def test(data_file, reps, n_trees, task_num,
default_transformer_class, default_transformer_kwargs):
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
kappa = np.zeros(reps)
for i in range(reps):
X_train, X_test, y_train, y_test, n_classes = load_data(data_file, task_num)
default_decider_kwargs = {"classes": np.arange(n_classes)}
pl = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs)
pl.add_task(X_train, y_train, num_transformers=n_trees)
y_hat = pl.predict(X_test, task_id=0)
acc = np.sum(y_test == y_hat) / len(y_test)
print("Accuracy after iteration ", i, ": ", acc)
chance_pred = 1 / n_classes
kappa[i] = (acc - chance_pred) / (1 - chance_pred)
return np.mean(kappa) * 100, (np.std(kappa) * 100) / np.sqrt(reps)
def test_nxor(self):
# tests proglearn on xor nxor simulation data
np.random.seed(12345)
reps = 10
errors = np.zeros((4, reps), dtype=float)
for ii in range(reps):
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
default_decider_kwargs = {"classes": np.arange(2)}
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs,
)
xor, label_xor = generate_gaussian_parity(750,
cov_scale=0.1,
angle_params=0)
test_xor, test_label_xor = generate_gaussian_parity(1000,
cov_scale=0.1,
angle_params=0)
nxor, label_nxor = generate_gaussian_parity(750,
cov_scale=0.1,
angle_params=np.pi / 2)
test_nxor, test_label_nxor = generate_gaussian_parity(
1000, cov_scale=0.1, angle_params=np.pi / 2)
progressive_learner.add_task(xor, label_xor, num_transformers=10)
progressive_learner.add_task(nxor, label_nxor, num_transformers=10)
uf_task1 = progressive_learner.predict(test_xor,
transformer_ids=[0],
task_id=0)
l2f_task1 = progressive_learner.predict(test_xor, task_id=0)
uf_task2 = progressive_learner.predict(test_nxor,
transformer_ids=[1],
task_id=1)
l2f_task2 = progressive_learner.predict(test_nxor, task_id=1)
errors[0, ii] = 1 - np.mean(uf_task1 == test_label_xor)
errors[1, ii] = 1 - np.mean(l2f_task1 == test_label_xor)
errors[2, ii] = 1 - np.mean(uf_task2 == test_label_nxor)
errors[3, ii] = 1 - np.mean(l2f_task2 == test_label_nxor)
bte = np.mean(errors[0, ]) / np.mean(errors[1, ])
fte = np.mean(errors[2, ]) / np.mean(errors[3, ])
assert bte > 1 and fte > 1
def LF_experiment(num_task_1_data, rep):
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleAverage
default_decider_kwargs = {}
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class)
X_train_task0, y_train_task0 = generate_gaussian_parity(n=num_task_1_data,
angle_params=0,
acorn=1)
X_train_task1, y_train_task1 = generate_gaussian_parity(n=100,
angle_params=10,
acorn=1)
X_test_task0, y_test_task0 = generate_gaussian_parity(n=10000,
angle_params=0,
acorn=2)
progressive_learner.add_task(
X_train_task0,
y_train_task0,
num_transformers=10,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(y_train_task0)})
llf_task = progressive_learner.predict(X_test_task0, task_id=0)
single_task_accuracy = np.nanmean(llf_task == y_test_task0)
single_task_error = 1 - single_task_accuracy
progressive_learner.add_transformer(X=X_train_task1,
y=y_train_task1,
transformer_data_proportion=1,
num_transformers=10,
backward_task_ids=[0])
llf_task = progressive_learner.predict(X_test_task0, task_id=0)
double_task_accuracy = np.nanmean(llf_task == y_test_task0)
double_task_error = 1 - double_task_accuracy
if double_task_error == 0 or single_task_error == 0:
te = 1
else:
te = (single_task_error + 1e-6) / (double_task_error + 1e-6)
df = pd.DataFrame()
df['te'] = [te]
print('n = {}, te = {}'.format(num_task_1_data, te))
file_to_save = 'result/' + str(num_task_1_data) + '_' + str(
rep) + '.pickle'
with open(file_to_save, 'wb') as f:
pickle.dump(df, f)
def LF_experiment(angle, reps=1, ntrees=10, acorn=None):
errors = np.zeros(2)
for rep in range(reps):
print("Starting Rep {} of Angle {}".format(rep, angle))
X_base_train, y_base_train = generate_gaussian_parity(n=100,
angle_params=0,
acorn=rep)
X_base_test, y_base_test = generate_gaussian_parity(n=10000,
angle_params=0,
acorn=rep)
X_rotated_train, y_rotated_train = generate_gaussian_parity(
n=100, angle_params=angle, acorn=rep)
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": 10}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleAverage
default_decider_kwargs = {}
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class)
progressive_learner.add_task(
X_base_train,
y_base_train,
num_transformers=ntrees,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(y_base_train)})
base_predictions_test = progressive_learner.predict(X_base_test,
task_id=0)
progressive_learner.add_transformer(X=X_rotated_train,
y=y_rotated_train,
transformer_data_proportion=1,
num_transformers=10,
backward_task_ids=[0])
all_predictions_test = progressive_learner.predict(X_base_test,
task_id=0)
errors[1] = errors[1] + (1 -
np.mean(all_predictions_test == y_base_test))
errors[0] = errors[0] + (1 -
np.mean(base_predictions_test == y_base_test))
errors = errors / reps
print("Errors For Angle {}: {}".format(angle, errors))
