def testTrainingConstructionClassificationSparse(self):
input_data = sparse_tensor.SparseTensor(
indices=[[0, 0], [0, 3], [1, 0], [1, 7], [2, 1], [3, 9]],
values=[-1.0, 0.0, -1., 2., 1., -2.0],
dense_shape=[4, 10])
input_labels = [0, 1, 2, 3]
params = tensor_forest.ForestHParams(num_classes=4,
num_features=10,
num_trees=10,
max_nodes=1000,
split_after_samples=25).fill()
graph_builder = tensor_forest.RandomForestGraphs(params)
graph = graph_builder.training_graph(input_data, input_labels)
self.assertTrue(isinstance(graph, ops.Operation))
def testTrainingConstructionClassification(self):
input_data = [
[-1., 0.],
[-1., 2.], # node 1
[1., 0.],
[1., -2.]
] # node 2
input_labels = [0, 1, 2, 3]
params = tensor_forest.ForestHParams(num_classes=4,
num_features=2,
num_trees=10,
max_nodes=1000,
split_after_samples=25).fill()
graph_builder = tensor_forest.RandomForestGraphs(params)
graph = graph_builder.training_graph(input_data, input_labels)
self.assertTrue(isinstance(graph, ops.Operation))
def testInferenceConstructionSparse(self):
input_data = sparse_tensor.SparseTensor(
indices=[[0, 0], [0, 3], [1, 0], [1, 7], [2, 1], [3, 9]],
values=[-1.0, 0.0, -1., 2., 1., -2.0],
dense_shape=[4, 10])
params = tensor_forest.ForestHParams(num_classes=4,
num_features=10,
num_trees=10,
max_nodes=1000,
regression=True,
split_after_samples=25).fill()
graph_builder = tensor_forest.RandomForestGraphs(params)
probs, paths, var = graph_builder.inference_graph(input_data)
self.assertTrue(isinstance(probs, ops.Tensor))
self.assertTrue(isinstance(paths, ops.Tensor))
self.assertTrue(isinstance(var, ops.Tensor))
def testInferenceConstruction(self):
input_data = [
[-1., 0.],
[-1., 2.], # node 1
[1., 0.],
[1., -2.]
] # node 2
params = tensor_forest.ForestHParams(num_classes=4,
num_features=2,
num_trees=10,
max_nodes=1000,
split_after_samples=25).fill()
graph_builder = tensor_forest.RandomForestGraphs(params)
probs, paths, var = graph_builder.inference_graph(input_data)
self.assertTrue(isinstance(probs, ops.Tensor))
self.assertTrue(isinstance(paths, ops.Tensor))
self.assertTrue(isinstance(var, ops.Tensor))
def testInfrenceFromRestoredModel(self):
input_data = [
[-1., 0.],
[-1., 2.], # node 1
[1., 0.],
[1., -2.]
] # node 2
expected_prediction = [[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]
hparams = tensor_forest.ForestHParams(num_classes=2,
num_features=2,
num_trees=1,
max_nodes=1000,
split_after_samples=25).fill()
tree_weight = {
'decisionTree': {
'nodes': [{
'binaryNode': {
'rightChildId': 2,
'leftChildId': 1,
'inequalityLeftChildTest': {
'featureId': {
'id': '0'
},
'threshold': {
'floatValue': 0
}
}
}
}, {
'leaf': {
'vector': {
'value': [{
'floatValue': 0.0
}, {
'floatValue': 1.0
}]
}
},
'nodeId': 1
}, {
'leaf': {
'vector': {
'value': [{
'floatValue': 0.0
}, {
'floatValue': 1.0
}]
}
},
'nodeId': 2
}]
}
}
restored_tree_param = ParseDict(
tree_weight, _tree_proto.Model()).SerializeToString()
graph_builder = tensor_forest.RandomForestGraphs(
hparams, [restored_tree_param])
probs, paths, var = graph_builder.inference_graph(input_data)
self.assertTrue(isinstance(probs, ops.Tensor))
self.assertTrue(isinstance(paths, ops.Tensor))
self.assertTrue(isinstance(var, ops.Tensor))
with self.cached_session():
variables.global_variables_initializer().run()
resources.initialize_resources(resources.shared_resources()).run()
self.assertEquals(probs.eval().shape, (4, 2))
self.assertEquals(probs.eval().tolist(), expected_prediction)