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test_tbcnn.py
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test_tbcnn.py
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import pytest
import numpy as np
import ast
import torch
from gensim.models import Word2Vec
from embeddings import Embedding
from main import training_and_validation_sets_creation, target_tensor_set_up, first_neural_network
from node import Node
from matrix_generator import MatrixGenerator
from node_object_creator import *
from first_neural_network import First_neural_network
from coding_layer import Coding_layer
from convolutional_layer import Convolutional_layer
from pooling_layer import Pooling_layer
from dynamic_pooling import Max_pooling_layer, Dynamic_pooling_layer
from hidden_layer import Hidden_layer
from get_targets import GetTargets
from second_neural_network import SecondNeuralNetwork
from validation_neural_network import Validation_neural_network
@pytest.fixture
def setup_get_targets():
get_targets = GetTargets('test\labels')
targets = get_targets.df_iterator()
return targets
'''
@pytest.fixture
def setup_training_dict():
training_dict = training_dict_set_up('test')
return training_dict
'''
@pytest.fixture
def setup_training_validation_sets_creation():
path = '..\\sets\\generators'
training_dict, validation_dict = training_and_validation_sets_creation(path)
return training_dict, validation_dict
@pytest.fixture
def setup_targets_tensor():
training_dict, validation_dict = training_and_validation_sets_creation('test')
targets = target_tensor_set_up('test', training_dict)
return targets
@pytest.fixture
def setup_first_neural_network():
training_dict, validation_dict = training_and_validation_sets_creation('test')
training_dict = first_neural_network(training_dict, 20, 0.1, 0.001)
return training_dict
@pytest.fixture
def set_up_dictionary():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
return tree, dict_ast_to_Node
@pytest.fixture
def set_up_embeddings():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
return embed
@pytest.fixture
def set_up_matrix():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
ls_nodes = embed.node_embedding()[:]
matrices = MatrixGenerator(20, 10)
return matrices
'''
@pytest.fixture
def set_up_update_vector():
tree = path_to_module('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
ls_nodes = embed.node_embedding()[:]
matrices = MatrixGenerator(20, 10)
w, b = matrices.w, matrices.b
nodes_vector_update(ls_nodes, w, b)
w, b = matrices.w, matrices.b
nodes_vector_update(ls_nodes, w, b)
return ls_nodes
'''
@pytest.fixture
def set_up_vector_representation():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
ls_nodes = embed.node_embedding()[:]
vector_representation = First_neural_network(ls_nodes, dict_ast_to_Node, 20, 0.1, 0.001)
ls_nodes, w_l, w_r, b_code = vector_representation.vector_representation()
return ls_nodes, w_l, w_r, b_code
@pytest.fixture
def set_up_coding_layer():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
ls_nodes = embed.node_embedding()[:]
vector_representation = First_neural_network(ls_nodes, dict_ast_to_Node, 20, 0.1, 0.001)
ls_nodes, w_l, w_r, b_code = vector_representation.vector_representation()
w_comb1 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
w_comb2 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
coding_layer = Coding_layer(20, w_comb1, w_comb2)
ls_nodes = coding_layer.coding_layer(ls_nodes, dict_ast_to_Node, w_l, w_r, b_code)
return ls_nodes, w_comb1, w_comb2
@pytest.fixture
def set_up_convolutional_layer():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
ls_nodes = node_position_assign(ls_nodes)
