def test_kmm_softmax():
print("========== Test KMM for multiclass classification ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_iris()
print("Number of training samples = {}".format(x_train.shape[0]))
print("Number of testing samples = {}".format(x_test.shape[0]))
clf = KMM(model_name="KMM_hinge",
D=4,
lbd=0.01,
gamma=0.01,
mode='batch',
loss='hinge',
num_kernels=4,
batch_size=100,
temperature=0.1,
num_epochs=10,
num_nested_epochs=1,
learning_rate=0.001,
learning_rate_mu=0.001,
learning_rate_gamma=0.001,
learning_rate_alpha=0.001,
random_state=random_seed())
clf.fit(x_train, y_train)
train_err = 1.0 - clf.score(x_train, y_train)
test_err = 1.0 - clf.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
clf = KMM(model_name="KMM_hinge",
D=100,
lbd=0.0,
gamma=0.01,
mode='online',
loss='hinge',
num_kernels=4,
batch_size=100,
temperature=0.1,
num_nested_epochs=1,
learning_rate=0.001,
learning_rate_mu=0.001,
learning_rate_gamma=0.001,
learning_rate_alpha=0.001,
random_state=random_seed(),
verbose=1)
clf.fit(x_train, y_train)
print("Mistake rate = %.4f" % clf.mistake)
def test_kmm_cv():
print("========== Test cross-validation for KMM ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_iris()
print("Number of training samples = {}".format(x_train.shape[0]))
print("Number of testing samples = {}".format(x_test.shape[0]))
x = np.vstack([x_train, x_test])
y = np.concatenate([y_train, y_test])
early_stopping = EarlyStopping(monitor='val_err', patience=2, verbose=1)
filepath = os.path.join(model_dir(),
"male/KMM/iris_{epoch:04d}_{val_err:.6f}.pkl")
checkpoint = ModelCheckpoint(filepath,
mode='min',
monitor='val_err',
verbose=0,
save_best_only=True)
clf = KMM(model_name="KMM_hinge",
D=20,
lbd=0.0,
gamma=0.1,
mode='batch',
loss='hinge',
num_kernels=3,
batch_size=100,
temperature=1.0,
num_epochs=10,
num_nested_epochs=1,
learning_rate=0.1,
learning_rate_mu=0.0,
learning_rate_gamma=0.1,
learning_rate_alpha=0.1,
metrics=['loss', 'err'],
callbacks=[early_stopping, checkpoint],
cv=[-1] * x_train.shape[0] + [0] * x_test.shape[0],
random_state=random_seed(),
verbose=1)
clf.fit(x, y)
train_err = 1.0 - clf.score(x_train, y_train)
test_err = 1.0 - clf.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)
def test_kmm_syn2d(show=False, block_figure_on_end=False):
print("========== Test KMM on 2D data ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_synthetic_2d()
idx_train, idx_test = next(
iter(
StratifiedShuffleSplit(n_splits=1,
test_size=40,
random_state=random_seed()).split(
x_train, y_train)))
x0 = x_train[idx_train]
y0 = y_train[idx_train]
x1 = x_train[idx_test]
y1 = y_train[idx_test]
x = np.vstack([x0, x1])
y = np.concatenate([y0, y1])
early_stopping = EarlyStopping(monitor='val_loss', patience=2, verbose=1)
filepath = os.path.join(
model_dir(), "male/KMM/syn2d_data_{epoch:04d}_{val_err:.6f}.pkl")
checkpoint = ModelCheckpoint(filepath,
mode='min',
monitor='val_err',
verbose=0,
save_best_only=True)
display = Display(layout=(3, 1),
dpi='auto',
show=show,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['loss', 'val_loss'],
'type': 'line',
'labels': ["training loss", "validation loss"],
'title': "Learning losses",
'xlabel': "epoch",
'ylabel': "loss",
},
{
'metrics': ['err', 'val_err'],
'type': 'line',
'title': "Learning errors",
'xlabel': "epoch",
'ylabel': "error",
},
{
'metrics': ['err'],
'type': 'line',
'labels': ["training error"],
'title': "Learning errors",
'xlabel': "epoch",
'ylabel': "error",
},
])
clf = KMM(model_name="KMM_hinge",
D=10,
lbd=0.0,
gamma=0.5,
mode='batch',
loss='hinge',
num_kernels=4,
batch_size=4,
temperature=0.1,
num_epochs=10,
num_nested_epochs=0,
learning_rate=0.001,
learning_rate_mu=0.0,
learning_rate_gamma=0.001,
learning_rate_alpha=0.001,
metrics=['loss', 'err'],
callbacks=[display, early_stopping, checkpoint],
cv=[-1] * x0.shape[0] + [0] * x1.shape[0],
random_state=random_seed())
clf.fit(x, y)
train_err = 1.0 - clf.score(x_train, y_train)
print("Training error = %.4f" % train_err)
if block_figure_on_end:
# save predictions
y_test_pred = clf.predict(x_test)
x_test[x_test == 0] = 1e-4
dump_svmlight_file(x_test,
y_test_pred,
os.path.join(
data_dir(),
"demo/synthetic_2D_data_test_predict.libsvm"),
zero_based=False)
def test_kmm_cv_disp(show=False, block_figure_on_end=False):
print("========== Test cross-validation for KMM with Display ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_iris()
print("Number of training samples = {}".format(x_train.shape[0]))
print("Number of testing samples = {}".format(x_test.shape[0]))
x = np.vstack([x_train, x_test])
y = np.concatenate([y_train, y_test])
early_stopping = EarlyStopping(monitor='val_err', patience=2, verbose=1)
filepath = os.path.join(model_dir(),
"male/KMM/iris_{epoch:04d}_{val_err:.6f}.pkl")
checkpoint = ModelCheckpoint(filepath,
mode='min',
monitor='val_err',
verbose=0,
save_best_only=True)
display = Display(layout=(3, 1),
dpi='auto',
show=show,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['loss', 'val_loss'],
'type': 'line',
'labels': ["training loss", "validation loss"],
'title': "Learning losses",
'xlabel': "epoch",
'ylabel': "loss",
},
{
'metrics': ['err', 'val_err'],
'type': 'line',
'title': "Learning errors",
'xlabel': "epoch",
'ylabel': "error",
},
{
'metrics': ['err'],
'type': 'line',
'labels': ["training error"],
'title': "Learning errors",
'xlabel': "epoch",
'ylabel': "error",
},
])
clf = KMM(model_name="KMM_hinge",
D=20,
lbd=0.0,
gamma=0.1,
mode='batch',
loss='hinge',
num_kernels=3,
batch_size=100,
temperature=1.0,
num_epochs=10,
num_nested_epochs=1,
learning_rate=0.1,
learning_rate_mu=0.0,
learning_rate_gamma=0.1,
learning_rate_alpha=0.1,
metrics=['loss', 'err'],
callbacks=[display, early_stopping, checkpoint],
cv=[-1] * x_train.shape[0] + [0] * x_test.shape[0],
random_state=random_seed(),
verbose=1)
clf.fit(x, y)
train_err = 1.0 - clf.score(x_train, y_train)
test_err = 1.0 - clf.score(x_test, y_test)
print("Training error = %.4f" % train_err)
print("Testing error = %.4f" % test_err)