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"""Tests for Python ops defined in sparse_ops."""
import tensorflow.python.platform
import numpy as np
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.framework import types
from tensorflow.python.ops import constant_op
from tensorflow.python.ops import sparse_ops
from tensorflow.python.platform import googletest
class SparseToIndicatorTest(test_util.TensorFlowTestCase):
def _SparseTensor_5x6(self, dtype):
ind = np.array([
[0, 0],
[1, 0], [1, 3], [1, 4],
[3, 2], [3, 3]])
val = np.array([0, 10, 13, 14, 32, 33])
shape = np.array([5, 6])
return ops.SparseTensor(
constant_op.constant(ind, types.int64),
constant_op.constant(val, dtype),
constant_op.constant(shape, types.int64))
def _SparseTensor_2x3x4(self, dtype):
ind = np.array([
[0, 0, 1],
[0, 1, 0], [0, 1, 2],
[1, 0, 3],
[1, 1, 1], [1, 1, 3],
[1, 2, 2]])
val = np.array([1, 10, 12, 103, 111, 113, 122])
shape = np.array([2, 3, 4])
return ops.SparseTensor(
constant_op.constant(ind, types.int64),
constant_op.constant(val, dtype),
constant_op.constant(shape, types.int64))
def testInt32(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_5x6(types.int32)
output = sparse_ops.sparse_to_indicator(sp_input, 50).eval()
expected_output = np.zeros((5, 50), dtype=np.bool)
expected_trues = ((0, 0), (1, 10), (1, 13), (1, 14), (3, 32), (3, 33))
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testInt64(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_5x6(types.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 50).eval()
expected_output = np.zeros((5, 50), dtype=np.bool)
expected_trues = [(0, 0), (1, 10), (1, 13), (1, 14), (3, 32), (3, 33)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
def testHigherRank(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_2x3x4(types.int64)
output = sparse_ops.sparse_to_indicator(sp_input, 200).eval()
expected_output = np.zeros((2, 3, 200), dtype=np.bool)
expected_trues = [(0, 0, 1), (0, 1, 10), (0, 1, 12),
(1, 0, 103), (1, 1, 111), (1, 1, 113), (1, 2, 122)]
for expected_true in expected_trues:
expected_output[expected_true] = True
self.assertAllEqual(output, expected_output)
class SparseRetainTest(test_util.TensorFlowTestCase):
def _SparseTensor_5x6(self):
ind = np.array([
[0, 0],
[1, 0], [1, 3], [1, 4],
[3, 2], [3, 3]])
val = np.array([0, 10, 13, 14, 32, 33])
shape = np.array([5, 6])
return ops.SparseTensor(
constant_op.constant(ind, types.int64),
constant_op.constant(val, types.int32),
constant_op.constant(shape, types.int64))
def testBasic(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_5x6()
to_retain = np.array([1, 0, 0, 1, 1, 0], dtype=np.bool)
sp_output = sparse_ops.sparse_retain(sp_input, to_retain)
output = sess.run(sp_output)
self.assertAllEqual(output.indices, [[0, 0], [1, 4], [3, 2]])
self.assertAllEqual(output.values, [0, 14, 32])
self.assertAllEqual(output.shape, [5, 6])
def testRetainNone(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_5x6()
to_retain = np.zeros((6,), dtype=np.bool)
sp_output = sparse_ops.sparse_retain(sp_input, to_retain)
output = sess.run(sp_output)
self.assertAllEqual(output.indices, np.array([]).reshape((0, 2)))
self.assertAllEqual(output.values, [])
self.assertAllEqual(output.shape, [5, 6])
def testMismatchedRetainShape(self):
with self.test_session(use_gpu=False):
sp_input = self._SparseTensor_5x6()
to_retain = np.array([1, 0, 0, 1, 0], dtype=np.bool)
with self.assertRaises(ValueError):
sparse_ops.sparse_retain(sp_input, to_retain)
class SparseFillEmptyRowsTest(test_util.TensorFlowTestCase):
def _SparseTensor_5x6(self):
ind = np.array([
[0, 0],
[1, 0], [1, 3], [1, 4],
[3, 2], [3, 3]])
val = np.array([0, 10, 13, 14, 32, 33])
shape = np.array([5, 6])
return ops.SparseTensor(
constant_op.constant(ind, types.int64),
constant_op.constant(val, types.int32),
constant_op.constant(shape, types.int64))
def _SparseTensor_String5x6(self):
ind = np.array([
[0, 0],
[1, 0], [1, 3], [1, 4],
[3, 2], [3, 3]])
val = np.array(["a", "b", "c", "d", "e", "f"])
shape = np.array([5, 6])
return ops.SparseTensor(
constant_op.constant(ind, types.int64),
constant_op.constant(val, types.string),
constant_op.constant(shape, types.int64))
def _SparseTensor_2x6(self):
ind = np.array([[0, 0], [1, 0], [1, 3], [1, 4]])
val = np.array([0, 10, 13, 14])
shape = np.array([2, 6])
return ops.SparseTensor(
constant_op.constant(ind, types.int64),
constant_op.constant(val, types.int32),
constant_op.constant(shape, types.int64))
def testFillNumber(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_5x6()
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, -1))
output, empty_row_indicator_out = sess.run(
[sp_output, empty_row_indicator])
self.assertAllEqual(
output.indices,
[[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]])
self.assertAllEqual(output.values, [0, 10, 13, 14, -1, 32, 33, -1])
self.assertAllEqual(output.shape, [5, 6])
self.assertAllEqual(empty_row_indicator_out,
np.array([0, 0, 1, 0, 1]).astype(np.bool))
def testFillString(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_String5x6()
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, ""))
output, empty_row_indicator_out = sess.run(
[sp_output, empty_row_indicator])
self.assertAllEqual(
output.indices,
[[0, 0], [1, 0], [1, 3], [1, 4], [2, 0], [3, 2], [3, 3], [4, 0]])
self.assertAllEqual(output.values, ["a", "b", "c", "d", "", "e", "f", ""])
self.assertAllEqual(output.shape, [5, 6])
self.assertAllEqual(empty_row_indicator_out,
np.array([0, 0, 1, 0, 1]).astype(np.bool))
def testNoEmptyRows(self):
with self.test_session(use_gpu=False) as sess:
sp_input = self._SparseTensor_2x6()
sp_output, empty_row_indicator = (
sparse_ops.sparse_fill_empty_rows(sp_input, -1))
output, empty_row_indicator_out = sess.run(
[sp_output, empty_row_indicator])
self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4]])
self.assertAllEqual(output.values, [0, 10, 13, 14])
self.assertAllEqual(output.shape, [2, 6])
self.assertAllEqual(empty_row_indicator_out, np.zeros(2).astype(np.bool))
if __name__ == "__main__":
googletest.main()
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