def create_model(current_game_name):
models = 'db_models.py'
model_found = False
ldb = LotteryDatabase(True)
curr_game = ldb.fetchone(LottoGame, {'game_name': current_game_name})
if ldb.check_model_exist_by_table_name(curr_game.input_model):
model_class = ldb.set_model_by_table_name(curr_game.input_model)
model_class.__table__.drop(ldb.engine)
with open(models, 'r') as models_file:
models_code = models_file.readlines()
try:
with open(models, 'w') as models_file:
for code_line in models_code:
if curr_game.input_model in code_line:
model_found = True
if model_found:
if code_line.strip() == '':
model_found = False
if not model_found:
models_file.write(code_line)
model_list = [
'class {}(BASE):'.format(curr_game.input_model) + '\n',
" __tablename__ = '{}'".format('INPUT_' +
curr_game.game_table) + '\n',
" id = Column('id', Integer, primary_key=True)" + '\n'
]
model_list += [
" {} = Column('{}', Integer)".format(
'NR_'.lower() + str(n), 'NR_' + str(n)) + '\n'
for n in range(1, curr_game.game_len + 1)
]
models_file.write('\n\n')
models_file.writelines(model_list)
BASE.metadata.create_all(bind=ldb.engine)
except Exception:
with open(models, 'w') as models_file:
models_file.writelines(models_code)
raise
def run(self):
process_name = self.window.select_thread
if process_name == "process_input":
if self.window.input_line.text() == "":
self.signal_infobox.emit(
'Missing input',
'No input numbers to proceed. Please try again.')
else:
self.table_name = 'PREDICT_' + self.window.combo_predict_model.currentText(
)
try:
convert = ConvertModel(self)
ml = MachineLearning(self)
convert.create_prediction_model(
self.window.input_line.text())
ml.random_forest_predict()
self.signal_progress_bar.emit(0)
self.signal_infobox.emit('Completed',
'Prediction model created!')
except Exception as exc:
self.signal_infobox.emit(
'Error', 'Something went wrong!! ' + str(exc))
self.signal_progress_bar.emit(0)
elif process_name == 'update_la_jolla':
try:
ldb = LotteryDatabase()
imported, rejected = ldb.import_la_jolla()
self.signal_infobox.emit(
'Completed',
str.format(
'Lottery data imported! \n Imported: {} \n Rejected: {}',
imported, rejected))
except Exception as exc:
self.signal_infobox.emit('Error',
'Something went wrong!! ' + str(exc))
self.signal_progress_bar.emit(0)
elif process_name == "create_model":
if self.window.combo_db.currentText() != '':
self.table_name = 'MODEL_' + self.window.combo_db.currentText()
self.signal_qbox.emit('Create',
'Do you want create new model?')
while self.window.response is None:
pass
if self.window.response == QMessageBox.Yes:
self.window.response = None
try:
convert = ConvertModel(self)
if self.window.check_win_loss.isChecked():
win, loss = convert.create_training_model()
self.signal_progress_bar.emit(0)
self.signal_infobox.emit(
'Completed', 'Training model created! \n' +
'Win Classification: ' + str(win) + '\n' +
'Loss Classification: ' + str(loss))
else:
zero, one, two, three, four = convert.create_training_model(
)
self.signal_progress_bar.emit(0)
self.signal_infobox.emit(
'Completed', 'Training model created! \n' +
'First Classification: ' + str(zero) + '\n' +
'Second Classification: ' + str(one) + '\n' +
'Third Classification: ' + str(two) + '\n' +
'Fourth Classification: ' + str(three) + '\n' +
'Fifth Classification: ' + str(four))
except Exception as exc:
self.signal_infobox.emit(
'Error', 'Something went wrong!! ' + str(exc))
self.signal_progress_bar.emit(0)
else:
self.signal_infobox.emit('Missing', 'Select model first!')
elif process_name == "process_embedded":
if self.window.input_line.text(
) == "" and not self.window.check_latest.isChecked():
self.signal_infobox.emit(
'Missing input',
'No input numbers to proceed. Please try again.')
