def testBasicQuantileBucketsMultipleResources(self):
with self.cached_session() as sess:
quantile_accumulator_handle_0 = self.create_resource("float_0", self.eps,
self.max_elements)
quantile_accumulator_handle_1 = self.create_resource("float_1", self.eps,
self.max_elements)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1], self._example_weights,
epsilon=self.eps)
summary_op_0 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_0,
[summaries[0]])
summary_op_1 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_1,
[summaries[1]])
flush_op_0 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_0, self.num_quantiles)
flush_op_1 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_1, self.num_quantiles)
bucket_0 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_0, num_features=1)
bucket_1 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_1, num_features=1)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], bucket_0 + bucket_1)
sess.run([summary_op_0, summary_op_1])
sess.run([flush_op_0, flush_op_1])
self.assertAllClose(self._feature_0_boundaries, bucket_0[0].eval())
self.assertAllClose(self._feature_1_boundaries, bucket_1[0].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def testBasicQuantileBucketsMultipleResources(self):
with self.test_session() as sess:
quantile_accumulator_handle_0 = self.create_resource("float_0", self.eps,
self.max_elements)
quantile_accumulator_handle_1 = self.create_resource("float_1", self.eps,
self.max_elements)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1], self._example_weights,
epsilon=self.eps)
summary_op_0 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_0,
[summaries[0]])
summary_op_1 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle_1,
[summaries[1]])
flush_op_0 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_0, self.num_quantiles)
flush_op_1 = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle_1, self.num_quantiles)
bucket_0 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_0, num_features=1)
bucket_1 = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle_1, num_features=1)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], bucket_0 + bucket_1)
sess.run([summary_op_0, summary_op_1])
sess.run([flush_op_0, flush_op_1])
self.assertAllClose(self._feature_0_boundaries, bucket_0[0].eval())
self.assertAllClose(self._feature_1_boundaries, bucket_1[0].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def testBasicQuantileBucketsSingleResourcesAddFlushed(self):
with self.cached_session():
quantile_accumulator_handle = self.create_resource(
"floats_0", self.eps, self.max_elements, 2)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1],
self._example_weights,
epsilon=self.eps)
summary_op = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle, summaries)
flushed_summaries = flush_quantile_summaries(
quantile_accumulator_handle, num_features=2)
# We are testing whether the flushed summaries output at the previous step
# will give the same expected results by inputing it to add_summaries
summary_op_2 = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle, flushed_summaries)
flush_op = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle, self.num_quantiles)
buckets = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle, num_features=2)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], buckets)
self.evaluate(summary_op)
self.evaluate(summary_op_2)
self.evaluate(flush_op)
self.assertAllClose(self._feature_0_boundaries, buckets[0].eval())
self.assertAllClose(self._feature_1_boundaries, buckets[1].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def call(self, inputs):
def _bucketize_op(bins):
bins = [gen_math_ops.cast(bins, dtypes.float32)]
return lambda inputs: boosted_trees_ops.boosted_trees_bucketize( # pylint: disable=g-long-lambda
float_values=[gen_math_ops.cast(inputs, dtypes.float32)],
bucket_boundaries=bins)[0]
if tf_utils.is_ragged(inputs):
integer_buckets = ragged_functional_ops.map_flat_values(
_bucketize_op(array_ops.squeeze(self.bins)),
inputs)
# Ragged map_flat_values doesn't touch the non-values tensors in the
# ragged composite tensor. If this op is the only op a Keras model,
# this can cause errors in Graph mode, so wrap the tensor in an identity.
return array_ops.identity(integer_buckets)
elif isinstance(inputs, sparse_tensor.SparseTensor):
integer_buckets = boosted_trees_ops.boosted_trees_bucketize(
[gen_math_ops.cast(inputs.values, dtypes.float32)],
bucket_boundaries=[gen_math_ops.cast(array_ops.squeeze(self.bins),
dtypes.float32)])[0]
return sparse_tensor.SparseTensor(
indices=array_ops.identity(inputs.indices),
values=integer_buckets,
dense_shape=array_ops.identity(inputs.dense_shape))
else:
input_shape = inputs.get_shape()
if any(dim is None for dim in input_shape.as_list()[1:]):
raise NotImplementedError(
"Discretization Layer requires known non-batch shape,"
"found {}".format(input_shape))
reshaped = array_ops.reshape(
inputs, [-1, gen_math_ops.prod(input_shape.as_list()[1:], axis=0)])
return array_ops.reshape(
control_flow_ops.vectorized_map(
_bucketize_op(array_ops.squeeze(self.bins)), reshaped),
array_ops.constant([-1] + input_shape.as_list()[1:]))
def testBasicQuantileBucketsSingleResource(self):
with self.cached_session() as sess:
quantile_accumulator_handle = self.create_resource("floats", self.eps,
self.max_elements, 2)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1], self._example_weights,
epsilon=self.eps)
summary_op = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle, summaries)
flush_op = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle, self.num_quantiles)
buckets = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle, num_features=2)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], buckets)
sess.run(summary_op)
sess.run(flush_op)
self.assertAllClose(self._feature_0_boundaries, buckets[0].eval())
self.assertAllClose(self._feature_1_boundaries, buckets[1].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def testBasicQuantileBucketsSingleResource(self):
with self.test_session() as sess:
quantile_accumulator_handle = self.create_resource("floats", self.eps,
self.max_elements, 2)
resources.initialize_resources(resources.shared_resources()).run()
summaries = boosted_trees_ops.make_quantile_summaries(
[self._feature_0, self._feature_1], self._example_weights,
epsilon=self.eps)
summary_op = boosted_trees_ops.quantile_add_summaries(
quantile_accumulator_handle, summaries)
flush_op = boosted_trees_ops.quantile_flush(
quantile_accumulator_handle, self.num_quantiles)
buckets = boosted_trees_ops.get_bucket_boundaries(
quantile_accumulator_handle, num_features=2)
quantiles = boosted_trees_ops.boosted_trees_bucketize(
[self._feature_0, self._feature_1], buckets)
sess.run(summary_op)
sess.run(flush_op)
self.assertAllClose(self._feature_0_boundaries, buckets[0].eval())
self.assertAllClose(self._feature_1_boundaries, buckets[1].eval())
self.assertAllClose(self._feature_0_quantiles, quantiles[0].eval())
self.assertAllClose(self._feature_1_quantiles, quantiles[1].eval())
def _bucketize_op(bins):
bins = [gen_math_ops.cast(bins, dtypes.float32)]
return lambda inputs: boosted_trees_ops.boosted_trees_bucketize( # pylint: disable=g-long-lambda
float_values=[gen_math_ops.cast(inputs, dtypes.float32)],
bucket_boundaries=bins)[0]
