def test_manhattan_cols_normalized(self):
assert_almost_equal = np.testing.assert_almost_equal
data = self.cont_data
dist = distance.Manhattan(data, axis=0, normalize=True)
assert_almost_equal(
dist,
[[0, 4.5833333, 2],
[4.5833333, 0, 4.25],
[2, 4.25, 0]])
with data.unlocked():
data.X[1, 1] = np.nan
dist = distance.Manhattan(data, axis=0, normalize=True)
assert_almost_equal(
dist,
[[0, 4.6666667, 2],
[4.6666667, 0, 4],
[2, 4, 0]])
with data.unlocked():
data.X[1, 0] = np.nan
dist = distance.Manhattan(data, axis=0, normalize=True)
assert_almost_equal(
dist,
[[0, 5.5, 4],
[5.5, 0, 4],
[4, 4, 0]])
def test_manhattan_cols(self):
assert_almost_equal = np.testing.assert_almost_equal
data = self.cont_data
dist = distance.Manhattan(data, axis=0, normalize=False)
assert_almost_equal(
dist,
[[0, 20, 7],
[20, 0, 15],
[7, 15, 0]])
with data.unlocked():
data.X[1, 1] = np.nan
dist = distance.Manhattan(data, axis=0, normalize=False)
assert_almost_equal(
dist,
[[0, 19, 7],
[19, 0, 14],
[7, 14, 0]])
with data.unlocked():
data.X[1, 0] = np.nan
dist = distance.Manhattan(data, axis=0, normalize=False)
assert_almost_equal(
dist,
[[0, 17, 9],
[17, 0, 14],
[9, 14, 0]])
def test_manhattan_cont(self):
assert_almost_equal = np.testing.assert_almost_equal
data = self.cont_data
dist = distance.Manhattan(data, axis=1, normalize=False)
assert_almost_equal(
dist,
[[0, 7, 6, 9],
[7, 0, 5, 16],
[6, 5, 0, 13],
[9, 16, 13, 0]])
with data.unlocked():
data.X[1, 0] = np.nan
dist = distance.Manhattan(data, axis=1, normalize=False)
assert_almost_equal(
dist,
[[0, 7, 6, 9],
[7, 0, 3, 14],
[6, 3, 0, 13],
[9, 14, 13, 0]])
with data.unlocked():
data.X[0, 0] = np.nan
dist = distance.Manhattan(data, axis=1, normalize=False)
assert_almost_equal(
dist,
[[0, 10, 10, 8],
[10, 0, 7, 13],
[10, 7, 0, 13],
[8, 13, 13, 0]])
def test_manhattan_cont_normalized(self):
assert_almost_equal = np.testing.assert_almost_equal
data = self.cont_data
model = distance.Manhattan(axis=1, normalize=True).fit(data)
assert_almost_equal(model.medians, [1.5, 4.5, 1.5])
assert_almost_equal(model.mads, [1.5, 2, 1])
assert_almost_equal(model.dist_missing2_cont, np.ones(3))
dist = model(data)
assert_almost_equal(
dist,
[
[0, 2.416666667, 1.833333333, 3],
[2.416666667, 0, 1.75, 5.416666667],
[1.833333333, 1.75, 0, 4.166666667],
[3, 5.416666667, 4.166666667, 0],
],
)
dist = distance.Manhattan(data, axis=1, normalize=True)
assert_almost_equal(
dist,
[
[0, 2.416666667, 1.833333333, 3],
[2.416666667, 0, 1.75, 5.416666667],
[1.833333333, 1.75, 0, 4.166666667],
[3, 5.416666667, 4.166666667, 0],
],
)
data.X[1, 0] = np.nan
model = distance.Manhattan(axis=1, normalize=True).fit(data)
assert_almost_equal(model.medians, [2, 4.5, 1.5])
assert_almost_equal(model.mads, [1, 2, 1])
dist = model(data)
assert_almost_equal(
dist,
[[0, 2.75, 2, 4], [2.75, 0, 1.25, 5.75], [2, 1.25, 0, 5],
[4, 5.75, 5, 0]],
)