with open('results/angle_' + str(angle) + '.pickle', 'wb') as f:
pickle.dump(errors, f, protocol=2)
def LF_experiment(data_x,
data_y,
ntrees,
shift,
slot,
model,
num_points_per_task,
acorn=None):
df = pd.DataFrame()
shifts = []
slots = []
accuracies_across_tasks = []
train_times_across_tasks = []
inference_times_across_tasks = []
train_x_task0, train_y_task0, test_x_task0, test_y_task0 = cross_val_data(
data_x,
data_y,
num_points_per_task,
total_task=10,
shift=shift,
slot=slot)
if model == "dnn":
default_transformer_class = NeuralClassificationTransformer
network = keras.Sequential()
network.add(
layers.Conv2D(filters=16,
kernel_size=(3, 3),
activation='relu',
input_shape=np.shape(train_x_task0)[1:]))
network.add(
layers.Conv2D(filters=32,
kernel_size=(3, 3),
strides=2,
padding="same",
activation='relu'))
network.add(
layers.Conv2D(filters=64,
kernel_size=(3, 3),
strides=2,
padding="same",
activation='relu'))
network.add(
layers.Conv2D(filters=128,
kernel_size=(3, 3),
strides=2,
padding="same",
activation='relu'))
network.add(
layers.Conv2D(filters=254,
kernel_size=(3, 3),
strides=2,
padding="same",
activation='relu'))
network.add(layers.Flatten())
network.add(layers.Dense(2000, activation='relu'))
network.add(layers.Dense(2000, activation='relu'))
network.add(layers.Dense(units=10, activation='softmax'))
default_transformer_kwargs = {
"network": network,
"euclidean_layer_idx": -2,
"num_classes": 10,
"optimizer": keras.optimizers.Adam(3e-4)
}
default_voter_class = KNNClassificationVoter
default_voter_kwargs = {"k": int(np.log2(num_points_per_task * .33))}
default_decider_class = SimpleArgmaxAverage
elif model == "uf":
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class)
train_start_time = time.time()
progressive_learner.add_task(
X=train_x_task0,
y=train_y_task0,
num_transformers=1 if model == "dnn" else ntrees,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(train_y_task0)})
train_end_time = time.time()
inference_start_time = time.time()
task_0_predictions = progressive_learner.predict(test_x_task0, task_id=0)
inference_end_time = time.time()
shifts.append(shift)
slots.append(slot)
accuracies_across_tasks.append(np.mean(task_0_predictions == test_y_task0))
train_times_across_tasks.append(train_end_time - train_start_time)
inference_times_across_tasks.append(inference_end_time -
inference_start_time)
for task_ii in range(1, 20):
train_x, train_y, _, _ = cross_val_data(data_x,
data_y,
num_points_per_task,
total_task=10,
shift=shift,
slot=slot,
task=task_ii)
print("Starting Task {} For Fold {} For Slot {}".format(
task_ii, shift, slot))
train_start_time = time.time()
progressive_learner.add_transformer(
X=train_x,
y=train_y,
transformer_data_proportion=1,
num_transformers=1 if model == "dnn" else ntrees,
backward_task_ids=[0])
train_end_time = time.time()
inference_start_time = time.time()
task_0_predictions = progressive_learner.predict(test_x_task0,
task_id=0)
inference_end_time = time.time()
shifts.append(shift)
slots.append(slot)
accuracies_across_tasks.append(
np.mean(task_0_predictions == test_y_task0))
train_times_across_tasks.append(train_end_time - train_start_time)
inference_times_across_tasks.append(inference_end_time -
inference_start_time)
print("Accuracy Across Tasks: {}".format(accuracies_across_tasks))
print("Train Times Across Tasks: {}".format(train_times_across_tasks))
print("Inference Times Across Tasks: {}".format(
inference_times_across_tasks))
df['data_fold'] = shifts
df['slot'] = slots
df['accuracy'] = accuracies_across_tasks
df['train_times'] = train_times_across_tasks
df['inference_times'] = inference_times_across_tasks
file_to_save = 'result/' + model + str(ntrees) + '_' + str(
shift) + '_' + str(slot) + '.pickle'
with open(file_to_save, 'wb') as f:
pickle.dump(df, f)
def experiment(n_task1,
n_task2,
n_test=1000,
task1_angle=0,
task2_angle=np.pi / 2,
n_trees=10,
max_depth=None,
random_state=None):
"""
A function to do progressive experiment between two tasks
where the task data is generated using Gaussian parity.
Parameters
----------
n_task1 : int
Total number of train sample for task 1.
n_task2 : int
Total number of train dsample for task 2
n_test : int, optional (default=1000)
Number of test sample for each task.
task1_angle : float, optional (default=0)
Angle in radian for task 1.
task2_angle : float, optional (default=numpy.pi/2)
Angle in radian for task 2.
n_trees : int, optional (default=10)
Number of total trees to train for each task.
max_depth : int, optional (default=None)
Maximum allowable depth for each tree.
random_state : int, RandomState instance, default=None
Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
Returns
-------
errors : array of shape [6]
Elements of the array is organized as single task error task1,
multitask error task1, single task error task2,
multitask error task2, naive UF error task1,
naive UF task2.
"""
if n_task1 == 0 and n_task2 == 0:
raise ValueError('Wake up and provide samples to train!!!')
