def train_mlp():
dataset = pd.read_csv(file_name, header=0)
data = dataset.iloc[:, :].values
np.random.shuffle(data)
X = data[:, :14]
for i in range(len(X)):
X[i] /= 100
y = data[:, 14:]
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=.2, random_state=42)
print("vai")
start = time.time()
mlp.fit(X, y.ravel())
end = time.time()
print("Iter number: " + str(mlp.n_iter_))
print("Tempo de treinamento: " + str(round(end - start, 2)) + " segundos")
y_pred = mlp.predict(X_test)
print("Training set score: %f" % mlp.score(X_train, y_train))
print("Test set score: %f" % mlp.score(X_test, y_test))
cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
plt.figure()
helper.plot_confusion_matrix(cnf_matrix,
classes=class_names,
title='Matriz de confusão')
plt.show()
def print_test_accuracy(show_example_errors=False,
show_confusion_matrix=False):
correct, cls_pred = predict_cls_test()
acc, num_correct = cls_accuracy(correct)
num_images = len(correct)
msg = 'Accuracy on Test-set: {0:.2%} ({1}/{2})'
print(msg.format(acc, num_correct, num_images))
if show_example_errors:
print('Example Errors:')
helper.plot_example_errors(data, cls_pred, correct)
if show_confusion_matrix:
print('Confusion Matrix:')
helper.plot_confusion_matrix(cls_pred, data.test.cls, num_classes)
def main():
# Prints hyperparameters
print("================ Hyperparameters ====================")
statprinter()
# Gets tranforms
transform = dataTransforms()
# input data
trainData = trainProcess(transform)
testData = testProcess(transform)
validData, input_dimension = validProcess(transform)
# Load Data
print("================LOADING DATA====================")
trainLoader, testLoader, validLoader = getLoaders(trainData, testData,
validData)
printDataStats(trainData, testData, validData)
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
criterion, optimizer = setCriteria(model)
print("================TRAINING MODEL====================")
saved_losses, validation_losses = trainModel(trainLoader, testLoader,
model, criterion, optimizer)
# Save the model checkpoint
torch.save(model.state_dict(), 'lstm_model.ckpt')
print("================TEST MODEL====================")
correct, total, actual, pred = testModel(testLoader, model, len(testData))
accuracy = 100 * correct / total
plot(saved_losses, validation_losses, accuracy)
confusionmMat = confusion_matrix(actual, pred)
print("...................................")
print("Confusion Matrix:", confusionmMat)
print("...................................")
plt.figure()
plot_confusion_matrix(confusionmMat,
classes=classes_str1,
normalize=True,
title='Normalized confusion matrix')
perClassAccuracy(correct, total, testLoader, model)
# helper.getPCASIFT() implments PCA-SIFT analysis on a given image for image_path in image_paths: des_list.append((image_path, helper.getPCASIFT(image_path))) # stack vertically descriptors = des_list[0][1] for image_path, descriptor in des_list[0:]: descriptors = np.vstack((descriptors, descriptor)) # histogram of features test_features = np.zeros((len(image_paths), num_clusters), "float32") for i in range(len(image_paths)): words, distance = vq(des_list[i][1], voc) for w in words: test_features[i][w] += 1 # classify (prediction) retval, results, neigh_resp, dists = clf.findNearest(test_features, k=num_neighbors) correct_count = 0 for i in range(0, len(results)): if(results[i] == image_classes[i]): correct_count += 1 accuracy = round(correct_count / len(results), 2) cnf_matrix = confusion_matrix(y_true=image_classes, y_pred=results) # Plot normalized confusion matrix helper.plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True, title=str(num_clusters) + ' clusters, ' + str(num_neighbors) + ' neighbors, accuracy=' + str(accuracy))
activation='tanh',
hidden_layer_sizes=(11, 5, 3, 2, 10),
learning_rate="constant",
early_stopping=True,
max_iter=200)
nn.fit(X_train, y_train)
nn_predictions = nn.predict(X_test)
nn_accuracy = accuracy_score(y_test, nn_predictions)
print 'Wine Quality Neural Network Accuracy:', nn_accuracy
print '-----------------'
cnf_matrix = confusion_matrix(y_test, nn_predictions)
plt.figure()
class_names = sorted(y_test.unique())
plot_confusion_matrix(cnf_matrix,
classes=class_names,
title='Wine Quality Neural Network Confusion Matrix')
plt.show()
#DecisionTreeClassifier
singleDT = DecisionTreeClassifier(max_depth=43, max_features=5)
singleDT.fit(X_train, y_train)
DT_predictions = singleDT.predict(X_test)
DT_accuracy = accuracy_score(y_test, DT_predictions)
print 'Wine Quality Decision Tree Accuracy:', DT_accuracy
print '-----------------'
cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=0)
plot_learning_curve(singleDT,
'Wine Quality Decision Tree Learning Curve',
X,
hidden_layer_sizes=(11, 5, 3, 2, 10),
learning_rate="constant",
early_stopping=True,
max_iter=200)
nn.fit(X_train, y_train)
nn_predictions = nn.predict(X_test)
nn_accuracy = accuracy_score(y_test, nn_predictions)
print 'Pen Digit Neural Network Accuracy:', nn_accuracy
print '-----------------'
cnf_matrix = confusion_matrix(y_test, nn_predictions)
plt.figure()
class_names = sorted(y_test.unique())
plot_confusion_matrix(cnf_matrix,
classes=class_names,
title='Pen Digits Neural Network Confusion Matrix')
plt.show()
#DecisionTreeClassifier
singleDT = DecisionTreeClassifier(max_depth=22, max_features=5)
singleDT.fit(X_train, y_train)
DT_predictions = singleDT.predict(X_test)
DT_accuracy = accuracy_score(y_test, DT_predictions)
print 'Pen Digit Decision Tree Accuracy:', DT_accuracy
print '-----------------'
cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=0)
plot_learning_curve(singleDT,
'Pen Digit Decision Tree Learning Curve',
X,