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unsupervised_embedding_generation.py
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"""
Graph-Induced Sum-Product Networks
Files: unsupervised_embedding_generation.py
Authors: Federico Errica ([email protected])
Mathias Niepert ([email protected])
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"""
import os
import torch
from pydgn.experiment.experiment import Experiment
from pydgn.static import LOSS, SCORE
def reorder(obj, perm):
assert len(obj) == len(perm) and len(obj) > 0
return [y for (x, y) in sorted(zip(perm, obj))]
class EmbeddingTask(Experiment):
def run_valid(self, dataset_getter, logger):
unsupervised_config = self.model_config.unsupervised_config
embeddings_folder = unsupervised_config['embeddings_folder']
batch_size = unsupervised_config['batch_size']
num_layers = unsupervised_config['num_layers']
if 'num_mixtures' in unsupervised_config:
num_mixtures = unsupervised_config['num_mixtures']
else:
num_mixtures = unsupervised_config['dim_embedding']
num_hidden_neurons = unsupervised_config['num_hidden_neurons']
avg_parameters_across_layers = unsupervised_config.get('avg_parameters_across_layers', True)
learning_rate = unsupervised_config['optimizer']['args']['lr']
if not os.path.exists(os.path.join(embeddings_folder, dataset_getter.dataset_name)):
os.makedirs(os.path.join(embeddings_folder, dataset_getter.dataset_name))
base_path = os.path.join(embeddings_folder,
dataset_getter.dataset_name,
f'{batch_size}_{num_layers}_{num_mixtures}_{num_hidden_neurons}_'
f'{avg_parameters_across_layers}_'
f'{learning_rate}_'
f'{dataset_getter.outer_k + 1}_'
f'{dataset_getter.inner_k + 1}')
batch_size = self.model_config.unsupervised_config['batch_size']
shuffle = self.model_config.unsupervised_config['shuffle'] \
if 'shuffle' in self.model_config.unsupervised_config else True
train_loader = dataset_getter.get_inner_train(batch_size=batch_size, shuffle=shuffle)
val_loader = dataset_getter.get_inner_val(batch_size=batch_size, shuffle=shuffle)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# NOTE: since the model is unsupervised, we already compute and store on disk the test embeddings
# WITHOUT TRAINING ON IT (we borrow this idea from CGMM's implementation on PyDGN)
# NOTE2: the outer kth TR set is the union of these internal TR and VL sets, so we can reuse the computed
# embeddings for the subsequent classifiers, see `unsup_model_classifier_experiment.py`
#
# NOTE3: storing unsup. embeddings allows to reuse them with different classifiers' configurations, which is
# very convenient in terms of computation, as we do not retrain an unsup. model each time.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
test_loader = dataset_getter.get_outer_test(batch_size=batch_size, shuffle=shuffle)
# Instantiate the Dataset
dim_node_features = dataset_getter.get_dim_node_features()
dim_edge_features = dataset_getter.get_dim_edge_features()
dim_target = dataset_getter.get_dim_target()
# Instantiate the Model
model = self.create_unsupervised_model(dim_node_features, dim_edge_features, dim_target)
# Instantiate the engine (it handles the training loop and the inference phase by abstracting the specifics)
unsupervised_training_engine = self.create_unsupervised_engine(model)
train_loss, train_score, train_data_list, \
val_loss, val_score, val_data_list, \
test_loss, test_score, test_data_list = unsupervised_training_engine.train(
train_loader=train_loader,
validation_loader=val_loader,
test_loader=test_loader,
max_epochs=self.model_config.unsupervised_config['epochs'],
logger=logger)
# This is fixed in PyDGN 1.3.0
# # If samples (already shuffled because of the data split in Subset) have been also shuffled according to a
# # permutation, i.e., in the last inference phase of the engine, use PyDGN RandomSampler to recover the
# # permutation and store the embedding data list as in the original split.
# # This way we recover the order of the [train/val/test]_idxs list in the split file.
# # This is useful for subsequent experiments about weak supervision, and it is better in general to avoid
# # confusion.
# if shuffle:
# train_data_list = reorder(train_data_list, train_loader.sampler.permutation)
# val_data_list = reorder(val_data_list, val_loader.sampler.permutation)
# test_data_list = reorder(test_data_list, test_loader.sampler.permutation)
torch.save(train_data_list, base_path + '_train.torch')
torch.save(val_data_list, base_path + '_val.torch')
torch.save(test_data_list, base_path + '_test.torch')
train_res = {LOSS: train_loss, SCORE: train_score}
val_res = {LOSS: val_loss, SCORE: val_score}
return train_res, val_res
def run_test(self, dataset_getter, logger):
# Dummy values for the library
tr_res = {LOSS: {'main_loss': torch.zeros(1)}, SCORE: {'main_score': torch.zeros(1)}}
vl_res = {LOSS: {'main_loss': torch.zeros(1)}, SCORE: {'main_score': torch.zeros(1)}}
te_res = {LOSS: {'main_loss': torch.zeros(1)}, SCORE: {'main_score': torch.zeros(1)}}
return tr_res, vl_res, te_res