def main():
data = Data()
logistic_regression = models.LogisticRegression()
neural_network = models.NeuralNet()
svm = models.SupportVectorMachine(C=1.0, kernel='rbf', gamma='scale')
random_forest = models.RandomForest(n_estimators=100,
max_depth=None,
random_state=None)
# Process dataset
training_data_features, training_data_labels, mnist_test_data_features, mnist_test_data_labels, \
usps_test_data_features, usps_test_data_labels, combined_test_data_features, combined_test_data_labels = \
data.pre_process()
# Logistic Regression
logistic_regression.fit(training_data_features,
training_data_labels,
learning_rate=0.01,
epochs=500)
accuracy_mnist, confusion_mnist = logistic_regression.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = logistic_regression.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = logistic_regression.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Logistic Regression', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)
# Neural Network
neural_network.fit(training_data_features, training_data_labels, epochs=10)
accuracy_mnist, confusion_mnist = neural_network.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = neural_network.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = neural_network.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Neural Network', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)
# Support Vector Machine
svm.fit(training_data_features, training_data_labels)
accuracy_mnist, confusion_mnist = svm.predict(mnist_test_data_features,
mnist_test_data_labels)
accuracy_usps, confusion_usps = svm.predict(usps_test_data_features,
usps_test_data_labels)
accuracy_combined, confusion_combined = svm.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('SVM', accuracy_mnist, accuracy_usps, accuracy_combined,
confusion_mnist, confusion_usps, confusion_combined)
# Random Forest
random_forest.fit(training_data_features, training_data_labels)
accuracy_mnist, confusion_mnist = random_forest.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = random_forest.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = random_forest.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Random Forest', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)
]
train_datasets = list()
for f in train_files:
temp = MatDNNDataset(dataset_dir + f, args.window_size)
train_datasets.append(temp)
validation_dataset = MatDNNDataset("data/Panasonic 18650PF Data/0degC/Drive cycles/06-02-17_10.43 0degC_HWFET_Pan18650PF.mat", args.window_size)
# Data loader
train_loaders = list()
for d in train_datasets:
temp = DataLoader(dataset=d, batch_size=args.batch_size, shuffle=True)
train_loaders.append(temp)
validation_loader = DataLoader(validation_dataset, batch_size=args.batch_size, shuffle=False)
# Model, loss, and optimizer
model = models.NeuralNet(args.input_size, args.hidden_size, args.num_classes, args.noise_std).to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
def evaluate(dataloader):
total_loss = 0.0
num_batches = 0.0
model.eval()
with torch.no_grad():
for (x,y) in dataloader:
x, y = x.to(device), y.to(device)
outputs = model(x)
loss = criterion(outputs, y)
total_loss += loss.item()
num_batches += 1
model.train()
def main():
data = Data()
logistic_regression = models.LogisticRegression()
neural_network = models.NeuralNet()
svm = models.SupportVectorMachine(C=1.0, kernel='rbf', gamma='scale')
random_forest = models.RandomForest(n_estimators=100,
max_depth=None,
random_state=None)
discriminant_analysis = DiscriminantAnalysis()
vaecnn = deep_learning_models.VAEConvolutionNeuralNet(
input_data.read_data_sets("data", one_hot=True), (28, 28), (28, 28))
# Process dataset
training_data_features, training_data_labels, mnist_test_data_features, mnist_test_data_labels, \
usps_test_data_features, usps_test_data_labels, combined_test_data_features, combined_test_data_labels = \
data.pre_process()
# Discriminant Analysis
IMAGE_SIZE = int(training_data_features.shape[-1]**0.5)
discriminant_analysis.fit(
training_data_features.reshape((-1, IMAGE_SIZE, IMAGE_SIZE)),
training_data_labels)
accuracy_mnist, confusion_mnist = discriminant_analysis.predict(
'MNIST dataset',
mnist_test_data_features.reshape((-1, IMAGE_SIZE, IMAGE_SIZE)),
mnist_test_data_labels)
accuracy_usps, confusion_usps = discriminant_analysis.predict(
'USPS dataset',
usps_test_data_features.reshape((-1, IMAGE_SIZE, IMAGE_SIZE)),
usps_test_data_labels)
accuracy_combined, confusion_combined = discriminant_analysis.predict(
'Combined dataset',
combined_test_data_features.reshape((-1, IMAGE_SIZE, IMAGE_SIZE)),
combined_test_data_labels)
print_and_plot('Bayesian Discriminant Analysis', accuracy_mnist,
accuracy_usps, accuracy_combined, confusion_mnist,
confusion_usps, confusion_combined)
# Logistic Regression
logistic_regression.fit(training_data_features,
training_data_labels,
learning_rate=0.01,
epochs=500)
accuracy_mnist, confusion_mnist = logistic_regression.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = logistic_regression.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = logistic_regression.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Logistic Regression', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)
# Neural Network
neural_network.fit(training_data_features, training_data_labels, epochs=10)
accuracy_mnist, confusion_mnist = neural_network.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = neural_network.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = neural_network.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Neural Network', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)
# Support Vector Machine
svm.fit(training_data_features, training_data_labels)
accuracy_mnist, confusion_mnist = svm.predict(mnist_test_data_features,
mnist_test_data_labels)
