def testAggregatesSummaryMultipleDimensionFeature(self):
"""Tests that MakeStatsSummary works for multiple features."""
expected_stats_summary = np.asarray([[
0, 0, 0, 0, .08, .09, 0, 0, 0, 0, .08, .09, 0, 0, 0, 0
], [
0, 0, .3, .5, .15, .36, 0, 0, .06, .07, -.05, .06, .1, .2, .06, .07
], [-.33, .58, .3, .4, 0, 0, 0, 0, .3, .4, -.4, .5, 0, 0, .07, .08]])
with self.cached_session():
max_splits = 3
num_buckets = 4
node_ids = [1, 1, 2, 2, 1, 1, 2, 0]
gradients = [[.1], [.2], [.3], [-.4], [-.05], [.06], [.07], [.08]]
hessians = [[.2], [.3], [.4], [.5], [.06], [.07], [.08], [.09]]
# Tests multiple features.
bucketized_features = [[3, 0], [1, 0], [2, 0], [0, 2], [1, 2],
[2, 3], [0, 3], [1, 2]]
result = boosted_trees_ops.boosted_trees_aggregate_stats(
node_ids, gradients, hessians, bucketized_features, max_splits,
num_buckets)
# Reshape to [max_splits, num_buckets, feature_dim, stats_dim]
expected_stats_summary = np.reshape(expected_stats_summary,
(3, 4, 2, 2))
# Swap feature_dim and bucket_id axis
expected_stats_summary = np.swapaxes(expected_stats_summary, 1, 2)
self.assertAllClose(expected_stats_summary, result)
def testAggregateStatsMultiClass(self):
"""Tests that Summary actually accumulates."""
with self.cached_session():
max_splits = 3
num_buckets = 4
node_ids = [1, 1, 2, 2, 1, 1, 2, 0]
gradients = [[.1, .2], [.2, .4], [.3, .6], [-.4, -.8], [-.05, -.1],
[.06, .12], [.07, .14], [.08, .16]]
hessians = [[.2, .6], [.3, .9], [.4, 1.2], [.5, 1.5], [.06, .18],
[.07, .21], [.08, .24], [.09, .27]]
# Tests a single feature.
bucketized_features = [[3], [1], [2], [0], [1], [2], [0], [1]]
result = boosted_trees_ops.boosted_trees_aggregate_stats(
node_ids, gradients, hessians, bucketized_features, max_splits,
num_buckets)
# shape=[max_splits, num_buckets, feature_dim, stats_dim]
expected_stats_summary = [
[[[0., 0., 0., 0.]], [[.08, .16, .09, .27]], [[0., 0., 0.,
0.]],
[[0., 0., 0., 0.]]],
[[[0., 0., 0., 0.]], [[.15, 0.3, .36, 1.08]],
[[.06, 0.12, .07, 0.21]], [[.1, .2, .2, .6]]],
[[[-.33, -.66, .58, 1.74]], [[0., 0., 0., 0.]],
[[.3, .6, .4, 1.2]], [[0., 0., 0., 0.]]],
]
expected_stats_summary = np.swapaxes(expected_stats_summary, 1, 2)
self.assertAllClose(expected_stats_summary, result)
def testAggregateStatsMultiClass(self):
"""Tests that Summary actually accumulates."""
with self.cached_session():
max_splits = 3
num_buckets = 4
node_ids = [1, 1, 2, 2, 1, 1, 2, 0]
gradients = [[.1, .2], [.2, .4], [.3, .6], [-.4, -.8], [-.05, -.1],
[.06, .12], [.07, .14], [.08, .16]]
hessians = [[.2, .6], [.3, .9], [.4, 1.2], [.5, 1.5], [.06, .18],
[.07, .21], [.08, .24], [.09, .27]]
# Tests a single feature.
bucketized_features = [[3], [1], [2], [0], [1], [2], [0], [1]]
result = boosted_trees_ops.boosted_trees_aggregate_stats(
node_ids, gradients, hessians, bucketized_features, max_splits,
num_buckets)
# shape=[max_splits, num_buckets, feature_dim, stats_dim]
expected_stats_summary = [
[[[0., 0., 0., 0.]], [[.08, .16, .09, .27]], [[0., 0., 0., 0.]],
[[0., 0., 0., 0.]]],
[[[0., 0., 0., 0.]], [[.15, 0.3, .36, 1.08]], [[.06, 0.12, .07,
0.21]],
[[.1, .2, .2, .6]]],
[[[-.33, -.66, .58, 1.74]], [[0., 0., 0., 0.]], [[.3, .6, .4, 1.2]],
[[0., 0., 0., 0.]]],
]
expected_stats_summary = np.swapaxes(expected_stats_summary, 1, 2)
self.assertAllClose(expected_stats_summary, result)
def testAggregateStatsSimple(self):
# Get the same result as MakeStatsSummary Op.
expected_stats_summary = np.asarray([1., 5., 2., 6., 3., 7., 4., 8.])
