def bench_float(m1=200, m2=200, rseed=0):
print 79 * '_'
print " real valued distance metrics"
print
np.random.seed(rseed)
X1 = np.random.random((m1, DTEST))
X2 = np.random.random((m2, DTEST))
for (metric, argdict) in METRIC_DICT.iteritems():
keys = argdict.keys()
for vals in itertools.product(*argdict.values()):
kwargs = dict(zip(keys, vals))
print metric, param_info(kwargs)
t0 = time()
try:
dist_metric = DistanceMetric(metric, **kwargs)
Yc1 = dist_metric.cdist(X1, X2)
except Exception as inst:
print " >>>>>>>>>> error in pyDistances cdist:"
print " ", inst
t1 = time()
try:
Yc2 = cdist(X1, X2, metric, **kwargs)
except Exception as inst:
print " >>>>>>>>>> error in scipy cdist:"
print " ", inst
t2 = time()
try:
dist_metric = DistanceMetric(metric, **kwargs)
Yp1 = dist_metric.pdist(X1)
except Exception as inst:
print " >>>>>>>>>> error in pyDistances pdist:"
print " ", inst
t3 = time()
try:
Yp2 = pdist(X1, metric, **kwargs)
except Exception as inst:
print " >>>>>>>>>> error in scipy pdist:"
print " ", inst
t4 = time()
if not np.allclose(Yc1, Yc2):
print " >>>>>>>>>> FAIL: cdist results don't match"
if not np.allclose(Yp1, Yp2):
print " >>>>>>>>>> FAIL: pdist results don't match"
print " - pyDistances: c: %.4f sec p: %.4f sec" % (t1 - t0,
t3 - t2)
print " - scipy: c: %.4f sec p: %.4f sec" % (t2 - t1,
t4 - t3)
print ''
def bench_float(m1=200, m2=200, rseed=0):
print 79 * '_'
print " real valued distance metrics"
print
np.random.seed(rseed)
X1 = np.random.random((m1, DTEST))
X2 = np.random.random((m2, DTEST))
for (metric, argdict) in METRIC_DICT.iteritems():
keys = argdict.keys()
for vals in itertools.product(*argdict.values()):
kwargs = dict(zip(keys, vals))
print metric, param_info(kwargs)
t0 = time()
try:
dist_metric = DistanceMetric(metric, **kwargs)
Yc1 = dist_metric.cdist(X1, X2)
except Exception as inst:
print " >>>>>>>>>> error in pyDistances cdist:"
print " ", inst
t1 = time()
try:
Yc2 = cdist(X1, X2, metric, **kwargs)
except Exception as inst:
print " >>>>>>>>>> error in scipy cdist:"
print " ", inst
t2 = time()
try:
dist_metric = DistanceMetric(metric, **kwargs)
Yp1 = dist_metric.pdist(X1)
except Exception as inst:
print " >>>>>>>>>> error in pyDistances pdist:"
print " ", inst
t3 = time()
try:
Yp2 = pdist(X1, metric, **kwargs)
except Exception as inst:
print " >>>>>>>>>> error in scipy pdist:"
print " ", inst
t4 = time()
if not np.allclose(Yc1, Yc2):
print " >>>>>>>>>> FAIL: cdist results don't match"
if not np.allclose(Yp1, Yp2):
print " >>>>>>>>>> FAIL: pdist results don't match"
print " - pyDistances: c: %.4f sec p: %.4f sec" % (t1 - t0,
t3 - t2)
print " - scipy: c: %.4f sec p: %.4f sec" % (t2 - t1,
t4 - t3)
print ''
def _check_metrics_float(self, k, metric, kwargs):
bt = BallTree(self.X, metric=metric, **kwargs)
dist_bt, ind_bt = bt.query(self.X, k=k)
dm = DistanceMetric(metric=metric, **kwargs)
D = dm.pdist(self.X, squareform=True)
ind_dm = np.argsort(D, 1)[:, :k]
dist_dm = D[np.arange(self.X.shape[0])[:, None], ind_dm]
# we don't check the indices here because if there is a tie for
# nearest neighbor, then the test may fail. Distances will reflect
# whether the search was successful
assert_array_almost_equal(dist_bt, dist_dm)
def _check_metrics_float(self, k, metric, kwargs):
bt = BallTree(self.X, metric=metric, **kwargs)
dist_bt, ind_bt = bt.query(self.X, k=k)
dm = DistanceMetric(metric=metric, **kwargs)
D = dm.pdist(self.X, squareform=True)
ind_dm = np.argsort(D, 1)[:, :k]
dist_dm = D[np.arange(self.X.shape[0])[:, None], ind_dm]
# we don't check the indices here because if there is a tie for
# nearest neighbor, then the test may fail. Distances will reflect
# whether the search was successful
assert_array_almost_equal(dist_bt, dist_dm)
def test_query_radius_count(self):
# center the data
X = 2 * self.X - 1
dm = DistanceMetric()
D = dm.pdist(X, squareform=True)
r = np.mean(D)
bt = BallTree(X)
count1 = bt.query_radius(X, r, count_only=True)
count2 = (D <= r).sum(1)
assert_array_almost_equal(count1, count2)
def test_query_radius_count(self):
# center the data
X = 2 * self.X - 1
dm = DistanceMetric()
D = dm.pdist(X, squareform=True)
r = np.mean(D)
bt = BallTree(X)
count1 = bt.query_radius(X, r, count_only=True)
count2 = (D <= r).sum(1)
assert_array_almost_equal(count1, count2)
