class MLPClassifierImpl():
def __init__(self, hidden_layer_sizes=(100,), activation='relu', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10):
self._hyperparams = {
'hidden_layer_sizes': hidden_layer_sizes,
'activation': activation,
'solver': solver,
'alpha': alpha,
'batch_size': batch_size,
'learning_rate': learning_rate,
'learning_rate_init': learning_rate_init,
'power_t': power_t,
'max_iter': max_iter,
'shuffle': shuffle,
'random_state': random_state,
'tol': tol,
'verbose': verbose,
'warm_start': warm_start,
'momentum': momentum,
'nesterovs_momentum': nesterovs_momentum,
'early_stopping': early_stopping,
'validation_fraction': validation_fraction,
'beta_1': beta_1,
'beta_2': beta_2,
'epsilon': epsilon,
'n_iter_no_change': n_iter_no_change}
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def predict(self, X):
return self._sklearn_model.predict(X)
def predict_proba(self, X):
return self._sklearn_model.predict_proba(X)
StandardScaler(copy=True, with_mean=True, with_std=True)
#now apply the transformations to the data:
x_train_nn = scaler.transform(X)
x_test_nn = scaler.transform(X_test)
nn = MLPClassifier(solver='lbfgs',
alpha=1e-5,
hidden_layer_sizes=(5, 2),
random_state=1)
print(nn.fit(x_train_nn, y))
print('Neural network model:')
nn_pred_test = nn.predict(x_test_nn)
#compute confusion matrix
from sklearn import metrics
#pred_obj = np.where(predictions==predictions[0],'N','Y')
#print(pred_obj)
cnf_matrix = metrics.confusion_matrix(y_test, nn_pred_test)
print(cnf_matrix)
#compute roc cureve
import matplotlib.pyplot as plt
y_pred_proba = nn.predict_proba(X_test)[::, 1]
y_binary = np.where(y == 'N', 0, 1)
fpr, tpr, _ = metrics.roc_curve(y_binary, y_pred_proba)
auc = metrics.roc_auc_score(y_binary, y_pred_proba)
plt.plot(fpr, tpr, label="data 1, auc=" + str(auc))
plt.legend(loc=4)
plt.show()