class SDML:
def __init__(self):
self.metric_model = SDML_Supervised(num_constraints=200)
self.X_tr = None
self.y_train = None
self.X_te = None
def fit(self, X_tr, y_train):
"""Fits the model to the prescribed data."""
self.X_tr = X_tr
self.y_train = y_train
return self.metric_model.fit(X_tr, y_train)
def transform(self, X):
"""Transforms the test data according to the model"""
return self.metric_model.transform(X)
def predict_proba(self, X_te):
"""Predicts the probabilities of each of the test samples"""
test_samples = X_te.shape[0]
self.X_tr = self.transform(self.X_tr)
clf = NearestCentroid()
clf.fit(self.X_tr, self.y_train)
centroids = clf.centroids_
probabilities = np.zeros((test_samples, centroids.shape[0]))
for sample in xrange(test_samples):
probabilities[sample] = sk_nearest_neighbour_proba(
centroids, X_te[sample, :])
return probabilities
def test_iris(self):
# Note: this is a flaky test, which fails for certain seeds.
# TODO: un-flake it!
rs = np.random.RandomState(5555)
sdml = SDML_Supervised(num_constraints=1500)
sdml.fit(self.iris_points, self.iris_labels, random_state=rs)
csep = class_separation(sdml.transform(), self.iris_labels)
self.assertLess(csep, 0.25)
def test_iris(self):
# Note: this is a flaky test, which fails for certain seeds.
# TODO: un-flake it!
rs = np.random.RandomState(5555)
sdml = SDML_Supervised(num_constraints=1500)
sdml.fit(self.iris_points, self.iris_labels, random_state=rs)
csep = class_separation(sdml.transform(), self.iris_labels)
self.assertLess(csep, 0.25)
def test_sdml_supervised(self):
seed = np.random.RandomState(1234)
sdml = SDML_Supervised(num_constraints=1500)
sdml.fit(self.X, self.y, random_state=seed)
res_1 = sdml.transform()
seed = np.random.RandomState(1234)
sdml = SDML_Supervised(num_constraints=1500)
res_2 = sdml.fit_transform(self.X, self.y, random_state=seed)
assert_array_almost_equal(res_1, res_2)
def test_sdml_supervised(self):
seed = np.random.RandomState(1234)
sdml = SDML_Supervised(n_constraints=1500, balance_param=1e-5,
prior='identity', random_state=seed)
sdml.fit(self.X, self.y)
res_1 = sdml.transform(self.X)
seed = np.random.RandomState(1234)
sdml = SDML_Supervised(n_constraints=1500, balance_param=1e-5,
prior='identity', random_state=seed)
res_2 = sdml.fit_transform(self.X, self.y)
assert_array_almost_equal(res_1, res_2)
def test_sdml_supervised(self):
seed = np.random.RandomState(1234)
sdml = SDML_Supervised(num_constraints=1500, balance_param=1e-5,
use_cov=False)
sdml.fit(self.X, self.y, random_state=seed)
res_1 = sdml.transform(self.X)
seed = np.random.RandomState(1234)
sdml = SDML_Supervised(num_constraints=1500, balance_param=1e-5,
use_cov=False)
res_2 = sdml.fit_transform(self.X, self.y, random_state=seed)
assert_array_almost_equal(res_1, res_2)
