def setUp(self):
self.input_data = [[-1., 10.], [-10., 2.], # node 1
[20., 50.], [1., -2.]] # node 2
self.node_map = [-1, 0, 1]
self.leaves = [1, 1, 2, 2]
self.split_features = [[-1, -1, -1], [1, 0, -1], [-1, -1, -1]]
self.split_thresholds = [[0., 0., 0.], [5., -2., 0.], [0., 0., 0.]]
self.ops = training_ops.Load()
def __init__(self, params):
self.params = params
self.variables = ForestTrainingVariables(self.params)
self.trees = [
RandomTreeGraphs(self.variables[i], self.params,
training_ops.Load(), inference_ops.Load())
for i in range(self.params.num_trees)
]
def setUp(self):
self.finished = [3, 5]
self.node_map = [-1, -1, -1, 0, -1, 3, -1, -1, -1]
self.candidate_counts = [[[50., 60., 40., 3.], [70., 30., 70., 30.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[10., 10., 10., 10.], [10., 5., 5., 10.]]]
self.total_counts = [[100., 100., 100., 100.], [0., 0., 0., 0.],
[0., 0., 0., 0.], [100., 100., 100., 100.]]
self.ops = training_ops.Load()
def setUp(self):
self.input_data = [[-1., 0.], [-1., 2.], # node 1
[1., 0.], [1., -2.]] # node 2
self.input_labels = [0, 1, 2, 3]
self.tree = [[1, 0], [-1, 0], [-1, 0]]
self.tree_thresholds = [0., 0., 0.]
self.node_map = [-1, 0, -1]
self.split_features = [[1], [-1]]
self.split_thresholds = [[1.], [0.]]
self.ops = training_ops.Load()
def setUp(self):
self.tree = tf.Variable([[1, 0], [-1, 0], [-1, 0], [-2, 0], [-2, 0],
[-2, 0], [-2, 0]])
self.tree_thresholds = tf.Variable([0., 0., 0., 0., 0., 0., 0.])
self.eot = tf.Variable([3])
self.node_map = [-1, 0, 1, -1, -1, -1, -1]
self.finished = [1, 2]
self.best_splits = [2, 3]
self.split_features = [[1, 2, 3, 4], [5, 6, 7, 8]]
self.split_thresholds = [[10., 20., 30., 40.], [50., 60., 70., 80.]]
self.ops = training_ops.Load()
def __init__(self, params, device_assigner=None, variables=None):
self.params = params
self.device_assigner = device_assigner or RandomForestDeviceAssigner()
tf.logging.info('Constructing forest with params = ')
tf.logging.info(self.params.__dict__)
self.variables = variables or ForestTrainingVariables(
self.params, device_assigner=self.device_assigner)
self.trees = [
RandomTreeGraphs(self.variables[i], self.params,
training_ops.Load(), inference_ops.Load())
for i in range(self.params.num_trees)
]
def setUp(self):
# tree is:
# 0
# 1 2
# 3 4 5 6
self.finished = [2]
self.non_fertile_leaves = [3, 4]
self.non_fertile_leaf_scores = [10., 15.]
self.end_of_tree = [5]
self.node_map = [-1, -1, 0, -1, -1, -1, -1]
self.total_counts = [[80., 40., 40.]]
self.ops = training_ops.Load()
self.stale_leaves = []
def setUp(self):
self.input_data = [[-1., 0.], [-1., 2.], # node 1
[1., 0.], [1., -2.]] # node 2
self.input_labels = [0, 1, 2, 3]
self.tree = [[1, 0], [-1, 0], [-1, 0]]
self.tree_thresholds = [0., 0., 0.]
self.node_map = [-1, 0, -1]
self.split_features = [[1], [-1]]
self.split_thresholds = [[1.], [0.]]
self.ops = training_ops.Load()
self.epochs = [0, 1, 1]
self.current_epoch = [1]
self.data_spec = [constants.DATA_FLOAT] * 2
def setUp(self):
# tree is:
# 0
# 1 2
# 3 4 5 6
self.finished = [2]
self.depths = [1, 2, 2, 3, 3, 3, 3]
self.non_fertile_leaves = [3, 4]
self.non_fertile_leaf_scores = [10., 15.]
self.end_of_tree = [5]
self.node_map = [-1, -1, 0, -1, -1, -1, -1]
self.candidate_counts = [[[10., 20.], [30., 10.]]]
self.total_counts = [[40., 40.]]
self.ops = training_ops.Load()
def setUp(self):
# tree is:
# 0
# 1 2
# 3 4 5 6
self.finished = [2]
self.non_fertile_leaves = [3, 4]
self.non_fertile_leaf_scores = [10., 15.]
self.end_of_tree = [5]
self.node_map = [-1, -1, 0, -1, -1, -1, -1]
self.total_counts = [[80., 40., 40.]]
self.ops = training_ops.Load()
self.stale_leaves = []
self.node_sums = [[3, 1, 2], [4, 2, 2], [5, 2, 3], [6, 1, 5],
[7, 5, 2], [8, 4, 4], [9, 7, 2]]
def setUp(self):
self.finished = [3, 5]
self.node_map = [-1, -1, -1, 0, -1, 3, -1, -1, -1]
self.candidate_sums = [[[5., 8., 8., 8.], [5., 10., 10., 10.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[10., 10., 20., 10.], [10., 5., 5., 5.]]]
self.candidate_squares = [[[5., 50., 50., 50.], [5., 50., 50., 50.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[0., 0., 0., 0.], [0., 0., 0., 0.]],
[[10., 40., 50., 60.], [10., 40., 40., 40.]]]
self.total_sums = [[15., 10., 10., 10.], [0., 0., 0., 0.],
[0., 0., 0., 0.], [20., 20., 20., 20.]]
self.total_squares = [[15., 50., 50., 50.], [0., 0., 0., 0.],
[0., 0., 0., 0.], [20., 60., 60., 60.]]
self.ops = training_ops.Load()
def setUp(self):
self.leaves = [1, 3, 4]
self.node_map = [-1, -1, -1, 0, 1, -1]
self.split_sums = [
# Accumulator 0
[[3, 0, 3], [2, 1, 1], [3, 1, 2]],
# Accumulator 1
[[6, 3, 3], [6, 2, 4], [5, 0, 5]],
# Accumulator 2
[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
# Accumulator 3
[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
# Accumulator 4
[[0, 0, 0], [0, 0, 0], [0, 0, 0]]
]
self.split_squares = []
self.accumulator_sums = [[6, 3, 3], [11, 4, 7], [0, 0, 0], [0, 0, 0],
[0, 0, 0]]
self.accumulator_squares = []
self.ops = training_ops.Load()
self.birth_epochs = [0, 0, 0, 1, 1, 1]
self.current_epoch = [1]
def setUp(self):
self.ops = training_ops.Load()
def setUp(self):
self.leaves = [1, 3, 4]
self.node_map = [-1, -1, -1, 0, 1, -1]
self.pcw_total_splits = [[3, 3], [4, 7], [0, 0], [0, 0], [0, 0]]
self.ops = training_ops.Load()
