Python ClassificationInterpretation Examples

Example #1

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')

Example #2

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}"

Example #3

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}"

Example #4

File: test_vision_train.py Project: tterava/fastai

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()

Example #5

File: test_vision_train.py Project: SiddharthTiwari/fastai

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}"

Example #6

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)

Example #7

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')

Example #8

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)

Example #9

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)

Example #10

File: test_vision_train.py Project: SiddharthTiwari/fastai

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)

Example #11

File: learner.py Project: nargroves/fastai_splunk

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)

Example #12

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()

Example #13

File: test_tabular_train.py Project: SiddharthTiwari/fastai

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)