def inference_graph(self, input_data, data_spec):
"""Constructs a TF graph for evaluating a random tree.
Args:
input_data: A tensor or SparseTensor or placeholder for input data.
data_spec: A list of tf.dtype values specifying the original types of
each column.
Returns:
The last op in the random tree inference graph.
"""
sparse_indices = []
sparse_values = []
sparse_shape = []
if isinstance(input_data, sparse_tensor.SparseTensor):
sparse_indices = input_data.indices
sparse_values = input_data.values
sparse_shape = input_data.dense_shape
input_data = []
return tensor_forest_ops.tree_predictions(
input_data,
sparse_indices,
sparse_values,
sparse_shape,
data_spec,
self.variables.tree,
self.variables.tree_thresholds,
self.variables.node_sums,
valid_leaf_threshold=self.params.valid_leaf_threshold)
def inference_graph(self, input_data, data_spec, sparse_features=None):
"""Constructs a TF graph for evaluating a random tree.
Args:
input_data: A tensor or placeholder for input data.
data_spec: A TensorForestDataSpec proto specifying the original
input columns.
sparse_features: A tf.SparseTensor for sparse input data.
Returns:
The last op in the random tree inference graph.
"""
if input_data is None:
input_data = []
sparse_indices = []
sparse_values = []
sparse_shape = []
if sparse_features is not None:
sparse_indices = sparse_features.indices
sparse_values = sparse_features.values
sparse_shape = sparse_features.dense_shape
return tensor_forest_ops.tree_predictions(
input_data,
sparse_indices,
sparse_values,
sparse_shape,
self.variables.tree,
self.variables.tree_thresholds,
self.variables.node_sums,
input_spec=data_spec.SerializeToString(),
valid_leaf_threshold=self.params.valid_leaf_threshold)
def inference_graph(self, input_data, data_spec):
"""Constructs a TF graph for evaluating a random tree.
Args:
input_data: A tensor or SparseTensor or placeholder for input data.
data_spec: A list of tf.dtype values specifying the original types of
each column.
Returns:
The last op in the random tree inference graph.
"""
sparse_indices = []
sparse_values = []
sparse_shape = []
if isinstance(input_data, sparse_tensor.SparseTensor):
sparse_indices = input_data.indices
sparse_values = input_data.values
sparse_shape = input_data.dense_shape
input_data = []
return tensor_forest_ops.tree_predictions(
input_data,
sparse_indices,
sparse_values,
sparse_shape,
data_spec,
self.variables.tree,
self.variables.tree_thresholds,
self.variables.node_sums,
valid_leaf_threshold=self.params.valid_leaf_threshold)
def testSparseInputDefaultIsZero(self):
sparse_shape = [3, 10]
sparse_indices = [[0, 0], [0, 4], [0, 9],
[1, 0], [1, 7],
[2, 0]]
sparse_values = [3.0, -1.0, 0.5,
1.5, 6.0,
-2.0]
sparse_data_spec = [constants.DATA_FLOAT] * 10
tree = [[1, 7], [-1, 0], [-1, 0]]
tree_thresholds = [3.0, 0., 0.]
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
[],
sparse_indices,
sparse_values,
sparse_shape,
sparse_data_spec,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=1)
self.assertAllClose([[0.1, 0.1, 0.8],
[0.5, 0.25, 0.25],
[0.1, 0.1, 0.8]],
predictions.eval())
def testSimple(self):
input_data = [
[-1., 0.],
[-1., 2.], # node 1
[1., 0.],
[1., -2.]
] # node 2
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0., 0.]
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
input_data, [], [], [],
self.data_spec,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=1)
self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
[0.5, 0.25, 0.25], [0.5, 0.25, 0.25]],
predictions.eval())
def testSparseInputDefaultIsZero(self):
sparse_shape = [3, 10]
sparse_indices = [[0, 0], [0, 4], [0, 9], [1, 0], [1, 7], [2, 0]]
sparse_values = [3.0, -1.0, 0.5, 1.5, 6.0, -2.0]
sparse_data_spec = [constants.DATA_FLOAT] * 10
tree = [[1, 7], [-1, 0], [-1, 0]]
tree_thresholds = [3.0, 0., 0.]
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
[],
sparse_indices,
sparse_values,
sparse_shape,
sparse_data_spec,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=1)
self.assertAllClose(
[[0.1, 0.1, 0.8], [0.5, 0.25, 0.25], [0.1, 0.1, 0.8]],
predictions.eval())
def testBackoffToParent(self):
input_data = [
[-1., 0.],
[-1., 2.], # node 1
[1., 0.],
[1., -2.]
] # node 2
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0., 0.]
node_pcw = [[15.0, 3.0, 9.0, 3.0], [5.0, 1.0, 1.0, 3.0],
[25.0, 5.0, 20.0, 0.0]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
input_data, [], [], [],
self.data_spec,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=10)
# Node 2 has enough data, but Node 1 needs to combine with the parent
# counts.
self.assertAllClose([[0.2, 0.4, 0.4], [0.2, 0.4, 0.4],
[0.2, 0.8, 0.0], [0.2, 0.8, 0.0]],
predictions.eval())
def testBadInput(self):
input_data = [
[-1., 0.],
[-1., 2.], # node 1
[1., 0.],
[1., -2.]
