def test_ted():
nb_words = 7500
maxlen = 20
embd_dim = 100
X_train, Y_train, X_test, Y_test, nb_classes = load_ted(nb_words, maxlen, 'self')
cnn1d_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
maxlen, nb_words, embd_dim,
100, 5, 50, 20, 'rmsprop')
def test_asap():
nb_words = 2900
maxlen = 75
embd_dim = 50
X_train, Y_train, X_test, Y_test, nb_classes = load_asap(nb_words, maxlen, 'self')
cnn1d_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
maxlen, nb_words, embd_dim,
100, 5, 50, 20, 'rmsprop')
def argu_cv():
maxlen = 40
nb_words = 8000
embd_dim = 100
folds = ['VC048263',
'VC048408',
'VC084849',
'VC084851',
'VC084853',
'VC101537',
'VC101541',
'VC140094',
'VC207640',
'VC248479']
trains = ['data/Argu/csv/generic_' + str(fold) + '_training.csv' for fold in folds]
tests = ['data/Argu/csv/generic_' + str(fold) + '_testing.csv' for fold in folds]
pairs = zip(trains, tests)
accs = []
for (train, test) in pairs:
print(train + '=>' + test)
X_train, Y_train, X_test, Y_test, nb_classes = load_csvs(train, test, nb_words, maxlen,
embd_type='self', w2v=None)
acc = cnn1d_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
maxlen, nb_words, embd_dim,
100, 5, 50, 20, 'rmsprop')
accs.append(acc)
acc_cv = np.mean(accs)
print('after 10-fold cv:' + str(acc_cv))
def pun_cv():
maxlen = 20
nb_words = 8000
embd_dim = 100
folds = range(1,11)
trains = ['data/pun_of_day/train'+str(fold)+'.csv' for fold in folds]
tests = ['data/pun_of_day/test'+str(fold)+'.csv' for fold in folds]
pairs = zip(trains, tests)
accs = []
for (train, test) in pairs:
print(train + '=>' + test)
X_train, Y_train, X_test, Y_test, nb_classes = load_csvs(train, test, nb_words, maxlen,
embd_type='self', w2v=None)
acc = cnn1d_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
maxlen, nb_words, embd_dim,
100, 5, 50, 20, 'rmsprop')
accs.append(acc)
acc_cv = np.mean(accs)
print('after 10-fold cv:' + str(acc_cv))
def asap_cv():
maxlen = 75
nb_words = 4500
embd_dim = 50
folds = (1,2,3,4,5,6,7,8,9,10)
trains = ['data/asap2/train'+str(fold)+'.csv' for fold in folds]
tests = ['data/asap2/test'+str(fold)+'.csv' for fold in folds]
pairs = zip(trains, tests)
kappas = []
for (train, test) in pairs:
print(train + '=>' + test)
X_train, Y_train, X_test, Y_test, nb_classes = load_csvs(train, test,
nb_words, maxlen, embd_type='self', w2v=None)
kappa = cnn1d_selfembd(X_train, Y_train, X_test, Y_test, nb_classes,
maxlen, nb_words, embd_dim,
100, 5, 50, 20, 'rmsprop')
kappas.append(kappa)
kappa_cv = metrics.mean_quadratic_weighted_kappa(kappas)
# TODO add other metrics.
print('after 10-fold cv:' + str(kappa_cv))