def _test_compute_quantiles_single_batch_helper(self, nptype):
self._test_combiner_spec_helper(
combiner_spec=analyzers._QuantilesCombinerSpec(
num_quantiles=3,
epsilon=0.00001,
bucket_numpy_dtype=np.float32),
batches=[[np.linspace(1, 100, 100, nptype)]],
expected_outputs=[np.array([35, 68], dtype=np.float32)])
def _test_compute_quantiles_multipe_batch_helper(self, nptype):
self._test_combiner_spec_helper(
combiner_spec=analyzers._QuantilesCombinerSpec(
num_quantiles=5,
epsilon=0.00001,
bucket_numpy_dtype=np.float32),
batches=[[np.linspace(1, 100, 100, dtype=nptype)],
[np.linspace(101, 200, 100, dtype=nptype)],
[np.linspace(201, 300, 100, dtype=nptype)]],
expected_outputs=[np.array([61, 121, 181, 241], dtype=np.float32)])
def __init__(self, num_quantiles, epsilon):
self._num_quantiles = num_quantiles
self._epsilon = epsilon
self._quantiles_spec = analyzers._QuantilesCombinerSpec(
num_quantiles=num_quantiles,
epsilon=epsilon,
bucket_numpy_dtype=np.float32,
always_return_num_quantiles=True)
# Initializes non-pickleable local state of the combiner spec.
self._quantiles_spec.initialize_local_state()
testcase_name='PCAWithDegenerateCovarianceMatrix',
combiner_spec=analyzers._PCACombinerSpec(numpy_dtype=np.float64),
batches=[
[np.array([[0, 0, 1]])],
[np.array([[4, 0, 1], [2, -1, 1]])],
[np.array([[2, 1, 1]])],
],
expected_outputs=[
np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.float64)
],
)
_QUANTILES_SINGLE_BATCH_TESTS = [
dict(
testcase_name='ComputeQuantilesSingleBatch-{}'.format(np_type),
combiner_spec=analyzers._QuantilesCombinerSpec(
num_quantiles=5, epsilon=0.00001, bucket_numpy_dtype=np.float32),
batches=[
[np.linspace(1, 100, 100, dtype=np_type)],
[np.linspace(101, 200, 100, dtype=np_type)],
[np.linspace(201, 300, 100, dtype=np_type)],
],
expected_outputs=[np.array([61, 121, 181, 241], dtype=np.float32)],
) for np_type in _NP_TYPES
]
_QUANTILES_MULTIPLE_BATCH_TESTS = [
dict(
testcase_name='ComputeQuantilesMultipleBatch-{}'.format(np_type),
combiner_spec=analyzers._QuantilesCombinerSpec(
num_quantiles=3, epsilon=0.00001, bucket_numpy_dtype=np.float32),
batches=[
axis=0),
_make_mean_and_var_accumulator_from_instance(
[[100, 200, 3000, 17, 27, 53]], axis=0),
],
expected_outputs=[
np.float32([36., 70., 1004., 10.33333333, 14.33333333, 23.66666667]),
np.float32(
[2054., 8456., 1992014., 28.22222222, 86.22222222, 436.22222222]),
],
)
_QUANTILES_NO_ELEMENTS_TEST = dict(
testcase_name='ComputeQuantilesNoElements',
combiner_spec=analyzers._QuantilesCombinerSpec(
num_quantiles=5,
epsilon=0.00001,
bucket_numpy_dtype=np.float32,
always_return_num_quantiles=False),
batches=[
(np.empty((0, 1), dtype=np.float32), ),
],
expected_outputs=[np.zeros((0, ), dtype=np.float32)],
)
_QUANTILES_EXACT_NO_ELEMENTS_TEST = dict(
testcase_name='ComputeExactQuantilesNoElements',
combiner_spec=analyzers._QuantilesCombinerSpec(
num_quantiles=5,
epsilon=0.00001,
bucket_numpy_dtype=np.float32,
always_return_num_quantiles=True),