'ANN__hidden_layer_sizes': [(d,) for d in [dim, dim//2]],
'ANN__solver': ['adam'],
'ANN__alpha': 10.0 ** -np.arange(1, 7),
'ANN__activation': ['relu', 'tanh', 'logistic'],
}
clf = basicResults(pipe, x_train, y_train, x_test,
y_test, params, 'ANN', dataset)
else:
print('Using presolved hyperparameters for ' + dataset)
clf = pipe.set_params(**solved_params)
# plot_learning_curve(clf, dataset + ' neural network',
# x, y, cv=5, n_jobs=4, scoring=scorer)
# plt.savefig('./graphs/' + dataset + '-ann.png')
# print('Creating timing curve for ' + dataset)
# plot_timing_curve(clf, x, y, 'neural network', dataset)
# plt.savefig('./graphs/' + dataset + '-ANN-timing.png')
# print('Creating iteration curve for ' + dataset)
# plot_iteration_curve(clf, x_train, y_train, x_test, y_test, iter_adjust, 'neural network', dataset)
# plt.savefig('./graphs/' + dataset + '-ANN-iteration.png')
conf = confusion_matrix(y_test, clf.predict(x_test))
conf = conf.astype('float') / conf.sum(axis=1)[:, np.newaxis]
print('Confusion matrix:')
print(conf)
np.savetxt('./output/ANN_{}_confusion.csv'.format(dataset), conf, delimiter=',', fmt='%.2f')
if __name__ == '__main__':
run_ann(read_wine(), 'wine')
run_ann(read_gestures(), 'gestures')
# plt.show();
# plt.savefig('./graphs/' + dataset + '-boost-timing.png')
# plot_iteration_curve(clf, x_train, y_train, x_test, y_test, params, 'boosted', dataset)
# plt.savefig('./graphs/' + dataset + '-boost-iteration.png')
conf = confusion_matrix(y_test, clf.predict(x_test))
conf = conf.astype('float') / conf.sum(axis=1)[:, np.newaxis]
print('Confusion matrix:')
print(conf)
np.savetxt('./output/Boosted_{}_confusion.csv'.format(dataset),
conf,
delimiter=',',
fmt='%.2f')
if __name__ == '__main__':
run_boost(read_wine(),
'wine',
dtparams={
'alpha': -100,
'class_weight': 'balanced',
'criterion': 'entropy',
'min_samples_split': 2
})
run_boost(read_gestures(),
'gestures',
dtparams={
'alpha': 0.0001,
'class_weight': 'balanced',
'criterion': 'gini',
'min_samples_split': 4
})
x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y)
if solved_params is None:
print("Doing a GridSearch for best hyperparameters")
params = {
'DT__criterion': ['gini', 'entropy'],
'DT__alpha': ALPHAS,
'DT__class_weight': ['balanced'],
'DT__min_samples_split': [2, 3, 4, 5],
}
clf = basicResults(pipe, x_train, y_train, x_test,
y_test, params, 'DT', title)
else:
print("Using pre-solved hyperparameters")
clf = pipe.set_params(**solved_params)
# print ("Plotting learning curve")
# plot_learning_curve(clf, title + ' decision tree', x,
# y, n_jobs=4, scoring=scorer, ylim=(0, 1))
# plt.savefig('./graphs/' + title + '-dt.png')
y_pred = clf.predict(x_test)
conf = confusion_matrix(y_test, clf.predict(x_test))
conf = conf.astype('float') / conf.sum(axis=1)[:, np.newaxis]
print('Confusion matrix:')
print(conf)
np.savetxt('./output/DT_{}_confusion.csv'.format(title), conf, delimiter=',', fmt='%.2f')
if __name__ == '__main__':
run_dt(read_gestures(), 'gestures')
run_dt(read_wine(), 'wine')
# plt.show()
x, y, pipeline = data
pipe = Pipeline([
*pipeline,
('SVM', svm.SVC(class_weight='balanced')),
])
print('Splitting data SVM RBF -- ' + dataset)
x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y)
print('Computing hyperparameters SVM RBF -- ' + dataset)
clf = basicResults(pipe, x_train, y_train, x_test,
y_test, params, 'SVM-RBF', dataset)
# plot_timing_curve(clf, x, y, 'rbf svm', dataset)
# plt.savefig('./graphs/' + dataset + '-svm-rbf-timing.png')
# plot_iteration_curve(clf, x_train, y_train, x_test,
# y_test, iter_adjust, 'rbf svm', dataset)
# plt.savefig('./graphs/' + dataset + '-svm-rbf-iteration.png')
conf = confusion_matrix(y_test, clf.predict(x_test))
conf = conf.astype('float') / conf.sum(axis=1)[:, np.newaxis]
print('Confusion matrix:')
print(conf)
np.savetxt('./output/SVM-RBF_{}_confusion.csv'.format(dataset), conf, delimiter=',', fmt='%.2f')
if __name__ == '__main__':
wine = read_wine()
gestures = read_gestures()
run_svm_linear(wine, 'wine')
run_svm_linear(gestures, 'gestures')
run_svm_rbf(wine, 'wine')
run_svm_rbf(gestures, 'gestures')
from multiprocessing import Process
from dt import run_dt
from ann import run_ann
from bdt import run_boost
from knn import run_knn
from svm import run_svm_linear, run_svm_rbf
from parse_data import read_gestures, read_wine
def run(data, dataset):
# print('running decision tree for ' + dataset)
# run_dt(data, dataset)
print('running neural network for ' + dataset)
run_ann(data, dataset)
print('running boosted dt for ' + dataset)
run_boost(data, dataset)
print('running knn for ' + dataset)
run_knn(data, dataset)
print('running svm (linear) for ' + dataset)
run_svm_linear(data, dataset)
print('running svm (rbf) for ' + dataset)
run_svm_rbf(data, dataset)
if __name__ == '__main__':
run(read_gestures(), 'gestures')
run(read_wine(), 'wine')