elif self.window.check_latest.isChecked():
ldb = LotteryDatabase()
ldb_original = 'INPUT_' + CONFIG['games']['mini_lotto'][
'database']
original_len = ldb.get_table_length(ldb_original)
for i in range(1, 32):
try:
fetch_one = list(
ldb.fetchone(ldb_original, original_len - i + 1))
for j in range(self.window.combo_db.count()):
self.window.combo_db.setCurrentIndex(j)
self.table_name = 'MODEL_' + self.window.combo_db.currentText(
)
self.currentThread().__name__ = "MainThread"
ml = MachineLearning(self)
_ = ml.embedded_learning(
" ".join(map(str, fetch_one[1:6])), i,
fetch_one[0])
except Exception as exc:
self.signal_infobox.emit(
'Error', 'Something went wrong!! \n' + str(exc))
self.signal_infobox.emit('Done', 'Finished!!')
else:
self.table_name = 'MODEL_' + self.window.combo_db.currentText()
self.currentThread().__name__ = "MainThread"
ml = MachineLearning(self)
try:
output = ml.embedded_learning(
self.window.input_line.text())
self.signal_infobox.emit(
'Completed', 'Embedded Training finished! \n' + output)
except Exception as exc:
self.signal_infobox.emit(
'Error', 'Something went wrong!! \n' + str(exc))
elif process_name == "sklearn_ml":
if len(self.window.list_ml.selectedItems()) > 0:
self.table_name = 'MODEL_' + self.window.combo_db.currentText()
self.currentThread().__name__ = "MainThread"
ml = MachineLearning(self)
try:
ml.sklearn_model_train()
self.signal_infobox.emit('Completed',
'Training model finished!')
except Exception as exc:
self.signal_infobox.emit(
'Error', 'Something went wrong!! \n' + str(exc))
else:
self.signal_infobox.emit('Missing',
'Algorithm has not been selected!')
elif process_name == "keras_ml":
self.table_name = 'MODEL_' + self.window.combo_db.currentText()
self.currentThread().__name__ = "MainThread"
ml = MachineLearning(self)
try:
ml.keras_model_train()
self.signal_infobox.emit('Completed',
'Training model finished!')
except Exception as exc:
self.signal_infobox.emit(
'Error', 'Something went wrong!! \n' + str(exc))
print(traceback.format_exc())
elif process_name == "export_to_csv":
export_to = ConvertModel(self, False)
try:
export_to.convert_to_original()
self.signal_progress_bar.emit(0)
self.signal_infobox.emit('Completed', 'Export complete!')
except Exception as exc:
self.signal_infobox.emit(
'Error', 'Something went wrong!! \n' + str(exc))
self.signal_progress_bar.emit(0)
elif process_name == "import_data":
options = QFileDialog.Options()
options |= QFileDialog.DontUseNativeDialog
file_name, _ = QFileDialog.getOpenFileName(
self.window,
"Import file",
"",
"All Files (*);;Text Files (*.txt)",
options=options)
if file_name:
ldb = LotteryDatabase()
curr_game = ldb.fetchone(
LottoGame, {'name': self.window.line_current_game.text()})
if not ldb.check_model_exist_by_table_name(
'INPUT_' + curr_game.input_table):
input_params = {
'__tablename__': 'INPUT_' + curr_game.input_table,
'id': Column('id', Integer, primary_key=True)
}
for i in range(1, curr_game.length + 1):
input_params['NR_' + str(i)] = Column(
'NR_' + str(i), Integer)
ldb.create_class_model(curr_game.input_model, input_params)
ldb.meta_create_all()
imported, rejected = ldb.import_file(
'INPUT_' + curr_game.input_table, file_name,
curr_game.length + 1)
self.signal_infobox.emit(
'Completed',
str.format(
'Lottery data imported! \n '
'Imported: {} \n '
'Rejected: {}', imported, rejected))