def main(args):
"""Main function of DVRL for corrupted sample discovery experiment.
Args:
args: data_name, train_no, valid_no, noise_rate,
normalization, network parameters
"""
# Data loading and sample corruption
data_name = args.data_name
# The number of training and validation samples
dict_no = dict()
dict_no['train'] = args.train_no
dict_no['valid'] = args.valid_no
# Additional noise ratio
noise_rate = args.noise_rate
# Checkpoint file name
checkpoint_file_name = args.checkpoint_file_name
# Data loading and label corruption
noise_idx = data_loading.load_tabular_data(data_name, dict_no, noise_rate)
# noise_idx: ground truth noisy label indices
print('Finished data loading.')
# Data preprocessing
# Normalization methods: 'minmax' or 'standard'
normalization = args.normalization
# Extracts features and labels. Then, normalizes features
x_train, y_train, x_valid, y_valid, x_test, y_test, _ = \
data_loading.preprocess_data(normalization, 'train.csv',
'valid.csv', 'test.csv')
print('Finished data preprocess.')
# Run DVRL
# Resets the graph
tf.reset_default_graph()
# Network parameters
parameters = dict()
parameters['hidden_dim'] = args.hidden_dim
parameters['comb_dim'] = args.comb_dim
parameters['activation'] = tf.nn.relu
parameters['iterations'] = args.iterations
parameters['layer_number'] = args.layer_number
parameters['batch_size'] = args.batch_size
parameters['learning_rate'] = args.learning_rate
# In this example, we consider a classification problem and we use Logistic
# Regression as the predictor model.
problem = 'classification'
pred_model = linear_model.LogisticRegression(solver='lbfgs')
# Flags for using stochastic gradient descent / pre-trained model
flags = {'sgd': False, 'pretrain': False}
# Initalizes DVRL
dvrl_class = dvrl.Dvrl(x_train, y_train, x_valid, y_valid, problem,
pred_model, parameters, checkpoint_file_name, flags)
# Trains DVRL
dvrl_class.train_dvrl('auc')
print('Finished dvrl training.')
# Outputs
# Data valuation
dve_out = dvrl_class.data_valuator(x_train, y_train)
print('Finished date valuation.')
# Evaluations
# Evaluation model
eval_model = lightgbm.LGBMClassifier()
# 1. Robust learning (DVRL-weighted learning)
robust_perf = dvrl_metrics.learn_with_dvrl(dve_out, eval_model, x_train,
y_train, x_valid, y_valid,
x_test, y_test, 'accuracy')
print('DVRL-weighted learning performance: ' +
str(np.round(robust_perf, 4)))
# 2. Performance after removing high/low values
_ = dvrl_metrics.remove_high_low(dve_out,
eval_model,
x_train,
y_train,
x_valid,
y_valid,
x_test,
y_test,
'accuracy',
plot=True)
# 3. Corrupted sample discovery
# If noise_rate is positive value.
if noise_rate > 0:
# Evaluates corrupted_sample_discovery
# and plot corrupted sample discovery results
_ = dvrl_metrics.discover_corrupted_sample(dve_out,
noise_idx,
noise_rate,
plot=True)
def main(args):
"""Main function of DVRL for domain adaptation experiment.
Args:
args: train_no, valid_no,
normalization, network parameters
"""
# Data loading
# The number of training and validation samples
dict_no = dict()
dict_no['source'] = args.train_no
dict_no['valid'] = args.valid_no
# Setting and target store type
setting = 'train-on-rest'
target_store_type = 'B'
# Network parameters
parameters = dict()
parameters['hidden_dim'] = args.hidden_dim
parameters['comb_dim'] = args.comb_dim
parameters['iterations'] = args.iterations
parameters['activation'] = tf.nn.tanh
parameters['layer_number'] = args.layer_number
parameters['batch_size'] = args.batch_size
parameters['learning_rate'] = args.learning_rate
# Checkpoint file name
checkpoint_file_name = args.checkpoint_file_name
# Data loading
data_loading.load_rossmann_data(dict_no, setting, target_store_type)
print('Finished data loading.')
# Data preprocessing
# Normalization methods: 'minmax' or 'standard'
normalization = args.normalization
# Extracts features and labels. Then, normalizes features
x_source, y_source, x_valid, y_valid, x_target, y_target, _ = \
data_loading.preprocess_data(normalization,
'source.csv', 'valid.csv', 'target.csv')
print('Finished data preprocess.')
# Run DVRL
# Resets the graph
tf.reset_default_graph()
problem = 'regression'
# Predictor model definition
pred_model = lightgbm.LGBMRegressor()
# Flags for using stochastic gradient descent / pre-trained model
flags = {'sgd': False, 'pretrain': False}
# Initializes DVRL
dvrl_class = dvrl.Dvrl(x_source, y_source, x_valid, y_valid, problem,
pred_model, parameters, checkpoint_file_name, flags)
# Trains DVRL
dvrl_class.train_dvrl('rmspe')
print('Finished dvrl training.')
# Outputs
# Data valuation
dve_out = dvrl_class.data_valuator(x_source, y_source)
print('Finished date valuation.')