ls_nodes, dict_sibling = node_sibling_assign(ls_nodes)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
ls_nodes = embed.node_embedding()[:]
vector_representation = First_neural_network(ls_nodes, dict_ast_to_Node, 20, 0.1, 0.001)
ls_nodes, w_l, w_r, b_code = vector_representation.vector_representation()
w_comb1 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
w_comb2 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
coding_layer = Coding_layer(20, w_comb1, w_comb2)
ls_nodes = coding_layer.coding_layer(ls_nodes, dict_ast_to_Node, w_l, w_r, b_code)
w_t = torch.randn(4, 20, requires_grad = True)
w_r = torch.randn(4, 20, requires_grad = True)
w_l = torch.randn(4, 20, requires_grad = True)
b_conv = torch.randn(4, requires_grad = True)
convolutional_layer = Convolutional_layer(20, w_t, w_r, w_l, b_conv, features_size=4)
ls_nodes = convolutional_layer.convolutional_layer(ls_nodes, dict_ast_to_Node)
return ls_nodes, w_t, w_l, w_r, b_conv
@pytest.fixture
def set_up_one_max_pooling_layer():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
ls_nodes = node_position_assign(ls_nodes)
ls_nodes, dict_sibling = node_sibling_assign(ls_nodes)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
ls_nodes = embed.node_embedding()[:]
vector_representation = First_neural_network(ls_nodes, dict_ast_to_Node, 20, 0.1, 0.001)
ls_nodes, w_l, w_r, b_code = vector_representation.vector_representation()
w_comb1 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
w_comb2 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
coding_layer = Coding_layer(20, w_comb1, w_comb2)
ls_nodes = coding_layer.coding_layer(ls_nodes, dict_ast_to_Node, w_l, w_r, b_code)
w_t = torch.randn(4, 20, requires_grad = True)
w_r = torch.randn(4, 20, requires_grad = True)
w_l = torch.randn(4, 20, requires_grad = True)
b_conv = torch.randn(4, requires_grad = True)
convolutional_layer = Convolutional_layer(20, w_t, w_r, w_l, b_conv, features_size=4)
ls_nodes = convolutional_layer.convolutional_layer(ls_nodes, dict_ast_to_Node)
pooling_layer = Pooling_layer()
pooled_tensor = pooling_layer.pooling_layer(ls_nodes)
return pooled_tensor
@pytest.fixture
def set_up_dynamic_pooling_layer():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
ls_nodes = node_position_assign(ls_nodes)
ls_nodes, dict_sibling = node_sibling_assign(ls_nodes)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
ls_nodes = embed.node_embedding()[:]
vector_representation = First_neural_network(ls_nodes, dict_ast_to_Node, 20, 0.1, 0.001)
ls_nodes, w_l, w_r, b_code = vector_representation.vector_representation()
w_comb1 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
w_comb2 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
coding_layer = Coding_layer(20, w_comb1, w_comb2)
ls_nodes = coding_layer.coding_layer(ls_nodes, dict_ast_to_Node, w_l, w_r, b_code)
w_t = torch.randn(4, 20, requires_grad = True)
w_r = torch.randn(4, 20, requires_grad = True)
w_l = torch.randn(4, 20, requires_grad = True)
b_conv = torch.randn(4, requires_grad = True)
convolutional_layer = Convolutional_layer(20, w_t, w_r, w_l, b_conv, features_size=4)
ls_nodes = convolutional_layer.convolutional_layer(ls_nodes, dict_ast_to_Node)
max_pooling_layer = Max_pooling_layer()
max_pooling_layer.max_pooling(ls_nodes)
dynamic_pooling = Dynamic_pooling_layer()
hidden_input = dynamic_pooling.three_way_pooling(ls_nodes, dict_sibling)
return ls_nodes, hidden_input
@pytest.fixture
def set_up_hidden_layer():
tree = file_parser('test\pruebas.py')
ls_nodes, dict_ast_to_Node = node_object_creator(tree)
ls_nodes = node_position_assign(ls_nodes)
ls_nodes, dict_sibling = node_sibling_assign(ls_nodes)
embed = Embedding(20, ls_nodes, dict_ast_to_Node)
ls_nodes = embed.node_embedding()[:]
