def test_can_pass_callbacks_to_tsne_object(self):
callback = MagicMock()
callback2 = MagicMock()
# We don't want individual callbacks to be iterable
del callback.__iter__
del callback2.__iter__
# Should be able to pass a single callback
TSNE(callbacks=callback,
callbacks_every_iters=1,
early_exaggeration_iter=0,
n_iter=1).fit(self.x)
self.assertEqual(callback.call_count, 1)
# Should be able to pass a list callbacks
callback.reset_mock()
TSNE(callbacks=[callback],
callbacks_every_iters=1,
early_exaggeration_iter=0,
n_iter=1).fit(self.x)
self.assertEqual(callback.call_count, 1)
# Should be able to change the callback on the object
callback.reset_mock()
tsne = TSNE(callbacks=callback,
callbacks_every_iters=1,
early_exaggeration_iter=0,
n_iter=1)
tsne.callbacks = callback2
tsne.fit(self.x)
callback.assert_not_called()
self.assertEqual(callback2.call_count, 1)
def test_same_results_on_fixed_random_state_pca_init(self):
"""Results should be exactly the same if we provide a random state."""
tsne1 = TSNE(random_state=1, initialization='pca')
embedding1 = tsne1.fit(self.x)
tsne2 = TSNE(random_state=1, initialization='pca')
embedding2 = tsne2.fit(self.x)
np.testing.assert_array_equal(
embedding1, embedding2,
'Same random state produced different initial embeddings')
def test_unfitted_pca_model(self):
"""Using PCA initialization in `transform` should fail when the initial
embedding was initialized with PCA."""
tsne = TSNE(initialization='random')
embedding = tsne.fit(self.x)
# Transforming using `pca` init on embedding that did not use
# `pca` init did not fail
with self.assertRaises(AssertionError):
embedding.transform(self.x_test, initialization='pca')
def transform(n_jobs=4, grad='bh', neighbors='approx'):
# iris = datasets.load_iris()
# x, y = iris['data'], iris['target']
x, y = get_mnist(20000)
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.33,
random_state=42)
tsne = TSNE(
n_components=2,
perplexity=30,
learning_rate=100,
early_exaggeration=12,
n_jobs=n_jobs,
theta=0.5,
initialization='random',
metric='euclidean',
n_iter=750,
early_exaggeration_iter=250,
neighbors=neighbors,
negative_gradient_method=grad,
min_num_intervals=10,
ints_in_interval=2,
late_exaggeration_iter=0,
late_exaggeration=4,
callbacks=[ErrorLogger()],
)
start = time.time()
embedding = tsne.fit(x_train)
print('tsne train', time.time() - start)
plt.subplot(121)
plot(embedding, y_train, show=False, ms=3)
start = time.time()
partial_embedding = embedding.transform(x_test, perplexity=20)
# partial_embedding = embedding.get_partial_embedding_for(
# x_test, perplexity=10, initialization='random')
# partial_embedding.optimize(200, exaggeration=2, inplace=True, momentum=0.1)
print('tsne transform', time.time() - start)
plt.subplot(122)
plot(embedding, y_train, show=False, ms=3, alpha=0.25)
plt.gca().set_color_cycle(None)
plot(partial_embedding, y_test, show=False, ms=3, alpha=0.8)
plt.show()
def test_partial_embedding_optimize(self, param_name, param_value,
gradient_descent):
# type: (str, Any, MagicMock) -> None
# Make sure mock still conforms to signature
gradient_descent.return_value = (1, MagicMock())
# Perform initial embedding - this is tested above
tsne = TSNE()
embedding = tsne.fit(self.x)
gradient_descent.reset_mock()
# `optimize` requires us to specify the `n_iter`
params = {'n_iter': 50, param_name: param_value}
partial_embedding = embedding.prepare_partial(self.x_test)
partial_embedding.optimize(**params, inplace=True)
self.assertEqual(1, gradient_descent.call_count)
check_call_contains_kwargs(gradient_descent.mock_calls[0], params)
def test_embedding_transform(self, param_name, param_value,
gradient_descent):
# type: (str, Any, MagicMock) -> None
# Make sure mock still conforms to signature
gradient_descent.return_value = (1, MagicMock())
# Perform initial embedding - this is tested above
tsne = TSNE()
embedding = tsne.fit(self.x)
gradient_descent.reset_mock()
embedding.transform(self.x_test, **{param_name: param_value})
# Early exaggeration training loop
if param_name == 'early_exaggeration_iter':
check_param_name = 'n_iter'
call_idx = 0
elif param_name == 'early_exaggeration':
check_param_name = 'exaggeration'
call_idx = 0
elif param_name == 'initial_momentum':
check_param_name = 'momentum'
call_idx = 0
# Main training loop
elif param_name == 'n_iter':
check_param_name = param_name
call_idx = 1
elif param_name == 'final_momentum':
check_param_name = 'momentum'
call_idx = 1
# If general parameter, should be applied to every call
else:
check_param_name = param_name
call_idx = 0
self.assertEqual(2, gradient_descent.call_count)
check_call_contains_kwargs(gradient_descent.mock_calls[call_idx],
{check_param_name: param_value})
def run(perplexity=30, learning_rate=100, n_jobs=4):
x, y = get_mouse_60k()
# x, y = get_fashion_mnist()
angle = 0.5
ee = 12
metric = 'euclidean'
print(x.shape)
start = time.time()
tsne = TSNE(
perplexity=perplexity,
learning_rate=learning_rate,
early_exaggeration=ee,
n_jobs=n_jobs,
theta=angle,
initialization='random',
metric=metric,
n_components=2,
n_iter=750,
early_exaggeration_iter=250,
neighbors='approx',
negative_gradient_method='fft',
min_num_intervals=10,
ints_in_interval=1,
late_exaggeration_iter=0,
late_exaggeration=2.,
callbacks=ErrorLogger(),
)
# x = PCA(n_components=50).fit_transform(x)
embedding = tsne.fit(x)
print('-' * 80)
print('tsne', time.time() - start)
plt.title('tsne')
plot(embedding, y)
return
x = np.ascontiguousarray(x.astype(np.float64))
from fitsne import FItSNE
start = time.time()
embedding = FItSNE(
x,
2,
perplexity=perplexity,
stop_lying_iter=250,
ann_not_vptree=True,
early_exag_coeff=ee,
nthreads=n_jobs,
theta=angle,
)
print('-' * 80)
print('fft interp %.4f' % (time.time() - start))
plt.title('fft interp')
plot(embedding, y)
plt.show()
return
init = PCA(n_components=2).fit_transform(x)
start = time.time()
embedding = MulticoreTSNE(early_exaggeration=ee,
learning_rate=learning_rate,
perplexity=perplexity,
n_jobs=n_jobs,
cheat_metric=False,
angle=angle,
init=init,
metric=metric,
verbose=True).fit_transform(x)
print('-' * 80)
print('mctsne', time.time() - start)
plt.title('mctsne')
plot(embedding, y)
plt.show()
start = time.time()
embedding = SKLTSNE(
early_exaggeration=ee,
learning_rate=learning_rate,
angle=angle,
perplexity=perplexity,
init='pca',
metric=metric,
).fit_transform(x)
print('-' * 80)
print('sklearn', time.time() - start)
plt.title('sklearn')
plot(embedding, y)
plt.show()
def test_fitted_pca_model(self):
"""Using PCA initialization in `transform` should work when the initial
embedding was initialized with PCA."""
tsne = TSNE(initialization='pca')
embedding = tsne.fit(self.x)
embedding.transform(self.x_test, initialization='pca')