if random_state != None:
np.random.seed(random_state)
errors = np.zeros(6, dtype=float)
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": max_depth}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
default_decider_kwargs = {"classes": np.arange(2)}
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs)
uf = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs)
naive_uf = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs)
#source data
X_task1, y_task1 = generate_gaussian_parity(n_task1,
angle_params=task1_angle)
test_task1, test_label_task1 = generate_gaussian_parity(
n_test, angle_params=task1_angle)
#target data
X_task2, y_task2 = generate_gaussian_parity(n_task2,
angle_params=task2_angle)
test_task2, test_label_task2 = generate_gaussian_parity(
n_test, angle_params=task2_angle)
if n_task1 == 0:
progressive_learner.add_task(X_task2,
y_task2,
num_transformers=n_trees)
errors[0] = 0.5
errors[1] = 0.5
uf_task2 = progressive_learner.predict(test_task2,
transformer_ids=[0],
task_id=0)
l2f_task2 = progressive_learner.predict(test_task2, task_id=0)
errors[2] = 1 - np.mean(uf_task2 == test_label_task2)
errors[3] = 1 - np.mean(l2f_task2 == test_label_task2)
errors[4] = 0.5
errors[5] = 1 - np.mean(uf_task2 == test_label_task2)
elif n_task2 == 0:
progressive_learner.add_task(X_task1,
y_task1,
num_transformers=n_trees)
uf_task1 = progressive_learner.predict(test_task1,
transformer_ids=[0],
task_id=0)
l2f_task1 = progressive_learner.predict(test_task1, task_id=0)
errors[0] = 1 - np.mean(uf_task1 == test_label_task1)
errors[1] = 1 - np.mean(l2f_task1 == test_label_task1)
errors[2] = 0.5
errors[3] = 0.5
errors[4] = 1 - np.mean(uf_task1 == test_label_task1)
errors[5] = 0.5
else:
progressive_learner.add_task(X_task1,
y_task1,
num_transformers=n_trees)
progressive_learner.add_task(X_task2,
y_task2,
num_transformers=n_trees)
uf.add_task(X_task1, y_task1, num_transformers=2 * n_trees)
uf.add_task(X_task2, y_task2, num_transformers=2 * n_trees)
naive_uf_train_x = np.concatenate((X_task1, X_task2), axis=0)
naive_uf_train_y = np.concatenate((y_task1, y_task2), axis=0)
naive_uf.add_task(naive_uf_train_x,
naive_uf_train_y,
num_transformers=n_trees)
uf_task1 = uf.predict(test_task1, transformer_ids=[0], task_id=0)
l2f_task1 = progressive_learner.predict(test_task1, task_id=0)
uf_task2 = uf.predict(test_task2, transformer_ids=[1], task_id=1)
l2f_task2 = progressive_learner.predict(test_task2, task_id=1)
naive_uf_task1 = naive_uf.predict(test_task1,
transformer_ids=[0],
task_id=0)
naive_uf_task2 = naive_uf.predict(test_task2,
transformer_ids=[0],
task_id=0)
errors[0] = 1 - np.mean(uf_task1 == test_label_task1)
errors[1] = 1 - np.mean(l2f_task1 == test_label_task1)
errors[2] = 1 - np.mean(uf_task2 == test_label_task2)
errors[3] = 1 - np.mean(l2f_task2 == test_label_task2)
errors[4] = 1 - np.mean(naive_uf_task1 == test_label_task1)
errors[5] = 1 - np.mean(naive_uf_task2 == test_label_task2)
return errors
def experiment(n_xor, n_nxor, n_test, reps, n_trees, max_depth, acorn=None):
#print(1)
if n_xor==0 and n_nxor==0:
raise ValueError('Wake up and provide samples to train!!!')
if acorn != None:
np.random.seed(acorn)
errors = np.zeros((reps,4),dtype=float)
for i in range(reps):
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs" : {"max_depth" : max_depth}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
default_decider_kwargs = {"classes" : np.arange(2)}
progressive_learner = ProgressiveLearner(default_transformer_class = default_transformer_class,
default_transformer_kwargs = default_transformer_kwargs,
default_voter_class = default_voter_class,
default_voter_kwargs = default_voter_kwargs,
default_decider_class = default_decider_class,
default_decider_kwargs = default_decider_kwargs)
uf = ProgressiveLearner(default_transformer_class = default_transformer_class,
default_transformer_kwargs = default_transformer_kwargs,
default_voter_class = default_voter_class,
default_voter_kwargs = default_voter_kwargs,
default_decider_class = default_decider_class,
default_decider_kwargs = default_decider_kwargs)
#source data
xor, label_xor = generate_gaussian_parity(n_xor,cov_scale=0.1,angle_params=0)
test_xor, test_label_xor = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=0)
#target data
nxor, label_nxor = generate_gaussian_parity(n_nxor,cov_scale=0.1,angle_params=np.pi/4)
test_nxor, test_label_nxor = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=np.pi/4)
if n_xor == 0:
progressive_learner.add_task(nxor, label_nxor, num_transformers=n_trees)
errors[i,0] = 0.5
errors[i,1] = 0.5
uf_task2=progressive_learner.predict(test_nxor, transformer_ids=[0], task_id=0)
l2f_task2=progressive_learner.predict(test_nxor, task_id=0)
errors[i,2] = 1 - np.sum(uf_task2 == test_label_nxor)/n_test
errors[i,3] = 1 - np.sum(l2f_task2 == test_label_nxor)/n_test
elif n_nxor == 0:
progressive_learner.add_task(xor, label_xor, num_transformers=n_trees)
uf_task1=progressive_learner.predict(test_xor, transformer_ids=[0], task_id=0)
l2f_task1=progressive_learner.predict(test_xor, task_id=0)
errors[i,0] = 1 - np.sum(uf_task1 == test_label_xor)/n_test
errors[i,1] = 1 - np.sum(l2f_task1 == test_label_xor)/n_test
errors[i,2] = 0.5
errors[i,3] = 0.5
else:
progressive_learner.add_task(xor, label_xor, num_transformers=n_trees)
progressive_learner.add_task(nxor, label_nxor, num_transformers=n_trees)
uf.add_task(xor, label_xor, num_transformers=2*n_trees)
uf.add_task(nxor, label_nxor, num_transformers=2*n_trees)
uf_task1=uf.predict(test_xor, transformer_ids=[0], task_id=0)
l2f_task1=progressive_learner.predict(test_xor, task_id=0)
uf_task2=uf.predict(test_nxor, transformer_ids=[1], task_id=1)
l2f_task2=progressive_learner.predict(test_nxor, task_id=1)
errors[i,0] = 1 - np.sum(uf_task1 == test_label_xor)/n_test
errors[i,1] = 1 - np.sum(l2f_task1 == test_label_xor)/n_test
errors[i,2] = 1 - np.sum(uf_task2 == test_label_nxor)/n_test
errors[i,3] = 1 - np.sum(l2f_task2 == test_label_nxor)/n_test
return np.mean(errors,axis=0)
def single_experiment(x, y, y_speaker, ntrees=19, model='uf', shuffle=False):
num_tasks = 6
num_points_per_task = 3000 / num_tasks
speakers = ['g', 'j', 'l', 'n', 't', 'y']
