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Module_Prognoze.py
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from rdflib import *
from ARappServer.NN_tools.relearn_nn import relearn_nn
from ARappServer.NN_tools.test_nn import test_nn
from ARappServer.NN_tools.learn_nn import learn_nn
#
# from rdflib import *
# from NN_tools.relearn_nn import relearn_nn
# from NN_tools.test_nn import test_nn
# from NN_tools.learn_nn import learn_nn
import os
import time
ACCEPTABLE_ERROR = 0.05
objectID = 1
sizeDataSet = 2000
sizeDataSet_test = 200
epochs = 100
window = 25
num_blocks_LSTM = 50
#
g = Graph()
file = open("KB.n3", "rb")
result = g.parse(source="KB.n3", format="n3")
file.close()
for subj, pred, obj in g:
if (subj, pred, obj) not in g:
raise Exception("N3 с ошибками!")
print("\n\033[30mГраф имеет {} триплетов!\033[30m".format(len(g)))
# извлекаем путь к нейросетевым моделям
path_nn = ''
q = g.query(
'''
PREFIX NN: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?path_model
WHERE
{
NN:NN_Model NN:path ?path_model .
}
''')
for item in q:
path_nn = item[0]
print("\nПуть к нейросетевому хранилищу: ", path_nn)
# Конвертирует словарь параметров в список
def convert_dict_to_ilst(dict_parametrs):
l = len(dict_parametrs)
list_parametrs = []
for i in range(l):
list_parametrs.append(dict_parametrs[i + 1])
return list_parametrs
def add_new_nn_to_KB(new_name):
NN = Namespace('file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#')
g.bind("NN", NN)
new_name = new_name.split("\\")[-1].split(".")[0]
new_obj = NN[:-1] + "Predicate_" + new_name
g.add((new_obj, RDF.type, NN.NN_Model))
print("\033[30mГраф имеет {} триплетов!".format(len(g)))
g.add((new_obj, NN.model_name, Literal(new_name)))
print("\033[30mГраф имеет {} триплетов!".format(len(g)))
g.add((new_obj, NN.active, Literal(True)))
print("\033[30mГраф имеет {} триплетов!".format(len(g)))
file = open("KB.n3", mode="wb")
file.write(g.serialize(format="n3"))
file.close()
# Изменение статуса
def change_status(new_name):
q = g.query(
'''
PREFIX NN: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?Name
WHERE
{
?Name a NN:NN_Model .
?Name NN:active true .
}
''')
for Name_Model in q:
nn_model_name = item[0]
# NN = URIRef(":")
NN = Namespace('file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#')
g.bind("NN", NN)
g.set((Name_Model[0], NN.active, Literal(False)))
add_new_nn_to_KB(new_name)
# Запись в базу знаний, сохранение
file = open("KB.n3", mode="wb")
file.write(g.serialize(format="n3"))
file.close()
while True:
# ////////////////////////////////////////////////////////////////////////////////
# узнаём базовые параметры для обучения
q = g.query('''
PREFIX NN: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?num_n ?num_l
WHERE
{
NN:base_parametrs_nn_model NN:num_blocks_LSTM ?num_n .
NN:base_parametrs_nn_model NN:window ?num_l .
}
''')
num_blocks_LSTM = 0
window = 0
for item in q:
num_blocks_LSTM = int(item[0])
window = int(item[1])
print("\033[95mКоличество ячеек LSTM - {}\n"
"Длина окна - {}\033[30m".format(num_blocks_LSTM, window))
# ////////////////////////////////////////////////////////////////////////////////
# while True:
# узнаём есть ли нейросетевые модели в базе и если есть забираем последнюю
q = g.query(
'''
PREFIX NN: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?instance
WHERE
{
?instance a NN:NN_Model .
?instance NN:active true .
}
''')
# затестить
# def objects(self, subject=None, predicate=None):
# """A generator of objects with the given subject and predicate"""
# formula s, p, o in self.triples((subject, predicate, None)):
# yield o
result = False
base_nn_model_name = ""
nn_model_name = ""
# Удаление лишних знаний
if len(q) != 0 and len(os.listdir(path_nn)) == 0:
print("\033[30mГраф имеет {} триплетов!\n"
"Удаление лишних знаний".format(len(g)))
q = g.query(
'''
PREFIX NN: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?instance ?status
WHERE
{
?instance a NN:NN_Model;
NN:active ?status .
}
''')
for name, status in q:
g.remove((name, None, None))
print("\033[30mГраф имеет {} триплетов!".format(len(g)))
# ---
# Запрос готовых нейросетевых моделей
q = g.query(
'''
PREFIX NN_tools: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/NN_tools/#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?instance ?action ?name
WHERE
{
?instance a NN_tools:algorithm .