] # node 2
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0.] # not enough nodes.
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
with self.assertRaisesOpError(
'Number of nodes should be the same in tree, tree_thresholds '
'and node_pcw.'):
predictions = tensor_forest_ops.tree_predictions(
input_data, [], [], [],
self.data_spec,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=10)
self.assertEquals((0, 3), predictions.eval().shape)
def testBackoffToParent(self):
input_data = [
[-1., 0.],
[-1., 2.], # node 1
[1., 0.],
[1., -2.]
] # node 2
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0., 0.]
node_pcw = [[15.0, 3.0, 9.0, 3.0], [5.0, 1.0, 1.0, 3.0],
[25.0, 5.0, 20.0, 0.0]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
input_data,
self.nothing,
self.nothing,
self.nothing,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=10,
input_spec=self.data_spec)
# Node 2 has enough data, but Node 1 needs to combine with the parent
# counts.
self.assertAllClose([[0.2, 0.4, 0.4], [0.2, 0.4, 0.4], [0.2, 0.8, 0.0],
[0.2, 0.8, 0.0]], predictions.eval())
def testSparseInput(self):
sparse_shape = [3, 10]
sparse_indices = [[0, 0], [0, 4], [0, 9],
[1, 0], [1, 7],
[2, 0]]
sparse_values = [3.0, -1.0, 0.5,
1.5, 6.0,
-2.0]
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0., 0.]
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
self.nothing,
sparse_indices,
sparse_values,
sparse_shape,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=1,
input_spec=self.data_spec)
self.assertAllClose([[0.5, 0.25, 0.25],
[0.5, 0.25, 0.25],
[0.1, 0.1, 0.8]],
predictions.eval())
def testBadInput(self):
input_data = [
[-1., 0.],
[-1., 2.], # node 1
[1., 0.],
[1., -2.]
] # node 2
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0.] # not enough nodes.
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
with self.assertRaisesOpError(
'Number of nodes should be the same in tree, tree_thresholds '
'and node_pcw.'):
predictions = tensor_forest_ops.tree_predictions(
input_data,
self.nothing,
self.nothing,
self.nothing,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=10,
input_spec=self.data_spec)
self.assertEquals((0, 3), predictions.eval().shape)
def testNoInput(self):
input_data = []
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0., 0.]
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
input_data, [], [], [],
self.data_spec,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=10)
self.assertEquals((0, 3), predictions.eval().shape)
def testNoInput(self):
input_data = []
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0., 0.]
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
input_data, [], [], [],
self.data_spec,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=10)
self.assertEquals((0, 3), predictions.eval().shape)
def testSimpleMixed(self):
# 0 1 2 3 4 5 6
tree = [[1, 0], [3, 2], [5, 5], [-1, 0], [-1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 15., 1., 0., 0., 0., 0.]
node_pcw = [[1.0, 0., 1.0, 0.4, 0.3], [1.0, 0., 0.1, 0.1, 0.8],
[1.0, 0., 0.5, 0.25, 0.25], [1.0, 1., 0., 0., 0.],
[1.0, 0., 1., 0., 0.], [1.0, 0., 0., 1., 0.],
[1.0, 0., 0., 0., 1.]]
input_data = [
[-1., 0., 15.], # node 3
[-1., 2., 11.], # node 4
[1., 0., 11.],
[1., -2., 30.]
]
sparse_shape = [4, 5]
sparse_indices = [
[0, 0],
[0, 1],
[0, 4],
[1, 0],
[1, 2],
[2, 1], # node 5
[3, 2]
] # node 6
sparse_values = [3.0, -1.0, 0.5, 1.5, 6.0, -2.0, 2.0]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
input_data,
sparse_indices,
sparse_values,
sparse_shape,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=1,
input_spec=self.data_spec)
self.assertAllClose([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.],
[0., 0., 0., 1.]], predictions.eval())
def testSimple(self):
input_data = [[-1., 0.], [-1., 2.], # node 1
[1., 0.], [1., -2.]] # node 2
tree = [[1, 0], [-1, 0], [-1, 0]]
tree_thresholds = [0., 0., 0.]
node_pcw = [[1.0, 0.3, 0.4, 0.3], [1.0, 0.1, 0.1, 0.8],
[1.0, 0.5, 0.25, 0.25]]
with self.test_session():
predictions = tensor_forest_ops.tree_predictions(
input_data, [], [], [],
self.data_spec,
tree,
tree_thresholds,
node_pcw,
valid_leaf_threshold=1)
self.assertAllClose([[0.1, 0.1, 0.8], [0.1, 0.1, 0.8],
[0.5, 0.25, 0.25], [0.5, 0.25, 0.25]],
predictions.eval())