accuracies_across_tasks = []
if model == 'dnn':
x_all = x
y_all = y
default_transformer_class = NeuralClassificationTransformer
network = keras.Sequential()
network.add(
layers.Conv2D(filters=16,
kernel_size=(3, 3),
activation='relu',
input_shape=np.shape(x_all)[1:]))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(filters=32,
kernel_size=(3, 3),
strides=2,
padding="same",
activation='relu'))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(filters=64,
kernel_size=(3, 3),
strides=2,
padding="same",
activation='relu'))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(filters=128,
kernel_size=(3, 3),
strides=2,
padding="same",
activation='relu'))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(filters=254,
kernel_size=(3, 3),
strides=2,
padding="same",
activation='relu'))
network.add(layers.Flatten())
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Dense(units=10, activation='softmax'))
default_transformer_kwargs = {
"network": network,
"euclidean_layer_idx": -2,
"loss": "categorical_crossentropy",
"optimizer": Adam(3e-4),
"fit_kwargs": {
"epochs": 100,
"callbacks": [EarlyStopping(patience=5, monitor="val_loss")],
"verbose": False,
"validation_split": 0.33,
"batch_size": 32,
},
}
default_voter_class = KNNClassificationVoter
default_voter_kwargs = {"k": int(np.log2(num_points_per_task))}
default_decider_class = SimpleArgmaxAverage
elif model == 'uf':
x_all = x.reshape(3000, -1)
y_all = y
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
if shuffle:
np.random.shuffle(speakers)
else:
pass
for j, task0_speaker in enumerate(speakers):
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class)
index = np.where(y_speaker == task0_speaker)
x_task0 = x_all[index]
y_task0 = y_all[index]
train_x_task0, test_x_task0, train_y_task0, test_y_task0 = train_test_split(
x_task0, y_task0, test_size=0.25)
progressive_learner.add_task(
X=train_x_task0,
y=train_y_task0,
task_id=0,
num_transformers=1 if model == "dnn" else ntrees,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(train_y_task0)},
)
task_0_predictions = progressive_learner.predict(test_x_task0,
task_id=0)
accuracies_across_tasks.append(
np.mean(task_0_predictions == test_y_task0))
for k, contribute_speaker in enumerate(speakers):
if k == j:
pass
else:
index = np.where(y_speaker == contribute_speaker)
x_train = x_all[index]
y_train = y_all[index]
progressive_learner.add_transformer(
X=x_train,
y=y_train,
transformer_data_proportion=1,
num_transformers=1 if model == "dnn" else ntrees,
backward_task_ids=[0],
)
task_0_predictions = progressive_learner.predict(test_x_task0,
task_id=0)
accuracies_across_tasks.append(
np.mean(task_0_predictions == test_y_task0))
return accuracies_across_tasks
def LF_experiment(train_x, train_y, test_x, test_y, ntrees, shift, slot, model, num_points_per_task, acorn=None):
df = pd.DataFrame()
shifts = []
tasks = []
base_tasks = []
accuracies_across_tasks = []
train_times_across_tasks = []
inference_times_across_tasks = []
if model == "dnn":
default_transformer_class = NeuralClassificationTransformer
network = keras.Sequential()
network.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu', input_shape=np.shape(train_x)[1:]))
network.add(layers.BatchNormalization())
network.add(layers.Conv2D(filters=32, kernel_size=(3, 3), strides = 2, padding = "same", activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Conv2D(filters=64, kernel_size=(3, 3), strides = 2, padding = "same", activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Conv2D(filters=128, kernel_size=(3, 3), strides = 2, padding = "same", activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Conv2D(filters=254, kernel_size=(3, 3), strides = 2, padding = "same", activation='relu'))
network.add(layers.Flatten())
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Dense(units=10, activation = 'softmax'))
default_transformer_kwargs = {"network" : network,
"euclidean_layer_idx" : -2,
"num_classes" : 10,
"optimizer" : keras.optimizers.Adam(3e-4)
}
default_voter_class = KNNClassificationVoter
default_voter_kwargs = {"k" : 16 * int(np.log2(num_points_per_task))}
default_decider_class = SimpleAverage
elif model == "uf":
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs" : {"max_depth" : 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleAverage
progressive_learner = ProgressiveLearner(default_transformer_class = default_transformer_class,
default_transformer_kwargs = default_transformer_kwargs,
default_voter_class = default_voter_class,
default_voter_kwargs = default_voter_kwargs,
default_decider_class = default_decider_class)
for task_ii in range(10):
print("Starting Task {} For Fold {} For Slot {}".format(task_ii, shift, slot))
if acorn is not None:
np.random.seed(acorn)
train_start_time = time.time()
progressive_learner.add_task(
X = train_x[task_ii*5000+slot*num_points_per_task:task_ii*5000+(slot+1)*num_points_per_task],
y = train_y[task_ii*5000+slot*num_points_per_task:task_ii*5000+(slot+1)*num_points_per_task],
num_transformers = 1 if model == "dnn" else ntrees,
transformer_voter_decider_split = [0.67, 0.33, 0],
decider_kwargs = {"classes" : np.unique(train_y[task_ii*5000+slot*num_points_per_task:task_ii*5000+(slot+1)*num_points_per_task])},
backward_task_ids=[0]
)
train_end_time = time.time()
inference_start_time = time.time()
llf_task=progressive_learner.predict(
test_x[:1000], task_id=0
)
inference_end_time = time.time()
acc = np.mean(
llf_task == test_y[:1000]
)
accuracies_across_tasks.append(acc)
shifts.append(shift)
train_times_across_tasks.append(train_end_time - train_start_time)
inference_times_across_tasks.append(inference_end_time - inference_start_time)
print("Accuracy Across Tasks: {}".format(accuracies_across_tasks))
print("Train Times Across Tasks: {}".format(train_times_across_tasks))
print("Inference Times Across Tasks: {}".format(inference_times_across_tasks))
df['data_fold'] = shifts
df['task'] = range(1, 11)
df['task_1_accuracy'] = accuracies_across_tasks
df['train_times'] = train_times_across_tasks
df['inference_times'] = inference_times_across_tasks
file_to_save = 'result/'+model+str(ntrees)+'_'+str(shift)+'_'+str(slot)+'.pickle'
with open(file_to_save, 'wb') as f:
