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()