# Evaluations
# Evaluation model
eval_model = lightgbm.LGBMRegressor()
# DVRL-weighted learning
dvrl_perf = dvrl_metrics.learn_with_dvrl(dve_out, eval_model, x_source,
y_source, x_valid, y_valid,
x_target, y_target, 'rmspe')
# Baseline prediction performance (treat all training samples equally)
base_perf = dvrl_metrics.learn_with_baseline(eval_model, x_source,
y_source, x_target, y_target,
'rmspe')
print('Finish evaluation.')
print('DVRL learning performance: ' + str(np.round(dvrl_perf, 4)))
print('Baseline performance: ' + str(np.round(base_perf, 4)))
def main(args):
"""Main function of DVRL with transfer learning for image data.
Main function of DVRL for corrupted sample discovery and robust learning
with transfer learning for image data.
Args:
args: data_name, train_no, valid_no, noise_rate,
normalization, network parameters
"""
# Data name (either cifar10 or cifar100)
data_name = args.data_name
# The number of training and validation samples
dict_no = dict()
dict_no['train'] = args.train_no
dict_no['valid'] = args.valid_no
dict_no['test'] = args.test_no
# Additional noise ratio
noise_rate = args.noise_rate
# Checkpoint file name
checkpoint_file_name = args.checkpoint_file_name
# Data loading and label corruption
noise_idx = data_loading.load_image_data(data_name, dict_no, noise_rate)
# noise_idx: ground truth noisy label indices
print('Finished data loading.')
# Extracts features and labels.
x_train, y_train, x_valid, y_valid, x_test, y_test = \
data_loading.load_image_data_from_file('train.npz', 'valid.npz', 'test.npz')
print('Finished data preprocess.')
# Encodes samples
# The preprocessing function used on the pre-training dataset is also
# applied while encoding the inputs.
preprocess_function = applications.inception_v3.preprocess_input
input_shape = (299, 299)
def encoder_model(architecture='inception_v3', pre_trained_dataset='imagenet',
downsample_factor=8):
"""Returns encoder model.
Defines the encoder model to learn the representations for image dataset.
In this example, we are considering the InceptionV3 model trained on
ImageNet dataset, followed by simple average pooling-based downsampling.
Args:
architecture: Base architecture of encoder model (e.g. 'inception_v3')
pre_trained_dataset: The dataset used to pre-train the encoder model
downsample_factor: Downsample factor for the outputs
Raises:
NameError: Returns name errors if architecture is not 'inception_v3'
"""
tf_input = layers.Input(shape=(input_shape[0], input_shape[1], 3))
if architecture == 'inception_v3':
model = applications.inception_v3.InceptionV3(
input_tensor=tf_input, weights=pre_trained_dataset, include_top=False)
output_pooled = \
layers.AveragePooling2D((downsample_factor, downsample_factor),
strides=(downsample_factor,
downsample_factor))(model.output)
else:
raise NameError('Invalid architecture')
return models.Model(model.input, output_pooled)
# Encodes training samples
enc_x_train = \
data_loading.encode_image(x_train,
encoder_model,
input_shape,
preprocess_function)
# Encodes validation samples
enc_x_valid = \
data_loading.encode_image(x_valid,
encoder_model,
input_shape,
preprocess_function)
# Encodes testing samples
enc_x_test = \
data_loading.encode_image(x_test,
encoder_model,
input_shape,
preprocess_function)
print('Finished data encoding')
# Run DVRL
# Resets the graph
tf.reset_default_graph()
keras.backend.clear_session()
# Network parameters
parameters = dict()
parameters['hidden_dim'] = args.hidden_dim
parameters['comb_dim'] = args.comb_dim
parameters['activation'] = tf.nn.relu
parameters['iterations'] = args.iterations
parameters['layer_number'] = args.layer_number
parameters['batch_size'] = args.batch_size
parameters['learning_rate'] = args.learning_rate
parameters['inner_iterations'] = args.inner_iterations
parameters['batch_size_predictor'] = args.batch_size_predictor
# Defines problem
problem = 'classification'
# Defines predictive model
pred_model = keras.models.Sequential()
pred_model.add(keras.layers.Dense(len(set(y_train)), activation='softmax'))
pred_model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
# Flags for using stochastic gradient descent / pre-trained model
flags = {'sgd': True, 'pretrain': False}
# Initalizes DVRL
dvrl_class = dvrl.Dvrl(enc_x_train, y_train, enc_x_valid, y_valid,
problem, pred_model, parameters,
checkpoint_file_name, flags)
# Trains DVRL
dvrl_class.train_dvrl('accuracy')
print('Finished DVRL training.')
# Outputs
# Data valuation
dve_out = dvrl_class.data_valuator(enc_x_train, y_train)
print('Finished data valuation.')
# Evaluations
# Evaluation model
eval_model = linear_model.LogisticRegression(solver='lbfgs',
multi_class='auto',
max_iter=2000)
# 1. Robust learning (DVRL-weighted learning)
robust_perf = dvrl_metrics.learn_with_dvrl(dve_out, eval_model,
enc_x_train, y_train,
enc_x_valid, y_valid,
enc_x_test, y_test, 'accuracy')
print('DVRL-weighted learning performance: ' + str(np.round(robust_perf, 4)))
# 2. Performance after removing high/low values
_ = dvrl_metrics.remove_high_low(dve_out, eval_model, enc_x_train, y_train,
enc_x_valid, y_valid, enc_x_test, y_test,
'accuracy', plot=True)
# 3. Corrupted sample discovery
# If noise_idx variable exist (explicit indices for noisy sample)
# and noise_rate is positive value.
if noise_rate > 0:
# Evaluates corrupted_sample_discovery
# and plot corrupted sample discovery results
_ = dvrl_metrics.discover_corrupted_sample(dve_out,
noise_idx, noise_rate,
plot=True)