pickle.dump(df, f)
def single_experiment(x, y, y_speaker, ntrees=10, model="odif", shuffle=False):
num_tasks = 6
num_points_per_task = 3000 / num_tasks
speakers = ["g", "j", "l", "n", "t", "y"]
single_task_accuracies = np.zeros(num_tasks, dtype=float)
accuracies = np.zeros(27, dtype=float)
if model == "odin":
x_all = x
y_all = y
clear_session() # clear GPU memory before each run, to avoid OOM error
default_transformer_class = NeuralClassificationTransformer
network = keras.Sequential()
network.add(
layers.Conv2D(
filters=16,
kernel_size=(3, 3),
activation="relu",
input_shape=np.shape(x_all)[1:],
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=32,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=64,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=128,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=254,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.Flatten())
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation="relu"))
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation="relu"))
network.add(layers.BatchNormalization())
network.add(layers.Dense(units=10, activation="softmax"))
default_transformer_kwargs = {
"network": network,
"euclidean_layer_idx": -2,
"loss": "categorical_crossentropy",
"optimizer": Adam(3e-4),
"fit_kwargs": {
"epochs": 100,
"callbacks": [EarlyStopping(patience=5, monitor="val_loss")],
"verbose": False,
"validation_split": 0.33,
"batch_size": 32,
},
}
default_voter_class = KNNClassificationVoter
default_voter_kwargs = {"k": int(np.log2(num_points_per_task))}
default_decider_class = SimpleArgmaxAverage
elif model == "odif":
x_all = x.reshape(3000, -1)
y_all = y
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
if shuffle:
np.random.shuffle(speakers)
else:
pass
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
)
train_x_task, test_x_task, train_y_task, test_y_task = (
[[], [], [], [], [], []],
[[], [], [], [], [], []],
[[], [], [], [], [], []],
[[], [], [], [], [], []],
)
for j, task0_speaker in enumerate(speakers):
index = np.where(y_speaker == task0_speaker)
x_task0 = x_all[index]
y_task0 = y_all[index]
(
train_x_task[j],
test_x_task[j],
train_y_task[j],
test_y_task[j],
) = train_test_split(x_task0, y_task0, test_size=0.45)
progressive_learner.add_task(
X=train_x_task[j],
y=train_y_task[j],
task_id=j,
num_transformers=1 if model == "odin" else ntrees,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(train_y_task[j])},
)
odi_predictions = progressive_learner.predict(X=test_x_task[j],
transformer_ids=[j],
task_id=j)
accuracies[j] = np.mean(odi_predictions == test_y_task[j])
for k, contribute_speaker in enumerate(speakers):
if k > j:
pass
else:
odi_predictions = progressive_learner.predict(test_x_task[k],
task_id=k)
accuracies[6 + k + (j * (j + 1)) // 2] = np.mean(
odi_predictions == test_y_task[k])
return accuracies
def L2_experiment(data_x,
data_y,
ntrees,
shift,
slot,
num_points_per_task,
task_num,
acorn=None):
# construct dataframes
df = pd.DataFrame()
shifts = []
slots = []
accuracies_across_tasks = []
# randomly separate the training and testing subsets
train_x_task0, train_y_task0, test_x_task0, test_y_task0 = cross_val_data(
data_x,
data_y,
num_points_per_task,
total_task=10,
shift=shift,
slot=slot)
# choose Uncertainty Forest as transformer
progressive_learner = ProgressiveLearner(
default_transformer_class=TreeClassificationTransformer,
default_transformer_kwargs={"kwargs": {
"max_depth": 30
}},
default_voter_class=TreeClassificationVoter,
default_voter_kwargs={},
default_decider_class=SimpleArgmaxAverage,
)
# training process
progressive_learner.add_task(
X=train_x_task0,
y=train_y_task0,
num_transformers=ntrees,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(train_y_task0)},
)
# testing process
task_0_predictions = progressive_learner.predict(test_x_task0, task_id=0)
# record results
shifts.append(shift)
slots.append(slot)
accuracies_across_tasks.append(np.mean(task_0_predictions == test_y_task0))
# repeating the tasks for task_num times
for task_ii in range(1, task_num):
# randomly separate the training and testing subsets
train_x, train_y, _, _ = cross_val_data(
data_x,
data_y,
num_points_per_task,
total_task=10,
shift=shift,
slot=slot,
task=task_ii,
)
# training process
progressive_learner.add_transformer(
X=train_x,
y=train_y,
transformer_data_proportion=1,
num_transformers=ntrees,
backward_task_ids=[0],
)
# testing process
task_0_predictions = progressive_learner.predict(test_x_task0,
task_id=0)
# record results
shifts.append(shift)
slots.append(slot)
accuracies_across_tasks.append(
np.mean(task_0_predictions == test_y_task0))
# finalize dataframes
df["data_fold"] = shifts
df["slot"] = slots
df["accuracy"] = accuracies_across_tasks
# save results
return df
def run_fte_bte_exp(data_x, data_y, which_task, model, ntrees=30, shift=0):
df_total = []
for slot in range(
1
): # Rotates the batch of training samples that are used from each class in each task
train_x, train_y, test_x, test_y = cross_val_data(
data_x, data_y, shift, slot)
if model == "odif":
# Reshape the data
train_x = train_x.reshape(
train_x.shape[0],
train_x.shape[1] * train_x.shape[2] * train_x.shape[3])
test_x = test_x.reshape(
test_x.shape[0],
test_x.shape[1] * test_x.shape[2] * test_x.shape[3])
if model == "odin":
clear_session(
) # clear GPU memory before each run, to avoid OOM error
default_transformer_class = NeuralClassificationTransformer
network = keras.Sequential()
network.add(
layers.Conv2D(
filters=16,
kernel_size=(3, 3),
activation="relu",
input_shape=np.shape(data_x)[1:],
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=32,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=64,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=128,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=254,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.Flatten())
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation="relu"))
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation="relu"))
network.add(layers.BatchNormalization())
network.add(layers.Dense(units=10, activation="softmax"))
default_transformer_kwargs = {
"network": network,
"euclidean_layer_idx": -2,
"loss": "categorical_crossentropy",
