def testPCAWithZeroAxis(self):
"""Test a PCA example with one zero variance axis."""
analyzer = analyzers._PCACombinerSpec(output_dim=2,
numpy_dtype=np.float64)
shards = [[[[0, 0, 1]]], [[[4, 0, 1], [2, -1, 1]]], [[[2, 1, 1]]]]
out = np.array([[1, 0], [0, 1], [0, 0]])
self.assertCombine(analyzer, shards, out)
def testPCAWithDegenerateCovarianceMatrix(self):
self._test_combiner_spec_helper(
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)
])
],
)
_COVARIANCE_WITH_LARGE_NUMBERS_TEST = dict(
testcase_name='CovarianceWithLargeNumbers',
combiner_spec=analyzers._CovarianceCombinerSpec(numpy_dtype=np.float64),
batches=[
[np.array([[2e15, 0], [1e15, 0]])],
[np.array([[-2e15, 0], [-1e15, 0]])],
],
expected_outputs=[np.array([[2.5e30, 0], [0, 0]], dtype=np.float64)],
)
_PCA_WITH_DEGENERATE_COVARIANCE_MATRIX_TEST = dict(
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),