data.X[0, 0] = np.nan
model = distance.Manhattan(axis=1, normalize=True).fit(data)
assert_almost_equal(model.medians, [4.5, 4.5, 1.5])
assert_almost_equal(model.mads, [2.5, 2, 1])
dist = model(data)
assert_almost_equal(
dist,
[
[0, 2.75, 2.5, 2],
[2.75, 0, 1.75, 3.75],
[2.5, 1.75, 0, 3.5],
[2, 3.75, 3.5, 0],
],
)
def test_manhattan_no_data(self):
np.testing.assert_almost_equal(
distance.Manhattan(Table(self.domain)),
np.zeros((0, 0)))
np.testing.assert_almost_equal(
distance.Manhattan(self.mixed_data, Table(self.domain)),
np.zeros((3, 0)))
np.testing.assert_almost_equal(
distance.Manhattan(Table(self.domain), self.mixed_data),
np.zeros((0, 3)))
self.assertRaises(
ValueError,
distance.Manhattan, Table(self.cont_domain), axis=0, normalize=True)
def test_manhattan_disc(self):
assert_almost_equal = np.testing.assert_almost_equal
data = self.disc_data
model = distance.Manhattan().fit(data)
assert_almost_equal(model.dist_missing_disc,
[[1 / 3, 2 / 3, 1, 1], [2 / 3, 2 / 3, 1, 2 / 3],
[2 / 3, 1 / 3, 1, 1]])
assert_almost_equal(model.dist_missing2_disc,
[1 - 5 / 9, 1 - 3 / 9, 1 - 5 / 9])
dist = model(data)
assert_almost_equal(dist, [[0, 2, 3], [2, 0, 2], [3, 2, 0]])
with data.unlocked():
data.X[1, 0] = np.nan
model = distance.Manhattan().fit(data)
assert_almost_equal(model.dist_missing_disc,
[[1 / 2, 1 / 2, 1, 1], [2 / 3, 2 / 3, 1, 2 / 3],
[2 / 3, 1 / 3, 1, 1]])
assert_almost_equal(model.dist_missing2_disc,
[1 - 2 / 4, 1 - 3 / 9, 1 - 5 / 9])
dist = model(data)
assert_almost_equal(dist, [[0, 2.5, 3], [2.5, 0, 1.5], [3, 1.5, 0]])
with data.unlocked():
data.X[0, 0] = np.nan
model = distance.Manhattan().fit(data)
assert_almost_equal(
model.dist_missing_disc,
[[1, 0, 1, 1], [2 / 3, 2 / 3, 1, 2 / 3], [2 / 3, 1 / 3, 1, 1]])
assert_almost_equal(model.dist_missing2_disc,
[1 - 1, 1 - 3 / 9, 1 - 5 / 9])
dist = model(data)
assert_almost_equal(dist, [[0, 2, 2], [2, 0, 1], [2, 1, 0]])
data = self.disc_data4
with data.unlocked():
data.X[:2, 0] = np.nan
model = distance.Manhattan().fit(data)
assert_almost_equal(model.dist_missing_disc,
[[1 / 2, 1 / 2, 1, 1], [3 / 4, 2 / 4, 1, 3 / 4],
[3 / 4, 1 / 4, 1, 1]])
assert_almost_equal(model.dist_missing2_disc,
[1 - 2 / 4, 1 - 6 / 16, 1 - 10 / 16])
dist = model(data)
assert_almost_equal(dist, [[0, 2.5, 2.5, 2.5], [2.5, 0, 0.5, 1.5],
[2.5, 0.5, 0, 2], [2.5, 1.5, 2, 0]])
def test_manhattan_mixed_cols(self):
self.assertRaises(ValueError,
distance.Manhattan,
self.mixed_data,
axis=0)
self.assertRaises(ValueError,
distance.Manhattan(axis=0).fit, self.mixed_data)
def test_two_tables(self):
assert_almost_equal = np.testing.assert_almost_equal
dist = distance.Manhattan(self.cont_data,
self.cont_data2,
normalize=True)
assert_almost_equal(dist, [[1.3333333, 0.25], [3.75, 2.6666667],
[2.5, 2.0833333], [1.6666667, 2.75]])
model = distance.Manhattan(normalize=True).fit(self.cont_data)
dist = model(self.cont_data, self.cont_data2)