"optimizer": Adam(3e-4),
"fit_kwargs": {
"epochs": 100,
"callbacks":
[EarlyStopping(patience=5, monitor="val_loss")],
"verbose": False,
"validation_split": 0.33,
"batch_size": 32,
},
}
default_voter_class = KNNClassificationVoter
default_voter_kwargs = {"k": int(np.log2(300))}
default_decider_class = SimpleArgmaxAverage
p_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
)
elif model == "odif":
p_learner = LifelongClassificationForest()
df = fte_bte_experiment(
train_x,
train_y,
test_x,
test_y,
ntrees,
shift,
slot,
model,
p_learner,
which_task,
acorn=12345,
)
df_total.append(df)
return df_total
def odif_experiment(angle, data_x, data_y, reps=1, ntrees=29, acorn=None):
if acorn is not None:
np.random.seed(acorn)
errors = np.zeros(2)
for _ in range(reps):
train_x1, train_y1, train_x2, train_y2, test_x, test_y = cross_val_data(
data_x, data_y, total_cls=10
)
# change data angle for second task
tmp_data = train_x2.copy()
total_data = tmp_data.shape[0]
for i in range(total_data):
tmp_ = image_aug(tmp_data[i], angle)
tmp_data[i] = tmp_
train_x1 = train_x1.reshape(
(
train_x1.shape[0],
train_x1.shape[1] * train_x1.shape[2] * train_x1.shape[3],
)
)
tmp_data = tmp_data.reshape(
(
tmp_data.shape[0],
tmp_data.shape[1] * tmp_data.shape[2] * tmp_data.shape[3],
)
)
test_x = test_x.reshape(
(test_x.shape[0], test_x.shape[1] * test_x.shape[2] * test_x.shape[3])
)
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
)
progressive_learner.add_task(
X=train_x1,
y=train_y1,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(train_y1)},
)
progressive_learner.add_transformer(
X=tmp_data, y=train_y2, transformer_data_proportion=1, backward_task_ids=[0]
)
llf_task1 = progressive_learner.predict(test_x, task_id=0)
llf_single_task = progressive_learner.predict(
test_x, task_id=0, transformer_ids=[0]
)
errors[1] = errors[1] + (1 - np.mean(llf_task1 == test_y))
errors[0] = errors[0] + (1 - np.mean(llf_single_task == test_y))
errors = errors / reps
return errors
def Odif_experiment(data_x,
data_y,
ntrees,
shift,
slot,
num_points_per_task,
acorn=None):
df = pd.DataFrame()
shifts = []
slots = []
accuracies_across_tasks = []
train_x_task0, train_y_task0, test_x_task0, test_y_task0 = cross_val_data(
data_x,
data_y,
num_points_per_task,
total_task=10,
shift=shift,
slot=slot)
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {
"kwargs": {
"max_depth": 30,
"max_features": "auto"
}
}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
)
progressive_learner.add_task(
X=train_x_task0,
y=train_y_task0,
num_transformers=ntrees,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(train_y_task0)},
)
task_0_predictions = progressive_learner.predict(test_x_task0, task_id=0)
shifts.append(shift)
slots.append(slot)
accuracies_across_tasks.append(np.mean(task_0_predictions == test_y_task0))
for task_ii in range(1, 20):
train_x, train_y, _, _ = cross_val_data(
data_x,
data_y,
num_points_per_task,
total_task=10,
shift=shift,
slot=slot,
task=task_ii,
)
progressive_learner.add_task(
X=train_x,
y=train_y,
num_transformers=ntrees,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(train_y)},
)
task_0_predictions = progressive_learner.predict(test_x_task0,
task_id=0)
shifts.append(shift)
slots.append(slot)
accuracies_across_tasks.append(
np.mean(task_0_predictions == test_y_task0))
df["data_fold"] = shifts
df["slot"] = slots
df["accuracy"] = accuracies_across_tasks
return df
def single_experiment(train_x_task,
test_x_task,
train_y_task,
test_y_task,
ntrees=10,
model="odif"):
num_tasks = 10
num_points_per_task = 1800
accuracies = np.zeros(65, dtype=float)
if model == "odin":
clear_session() # clear GPU memory before each run, to avoid OOM error
default_transformer_class = NeuralClassificationTransformer
network = keras.Sequential()
network.add(
layers.Conv2D(
filters=16,
kernel_size=(3, 3),
activation="relu",
input_shape=np.shape(train_x_task[0])[1:],
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=32,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=64,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=128,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.BatchNormalization())
network.add(
layers.Conv2D(
filters=254,
kernel_size=(3, 3),
strides=2,
padding="same",
activation="relu",
))
network.add(layers.Flatten())
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation="relu"))
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation="relu"))
network.add(layers.BatchNormalization())
network.add(layers.Dense(units=20, activation="softmax")) # units=10
default_transformer_kwargs = {
"network": network,
"euclidean_layer_idx": -2,
"loss": "categorical_crossentropy",
"optimizer": Adam(3e-4),
"fit_kwargs": {
"epochs": 100,
"callbacks": [EarlyStopping(patience=5, monitor="val_loss")],
"verbose": False,
"validation_split": 0.33,
"batch_size": 32,
},
}
default_voter_class = KNNClassificationVoter
default_voter_kwargs = {"k": int(np.log2(num_points_per_task))}
default_decider_class = SimpleArgmaxAverage
elif model == "odif":
for i in range(num_tasks):
train_x_task[i] = train_x_task[i].reshape(1080, -1)
test_x_task[i] = test_x_task[i].reshape(720, -1)
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
)
for i in range(num_tasks):
progressive_learner.add_task(
X=train_x_task[i],
y=train_y_task[i],
task_id=i,
num_transformers=1 if model == "odin" else ntrees,
transformer_voter_decider_split=[0.67, 0.33, 0],
decider_kwargs={"classes": np.unique(train_y_task[i])},
)
prediction = progressive_learner.predict(X=test_x_task[i],
transformer_ids=[i],
task_id=i)
accuracies[i] = np.mean(prediction == test_y_task[i])
for j in range(num_tasks):
if j > i:
pass # this is not wrong but misleading, should be continue
else:
odif_predictions = progressive_learner.predict(test_x_task[j],
task_id=j)
accuracies[10 + j + (i * (i + 1)) // 2] = np.mean(
odif_predictions == test_y_task[j])
# print('single experiment done!')
return accuracies
def experiment(
n_task1,
n_task2,
n_test=1000,
task1_angle=0,
task2_angle=np.pi / 2,
n_trees=10,
max_depth=None,
random_state=None,
register=False,
):
"""
A function to do backwards transfer efficiency experiment
between two tasks. Task 1 is XOR. Task 2 is RXOR.
