for j, lmbd in enumerate(lmbd_vals):
for k, act_h in enumerate(acts_hidden):
for l, hn in enumerate(hidden_neurons):
dnn = NeuralNetwork(XTrain, Y_train_onehot.toarray(), XTest, Y_test_onehot.toarray(), cost=cost, batch_size=batch_size, eta=eta, lmbd=lmbd, n_hidden_neurons=hn, act_h=act_h, epochs=epochs, tol = tol)
costs, scores = dnn.train()
accuracy = scores[-1,1,0]
rocauc = scores[-1,1,1]
#dnn.plot_costs(color_iter)
dnn.plot_scores(color_iter)
color_iter += 1
yTrue, yPred = yTest, dnn.predict(XTest)
logreg.own_classification_report(yTest,yPred)
if metric=="accuracy":
if accuracy > best_accuracy:
best_accuracy = accuracy
best_eta = eta
best_lmbd = lmbd
best_hn = hn
best_act_h = act_h
best_rocauc = rocauc
elif metric=="roc_auc":
if rocauc > best_rocauc:
best_accuracy = accuracy
best_eta = eta
best_lmbd = lmbd
best_hn = hn
best_act_h = act_h
batch_size=100,
n_epoch=25,
verbosity=1,
n_iter=10,
new_per_iter=False
) # Fit using SGD. This can be looped over for best lambda (i.e. learning rate 'lr').
else:
beta, costs = logreg.GD(
XTrain, yTrain.ravel(), lr=lrs[j], rnd_seed=True, tol=1e-2
) # Fit using GD. This can be looped over for best lambda (i.e. learning rate 'lr').
betas = beta.copy()
if plt_cost:
plt.plot(costs, label='%5.3f' % lrs[i])
yPred = logreg.predict(XTest) #predict
f1, ac1 = logreg.own_classification_report(yTest,
yPred,
return_f1=True,
return_ac=True)
yPred = logreg.predict(XTrain) #predict
f2, ac2 = logreg.own_classification_report(yTrain,
yPred,
return_f1=True,
return_ac=True)
f3 = (f1 + f2) / 2.0
ac = (ac1 + ac2) / 2.0
f1_log.append(f1)
f3_log.append(f3)
ac_log.append(ac)
ac1_log.append(ac1)
if (return_ar):
ar = logreg.plot_cumulative(XTest, yTest, return_ar=return_ar)
ar_log.append(ar)
