def test_random_walk(self):
nsamples = 10
weights_hit = np.linspace(10, 1, nsamples)
weights_miss = weights_hit.copy()
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(nsamples, dtype=np.int)
membership = np.ones(nsamples, dtype=INT_DTYPE)
# test all combinations of membership
# with perfect enrichment (all hits before misses)
for i in xrange(1, nsamples):
membership[:] = 1
membership[i:] = 0
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == 1.0)
self.assertTrue(es_rank == (i - 1))
self.assertTrue(es_run[es_rank] == 1.0)
# test all combinations of membership
# with perfect negative enrichment (all misses before hits)
for i in xrange(nsamples - 1, 0, -1):
membership[:] = 1
membership[:i] = 0
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == -1.0)
self.assertTrue(es_rank == (i - 1))
self.assertTrue(es_run[es_rank] == -1.0)
def test_random_walk(self):
nsamples = 10
weights_hit = np.linspace(10, 1, nsamples)
weights_miss = weights_hit.copy()
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(nsamples, dtype=np.int)
membership = np.ones(nsamples, dtype=INT_DTYPE)
# test all combinations of membership
# with perfect enrichment (all hits before misses)
for i in xrange(1,nsamples):
membership[:] = 1
membership[i:] = 0
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == 1.0)
self.assertTrue(es_rank == (i-1))
self.assertTrue(es_run[es_rank] == 1.0)
# test all combinations of membership
# with perfect negative enrichment (all misses before hits)
for i in xrange(nsamples-1,0,-1):
membership[:] = 1
membership[:i] = 0
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == -1.0)
self.assertTrue(es_rank == (i-1))
self.assertTrue(es_run[es_rank] == -1.0)
def test_random_walk_boundary_cases(self):
nsamples = 100
# test all zeros
weights_hit = np.zeros(nsamples, dtype=np.float)
weights_miss = np.zeros(nsamples, dtype=np.float)
membership = np.zeros(nsamples, dtype=INT_DTYPE)
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(nsamples, dtype=np.int)
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == 0.0)
self.assertTrue(es_rank == 0.0)
self.assertTrue(es_run.shape[0] == nsamples)
# nonzero weights but empty membership
weights_hit = np.linspace(1, 10, nsamples)
weights_miss = weights_hit.copy()
membership = np.zeros(nsamples, dtype=INT_DTYPE)
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(nsamples, dtype=np.int)
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == -1.0)
self.assertTrue(es_rank == (nsamples - 1))
self.assertTrue(es_run.shape[0] == nsamples)
self.assertTrue(np.all(es_run == -1.0))
# nonzero weights but full membership
weights_hit = np.linspace(1, 10, nsamples)
weights_miss = weights_hit.copy()
membership = np.ones(nsamples, dtype=INT_DTYPE)
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(nsamples, dtype=np.int)
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == 1.0)
self.assertTrue(es_rank == 0)
self.assertTrue(es_run.shape[0] == nsamples)
self.assertTrue(np.all(es_run == 1.0))
def test_random_walk_boundary_cases(self):
nsamples = 100
# test all zeros
weights_hit = np.zeros(nsamples, dtype=np.float)
weights_miss = np.zeros(nsamples, dtype=np.float)
membership = np.zeros(nsamples, dtype=INT_DTYPE)
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(nsamples, dtype=np.int)
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == 0.0)
self.assertTrue(es_rank == 0.0)
self.assertTrue(es_run.shape[0] == nsamples)
# nonzero weights but empty membership
weights_hit = np.linspace(1, 10, nsamples)
weights_miss = weights_hit.copy()
membership = np.zeros(nsamples, dtype=INT_DTYPE)
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(nsamples, dtype=np.int)
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == -1.0)
self.assertTrue(es_rank == (nsamples-1))
self.assertTrue(es_run.shape[0] == nsamples)
self.assertTrue(np.all(es_run == -1.0))
# nonzero weights but full membership
weights_hit = np.linspace(1, 10, nsamples)
weights_miss = weights_hit.copy()
membership = np.ones(nsamples, dtype=INT_DTYPE)
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(nsamples, dtype=np.int)
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership,
ranks, perm)
self.assertTrue(es_val == 1.0)
self.assertTrue(es_rank == 0)
self.assertTrue(es_run.shape[0] == nsamples)
self.assertTrue(np.all(es_run == 1.0))
def test_random_walk_empty(self):
'''
test trying to run random walk with an empty array
'''
weights_hit = np.array([], dtype=np.float)
weights_miss = np.array([], dtype=np.float)
membership = np.empty((0, ), dtype=INT_DTYPE)
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(0)
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership, ranks, perm)
self.assertTrue(es_val == 0)
self.assertTrue(es_rank == 0)
self.assertTrue(np.all(es_run == 0.0))
def test_random_walk_empty(self):
'''
test trying to run random walk with an empty array
'''
weights_hit = np.array([], dtype=np.float)
weights_miss = np.array([], dtype=np.float)
membership = np.empty((0,), dtype=INT_DTYPE)
ranks = np.argsort(weights_hit)[::-1]
perm = np.arange(0)
es_val, es_rank, es_run = \
kernel.random_walk(weights_miss, weights_hit, membership, ranks, perm)
self.assertTrue(es_val == 0)
self.assertTrue(es_rank == 0)
self.assertTrue(np.all(es_run == 0.0))
