def test_error(test_activites, hmms):
total = 0.
incorrect = 0.
y_test = []
y_pred = []
activity_error = [0]*(len(hmms)+1);
activity_count = [0]*(len(hmms)+1);
for a in test_activites:
for x in test_activites[a]:
total += 1
activity_count[a] += 1;
y_test.append(a)
pred = classify_activity(x, hmms)
y_pred.append(pred)
#print pred
if a != pred:
incorrect += 1
activity_error[a] += 1;
for a in test_activites:
print "Activity " + str(a) + ": " + str(float(activity_error[a])/activity_count[a]);
utils.show_confusion_matrix(y_test, y_pred)
return incorrect/total
nbAccuracy = nbClf.score(X_test, Y_test)
# Predictions
treePrediction = treeClf.predict(X_test)
neighborsPrediction = neighborsClf.predict(X_test)
logPrediction = logClf.predict(X_test)
nbPrediction = nbClf.predict(X_test)
# Confusion matrices
treeCm = metrics.confusion_matrix(treePrediction, Y_test)
neighborsCm = metrics.confusion_matrix(neighborsPrediction, Y_test)
logCm = metrics.confusion_matrix(logPrediction, Y_test)
nbCm = metrics.confusion_matrix(nbPrediction, Y_test)
# Matplotlib Images
show_confusion_matrix('Tree', treeCm, labels)
show_confusion_matrix('K-Neighbors', neighborsCm, labels)
show_confusion_matrix('Logistic Regression', logCm, labels)
show_confusion_matrix('Naive Bayes', nbCm, labels)
# Terminal Outputs ...
# Accuracies
print('Tree accuracy: ', treeAccuracy)
print('Neighbors accuracy: ', neighborsAccuracy)
print('Logistic regression accuracy: ', logAccuracy)
print('Naive bayes accuracy: ', nbAccuracy)
# Predicitons
print('Tree predicition: ', treePrediction)
print('Neighbor prediction: ', neighborsPrediction)
print('Logistic regression prediction: ', logPrediction)
if args.load:
net = torch.load(args.load)
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
criterion = nn.CrossEntropyLoss()
model = Model(net, optimizer, criterion)
if args.trainable:
train_history = model.train(train_loader,
epochs=args.epochs,
val_loader=test_loader)
show_history(train_history,
f'history_{args.net}_{args.pretrained}.jpg')
with open(f'score_{args.net}_{args.pretrained}.txt',
'w') as score_file:
print(train_history['accuracy'], file=score_file)
print(train_history['val_accuracy'], file=score_file)
if args.save:
model.save(args.save)
test_history = model.test(test_loader)
show_confusion_matrix(
test_history['truth'], test_history['predict'],
f'confusion_matrix_{args.net}_{args.pretrained}.jpg')
def run_method(clf, X, y):
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=0)
clf.fit(X_train, y_train)
return clf.predict(X_test), y_test, (1 - clf.score(X_train, y_train)), (1 - clf.score(X_test, y_test))
if __name__ == "__main__":
methods = {"random_forest": RandomForestClassifier, "svm": svm.SVC}
sources = {"time": data.load_time, "fourier": data.load_fourier, "raw": data.load_raw}
if len(sys.argv) > 1:
method = sys.argv[1]
source = "time"
if len(sys.argv) > 2:
source = sys.argv[2]
X, y = sources[source]()
y_pred, y_test, train, test = run_method(methods[method](), X, y)
utils.show_confusion_matrix(y_test, y_pred)
print "training error: %f" % (train)
print "testing error: %f" % (test)
1 - clf.score(X_test, y_test))
if __name__ == "__main__":
methods = {
"random_forest": RandomForestClassifier,
"svm": svm.SVC,
}
sources = {
'time': data.load_time,
'fourier': data.load_fourier,
'raw': data.load_raw
}
if len(sys.argv) > 1:
method = sys.argv[1]
source = "time"
if len(sys.argv) > 2:
source = sys.argv[2]
X, y = sources[source]()
y_pred, y_test, train, test = run_method(methods[method](), X, y)
utils.show_confusion_matrix(y_test, y_pred)
print "training error: %f" % (train)
print "testing error: %f" % (test)
