def interpret(learn):
interp = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix()
plt.savefig(PATH / 'confusion_matrix.jpg')
interp.plot_top_losses(8)
plt.savefig(PATH / 'top_losses.jpg')
def test_ClassificationInterpretation(learn):
interp = ClassificationInterpretation.from_learner(learn)
assert isinstance(interp.confusion_matrix(), (np.ndarray))
assert interp.confusion_matrix().sum() == len(learn.data.valid_ds)
conf = interp.most_confused()
print(conf) # debug
assert len(conf) == 0 or len(conf) == 2 and (set(conf[0][:2]) == set(
conf[1][:2]) == {'3', '7'}), f"conf={conf}"
def test_ClassificationInterpretation(learn):
this_tests(ClassificationInterpretation)
interp = ClassificationInterpretation.from_learner(learn)
assert isinstance(interp.confusion_matrix(), (np.ndarray))
assert interp.confusion_matrix().sum() == len(learn.data.valid_ds)
conf = interp.most_confused()
expect = {'3', '7'}
assert (len(conf) == 0 or len(conf) == 1 and (set(conf[0][:2]) == expect)
or len(conf) == 2 and
(set(conf[0][:2]) == set(conf[1][:2]) == expect)), f"conf={conf}"
def test_ClassificationInterpretation(learn):
interp = ClassificationInterpretation.from_learner(learn)
print(interp.confusion_matrix())
interp.plot_confusion_matrix()
plt.show()
print(interp.most_confused())
losses, idxs = interp.top_losses()
print([losses[:10], idxs[:10]])
interp.plot_top_losses(4)
plt.show()
def test_ClassificationInterpretation(learn):
this_tests(ClassificationInterpretation)
interp = ClassificationInterpretation.from_learner(learn)
assert isinstance(interp.confusion_matrix(), (np.ndarray))
assert interp.confusion_matrix().sum() == len(learn.data.valid_ds)
conf = interp.most_confused()
expect = {'3', '7'}
assert (len(conf) == 0 or
len(conf) == 1 and (set(conf[0][:2]) == expect) or
len(conf) == 2 and (set(conf[0][:2]) == set(conf[1][:2]) == expect)
), f"conf={conf}"
def test_interp(learn):
this_tests(ClassificationInterpretation.from_learner)
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses()
assert len(learn.data.valid_ds) == len(losses) == len(idxs)
plt.savefig(base_path / 'classification' / 'batch_example_valid.svg') learn = cnn_learner(data, arch, pretrained=True, metrics=[accuracy]) # learn.unfreeze() # learn.load(arch.__name__) # learn.lr_find() # learn.recorder.plot() # plt.show() cbs = [SaveModelCallback(learn, monitor='accuracy', name='best')] learn.fit_one_cycle(5, max_lr=1e-3, callbacks=cbs) learn.save(arch.__name__) learn.recorder.plot_losses() plt.savefig(base_path / 'classification' / 'loss.svg') learn.show_results(rows=20) plt.savefig(base_path / 'classification' / 'show_results.svg') preds, y, losses = learn.get_preds(with_loss=True) interpreter = ClassificationInterpretation(learn, preds, y, losses) interpreter.plot_confusion_matrix(normalize=True, figsize=(8, 8)) plt.savefig(base_path / 'classification' / 'cm.svg') interpreter.most_confused() interpreter.plot_top_losses(10, figsize=(10, 10)) plt.savefig(base_path / 'classification' / 'top_losses.svg')
def test_confusion_tabular(learn):
interp = ClassificationInterpretation.from_learner(learn)
assert isinstance(interp.confusion_matrix(), (np.ndarray))
assert interp.confusion_matrix().sum() == len(learn.data.valid_ds)
this_tests(interp.confusion_matrix)
def _learner_interpret(learn:Learner, ds_type:DatasetType=DatasetType.Valid, tta=False):
"Create a `ClassificationInterpretation` object from `learner` on `ds_type` with `tta`."
return ClassificationInterpretation.from_learner(learn, ds_type=ds_type, tta=tta)
def test_interp(learn):
this_tests(ClassificationInterpretation.from_learner)
interp = ClassificationInterpretation.from_learner(learn)
losses,idxs = interp.top_losses()
assert len(learn.data.valid_ds)==len(losses)==len(idxs)
def _learner_interpret(learn: Learner,
ds_type: DatasetType = DatasetType.Valid):
"Create a 'ClassificationInterpretation' object from 'learner' on 'ds_type'."
return ClassificationInterpretation.from_learner(learn, ds_type=ds_type)
def test_confusion_tabular(learn, out=True):
interp = ClassificationInterpretation.from_learner(learn)
assert isinstance(interp.confusion_matrix(), (np.ndarray))
print(interp.confusion_matrix())
interp.plot_confusion_matrix()
if out: plt.show()
def test_confusion_tabular(learn):
interp = ClassificationInterpretation.from_learner(learn)
assert isinstance(interp.confusion_matrix(), (np.ndarray))
assert interp.confusion_matrix().sum() == len(learn.data.valid_ds)
this_tests(interp.confusion_matrix)