# shape=[max_splits, num_buckets, feature_dim, stats_dim]
expected_stats_summary = np.reshape(expected_stats_summary, (2, 2, 1, 2))
# Reshape feature dim and bucket id axes
expected_stats_summary = np.swapaxes(expected_stats_summary, 1, 2)
self.assertAllClose(
expected_stats_summary,
boosted_trees_ops.boosted_trees_aggregate_stats(
node_ids=[0, 0, 1, 1],
gradients=[[1.], [2.], [3.], [4.]],
hessians=[[5.], [6.], [7.], [8.]],
feature=[[0], [1], [0], [1]],
max_splits=2,
num_buckets=2))
def testAggregateStatsAccumulate(self):
"""Tests that Summary actually accumulates."""
max_splits = 3
num_buckets = 4
node_ids = [1, 1, 2, 2, 1, 1, 2, 0]
gradients = [[.1], [.2], [.3], [-.4], [-.05], [.06], [.07], [.08]]
hessians = [[.2], [.3], [.4], [.5], [.06], [.07], [.08], [.09]]
# Tests a single feature.
bucketized_features = [[3], [1], [2], [0], [1], [2], [0], [1]]
result = boosted_trees_ops.boosted_trees_aggregate_stats(
node_ids, gradients, hessians, bucketized_features, max_splits,
num_buckets)
# shape=[max_splits, num_buckets, feature_dim, stats_dim]
# Get the same result as MakeStatsSummary Op.
expected_stats_summary = [
[[[0., 0.]], [[.08, .09]], [[0., 0.]], [[0., 0.]]],
[[[0., 0.]], [[.15, .36]], [[.06, .07]], [[.1, .2]]],
[[[-.33, .58]], [[0., 0.]], [[.3, .4]], [[0., 0.]]],
]
# Swap feature dim and bucket id axis
expected_stats_summary = np.swapaxes(expected_stats_summary, 1, 2)
self.assertAllClose(expected_stats_summary, result)
def testAggregateStatsAccumulate(self):
"""Tests that Summary actually accumulates."""
max_splits = 3
num_buckets = 4
node_ids = [1, 1, 2, 2, 1, 1, 2, 0]
gradients = [[.1], [.2], [.3], [-.4], [-.05], [.06], [.07], [.08]]
hessians = [[.2], [.3], [.4], [.5], [.06], [.07], [.08], [.09]]
# Tests a single feature.
bucketized_features = [[3], [1], [2], [0], [1], [2], [0], [1]]
result = boosted_trees_ops.boosted_trees_aggregate_stats(
node_ids, gradients, hessians, bucketized_features, max_splits,
num_buckets)
# shape=[max_splits, num_buckets, feature_dim, stats_dim]
# Get the same result as MakeStatsSummary Op.
expected_stats_summary = [
[[[0., 0.]], [[.08, .09]], [[0., 0.]], [[0., 0.]]],
[[[0., 0.]], [[.15, .36]], [[.06, .07]], [[.1, .2]]],
[[[-.33, .58]], [[0., 0.]], [[.3, .4]], [[0., 0.]]],
]
# Swap feature dim and bucket id axis
expected_stats_summary = np.swapaxes(expected_stats_summary, 1, 2)
self.assertAllClose(expected_stats_summary, result)
def testAggregatesSummaryMultipleDimensionFeature(self):
"""Tests that MakeStatsSummary works for multiple features."""
expected_stats_summary = np.asarray(
[[0, 0, 0, 0, .08, .09, 0, 0, 0, 0, .08, .09, 0, 0, 0, 0],
[0, 0, .3, .5, .15, .36, 0, 0, .06, .07, -.05, .06, .1, .2, .06, .07],
[-.33, .58, .3, .4, 0, 0, 0, 0, .3, .4, -.4, .5, 0, 0, .07, .08]])
with self.cached_session():
max_splits = 3
num_buckets = 4
node_ids = [1, 1, 2, 2, 1, 1, 2, 0]
gradients = [[.1], [.2], [.3], [-.4], [-.05], [.06], [.07], [.08]]
hessians = [[.2], [.3], [.4], [.5], [.06], [.07], [.08], [.09]]
# Tests multiple features.
bucketized_features = [[3, 0], [1, 0], [2, 0], [0, 2], [1, 2], [2, 3],
[0, 3], [1, 2]]
result = boosted_trees_ops.boosted_trees_aggregate_stats(
node_ids, gradients, hessians, bucketized_features, max_splits,
num_buckets)
# Reshape to [max_splits, num_buckets, feature_dim, stats_dim]
expected_stats_summary = np.reshape(expected_stats_summary, (3, 4, 2, 2))
# Swap feature_dim and bucket_id axis
expected_stats_summary = np.swapaxes(expected_stats_summary, 1, 2)
self.assertAllClose(expected_stats_summary, result)
