def test_input_types():
# Both strings and bytestrings are unpackable
assert np.all(unpack64('abcd') == unpack64(b'abcd'))
# Anything that can be converted to a NumPy array is packable
eq_(pack64([1.0, 2.0]), 'ZIAAQAA')
eq_(pack64((1.0, 2.0)), 'ZIAAQAA')
eq_(pack64(np.array([1.0, 2.0], dtype=np.float32)), 'ZIAAQAA')
eq_(pack64(np.array([1.0, 2.0], dtype=np.float64)), 'ZIAAQAA')
eq_(pack64(np.array([1.0, 2.0], dtype=np.int32)), 'ZIAAQAA')
def test_speed():
vectors = [np.random.normal(size=(i%40+1,)) for i in xrange(40)]
start1 = time.time()
for vec in vectors:
reference_unpack64(reference_pack64(vec))
time_reference = (time.time() - start1)*1000
start2 = time.time()
for vec in vectors:
unpack64(pack64(vec))
time_ours = (time.time() - start2)*1000
assert time_ours < time_reference,\
"Took %4.4f ms. Time to beat: %4.4f ms." % (time_ours, time_reference)
def _check(vector, expected=None, exact=False):
# Checks that the given vector:
# * Encodes successfully (and to a particular string, if specified)
# * Decodes successfully to exactly the same value (if specified) or to
# within the expected tolerance (see below for what this tolerance is)
# * Produces exactly the same string when the decoded value is reencoded
# Returns the maximum absolute deviation between the given and decoded
# vectors, and the tolerance to which it was compared.
encoded = pack64(vector)
if expected is not None:
eq_(encoded, expected)
decoded = unpack64(encoded)
eq_(pack64(decoded), encoded)
if not len(vector):
deviation = 0.0
else:
deviation = np.max(np.abs(decoded - vector))
if exact:
tolerance = 0.0
else:
# Generally pack64 guarantees a precision of 2 ** -17 times the largest
# magnitude entry. However, we have to adjust for two details.
# * The largest magnitude entry may be rounded for packing in such a
# way that the precision is slightly less than that guarantee.
# * The smallest positive number that can be packed at all is
# 2 ** -40, so the absolute precision available for very small
# vectors, regardless of the size of the vector, is 2 ** -41.
tolerance = max(np.max(np.abs(vector)) / (2.0 ** 17 - 0.5), 2.0 ** -41)
assert deviation <= tolerance
return deviation, tolerance
def unpack_vectors(dataframecolumn):
"""
Arguments:
dataframecolumn (pandas dataframe column):
single column of a pandas dataframe containing pack64'd document vectors
Returns:
newarray (numpy array): array of unpacked document vectors
"""
newarray = np.asarray([unpack64(x) for x in dataframecolumn])
return newarray
def round_trip_check(vec):
newvec = unpack64(pack64(vec, rounded=True))
if len(vec) == 0:
precision = 0.
maxdiff = 0.
else:
precision = np.max(np.abs(vec)) * (2**-17) + 2**-40
maxdiff = np.max(np.abs(newvec - vec))
assert np.allclose(newvec, vec, 1e-10, precision),\
"%s isn't close enough to %s; difference=%s, precision=%s" % (newvec,
vec, maxdiff, precision)
def test_errors():
# Nonfinite values are rejected
for value in (float('inf'), float('nan')):
with assert_raises(ValueError):
pack64([value])
# Out of range values are rejected; check near the edge of the range
with assert_raises(OverflowError):
pack64([(2.0 ** 17 - 0.5) * 2.0 ** 23])
_check([(2.0 ** 17 - 0.6) * 2.0 ** 23], expected='_f__')
with assert_raises(OverflowError):
# (This could actually be encoded as '_gAA'.)
pack64([-(2.0 ** 17 - 0.5) * 2.0 ** 23])
_check([-(2.0 ** 17 - 0.6) * 2.0 ** 23], expected='_gAB')
# Strings with bad lengths or characters are rejected
for string in ('', 'xx', b'xx', '\U0001f43c', 'Hey!', 'panda', 'rutabaga'):
with assert_raises(ValueError):
unpack64(string)
# Some (but not all) bad strings are accepted if error checking is disabled
for string in ('xx', 'Hey!', 'panda'):
unpack64(string, check=False)
with assert_raises(ValueError):
unpack64('rutabaga', check=False)
def decoding_check(vec):
encoded = reference_pack64(vec)
a = reference_unpack64(encoded)
b = unpack64(encoded)
assert np.allclose(a, b), '%s should have decoded to %s, got %s' % (encoded, a, b)
