def testHistogramExecution(self):
rs = np.random.RandomState(0)
raw = rs.randint(10, size=(20,))
a = tensor(raw, chunk_size=3)
raw_weights = rs.random(20)
weights = tensor(raw_weights, chunk_size=4)
# range provided
for range_ in [(0, 10), (3, 11), (3, 7)]:
bin_edges = histogram(a, range=range_)[0]
result = self.executor.execute_tensor(bin_edges)[0]
expected = np.histogram(raw, range=range_)[0]
np.testing.assert_array_equal(result, expected)
for wt in (raw_weights, weights):
for density in (True, False):
bins = [1, 4, 6, 9]
bin_edges = histogram(a, bins=bins, weights=wt, density=density)[0]
result = self.executor.execute_tensor(bin_edges)[0]
expected = np.histogram(
raw, bins=bins, weights=raw_weights, density=density)[0]
np.testing.assert_almost_equal(result, expected)
this = self
class MockSession:
def __init__(self):
self.executor = this.executor
ctx = LocalContext(MockSession())
executor = ExecutorForTest('numpy', storage=ctx)
with ctx:
raw2 = rs.randint(10, size=(1,))
b = tensor(raw2)
raw3 = rs.randint(10, size=(0,))
c = tensor(raw3)
for t, r in [(a, raw), (b, raw2), (c, raw3), (sort(a), raw)]:
for density in (True, False):
test_bins = [10, 'stone', 'auto', 'doane', 'fd',
'rice', 'scott', 'sqrt', 'sturges']
for bins in test_bins:
hist = histogram(t, bins=bins, density=density)[0]
if r.size > 0:
with self.assertRaises(TilesError):
executor.execute_tensor(hist)
result = executor.execute_tensors([hist])[0]
expected = np.histogram(r, bins=bins, density=density)[0]
np.testing.assert_array_equal(result, expected)
test_bins = [[0, 4, 8], tensor([0, 4, 8], chunk_size=2)]
for bins in test_bins:
hist = histogram(t, bins=bins, density=density)[0]
result = executor.execute_tensors([hist])[0]
expected = np.histogram(r, bins=[0, 4, 8], density=density)[0]
np.testing.assert_array_equal(result, expected)
def testHistogramExecution(self):
rs = np.random.RandomState(0)
raw = rs.randint(10, size=(20, ))
a = tensor(raw, chunk_size=3)
raw_weights = rs.random(20)
weights = tensor(raw_weights, chunk_size=4)
# range provided
for range_ in [(0, 10), (3, 11), (3, 7)]:
bin_edges = histogram(a, range=range_)[0]
result = self.executor.execute_tensor(bin_edges)[0]
expected = np.histogram(raw, range=range_)[0]
np.testing.assert_array_equal(result, expected)
for wt in (raw_weights, weights):
for density in (True, False):
bins = [1, 4, 6, 9]
bin_edges = histogram(a,
bins=bins,
weights=wt,
density=density)[0]
result = self.executor.execute_tensor(bin_edges)[0]
expected = np.histogram(raw,
bins=bins,
weights=raw_weights,
density=density)[0]
np.testing.assert_almost_equal(result, expected)
ctx, executor = self._create_test_context(self.executor)
with ctx:
raw2 = rs.randint(10, size=(1, ))
b = tensor(raw2)
raw3 = rs.randint(10, size=(0, ))
c = tensor(raw3)
for t, r in [(a, raw), (b, raw2), (c, raw3), (sort(a), raw)]:
for density in (True, False):
test_bins = [
10, 'stone', 'auto', 'doane', 'fd', 'rice', 'scott',
'sqrt', 'sturges'
]
for bins in test_bins:
hist = histogram(t, bins=bins, density=density)[0]
if r.size > 0:
with self.assertRaises(TilesError):
executor.execute_tensor(hist)
result = executor.execute_tensors([hist])[0]
expected = np.histogram(r, bins=bins,
density=density)[0]
np.testing.assert_array_equal(result, expected)
test_bins = [[0, 4, 8], tensor([0, 4, 8], chunk_size=2)]
for bins in test_bins:
hist = histogram(t, bins=bins, density=density)[0]
result = executor.execute_tensors([hist])[0]
expected = np.histogram(r,
bins=[0, 4, 8],
density=density)[0]
np.testing.assert_array_equal(result, expected)
# test unknown shape
raw4 = rs.rand(10)
d = tensor(raw4, chunk_size=3)
d = d[d < 0.9]
hist = histogram(d)
result = executor.execute_tensors(hist)[0]
expected = np.histogram(raw4[raw4 < 0.9])[0]
np.testing.assert_array_equal(result, expected)
raw5 = np.arange(3, 10)
e = arange(10, chunk_size=3)
e = e[e >= 3]
hist = histogram(e)
result = executor.execute_tensors(hist)[0]
expected = np.histogram(raw5)[0]
np.testing.assert_array_equal(result, expected)
def test_histogram_execution(setup):
rs = np.random.RandomState(0)
raw = rs.randint(10, size=(20, ))
a = tensor(raw, chunk_size=6)
raw_weights = rs.random(20)
weights = tensor(raw_weights, chunk_size=8)
# range provided
for range_ in [(0, 10), (3, 11), (3, 7)]:
bin_edges = histogram(a, range=range_)[0]
result = bin_edges.execute().fetch()
expected = np.histogram(raw, range=range_)[0]
np.testing.assert_array_equal(result, expected)
for wt in (raw_weights, weights):
for density in (True, False):
bins = [1, 4, 6, 9]
bin_edges = histogram(a, bins=bins, weights=wt, density=density)[0]
result = bin_edges.execute().fetch()
expected = np.histogram(raw,
bins=bins,
weights=raw_weights,
density=density)[0]
np.testing.assert_almost_equal(result, expected)
raw2 = rs.randint(10, size=(1, ))
b = tensor(raw2)
raw3 = rs.randint(10, size=(0, ))
c = tensor(raw3)
for t, r in [(a, raw), (b, raw2), (c, raw3), (sort(a), raw)]:
for density in (True, False):
test_bins = [
10, 'stone', 'auto', 'doane', 'fd', 'rice', 'scott', 'sqrt',
'sturges'
]
for bins in test_bins:
hist = histogram(t, bins=bins, density=density)[0]
result = hist.execute().fetch()
expected = np.histogram(r, bins=bins, density=density)[0]
np.testing.assert_array_equal(result, expected)
test_bins = [[0, 4, 8], tensor([0, 4, 8], chunk_size=2)]
for bins in test_bins:
hist = histogram(t, bins=bins, density=density)[0]
result = hist.execute().fetch()
expected = np.histogram(r, bins=[0, 4, 8], density=density)[0]
np.testing.assert_array_equal(result, expected)
# test unknown shape
raw4 = rs.rand(10)
d = tensor(raw4, chunk_size=6)
d = d[d < 0.9]
hist = histogram(d)
result = hist.execute().fetch()[0]
expected = np.histogram(raw4[raw4 < 0.9])[0]
np.testing.assert_array_equal(result, expected)
raw5 = np.arange(3, 10)
e = arange(10, chunk_size=6)
e = e[e >= 3]
hist = histogram(e)
result = hist.execute().fetch()[0]
expected = np.histogram(raw5)[0]
np.testing.assert_array_equal(result, expected)
