validation_alphas = linspace(0.015, 0.01, 20)
for realization in range(1):
train, test = split_random(iris_base, train_percentage=0.8)
train, train_val = split_random(train, train_percentage=0.8)
x_train = train.drop(['Species'], axis=1)
y_train = train['Species']
x_train_val = train_val.drop(['Species'], axis=1)
y_train_val = train_val['Species']
x_test = test.drop(['Species'], axis=1)
y_test = test['Species']
classifier_perceptron = sigmoid_perceptron_network(
epochs=10000, learning_rate=0.01)
classifier_perceptron.fit(x_train.to_numpy(),
y_train.to_numpy(),
x_train_val.to_numpy(),
y_train_val.to_numpy(),
alphas=validation_alphas)
y_out_perceptron = classifier_perceptron.predict(x_test.to_numpy())
metrics_calculator = metric(
list(y_test),
y_out_perceptron,
types=['ACCURACY', 'AUC', 'precision', 'recall', 'f1_score'])
metric_results = metrics_calculator.calculate(average='macro')
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[:, :2]
y_train = train[:, 2:]
x_train_val = train_val[:, :2]
y_train_val = train_val[:, 2:]
x_test = test[:, :2]
y_test = test[:, 2:]
validation_alphas = linspace(0.015, 0.1, 20)
simple_net = sigmoid_perceptron_network(epochs=10000,
number_of_neurons=3,
learning_rate=0.1)
simple_net.fit(x_train,
y_train,
x_train_val,
y_train_val,
alphas=validation_alphas)
y_out_simple_net = simple_net.predict(x_test)
y_out = out_of_c_to_label(y_out_simple_net)
y_test = out_of_c_to_label(y_test)
metrics_calculator = metric(
y_test,
y_out,
types=['ACCURACY', 'precision', 'recall', 'f1_score'])
train, test = split_random(base, train_percentage=.8)
train, train_val = split_random(train, train_percentage=.8)
x_train = train[:, :2]
y_train = train[:, 2:]
x_train_val = train_val[:, :2]
y_train_val = train_val[:, 2:]
x_test = test[:, :2]
y_test = test[:, 2:]
validation_alphas = linspace(0.015, 0.1, 20)
sigmoid_net = sigmoid_perceptron_network(
epochs=1000,
number_of_neurons=1,
learning_rate=0.1,
activation_function='sigmoid logistic')
sigmoid_net.fit(x_train,
y_train,
x_train_val,
y_train_val,
alphas=validation_alphas,
validation=False)
y_out = sigmoid_net.predict(x_test)
metrics_calculator = metric(
y_test,
y_out,
types=['ACCURACY', 'precision', 'recall', 'f1_score'])