def test_finite_differences(self):
"""Test gradient of loss function
Assert that the gradient is almost equal to its finite differences
approximation.
"""
# Initialize the transformation `M`, as well as `X`, and `y` and `MLKR`
X, y = make_regression(n_features=4, random_state=1, n_samples=20)
X, y = check_X_y(X, y)
M = np.random.randn(2, X.shape[1])
mlkr = MLKR()
mlkr.n_iter_ = 0
def fun(M):
return mlkr._loss(M, X, y)[0]
def grad_fn(M):
return mlkr._loss(M, X, y)[1].ravel()
# compute relative error
rel_diff = check_grad(fun, grad_fn, M.ravel()) / np.linalg.norm(grad_fn(M))
np.testing.assert_almost_equal(rel_diff, 0.)
def test_mlkr(self):
mlkr = MLKR(num_dims=2)
mlkr.fit(self.X, self.y)
res_1 = mlkr.transform(self.X)
mlkr = MLKR(num_dims=2)
res_2 = mlkr.fit_transform(self.X, self.y)
assert_array_almost_equal(res_1, res_2)
def test_iris(self): mlkr = MLKR() mlkr.fit(self.iris_points, self.iris_labels) csep = class_separation(mlkr.transform(self.iris_points), self.iris_labels) self.assertLess(csep, 0.25)
[learner for (learner, _) in pairs_learners]))
classifiers = [(Covariance(), build_classification),
(LFDA(), build_classification), (LMNN(), build_classification),
(NCA(), build_classification), (RCA(), build_classification),
(ITML_Supervised(max_iter=5), build_classification),
(LSML_Supervised(), build_classification),
(MMC_Supervised(max_iter=5), build_classification),
(RCA_Supervised(num_chunks=5), build_classification),
(SDML_Supervised(prior='identity',
balance_param=1e-5), build_classification)]
ids_classifiers = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in classifiers]))
regressors = [(MLKR(init='pca'), build_regression)]
ids_regressors = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in regressors]))
WeaklySupervisedClasses = (_PairsClassifierMixin, _QuadrupletsClassifierMixin)
tuples_learners = pairs_learners + quadruplets_learners
ids_tuples_learners = ids_pairs_learners + ids_quadruplets_learners
supervised_learners = classifiers + regressors
ids_supervised_learners = ids_classifiers + ids_regressors
metric_learners = tuples_learners + supervised_learners
ids_metric_learners = ids_tuples_learners + ids_supervised_learners
def test_mlkr(self): mlkr = MLKR(num_dims=2) mlkr.fit(self.X, self.y) L = mlkr.transformer_ assert_array_almost_equal(L.T.dot(L), mlkr.get_mahalanobis_matrix())
[learner for (learner, _) in pairs_learners]))
classifiers = [(Covariance(), build_classification),
(LFDA(), build_classification), (LMNN(), build_classification),
(NCA(), build_classification), (RCA(), build_classification),
(ITML_Supervised(max_iter=5), build_classification),
(LSML_Supervised(), build_classification),
(MMC_Supervised(max_iter=5), build_classification),
(RCA_Supervised(num_chunks=10), build_classification),
(SDML_Supervised(use_cov=False,
balance_param=1e-5), build_classification)]
ids_classifiers = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in classifiers]))
regressors = [(MLKR(), build_regression)]
ids_regressors = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in regressors]))
WeaklySupervisedClasses = (_PairsClassifierMixin, _QuadrupletsClassifierMixin)
tuples_learners = pairs_learners + quadruplets_learners
ids_tuples_learners = ids_pairs_learners + ids_quadruplets_learners
supervised_learners = classifiers + regressors
ids_supervised_learners = ids_classifiers + ids_regressors
metric_learners = tuples_learners + supervised_learners
ids_metric_learners = ids_tuples_learners + ids_supervised_learners
def test_mlkr(self): mlkr = MLKR(n_components=2) mlkr.fit(self.X, self.y) L = mlkr.components_ assert_array_almost_equal(L.T.dot(L), mlkr.get_mahalanobis_matrix())
def test_iris(self): mlkr = MLKR() mlkr.fit(self.iris_points, self.iris_labels) csep = class_separation(mlkr.transform(), self.iris_labels) self.assertLess(csep, 0.25)
def test_mlkr(self):
check_estimator(MLKR())
class MetricLearner:
def __init__(self):
self._learner = None
self._trained = ObservableObject(False)
def train_metric_learner_supervised(self, landmark_fin_ok,
landmark_fin_bad, path):
landmarks = np.concatenate((landmark_fin_ok, landmark_fin_bad))
landmarks = landmarks.reshape(landmarks.shape[0],
landmarks.shape[1] * landmarks.shape[2])
labels = []
for i in range(len(landmark_fin_ok)):
labels.append(1)
for i in range(len(landmark_fin_bad)):
labels.append(-1)
self._learner = MLKR(preprocessor=landmarks)
self._learner.fit(landmarks, labels)
self.save_learner(path)
def train_metric_learner_supervised_all_landmarks(self, landmark_init_ok,
landmark_fin_ok,
landmark_fin_bad, path):
landmarks = np.concatenate(
(landmark_init_ok, landmark_fin_ok, landmark_fin_bad))
landmarks = landmarks.reshape(landmarks.shape[0],
landmarks.shape[1] * landmarks.shape[2])
labels = []
for i in range(len(landmark_init_ok)):
labels.append(0)
for i in range(len(landmark_fin_ok)):
labels.append(1)
for i in range(len(landmark_fin_bad)):
labels.append(-1)
# Utilizzo anche i video corretti degli altri esercizi
for categoria in os.listdir("Exercises"):
cat_path = Path("Exercises/" + categoria)
if cat_path.is_dir():
for name in os.listdir(cat_path):
ex_path = Path("Exercises/" + categoria + "/" + name)
if ex_path.is_dir() and ex_path != Path(path):
pathCorrect = Path("Exercises/" + categoria + "/" +
name + "/Correct")
for file in os.listdir(pathCorrect):
if file.endswith(".npy"):
pathFile = str(pathCorrect) + "\\" + file
landmark = np.load(
pathFile,
allow_pickle=True)[38].reshape(1, 68 * 2)
landmarks = np.concatenate(
(landmarks, landmark))
labels.append(-1)
self._learner = MLKR(preprocessor=landmarks)
self._learner.fit(landmarks, labels)
self.save_learner(path)
def get_learner(self):
return self._learner
def save_learner(self, path):
joblib.dump(self._learner, path + "TreanedModel.sav")
def load_learner(self, path):
self._learner = joblib.load(path + "TreanedModel.sav")
def remove_learner(self, path):
os.remove(path + ".sav")
def test_mlkr(self): mlkr = MLKR(num_dims=2) mlkr.fit(self.X, self.y) L = mlkr.transformer_ assert_array_almost_equal(L.T.dot(L), mlkr.metric())
def mlkr(data, label, dim):
mlkr = MLKR(num_dims=dim)
mlkr.fit(data, label)
result = mlkr.transform(data)
return result
def test_mlkr(self): mlkr = MLKR(num_dims=2) mlkr.fit(self.X, self.y) L = mlkr.transformer_ assert_array_almost_equal(L.T.dot(L), mlkr.get_mahalanobis_matrix())
def test_mlkr(self): mlkr = MLKR(num_dims=2) mlkr.fit(self.X, self.y) L = mlkr.transformer() assert_array_almost_equal(L.T.dot(L), mlkr.metric())
