def _evaluate_solution(self, encoded_solution):
decoded = self._decode_solution(encoded_solution)
print('Evaluate: ' + str(decoded['layers']) + ' ...')
model_hash = hashlib.sha224(str(decoded['look_back']).encode('UTF-8') + str(decoded['weights']).encode('UTF-8')).hexdigest()
metrics = self.cache.upsert_cache(model_hash, None)
if metrics is None:
rnn_solution = nn.RNNBuilder(decoded['layers'], decoded['weights'], dense_activation=self.config.dense_activation)
if self.config.blind:
y_predicted = rnn_solution.predict_blind(self.data['train'],
self.data['test'],
self.config.x_features,
self.config.y_features,
decoded['look_back'])
y_gt = self.data['test'][self.config.y_features].values[:,:]
else:
y_predicted = rnn_solution.predict(self.data[self.config.x_features], decoded['look_back'])
y_gt = self.data[self.config.y_features].values[decoded['look_back']:,:]
mse = ut.mse_loss(y_predicted, y_gt)
mae = ut.mae_loss(y_predicted, y_gt)
metrics = { 'trainable_params':int(rnn_solution.trainable_params),
'num_hidden_layers':int(rnn_solution.hidden_layers),
'layers':'-'.join(map(str, decoded['layers'])),
'mse':mse,
'mae':mae,
'num_hidden_neurons':int(np.sum(decoded['layers'][1:-1])),
'look_back':int(decoded['look_back'])
}
del rnn_solution
self.cache.upsert_cache(model_hash, metrics)
else:
print('Metrics load from cache')
print(metrics)
self.memory_tracker.print_diff()
return metrics
def _sample_architecture(self, layers, look_back):
rnn_solution = nn.RNNBuilder(layers, dense_activation=self.config.dense_activation)
maes = list()
for i in range(self.config.samples):
weights = self._generate_weights(layers)
rnn_solution.update_weights( weights )
y_predicted = rnn_solution.predict(self.data[self.config.x_features], look_back)
y_gt = self.data[self.config.y_features].values[look_back:,:]
mae = ut.mae_loss(y_predicted, y_gt)
maes.append(mae)
del rnn_solution
mean = np.mean(maes)
sd = np.std(maes)
metrics = {'mean':mean, 'sd':sd, 'maes':maes, 'arch':layers, 'look_back':look_back}
return metrics