A registered Task 2
Parameters
----------
n_task1 : int
Total number of train sample for task 1.
n_task2 : int
Total number of train dsample for task 2
n_test : int, optional (default=1000)
Number of test sample for each task.
task1_angle : float, optional (default=0)
Angle in radian for task 1.
task2_angle : float, optional (default=numpy.pi/2)
Angle in radian for task 2.
n_trees : int, optional (default=10)
Number of total trees to train for each task.
max_depth : int, optional (default=None)
Maximum allowable depth for each tree.
random_state : int, RandomState instance, default=None
Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
register: boolean, default=False
Register task2 to task1 before feeding to forest.
Returns
-------
errors : array of shape [6]
Elements of the array is organized as single task error task1,
multitask error task1, single task error task2,
multitask error task2, naive UF error task1,
naive UF task2.
"""
if n_task1 == 0 and n_task2 == 0:
raise ValueError("Wake up and provide samples to train!!!")
if random_state != None:
np.random.seed(random_state)
errors = np.zeros(6, dtype=float)
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": max_depth}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
default_decider_kwargs = {"classes": np.arange(2)}
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs,
)
uf = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs,
)
naive_uf = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs,
)
# source data
X_task1, y_task1 = generate_gaussian_parity(n_task1,
angle_params=task1_angle)
test_task1, test_label_task1 = generate_gaussian_parity(
n_test, angle_params=task1_angle)
# target data
X_task2, y_task2 = generate_gaussian_parity(n_task2,
angle_params=task2_angle)
test_task2, test_label_task2 = generate_gaussian_parity(
n_test, angle_params=task2_angle)
if register:
X_task2 = cpd_reg(X_task2.copy(), X_task1.copy())
progressive_learner.add_task(X_task1, y_task1, num_transformers=n_trees)
progressive_learner.add_task(X_task2, y_task2, num_transformers=n_trees)
uf.add_task(X_task1, y_task1, num_transformers=2 * n_trees)
uf.add_task(X_task2, y_task2, num_transformers=2 * n_trees)
uf_task1 = uf.predict(test_task1, transformer_ids=[0], task_id=0)
l2f_task1 = progressive_learner.predict(test_task1, task_id=0)
errors[0] = 1 - np.mean(uf_task1 == test_label_task1)
errors[1] = 1 - np.mean(l2f_task1 == test_label_task1)
return errors
def label_shuffle_experiment(
train_x,
train_y,
test_x,
test_y,
ntrees,
shift,
slot,
num_points_per_task,
acorn=None,
):
df = pd.DataFrame()
single_task_accuracies = np.zeros(10, dtype=float)
shifts = []
tasks = []
base_tasks = []
accuracies_across_tasks = []
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {
"kwargs": {
"max_depth": 30,
"max_features": "auto"
}
}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
)
for task_ii in range(10):
if acorn is not None:
np.random.seed(acorn)
tmp_y = train_y[task_ii * 5000 +
slot * num_points_per_task:task_ii * 5000 +
(slot + 1) * num_points_per_task]
if task_ii != 0:
np.random.shuffle(tmp_y)
progressive_learner.add_task(
X=train_x[task_ii * 5000 +
slot * num_points_per_task:task_ii * 5000 +
(slot + 1) * num_points_per_task],
y=tmp_y,
num_transformers=ntrees,
transformer_voter_decider_split=[0.63, 0.37, 0],
decider_kwargs={
"classes":
np.unique(train_y[task_ii * 5000 +
slot * num_points_per_task:task_ii * 5000 +
(slot + 1) * num_points_per_task])
},
)
llf_task = progressive_learner.predict(X=test_x[0:1000, :], task_id=0)
shifts.append(shift)
accuracies_across_tasks.append(np.mean(llf_task == test_y[0:1000]))
df["data_fold"] = shifts
df["task"] = range(1, 11)
df["task_1_accuracy"] = accuracies_across_tasks
return df
def LF_experiment(data_x, data_y, angle, model, granularity, reps=1, ntrees=29, acorn=None):
if acorn is not None:
np.random.seed(acorn)
errors = np.zeros(2)
with tf.device('/gpu:'+str(int(angle // granularity) % 4)):
for rep in range(reps):
train_x1, train_y1, train_x2, train_y2, test_x, test_y = cross_val_data(data_x, data_y, total_cls=10)
#change data angle for second task
tmp_data = train_x2.copy()
_tmp_ = np.zeros((32,32,3), dtype=int)
total_data = tmp_data.shape[0]
for i in range(total_data):
tmp_ = image_aug(tmp_data[i],angle)
tmp_data[i] = tmp_
if model == "uf":
train_x1 = train_x1.reshape((train_x1.shape[0], train_x1.shape[1] * train_x1.shape[2] * train_x1.shape[3]))
tmp_data = tmp_data.reshape((tmp_data.shape[0], tmp_data.shape[1] * tmp_data.shape[2] * tmp_data.shape[3]))
test_x = test_x.reshape((test_x.shape[0], test_x.shape[1] * test_x.shape[2] * test_x.shape[3]))
if model == "dnn":
default_transformer_class = NeuralClassificationTransformer
network = keras.Sequential()
network.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu', input_shape=np.shape(train_x1)[1:]))
network.add(layers.BatchNormalization())
