class PyNNSim(LoadPyNN):
    file_path = os.path.join(PyNN_path, 'some_file.h5')
    params = copy(LoadPyNN.params)
    params.update(color='#01796f', simulator='PyNN')

Nest = NestSim(name='Nest')
PyNN = PyNNSim(name='PyNN')
joint = joint_test()
judge = joint.judge(models=[PyNN, Nest])

Error thrown:

judge = joint.judge(models=[PyNN, Nest])
/p/home/jusers/albers2/jureca/.local/lib/python3.8/site-packages/elephant/statistics.py:334: UserWarning: The input size is too small. Please providean input with more than 1 entry. Returning `NaN`since the argument `with_nan` is `True`
  warnings.warn("The input size is too small. Please provide"
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/p/project/cjinb33/albers2/projects/mam_benchmarking/PyNN/validation/validate.py in <module>
----> 1 judge = joint.judge(models=[PyNN, Nest])

~/.local/lib/python3.8/site-packages/sciunit/tests.py in judge(self, models, skip_incapable, stop_on_error, deep_error, only_lower_triangle)
    861                     scores[i][j] = scores[j][i]
    862                 else:
--> 863                     scores[i][j] = self._judge(
    864                         predictions[i], predictions[j], model1, model2
    865                     )

~/.local/lib/python3.8/site-packages/sciunit/tests.py in _judge(self, prediction1, prediction2, model1, model2)
    706
    707         # 6.
--> 708         score = self.compute_score(prediction1, prediction2)
    709         if self.converter:
    710             score = self.converter.convert(score)

/p/project/cjinb33/albers2/projects/mam_benchmarking/PyNN/validation/validate.py in compute_score(self, observation, prediction)
    177                 layer_observation = observation[:,
    178                                                 np.where(obsv_labels == label)[0]]
--> 179                 scores.append(self.score_type.compute(layer_observation,
    180                                                       layer_prediction))
    181

/p/project/cjinb33/albers2/repositories/NetworkUnit/networkunit/scores/kl_divergence.py in compute(self, data_sample_1, data_sample_2, kl_binsize, **kwargs)
     41         min_value = min([min(sample1),min(sample2)])
     42         bins = (max_value - min_value) / kl_binsize
---> 43         edges = np.linspace(min_value, max_value, bins)
     44
     45         P, edges = np.histogram(sample1, bins=edges, density=True)

<__array_function__ internals> in linspace(*args, **kwargs)

/p/software/jurecadc/stages/2020/software/SciPy-Stack/2020-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/numpy-1.19.1-py3.8-linux-x86_64.egg/numpy/core/function_base.py in linspace(start, stop, num, endpoint, retstep, dtype, axis)
    111
    112     """
--> 113     num = operator.index(num)
    114     if num < 0:
    115         raise ValueError("Number of samples, %s, must be non-negative." % num)

TypeError: 'numpy.float64' object cannot be interpreted as an integer

[rgerkin]

Try setting bins to an integer by adding a bins = int(bins) line right after line 42 of kl_divergence.py.

[jasperalbers]

Your suggestion makes the code not run into the error! However, the resulting scores are not numbers:

In: print(judge)
Out:
        PyNN                                               Nest
PyNN  \n\nKullback-Leibler-Divergence\n\tdat...  \n\nKullback-Leibler-Divergence\n\tdat...
Nest  \n\nKullback-Leibler-Divergence\n\tdat...  \n\nKullback-Leibler-Divergence\n\tdat...

[rgutzen]

The judge function returns a score object (or for multiple tests a score collection object). Printing the object invokes its __str__ attribute which is for the networkunit score types a string describing the score. To only get the numerical score you can do print(score.score).

[rgerkin]

Actually I think printing a score object itself should just print the .score attribute already: https://github.com/scidash/sciunit/blob/master/sciunit/scores/base.py#L288. But you have a score matrix (models x models) which is like a pandas dataframe. Just enter judge directly on a line and you will get a rich html representation of it (in a Jupyter notebook) or in a console you may still get something like what you see above. Probably I should consider overriding the default Pandas representation in the console with one that contains the .score attribute for each score in the matrix.

[jasperalbers]

judge.score does return the desired matrix of scores, thanks!

[rgerkin]

Of course, you are right! I forgot about this, which maps the single-score-level score call through the whole ScoreMatrix.