mean_tpr += interp(mean_fpr, fpr, tpr)
mean_tpr[0] = 0.0
roc_auc = compute_auc(homesite.train_y[test], probas_[:, 1])
fold_cm = confusion_matrix(homesite.train_y[test], np.round(probas_)[:, 1])
confusion_matrix_history = np.dstack((confusion_matrix_history, fold_cm))
accuracy, precision, recall = compute_performance_metrics(fold_cm)
mean_acc += accuracy
mean_recall += recall
mean_precision += precision
accuracy_history.append(accuracy)
precision_history.append(precision)
recall_history.append(recall)
auc_history.append(roc_auc)
save_np_array("../../results/random_forests/rf_accuracy_" + str(c) + ".bin", np.array(accuracy_history))
save_np_array("../../results/random_forests/rf_precision_" + str(c) + ".bin", np.array(precision_history))
save_np_array("../../results/random_forests/rf_recall_" + str(c) + ".bin", np.array(recall_history))
save_np_array("../../results/random_forests/rf_auc_" + str(c) + ".bin", np.array(auc_history))
save_np_array("../../results/random_forests/rf_confusion_matrix_" + str(c) + ".bin", np.array(confusion_matrix_history))
plt.plot(fpr, tpr, lw = 1, label = 'ROC fold %d (area = %0.2f)' % (i, roc_auc))
mean_acc /= len(cvs)
mean_recall /= len(cvs)
mean_precision /= len(cvs)
mean_tpr /= len(cvs)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
plot_roc(mean_fpr, mean_tpr, mean_auc)
np.round(probas_)[:, 1])
confusion_matrix_history = np.dstack(
(confusion_matrix_history, fold_cm))
accuracy, precision, recall = compute_performance_metrics(fold_cm)
mean_acc += accuracy
mean_recall += recall
mean_precision += precision
accuracy_history.append(accuracy)
precision_history.append(precision)
recall_history.append(recall)
auc_history.append(roc_auc)
save_np_array("results/ada_accuracy_" + str(c) + ".bin",
np.array(accuracy_history))
save_np_array("results/ada_precision_" + str(c) + ".bin",
np.array(precision_history))
save_np_array("results/ada_recall_" + str(c) + ".bin",
np.array(recall_history))
save_np_array("results/ada_auc_" + str(c) + ".bin",
np.array(auc_history))
plt.plot(fpr,
tpr,
lw=1,
label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
mean_acc /= len(cvs)
mean_recall /= len(cvs)
mean_precision /= len(cvs)
# homesite.train_y = homesite.train_y[reduced_range]
C = [0.2, 0.4, 0.6, 0.8, 1]
for c in C:
# Creating classifier.
clf = svm.SVC(kernel='linear', class_weight='balanced', C=c)
# Train classifier.
print "Training classifier."
clf.fit(homesite.train_x, homesite.train_y)
# Test classifier.
print 'Testing classifier.'
predicted_labels = clf.predict(homesite.validation_x)
# Show final results.
results = confusion_matrix(homesite.validation_y,
np.round(predicted_labels))
accuracy, precision, recall = compute_performance_metrics(results)
auc = compute_auc(homesite.validation_y, predicted_labels)
result = [c, precision, recall, accuracy, auc]
wr.writerow(result)
save_np_array("results/svm_accuracy.bin", np.array(accuracy_history))
save_np_array("results/svm_precision.bin", np.array(precision_history))
save_np_array("results/svm_recall.bin", np.array(recall_history))
save_np_array("results/svm_auc.bin", np.array(auc_history))
del clf
# Test classifier.
print 'Testing classifier.'
predicted_labels = clf.predict_proba(homesite.validation_x)[:, 1]
# Show final results.
results = confusion_matrix(homesite.validation_y,
np.round(predicted_labels))
accuracy, precision, recall = compute_performance_metrics(results)
auc = compute_auc(homesite.validation_y, predicted_labels)
accuracy_history.append(accuracy)
precision_history.append(precision)
recall_history.append(recall)
auc_history.append(auc)
print 'Saving result.', i * 10
save_np_array(
"../homesite_data/results/random_forest_grid_search_accuracy.bin",
np.array(accuracy_history))
save_np_array(
"../homesite_data/results/random_forest_grid_search_precision.bin",
np.array(precision_history))
save_np_array(
"../homesite_data/results/random_forest_grid_search_recall.bin",
np.array(recall_history))
save_np_array(
"../homesite_data/results/random_forest_grid_search_auc.bin",
np.array(auc_history))
del clf
roc_auc = compute_auc(homesite.train_y[test], probas_[:, 1])
fold_cm = confusion_matrix(homesite.train_y[test],
np.round(probas_)[:, 1])
confusion_matrix_history = np.dstack(
(confusion_matrix_history, fold_cm))
accuracy, precision, recall = compute_performance_metrics(fold_cm)
mean_acc += accuracy
mean_recall += recall
mean_precision += precision
accuracy_history.append(accuracy)
precision_history.append(precision)
recall_history.append(recall)
auc_history.append(roc_auc)
save_np_array("../../results/ANN/ann_accuracy_" + str(c) + ".bin",
np.array(accuracy_history))
save_np_array("../../results/ANN/ann_precision_" + str(c) + ".bin",
np.array(precision_history))
save_np_array("../../results/ANN/ann_recall_" + str(c) + ".bin",
np.array(recall_history))
save_np_array("../../results/ANN/ann_auc_" + str(c) + ".bin",
np.array(auc_history))
save_np_array(
"../../results/ANN/ann_confusion_matrix_" + str(c) + ".bin",
np.array(confusion_matrix_history))
plt.plot(fpr,
tpr,
lw=1,
label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
mean_acc /= len(cvs)