network.add(layers.Conv2D(filters=32, kernel_size=(3, 3), strides = 2, padding = "same", activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Conv2D(filters=64, kernel_size=(3, 3), strides = 2, padding = "same", activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Conv2D(filters=128, kernel_size=(3, 3), strides = 2, padding = "same", activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Conv2D(filters=254, kernel_size=(3, 3), strides = 2, padding = "same", activation='relu'))
network.add(layers.Flatten())
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Dense(2000, activation='relu'))
network.add(layers.BatchNormalization())
network.add(layers.Dense(units=10, activation = 'softmax'))
default_transformer_kwargs = {"network" : network,
"euclidean_layer_idx" : -2,
"num_classes" : 10,
"optimizer" : keras.optimizers.Adam(3e-4)
}
default_voter_class = KNNClassificationVoter
default_voter_kwargs = {"k" : int(np.log2(len(train_x1)))}
default_decider_class = SimpleArgmaxAverage
elif model == "uf":
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs" : {"max_depth" : 30}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
progressive_learner = ProgressiveLearner(default_transformer_class = default_transformer_class,
default_transformer_kwargs = default_transformer_kwargs,
default_voter_class = default_voter_class,
default_voter_kwargs = default_voter_kwargs,
default_decider_class = default_decider_class)
progressive_learner.add_task(
X = train_x1,
y = train_y1,
transformer_voter_decider_split = [0.67, 0.33, 0],
decider_kwargs = {"classes" : np.unique(train_y1)}
)
progressive_learner.add_transformer(
X = tmp_data,
y = train_y2,
transformer_data_proportion = 1,
backward_task_ids = [0]
)
llf_task1=progressive_learner.predict(test_x, task_id=0)
llf_single_task=progressive_learner.predict(test_x, task_id=0, transformer_ids=[0])
errors[1] = errors[1]+(1 - np.mean(llf_task1 == test_y))
errors[0] = errors[0]+(1 - np.mean(llf_single_task == test_y))
errors = errors/reps
print("Errors For Angle {}: {}".format(angle, errors))
with open('results/angle_'+str(angle)+'_'+model+'.pickle', 'wb') as f:
pickle.dump(errors, f, protocol = 2)
def experiment(
n_task1,
n_task2,
n_test=0.4,
task1_angle=0,
task2_angle=np.pi / 2,
n_trees=10,
max_depth=None,
random_state=None,
register_cpd=False,
register_otp=False,
register_icp=False,
):
if n_task1 == 0 and n_task2 == 0:
raise ValueError("Wake up and provide samples to train!!!")
if random_state != None:
np.random.seed(random_state)
errors = np.zeros(6, dtype=float)
default_transformer_class = TreeClassificationTransformer
default_transformer_kwargs = {"kwargs": {"max_depth": max_depth}}
default_voter_class = TreeClassificationVoter
default_voter_kwargs = {}
default_decider_class = SimpleArgmaxAverage
default_decider_kwargs = {"classes": np.arange(2)}
progressive_learner = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs,
)
uf = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs,
)
naive_uf = ProgressiveLearner(
default_transformer_class=default_transformer_class,
default_transformer_kwargs=default_transformer_kwargs,
default_voter_class=default_voter_class,
default_voter_kwargs=default_voter_kwargs,
default_decider_class=default_decider_class,
default_decider_kwargs=default_decider_kwargs,
)
# source data
X_task1, y_task1 = sample_cc18(n_task1, angle_params=task1_angle)
test_task1, test_label_task1 = sample_cc18(n_test,
angle_params=task1_angle)
# target data
tform1 = transform.AffineTransform(shear=0 * np.pi / 180)
tform2 = transform.AffineTransform(shear=task2_angle)
X_task2, y_task2 = sample_cc18(n_task2, angle_params=task1_angle)
test_task2, test_label_task2 = sample_cc18(n_test,
angle_params=task1_angle)
# Transform training set
X1_top = X_task2[X_task2[:, 1] >= 0]
X1_bottom = X_task2[X_task2[:, 1] < 0]
y1_top = y_task2[X_task2[:, 1] >= 0]
y1_bottom = y_task2[X_task2[:, 1] < 0]
m = X1_top.shape[1]
src = np.ones((m + 1, X1_top.shape[0]))
src[:m, :] = np.copy(X1_top.T)
src = np.dot(tform1.params, src)
X2_top = src.T[:, 0:2]
y2_top = y1_top
m = X1_bottom.shape[1]
src = np.ones((m + 1, X1_bottom.shape[0]))
src[:m, :] = np.copy(X1_bottom.T)
src = np.dot(tform2.params, src)
X2_bottom = src.T[:, 0:2]
y2_bottom = y1_bottom
X_task2 = np.concatenate((X2_top, X2_bottom))
y_task2 = np.concatenate((y2_top, y2_bottom))
if register_cpd:
X_task2 = cpd_reg(X_task2.copy(), X_task1.copy())
if register_otp:
ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-2)
ot_sinkhorn.fit(Xs=X_task2.copy(),
Xt=X_task1.copy(),
ys=y_task2.copy(),
yt=y_task1.copy())
transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X_task2.copy())
X_task2 = transp_Xs_sinkhorn
if register_icp:
T, X_3, i = icp(X_task2.copy(), X_task1.copy(), y_task2.copy(),
y_task1.copy())
X_task2 = X_3.T[:, 0:2]
progressive_learner.add_task(X_task1, y_task1, num_transformers=n_trees)
progressive_learner.add_task(X_task2, y_task2, num_transformers=n_trees)
uf.add_task(X_task1, y_task1, num_transformers=2 * n_trees)
uf.add_task(X_task2, y_task2, num_transformers=2 * n_trees)
uf_task1 = uf.predict(test_task1, transformer_ids=[0], task_id=0)
l2f_task1 = progressive_learner.predict(test_task1, task_id=0)
errors[0] = 1 - np.mean(uf_task1 == test_label_task1)
errors[1] = 1 - np.mean(l2f_task1 == test_label_task1)
return errors