assert_almost_equal(dist, [[1.3333333, 0.25], [3.75, 2.6666667],
[2.5, 2.0833333], [1.6666667, 2.75]])
dist = model(self.cont_data2)
assert_almost_equal(dist, [[0, 1.083333333], [1.083333333, 0]])
def test_manhattan_mixed(self):
assert_almost_equal = np.testing.assert_almost_equal
data = self.mixed_data
data.X[2, 0] = 2 # prevent mads[0] = 0
model = distance.Manhattan(axis=1, normalize=True).fit(data)
assert_almost_equal(model.medians, [1, 3, 1])
assert_almost_equal(model.mads, [1, 2, 1])
assert_almost_equal(model.dist_missing_disc,
[[1 / 3, 2 / 3, 1, 1], [2 / 3, 2 / 3, 1, 2 / 3],
[2 / 3, 1 / 3, 1, 1]])
assert_almost_equal(model.dist_missing2_disc,
[1 - 5 / 9, 1 - 3 / 9, 1 - 5 / 9])
dist = model(data)
assert_almost_equal(dist, [[0, 4.5, 4.5], [4.5, 0, 5], [4.5, 5, 0]])
print "Count of documents in Reuters dataset: " + str(numDocs) + "\n"
print "1. Constructing Distance Matrices\n"
starter = time.time()
constructorEuclidean = distance.Euclidean()
EuclideanDistanceMat = distance.distance_matrix(
data, distance_constructor=constructorEuclidean)
euclidean_hierarchical_clustering = clustering.hierarchical.HierarchicalClustering(
)
euclidean_hierarchical_clustering.linkage = clustering.hierarchical.AVERAGE
euclideanRoot = euclidean_hierarchical_clustering(EuclideanDistanceMat)
ender = time.time()
timer = ender - starter
starter1 = time.time()
constructorManhattan = distance.Manhattan()
ManhattanDistanceMat = distance.distance_matrix(
data, distance_constructor=constructorManhattan)
manhattan_hierarchical_clustering = clustering.hierarchical.HierarchicalClustering(
)
manhattan_hierarchical_clustering.linkage = clustering.hierarchical.AVERAGE
manhattanRoot = manhattan_hierarchical_clustering(ManhattanDistanceMat)
ender1 = time.time()
timer1 = ender1 - starter1
print "2. Time: Hierarchical clustering: "
print "With Euclidean distance = " + str(timer) + "sec"
print "With Manhattan distance = " + str(timer1) + "sec\n"
euclideanRoot.mapping.objects = data
manhattanRoot.mapping.objects = data
import Orange.data
import Orange.misc
from Orange.widgets import widget, gui, settings
from Orange import distance
_METRICS = [
("Euclidean", distance.Euclidean()),
("Manhattan", distance.Manhattan()),
("Cosine", distance.Cosine()),
("Jaccard", distance.Jaccard()),
("Mahalanobis", distance.Mahalanobis()),
("Spearman", distance.SpearmanR()),
("Spearman absolute", distance.SpearmanRAbsolute()),
("Pearson", distance.PearsonR()),
("Pearson absolute", distance.PearsonRAbsolute()),
]
class OWDistances(widget.OWWidget):
name = "Distances"
description = "Compute a matrix of pairwise distances."
icon = "icons/Distance.svg"
inputs = [("Data", Orange.data.Table, "set_data")]
outputs = [("Distances", Orange.misc.DistMatrix)]
axis = settings.Setting(0)
metric_idx = settings.Setting(0)
autocommit = settings.Setting(False)
want_main_area = False
