def __init__(self, data_stream, probs, labels, **kwargs):
self.data_stream = data_stream
self.evaluator = DataStreamEvaluator(
[probs.copy('probs'), labels.copy('targets')])
logger.info("compiling auc logger")
self.evaluator._compile()
super(AUCMonitor, self).__init__(**kwargs)
def __init__(self, data_stream, prediction, targets, label_i_to_c,
**kwargs):
self.data_stream = data_stream
self.label_i_to_c = label_i_to_c
self.evaluator = DataStreamEvaluator(
[prediction.copy('prediction'),
targets.copy('targets')])
if prediction.ndim != 1 or targets.ndim != 1:
raise ValueError("targets and predictions must be 1d vectors")
logger.info("compiling perclass accuracy logger")
self.evaluator._compile()
super(PerClassAccuracyMonitor, self).__init__(**kwargs)
def test_datastream_evaluator():
stream = IndexableDataset(indexables=OrderedDict([
("data", np.ones((10, 4, 9), dtype="float32")),
])).get_example_stream()
x = T.matrix("data")
mon = x.sum(axis=1)
mon.name = "mon"
evaluator = DataStreamEvaluator([mon])
results = evaluator.evaluate(stream)
assert set(results.keys()) == set(['mon'])
assert_allclose(results['mon'], np.ones((4 * 10)) * 9)
def __init__(self, data_stream, prediction, targets, dest_directory,
**kwargs):
self.data_stream = data_stream
self.evaluator = DataStreamEvaluator(
[prediction.copy('prediction'),
targets.copy('targets')])
self.dest_directory = dest_directory
if not os.path.exists(self.dest_directory):
os.mkdir(self.dest_directory)
if prediction.ndim != 1 or targets.ndim != 1:
raise ValueError("targets and predictions must be 1d vectors")
logger.info("compiling confusion matrix monitor")
self.evaluator._compile()
super(ConfusionMatrixMonitor, self).__init__(**kwargs)
