def runMMC():
# Run MMC
from metric_learn import MMC
"""
Learn MMC (Mahalanobis Metrics for Clustering) Model
"""
mmc = MMC()
mmc.fit(pairs, y) # learn the MMC model
print("Mahalanobis Matrix : ", mmc.get_mahalanobis_matrix())
def test_iris(self):
# Generate full set of constraints for comparison with reference
# implementation
mask = self.iris_labels[None] == self.iris_labels[:, None]
a, b = np.nonzero(np.triu(mask, k=1))
c, d = np.nonzero(np.triu(~mask, k=1))
# Full metric
n_features = self.iris_points.shape[1]
mmc = MMC(convergence_threshold=0.01, init=np.eye(n_features) / 10)
mmc.fit(*wrap_pairs(self.iris_points, [a, b, c, d]))
expected = [[+0.000514, +0.000868, -0.001195, -0.001703],
[+0.000868, +0.001468, -0.002021, -0.002879],
[-0.001195, -0.002021, +0.002782, +0.003964],
[-0.001703, -0.002879, +0.003964, +0.005648]]
assert_array_almost_equal(expected,
mmc.get_mahalanobis_matrix(),
decimal=6)
# Diagonal metric
mmc = MMC(diagonal=True)
mmc.fit(*wrap_pairs(self.iris_points, [a, b, c, d]))
expected = [0, 0, 1.210220, 1.228596]
assert_array_almost_equal(np.diag(expected),
mmc.get_mahalanobis_matrix(),
decimal=6)
# Supervised Full
mmc = MMC_Supervised()
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(self.iris_points),
self.iris_labels)
self.assertLess(csep, 0.15)
# Supervised Diagonal
mmc = MMC_Supervised(diagonal=True)
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(self.iris_points),
self.iris_labels)
self.assertLess(csep, 0.2)
def test_iris(self):
# Generate full set of constraints for comparison with reference implementation
n = self.iris_points.shape[0]
mask = (self.iris_labels[None] == self.iris_labels[:,None])
a, b = np.nonzero(np.triu(mask, k=1))
c, d = np.nonzero(np.triu(~mask, k=1))
# Full metric
mmc = MMC(convergence_threshold=0.01)
mmc.fit(*wrap_pairs(self.iris_points, [a,b,c,d]))
expected = [[+0.000514, +0.000868, -0.001195, -0.001703],
[+0.000868, +0.001468, -0.002021, -0.002879],
[-0.001195, -0.002021, +0.002782, +0.003964],
[-0.001703, -0.002879, +0.003964, +0.005648]]
assert_array_almost_equal(expected, mmc.get_mahalanobis_matrix(),
decimal=6)
# Diagonal metric
mmc = MMC(diagonal=True)
mmc.fit(*wrap_pairs(self.iris_points, [a,b,c,d]))
expected = [0, 0, 1.210220, 1.228596]
assert_array_almost_equal(np.diag(expected), mmc.get_mahalanobis_matrix(),
decimal=6)
# Supervised Full
mmc = MMC_Supervised()
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(self.iris_points), self.iris_labels)
self.assertLess(csep, 0.15)
# Supervised Diagonal
mmc = MMC_Supervised(diagonal=True)
mmc.fit(self.iris_points, self.iris_labels)
csep = class_separation(mmc.transform(self.iris_points), self.iris_labels)
self.assertLess(csep, 0.2)
# in this task we want points where the first feature is close to be closer to each other,
# no matter how close the second feature is
y = [1, 1, -1, -1]
"""
Learn MMC (Mahalanobis Metrics for Clustering) Model
"""
mmc = MMC()
mmc.fit(pairs, y) # learn the MMC model
"""
Return the decision function used to classify the pairs
"""
print("debug 1: ", mmc.decision_function(pairs))
"""
Returns a copy of the Mahalanobis matrix learned by the metric learner
"""
print("debug 2: ", mmc.get_mahalanobis_matrix())
"""
Returns a function that takes as input two 1D arrays and outputs the learned metric score on these two points.
"""
f = mmc.get_metric()
print("debug 3: ", f)
"""
Predicts the learned metric between input pairs
"""
example_pairs = [
[[1.2, 7.5], [1.3, 8.5]]
] #[1.2, 7.5] # error - ValueError: 3D array of formed tuples expected by MMC.
print("debug 4 : ", mmc.predict(example_pairs))
pairs.append(pair)
y.append(1)
for row2 in normalized_n_pairs.iterrows():
print('row2 : ', row2)
lon2 = row2[1]["lon_0"]
lat2 = row2[1]["lat_0"]
alt2 = row2[1]["properties_alt_m"]
gt_tuple2 = [lon2, lat2, alt2]
obs_lon2 = row2[1]["position::longitude_degrees"]
obs_lat2 = row2[1]["position::latitude_degrees"]
obs_alt2 = row2[1]["position::altitude_meters"]
obs_tuple2 = [obs_lon2, obs_lat2, obs_alt2]
pair2 = [gt_tuple2, obs_tuple2]
pairs.append(pair2)
y.append(-1)
print('debug : pairs >> ', pairs)
print('debug : y >> ', y)
# Run MMC
from metric_learn import MMC
"""
Learn MMC (Mahalanobis Metrics for Clustering) Model
"""
mmc = MMC()
mmc.fit(pairs, y) # learn the MMC model
print("Mahalanobis Matrix : ", mmc.get_mahalanobis_matrix())