def get_metrics(dict_test_features):
metrics_matrix = []
n = len(classes())
m = 4
for subset in itertools.combinations(classes(), 2):
class_0 = subset[0]
class_1 = subset[1]
X_train_0 = dataloader.dict_train_features(class_0)
X_train_1 = dataloader.dict_train_features(class_1)
X_train = np.concatenate((X_train_0, X_train_1), axis=0)
X_train = X_train[:, dataloader.columns_selected()]
y_train_0 = np.zeros((X_train_0.shape[0],))
y_train_1 = np.ones((X_train_1.shape[0],))
y_train = np.concatenate((y_train_0, y_train_1), axis=0)
X_test_0 = dict_test_features[class_0]
X_test_1 = dict_test_features[class_1]
X_test = np.concatenate((X_test_0, X_test_1), axis=0)
X_test = X_test[:, dataloader.columns_selected()]
y_test_0 = np.zeros((X_test_0.shape[0],))
y_test_1 = np.ones((X_test_1.shape[0],))
y_test = np.concatenate((y_test_0, y_test_1), axis=0)
feat_max = np.max(X_train, axis=0)
feat_min = np.min(X_train, axis=0)
X_train_normalized = (X_train - feat_min) / (feat_max - feat_min)
X_test_normalized = (X_test - feat_min) / (feat_max - feat_min)
SVM_parameters = {
'C': 1,
'kernel': 'rbf',
}
clf = sklearn.svm.SVC(**SVM_parameters)
clf.fit(X_train_normalized, y_train)
y_test_predicted = clf.predict(X_test_normalized)
print("{} // {}".format(class_0, class_1))
metrics_couple = compute_metrics(y_test, y_test_predicted)
metrics_matrix = np.append(metrics_matrix, metrics_couple)
metrics_matrix = np.reshape(metrics_matrix, (n, 4))
compute_overall_metrics(metrics_matrix)
def test():
# begin compute and select features
path = pathlib.Path(__file__).parent.absolute()
classes = user_interface.classes()
if user_interface.generate_datasets():
dict_test_features = {'NoFX': [], 'Distortion': [], 'Tremolo': []}
for c in classes:
dict_test_features[c] = dataloader.dict_test_feats(c)
else:
dict_test_features = testloop.getdicttestfeatures(
path) # test features
X_test = [dict_test_features[c] for c in user_interface.classes()]
columns_selected = dataloader.columns_selected(
) # positions of selected features
X_test_selected = [
X_test[i][:, columns_selected]
for i in np.arange(len(user_interface.classes()))
] # selection
y_test = [
np.ones(X_test[i].shape[0], ) * i
for i in np.arange(len(user_interface.classes()))
] # keys
y_test_mc = np.concatenate((y_test[0], y_test[1], y_test[2]), axis=0)
X_test_normalized = [
((X_test_selected[c] - dataloader.featmin()[columns_selected]) /
(dataloader.featmax()[columns_selected] -
dataloader.featmin()[columns_selected]))
for c in np.arange(len(user_interface.classes()))
] # normalized matrix
X_train_normalized_loaded = [
(dataloader.dict_train_features(c) - dataloader.featmin()) /
(dataloader.featmax() - dataloader.featmin())
for c in user_interface.classes()
] # train features
X_train_normalized_loaded_selected = [
X_train_normalized_loaded[i][:, columns_selected]
for i in np.arange(len(user_interface.classes()))
] # selection
X_test_mc_normalized = np.concatenate(
(X_test_normalized[0], X_test_normalized[1], X_test_normalized[2]),
axis=0)
y_train_selected = [
np.ones(X_train_normalized_loaded_selected[i].shape[0], ) * i
for i in np.arange(len(user_interface.classes()))
]
# end compute and select features
y_test_predicted_mv = supportvectormachines.getpredictions(
X_train_normalized_loaded_selected, y_train_selected,
X_test_mc_normalized) # SVM
print('\n\nMetrics:')
metrics.get_metrics(dict_test_features)
print('\n\nConfusion matrix:')
confusionmatrix.compute_cm_multiclass(
y_test_mc, y_test_predicted_mv) # print confusion matrix
return True
def train():
path = pathlib.Path(__file__).parent.absolute()
classes = user_interface.classes()
if user_interface.generate_datasets():
main_traintest.traintest(path)
dict_train_features = {'NoFX': [], 'Distortion': [], 'Tremolo': []}
for c in classes:
dict_train_features[c] = dataloader.dict_train_features(c)
else:
dict_train_features = trainingloop.getdicttrainfeatures(
path) # compute train features
X_train = [dict_train_features[c] for c in classes]
y_train = [
np.ones(X_train[i].shape[0], ) * i
for i in np.arange(len(user_interface.classes()))
] # keys
feat_max = np.max(np.concatenate((X_train[0], X_train[1], X_train[2]),
axis=0),
axis=0)
feat_min = np.min(np.concatenate((X_train[0], X_train[1], X_train[2]),
axis=0),
axis=0)
X_train_normalized = [(X_train[c] - feat_min) / (feat_max - feat_min)
for c in np.arange(len(user_interface.classes()))
] # normalized matrix
X_train_mc_normalized = np.concatenate(
(X_train_normalized[0], X_train_normalized[1], X_train_normalized[2]),
axis=0)
y_train_mc = np.concatenate((y_train[0], y_train[1], y_train[2]), axis=0)
featurelst = featureselection.getfeaturelist(
X_train_mc_normalized, y_train_mc) # feature selection
if user_interface.do_plot():
plotfeatures.plotfeats(X_train_normalized,
mask=np.arange(10) + 7) # plot train features
else:
plotselected.plotsel(X_train_normalized, featurelst['selectedcolumns'])
savetraindata.savedata(dict_train_features, featurelst, feat_max,
feat_min) # save data
print('All features')
print(user_interface.featuresnames())
print_feature_sel.print_features(featurelst)
return True
import plotfeatures
def plotsel(features, colums_selected):
plotfeatures.plotfeats(features, colums_selected)
if __name__ == "__main__": # executable from terminal
from environment.modules import dataloader
from environment.modules.analysislab.user_interface import classes
dict_tr = [(dataloader.dict_train_features(c) - dataloader.featmin()) /
(dataloader.featmax() - dataloader.featmin())
for c in classes()]
plotsel(dict_tr, dataloader.colums_selected())