vector_representation = First_neural_network(ls_nodes, dict_ast_to_Node, 20, 0.1, 0.001)
ls_nodes, w_l, w_r, b_code = vector_representation.vector_representation()
w_comb1 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
w_comb2 = torch.diag(torch.randn(20, dtype=torch.float32)).requires_grad_()
coding_layer = Coding_layer(20, w_comb1, w_comb2)
ls_nodes = coding_layer.coding_layer(ls_nodes, dict_ast_to_Node, w_l, w_r, b_code)
w_t = torch.randn(4, 20, requires_grad = True)
w_r = torch.randn(4, 20, requires_grad = True)
w_l = torch.randn(4, 20, requires_grad = True)
b_conv = torch.randn(4, requires_grad = True)
convolutional_layer = Convolutional_layer(20, w_t, w_r, w_l, b_conv, features_size=4)
ls_nodes = convolutional_layer.convolutional_layer(ls_nodes, dict_ast_to_Node)
max_pooling_layer = Max_pooling_layer()
max_pooling_layer.max_pooling(ls_nodes)
dynamic_pooling = Dynamic_pooling_layer()
hidden_input = dynamic_pooling.three_way_pooling(ls_nodes, dict_sibling)
w_hidden = torch.randn(3, requires_grad = True)
b_hidden = torch.randn(1, requires_grad = True)
hidden = Hidden_layer(w_hidden, b_hidden)
output_hidden = hidden.hidden_layer(hidden_input)
return output_hidden, w_hidden, b_hidden
@pytest.fixture
def setup_second_neural_network():
training_dict, validation_dict = training_and_validation_sets_creation('test')
targets = target_tensor_set_up('test', training_dict)
training_dict = first_neural_network(training_dict, 20)
secnn = SecondNeuralNetwork(20, 4)
outputs = secnn.forward(training_dict)
return outputs
@pytest.fixture
def setup_validation_neural_network():
training_dict, validation_dict = training_and_validation_sets_creation('test')
targets = target_tensor_set_up('test', training_dict)
training_dict = first_neural_network(training_dict, 20)
secnn = SecondNeuralNetwork(20, 4)
secnn.train(targets, training_dict)
val = Validation_neural_network(20, 4)
targets = val.target_tensor_set_up('test', validation_dict)
predicts = val.prediction(validation_dict)
accuracy = val.accuracy(predicts, targets)
return predicts, accuracy
def test_get_targets(setup_get_targets):
targets = setup_get_targets
assert isinstance(targets, dict)
assert targets != {}
for target_key in targets:
target = targets[target_key]
break
assert isinstance(target, torch.Tensor)
assert len(target.shape) == 1
assert target.shape[0] == 1
assert target.numpy()[0] == 0
'''
def test_training_dict(setup_training_dict):
training_dict = setup_training_dict
assert isinstance(training_dict, dict)
assert training_dict != {}
'''
def test_training_validation_sets_creation(setup_training_validation_sets_creation):
training_dict, validation_dict = setup_training_validation_sets_creation
assert isinstance(training_dict, dict)
assert isinstance(validation_dict, dict)
assert training_dict != {}
assert validation_dict != {}
def targets_tensor_dict(setup_targets_tensor):
targets = setup_targets_tensor
assert isinstance(targets, torch.Tensor)
assert len(targets) == 1
def test_first_neural_network(setup_first_neural_network):
training_dict = setup_first_neural_network
assert isinstance(training_dict, dict)
assert training_dict != {}
for data in training_dict:
data = training_dict[data]
break
assert isinstance(data, list)
assert len(data) == 6
assert isinstance(data[0], list)
assert isinstance(data[1], dict)
assert isinstance(data[2], dict)
assert isinstance(data[3], torch.Tensor)
assert isinstance(data[4], torch.Tensor)
assert isinstance(data[5], torch.Tensor)
def test_dictionary_Node(set_up_dictionary):
tree, dict_ast_to_Node = set_up_dictionary
for node in ast.iter_child_nodes(tree):
assert node in dict_ast_to_Node
assert dict_ast_to_Node[node].__class__.__name__ == "Node"
