for realization in range(20):
train, test = split_random(base, train_percentage=.8)
train, train_val = split_random(train, train_percentage=.8)
x_train = train[:, :4]
y_train = train[:, 4:]
x_train_val = train_val[:, :4]
y_train_val = train_val[:, 4:]
x_test = test[:, :4]
y_test = test[:, 4:]
validation_alphas = [0.15]
hidden = 3 * np.arange(1, 5)
simple_net = MultiLayerPerceptron(M, C, epochs=10000)
simple_net.fit(x_train, y_train, x_train_val=x_train_val, y_train_val=y_train_val, alphas=validation_alphas, hidden=hidden)
y_out_simple_net = simple_net.predict(x_test, bias=True)
y_test = simple_net.predicao(y_test)
metrics_calculator = metric(y_test, y_out_simple_net, types=['ACCURACY', 'precision', 'recall', 'f1_score'])
metric_results = metrics_calculator.calculate(average='macro')
print(metric_results)
results['cf'].append((metric_results['ACCURACY'], metrics_calculator.confusion_matrix(list(y_test),
y_out_simple_net, labels=[0,1,2])))
results['alphas'].append(simple_net.learning_rate)
train, test = split_random(df, train_percentage=.8)
train, train_val = split_random(train, train_percentage=.8)
x_train = train[features]
y_train = train[target].to_numpy().reshape(train[target].shape[0], 1)
x_train_val = train_val[features]
y_train_val = train_val[target]
x_test = test[features]
y_test = test[target]
y_test.to_numpy().reshape(y_test.shape[0], 1)
validation_alphas = [0.008]
hidden = [8, 10, 12, 16]
simple_net = MultiLayerPerceptron(M, C, epochs=epochs, Regressao=True)
simple_net.fit(x_train.to_numpy(),
y_train,
x_train_val=x_train_val.to_numpy(),
y_train_val=y_train_val,
alphas=validation_alphas,
hidden=hidden,
validation=False)
y_out = simple_net.predict(x_test, bias=True)
metrics_calculator = metric(y_test, y_out, types=['MSE', 'RMSE', 'R2'])
metric_results = metrics_calculator.calculate()
print(metric_results)
results['error_per_epoch'].append(
# ------------------------------ x and y for validation -----------------------------------
# x_train_val = train_val[features]
# y_train_val = train_val[different_target]
# ------------------------------ x and y for test ------------------------------------------
x_test = test[features]
y_test = test[different_target]
y_test.to_numpy().reshape(y_test.shape[0], 1)
# ---------------------------------- modeling ----------------------------------------------
validation_alphas = [0.15]
hidden = 4 * np.arange(1, 5)
simple_net = MultiLayerPerceptron(9, C, epochs=1000, Regressao=True, hidden_layer_neurons=12, learning_rate=0.15)
simple_net.fit(x_train.to_numpy(), y_train,
x_train_val=[], y_train_val=[],
alphas=validation_alphas,
hidden=hidden,
validation=False)
y_out = simple_net.predict(x_test, bias=True)
metrics_calculator = metric(y_test, y_out, types=['MSE', 'RMSE'])
metric_results = metrics_calculator.calculate()
print(metric_results)
results['alphas'].append(simple_net.lr)
results['realization'].append(realization)
for type in ['MSE', 'RMSE']:
train, train_val = split_random(train, train_percentage=.8)
x_train = train[['x1']]
y_train = train[['y']]
x_train_val = train_val[['x1']]
y_train_val = train_val[['y']]
x_test = test[['x1']]
y_test = test[['y']]
validation_alphas = [0.15]
hidden = 3 * np.arange(1, 6)
simple_net = MultiLayerPerceptron(M,
C,
epochs=epochs,
Regressao=True,
learning_rate=0.05,
hidden_layer_neurons=12)
simple_net.fit(x_train,
y_train.to_numpy(),
x_train_val=x_train_val,
y_train_val=y_train_val.to_numpy(),
alphas=validation_alphas,
hidden=hidden)
y_out = simple_net.predict(x_test, bias=True)
metrics_calculator = metric(y_test, y_out, types=['MSE', 'RMSE', 'R2'])
metric_results = metrics_calculator.calculate()
print(metric_results)