?instance NN_tools:action ?action .
?instance NN_tools:name ?name .
}
''')
NN_tools = {}
for _, action, name in q:
NN_tools[name] = action
# Запрос готовых нейросетевых моделей
q = g.query(
'''
PREFIX NN: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?instance
WHERE
{
?instance a NN:NN_Model .
?instance NN:active true .
}
''')
if len(q) == 0:
print("\033[95mнейросетевой модели нет\033[30m")
q = g.query(
'''
PREFIX NN: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#>
SELECT ?model_name
WHERE
{
NN:NN_Model NN:base_model_name ?model_name.
}
'''
)
for item in q:
base_nn_model_name = item[0]
base_nn_model_name += "1"
nn_model_name = os.path.join(path_nn, base_nn_model_name + ".h5")
# обучение нейросети
error = 10000000
while error > ACCEPTABLE_ERROR:
print("Запуск обучения нейросети!")
learn_nn(
[nn_model_name],
objectID,
sizeDataSet,
epochs,
window,
num_blocks_LSTM
)
print("Процесс тестирования нейросетевой модели")
error = test_nn(
[nn_model_name],
objectID,
sizeDataSet_test,
window
)
print("\033[95mКоличество ячеек LSTM - {}\n"
"Длина окна - {}\n"
"Текущая ошибка прогностической модели - {}\033[30m".format(num_blocks_LSTM, window, error))
if num_blocks_LSTM < 100:
num_blocks_LSTM += 1
elif window < 40:
window += 1
else:
break
print("\033[95mПрогностическая модель - {}\033[30m".format(base_nn_model_name))
# add_new_nn_to_KB(nn_model_name)
change_status(nn_model_name)
else:
print("\033[95mнейросетевая модель есть\033[30m")
# print(len(q))
for item in q:
nn_model_name = item[0].split("#")[1]
# print(nn_model_name)
q = g.query(
'''
PREFIX NN: <file:///U:/7%20%D1%81%D0%B5%D0%BC%D0%B5%D1%81%D1%82%D1%80/pythonProject/MyBase/NN/#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?Name
WHERE
{
NN:''' + nn_model_name + ''' NN:model_name ?Name .
NN:''' + nn_model_name + ''' NN:active true .
}
''')
for item in q:
nn_model_name = item[0]
print("\033[95mПрогностическая модель - {}\033[30m".format(nn_model_name))
full_path_nn_model = os.path.join(path_nn, nn_model_name + ".h5")
# print(full_path_nn_model)
print("Процесс тестирования нейросетевой модели")
error = test_nn(
[full_path_nn_model],
objectID,
sizeDataSet_test,
window
)
#проверка нейросети
print("\033[95mКолличество ячеек LSTM - {}\n"
"Длина окна - {}\n"
"Текущая ошибка прогностической модели - {}\033[30m".format(num_blocks_LSTM, window, error))
if error > ACCEPTABLE_ERROR:
print("Запуск переобучения нейросети!")
relearn_nn(
[full_path_nn_model],
objectID,
sizeDataSet,
epochs,
window,
num_blocks_LSTM
)
print("Процесс тестирования нейросетевой модели")
error = test_nn(
[full_path_nn_model],
objectID,
sizeDataSet_test,
window
) # проверка нейросети
print("\033[95mКолличество ячеек LSTM - {}\n"
"Длина окна - {}\n"
"Текущая ошибка прогностической модели - {}\033[30m".format(num_blocks_LSTM, window, error))
if error > ACCEPTABLE_ERROR:
name, i_num = nn_model_name.split("_")
name += "_{}".format(int(i_num) + 1)
nn_model_name = os.path.join(path_nn, name + ".h5")
# обучение нейросети
error = 10000000
while error > ACCEPTABLE_ERROR:
print("Запуск обучения нейросети!")
learn_nn(
[nn_model_name],
objectID,
sizeDataSet,
epochs,
window,
num_blocks_LSTM
)
print("Процесс тестирования нейросетевой модели")
error = test_nn(
[nn_model_name],
objectID,
sizeDataSet_test,
window
)
print("\033[95mКоличество ячеек LSTM - {}\n"
"Длина окна - {}\n"
"Текущая ошибка прогностической модели - {}\033[30m".format(num_blocks_LSTM, window, error))
if num_blocks_LSTM < 100:
num_blocks_LSTM += 1
elif window < 40:
window += 1
else:
break
# add_new_nn_to_KB(nn_model_name)
change_status(nn_model_name)
print("\033[95mНовая прогностическая модель - {}\033[30m".format(name))
else:
# print(1)
change_status(nn_model_name)
print("system of for working with neural networks is waiting...")
time.sleep(5)