# Error a solucionar (file Embeddings.py line 37)
# Hay un error porque no reconoce como argumento "vector_size" en el comando word2vec.
# Al escribir "size" como argumento funcionan los test pero da error al ejecutar el código.
# Viceversa cuando escribimos "vector_size" funciona el código pero da error en los tests.
def test_node_embedding(set_up_embeddings):
result = set_up_embeddings.node_embedding()[:]
length_expected = 20
for el in result:
assert len(el.vector) == length_expected
def test_matrix_length(set_up_matrix):
w, b = set_up_matrix.w, set_up_matrix.b
assert w.shape == (20, 10)
assert len(b) == 20
'''
def test_update_vector(set_up_update_vector):
for node in set_up_update_vector:
assert len(node.new_vector) > 0
'''
def test_vector_representation(set_up_vector_representation):
ls_nodes, w_l, w_r, b_code = set_up_vector_representation
feature_size_expected = 20
for node in ls_nodes:
vector = node.vector.detach().numpy()
assert len(vector) == feature_size_expected
assert np.count_nonzero(vector) != 0
assert w_l.shape == (feature_size_expected, feature_size_expected)
w_l = w_l.detach().numpy()
assert np.count_nonzero(w_l) != 0
assert w_r.shape == (feature_size_expected, feature_size_expected)
w_r = w_r.detach().numpy()
assert np.count_nonzero(w_r) != 0
assert len(b_code) == feature_size_expected
def test_coding_layer(set_up_coding_layer):
ls_nodes, w_comb1, w_comb2 = set_up_coding_layer
feature_size_expected = 20
for node in ls_nodes:
assert len(node.combined_vector) == feature_size_expected
vector = node.combined_vector.detach().numpy()
assert np.count_nonzero(vector) != 0
assert w_comb1.shape == (feature_size_expected, feature_size_expected)
w_comb1 = w_comb1.detach().numpy()
assert np.count_nonzero(w_comb1) != 0
assert w_comb2.shape == (feature_size_expected, feature_size_expected)
w_comb2 = w_comb2.detach().numpy()
assert np.count_nonzero(w_comb2) != 0
def test_convolutional_layer(set_up_convolutional_layer):
ls_nodes, w_t, w_l, w_r, b_conv = set_up_convolutional_layer
feature_size_expected = 20
output_size_expected = 4
for node in ls_nodes:
assert len(node.y) == output_size_expected
assert w_t.shape == (output_size_expected, feature_size_expected)
w_t = w_t.detach().numpy()
assert np.count_nonzero(w_t) != 0
assert w_l.shape == (output_size_expected, feature_size_expected)
w_l = w_l.detach().numpy()
assert np.count_nonzero(w_l) != 0
assert w_r.shape == (output_size_expected, feature_size_expected)
w_r = w_r.detach().numpy()
assert np.count_nonzero(w_r) != 0
assert len(b_conv) == output_size_expected
def test_one_max_pooling_layer(set_up_one_max_pooling_layer):
pooled_tensor = set_up_one_max_pooling_layer
expected_dimension = 1
expected_size = 4
assert isinstance(pooled_tensor, torch.Tensor)
assert len(pooled_tensor.shape) == expected_dimension
assert pooled_tensor.shape[0] == expected_size
def test_dynamic_pooling_layer(set_up_dynamic_pooling_layer):
ls_nodes, hidden_input = set_up_dynamic_pooling_layer
for node in ls_nodes:
pool = node.pool.detach().numpy()
assert pool.size == 1
assert len(hidden_input) == 3
def test_hidden_layer(set_up_hidden_layer):
output_hidden, w_hidden, b_hidden = set_up_hidden_layer
assert len(output_hidden) == 1
output_hidden = output_hidden.detach().numpy()
assert np.count_nonzero(output_hidden) != 0
assert len(w_hidden) == 3
w_hidden = w_hidden.detach().numpy()
assert np.count_nonzero(w_hidden) != 0
assert len(b_hidden) == 1
def test_second_neural_network(setup_second_neural_network):
outputs = setup_second_neural_network
assert isinstance(outputs, torch.Tensor)
assert len(outputs) == 2
assert 0 <= outputs[0] <= 1
def test_validation(setup_validation_neural_network):
predicts, accuracy = setup_validation_neural_network
assert isinstance(predicts, torch.Tensor)
assert len(predicts) == 1
assert 0 <= predicts <= 1
assert isinstance(accuracy, torch.Tensor)
assert 0 <= accuracy <= 1