def gridsearch_reservoir_computing(self,
train_X,
train_y,
test_X,
test_y,
per_time_step=False,
error='mse',
gridsearch_training_frac=0.7):
tuned_parameters = {
'a': [0.6, 0.8],
'reservoir_size': [400, 700, 1000]
}
# tuned_parameters = {'a': [0.4], 'reservoir_size':[250]}
params = tuned_parameters.keys()
combinations = self.generate_parameter_combinations(
tuned_parameters, params)
split_point = int(gridsearch_training_frac * len(train_X.index))
train_params_X = train_X.ix[0:split_point, ]
test_params_X = train_X.ix[split_point:len(train_X.index), ]
train_params_y = train_y.ix[0:split_point, ]
test_params_y = train_y.ix[split_point:len(train_X.index), ]
if error == 'mse':
best_error = sys.float_info.max
elif error == 'accuracy':
best_error = 0
best_combination = []
for comb in combinations:
print comb
# Order of the keys might have changed.
keys = tuned_parameters.keys()
pred_train_y, pred_test_y, pred_train_y_prob, pred_test_y_prob = self.reservoir_computing(
train_params_X,
train_params_y,
test_params_X,
test_params_y,
reservoir_size=comb[keys.index('reservoir_size')],
a=comb[keys.index('a')],
per_time_step=per_time_step,
gridsearch=False)
if error == 'mse':
eval = RegressionEvaluation()
mse = eval.mean_squared_error(test_params_y, pred_test_y_prob)
if mse < best_error:
best_error = mse
best_combination = comb
elif error == 'accuracy':
eval = ClassificationEvaluation()
acc = eval.accuracy(test_params_y, pred_test_y)
if acc > best_error:
best_error = acc
best_combination = comb
print '-------'
print best_combination
print '-------'
return best_combination[keys.index(
'reservoir_size')], best_combination[keys.index('a')]
def gridsearch_recurrent_neural_network(self,
train_X,
train_y,
test_X,
test_y,
error='accuracy',
gridsearch_training_frac=0.7):
tuned_parameters = {
'n_hidden_neurons': [50, 100],
'iterations': [250, 500],
'outputbias': [True]
}
params = list(tuned_parameters.keys())
combinations = self.generate_parameter_combinations(
tuned_parameters, params)
split_point = int(gridsearch_training_frac * len(train_X.index))
train_params_X = train_X.iloc[0:split_point, ]
test_params_X = train_X.iloc[split_point:len(train_X.index), ]
train_params_y = train_y.iloc[0:split_point, ]
test_params_y = train_y.iloc[split_point:len(train_X.index), ]
if error == 'mse':
best_error = sys.float_info.max
elif error == 'accuracy':
best_error = 0
best_combination = []
for comb in combinations:
print(comb)
# Order of the keys might have changed.
keys = list(tuned_parameters.keys())
# print(keys)
pred_train_y, pred_test_y, pred_train_y_prob, pred_test_y_prob = self.recurrent_neural_network(
train_params_X,
train_params_y,
test_params_X,
test_params_y,
n_hidden_neurons=comb[keys.index('n_hidden_neurons')],
iterations=comb[keys.index('iterations')],
outputbias=comb[keys.index('outputbias')],
gridsearch=False)
if error == 'mse':
eval = RegressionEvaluation()
mse = eval.mean_squared_error(test_params_y, pred_test_y_prob)
if mse < best_error:
best_error = mse
best_combination = comb
elif error == 'accuracy':
eval = ClassificationEvaluation()
acc = eval.accuracy(test_params_y, pred_test_y)
if acc > best_error:
best_error = acc
best_combination = comb
print('-------')
print(best_combination)
print('-------')
return best_combination[params.index(
'n_hidden_neurons')], best_combination[params.index(
'iterations')], best_combination[params.index('outputbias')]
