def setUp(self): super(TestOnDenseGrid, self).setUp() sparse_grid_context = nql_test_lib.make_grid() context = nql.NeuralQueryContext() # copy the grid but densify some of it context.declare_relation('n', 'place_t', 'place_t') context.declare_relation('s', 'place_t', 'place_t') context.declare_relation('e', 'place_t', 'place_t') context.declare_relation('w', 'place_t', 'place_t') context.declare_relation('color', 'place_t', 'color_t', dense=True) context.declare_relation('distance_to', 'place_t', 'corner_t') # copy the type definitions for type_name in sparse_grid_context.get_type_names(): entity_list = [ sparse_grid_context.get_entity_name(i, type_name) for i in range(sparse_grid_context.get_max_id(type_name)) ] context.extend_type(type_name, entity_list) # copy the data over for r in sparse_grid_context.get_relation_names(): m = sparse_grid_context.get_initial_value(r) if context.is_dense(r): context.set_initial_value(r, m.todense()) else: context.set_initial_value(r, m) self.context = context self.session = tf.Session()
def make_grid(): """Create a grid, with relations for going n, s, e, w.""" result = nql.NeuralQueryContext() result.declare_relation('n', 'place_t', 'place_t') result.declare_relation('s', 'place_t', 'place_t') result.declare_relation('e', 'place_t', 'place_t') result.declare_relation('w', 'place_t', 'place_t') result.declare_relation('color', 'place_t', 'color_t') result.declare_relation('distance_to', 'place_t', 'corner_t') kg_lines = [] dij = {'n': (-1, 0), 's': (+1, 0), 'e': (0, +1), 'w': (0, -1)} for i in range(0, 4): for j in range(0, 4): cell_color = 'black' if (i % 2) == (j % 2) else 'white' kg_lines.append( '\t'.join(['color', cell(i, j), cell_color]) + '\n') kg_lines.append('\t'.join( ['distance_to', cell(i, j), 'ul', str(i + j)]) + '\n') for direction, (di, dj) in dij.items(): if (0 <= i + di < 4) and (0 <= j + dj < 4): kg_lines.append('\t'.join([ direction, cell(i, j), cell(i + di, j + dj) ]) + '\n') result.load_kg(lines=kg_lines, freeze=True) return result
def setUp(self): super(TestTFDataset, self).setUp() self.clean_examples = ['a|A', 'b|B', 'c|C,D'] self.noisy_examples = ['a|A', 'b|Beta', 'c|C,noise', 'd|D'] self.empty_examples = ['a|A,', 'b|Beta,', 'c|'] self.noisy_examples_good_count = 4 self.empty_examples_good_count = 3 self.context = nql.NeuralQueryContext() self.context.extend_type('uc_t', ['A', 'B', 'C', 'D']) self.context.freeze('uc_t') self.tf_string_type = bytes
def setUp(self): super(TestTFDataset, self).setUp() self.clean_examples = ['a|A', 'b|B', 'c|C,D'] self.noisy_examples = ['a|A', 'b|Beta', 'c|C,noise', 'd|D'] self.empty_examples = ['a|A,', 'b|Beta,', 'c|'] self.noisy_examples_good_count = 4 self.empty_examples_good_count = 3 self.context = nql.NeuralQueryContext() self.context.extend_type('uc_t', ['A', 'B', 'C', 'D']) self.context.freeze('uc_t') with tf.Session() as session: s_const = tf.constant('hello world', dtype=tf.string) s_eval = session.run(s_const) self.tf_string_type = type(s_eval) self.tf_string_type = bytes
def setUp(self): super(TestDeclaredTypes, self).setUp() self.context = nql.NeuralQueryContext()
def setUp(self): super(TestLoad, self).setUp() self.context = nql.NeuralQueryContext() self.context.declare_relation('foo', 'foo_d', 'foo_r') self.context.declare_relation('bat', 'bat_d', 'bat_r')
def setUp(self): super(TestKhotOverFrozenWithNone, self).setUp() self.context = nql.NeuralQueryContext() self.context.declare_entity_type('uc_t', fixed_vocab=['A', 'B', 'C', 'D'], unknown_marker=None)
def setUp(self): self.context = nql.NeuralQueryContext() self.context.declare_entity_type('uc_t', fixed_vocab=['A', 'B', 'C', 'D'], unknown_marker=None)