accuracy_usps, confusion_usps = svm.predict(usps_test_data_features,
usps_test_data_labels)
accuracy_combined, confusion_combined = svm.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('SVM', accuracy_mnist, accuracy_usps, accuracy_combined,
confusion_mnist, confusion_usps, confusion_combined)
# Random Forest
random_forest.fit(training_data_features, training_data_labels)
accuracy_mnist, confusion_mnist = random_forest.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = random_forest.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = random_forest.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Random Forest', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)
# Restricted Boltzmann Machine
num_hidden_nodes_list = [20, 100, 500]
for num_hidden_nodes in num_hidden_nodes_list:
rbm = deep_learning_models.RBM(images=input_data.read_data_sets(
"data", one_hot=True),
n_components=num_hidden_nodes,
learning_rate=0.02,
batch_size=100,
n_iter=1000,
random_state=0)
rbm.fit()
rbm.gibbs_sampling(1000)
rbm.generate_images(num_hidden_nodes)
# Variational Auto Encoders
code_unit_list = [2, 8, 16]
for code_unit in code_unit_list:
vae = deep_learning_models.VAE(
input_data.read_data_sets("data", one_hot=True), code_unit)
vae.generate_images(epochs=20)
# Variational Auto Encoders with Convolutional Neural Networks
vaecnn.encode()
vaecnn.decode()
vaecnn.compile_()
vaecnn.train(epochs=10, batch_size=100)
for train_index, test_index in group_kfold.split(Xdata, Ydata, groups):
model.train(Xdata[train_index], Ydata[train_index])
Ypred = model.test(Xdata[test_index])
confusion = sklearn.metrics.confusion_matrix(Ydata[test_index], Ypred,
labels=features.labels)
if sum_confusion is None:
sum_confusion = np.zeros(confusion.shape)
sum_confusion += confusion
return sum_confusion / k
def select_best_model(Xdata, Ydata, models):
avg_accuracies = [(i, k_fold_cross_validate(Xdata, Ydata, 4, model)) for
i, model in enumerate(models)]
print(avg_accuracies)
return max(avg_accuracies, key=operator.itemgetter(1))
allfeatures = features.compute_or_read_features()
Xdata, Ydata = to_numpy_arrays(allfeatures)
models = [models.RandomForest(200, 'gini'), models.LogisticRegression(),
models.SVMNonLinear('rbf'), models.SVMNonLinear('sigmoid'),
models.NeuralNet(), models.KNN()]
#best = select_best_model(Xdata, Ydata, models)
#print(best)
for model in models:
cm = k_fold_confusion_matrix(Xdata, Ydata, 4, model)
save_confusion_matrix(cm, model._name)
print(f"Confusion matrix for {model._name} saved")
def main():
data = Data()
logistic_regression = models.LogisticRegression()
neural_network = models.NeuralNet()
svm = models.SupportVectorMachine(C=1.0, kernel='rbf', gamma='scale')
random_forest = models.RandomForest(n_estimators=100,
max_depth=None,
random_state=None)
discriminant_analysis = DiscriminantAnalysis()
# Process dataset
training_data_features, training_data_labels, mnist_test_data_features, mnist_test_data_labels, \
usps_test_data_features, usps_test_data_labels, combined_test_data_features, combined_test_data_labels = \
data.pre_process()
# Discriminant Analysis
IMAGE_SIZE = int(training_data_features.shape[-1]**0.5)
discriminant_analysis.fit(
training_data_features.reshape((-1, IMAGE_SIZE, IMAGE_SIZE)),
training_data_labels)
accuracy_mnist, confusion_mnist = discriminant_analysis.predict(
'MNIST dataset',
mnist_test_data_features.reshape((-1, IMAGE_SIZE, IMAGE_SIZE)),
mnist_test_data_labels)
accuracy_usps, confusion_usps = discriminant_analysis.predict(
'USPS dataset',
usps_test_data_features.reshape((-1, IMAGE_SIZE, IMAGE_SIZE)),
usps_test_data_labels)
accuracy_combined, confusion_combined = discriminant_analysis.predict(
'Combined dataset',
combined_test_data_features.reshape((-1, IMAGE_SIZE, IMAGE_SIZE)),
combined_test_data_labels)
print_and_plot('Bayesian Discriminant Analysis', accuracy_mnist,
accuracy_usps, accuracy_combined, confusion_mnist,
confusion_usps, confusion_combined)
# Logistic Regression
logistic_regression.fit(training_data_features,
training_data_labels,
learning_rate=0.01,
epochs=500)
accuracy_mnist, confusion_mnist = logistic_regression.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = logistic_regression.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = logistic_regression.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Logistic Regression', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)
# Neural Network
neural_network.fit(training_data_features, training_data_labels, epochs=10)
accuracy_mnist, confusion_mnist = neural_network.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = neural_network.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = neural_network.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Neural Network', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)
# Support Vector Machine
svm.fit(training_data_features, training_data_labels)
accuracy_mnist, confusion_mnist = svm.predict(mnist_test_data_features,
mnist_test_data_labels)
accuracy_usps, confusion_usps = svm.predict(usps_test_data_features,
usps_test_data_labels)
accuracy_combined, confusion_combined = svm.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('SVM', accuracy_mnist, accuracy_usps, accuracy_combined,
confusion_mnist, confusion_usps, confusion_combined)
# Random Forest
random_forest.fit(training_data_features, training_data_labels)
accuracy_mnist, confusion_mnist = random_forest.predict(
mnist_test_data_features, mnist_test_data_labels)
accuracy_usps, confusion_usps = random_forest.predict(
usps_test_data_features, usps_test_data_labels)
accuracy_combined, confusion_combined = random_forest.predict(
combined_test_data_features, combined_test_data_labels)
print_and_plot('Random Forest', accuracy_mnist, accuracy_usps,
accuracy_combined, confusion_mnist, confusion_usps,
confusion_combined)