Does graphvite support reading embedding from file instead of train it from scratch

[xptree]

I wonder what is the best way to only do evaluation with GraphVite, i.e., import embedding from file and evaluation it on some tasks.

[KiddoZhu]

Importing embedding from file is supported in v0.2.1. See the load section in configuration file.

To only do evaluation, you can skip the train section by setting num_epoch to 0 in the train section.

[AdrianKs]

I am trying to evaluate a trained model as specified in the load section, but it seems to initialize the embeddings again, instead of loading the trained ones. Do you have a hint on what I am doing wrong in my configuration:

application: knowledge graph

resource: gpus: [] cpu_per_gpu: auto dim: 512

graph: file_name:

build: optimizer: type: Adam lr: 5.0e-6 weight_decay: 0 num_partition: auto num_negative: 64 batch_size: 100000 episode_size: 1

load: file_name: transe_wn18.pkl

train: model: TransE num_epoch: 0 margin: 12 sample_batch_size: 2000 adversarial_temperature: 2 log_frequency: 100 resume: true

evaluate: task: link prediction file_name: filter_files:

[nxznm]

I am trying to evaluate a trained model as specified in the load section, but it seems to initialize the embeddings again, instead of loading the trained ones. Do you have a hint on what I am doing wrong in my configuration:

application: knowledge graph

resource: gpus: [] cpu_per_gpu: auto dim: 512

graph: file_name:

build: optimizer: type: Adam lr: 5.0e-6 weight_decay: 0 num_partition: auto num_negative: 64 batch_size: 100000 episode_size: 1

load: file_name: transe_wn18.pkl

train: model: TransE num_epoch: 0 margin: 12 sample_batch_size: 2000 adversarial_temperature: 2 log_frequency: 100 resume: true

evaluate: task: link prediction file_name: filter_files:

I meet the same situation. Have you solved it?

[nxznm]

After reading the doc, I find my problem. I can successfully load the saved embedding model, and test it without training. Here is my configure.

application:
  knowledge graph

resource:
  gpus: []
  cpu_per_gpu: auto
  dim: 1024

graph:
  file_name: <wn18.train>

build:
  optimizer:
    type: Adam
    lr: 5.0e-6
    weight_decay: 0
  num_partition: auto
  num_negative: 64
  batch_size: 100000
  episode_size: 1

train:
  model: RotatE
  num_epoch: 0
  margin: 9
  sample_batch_size: 2000
  adversarial_temperature: 2
  log_frequency: 100
  resume: True

load:
  file_name: rotate_wn18.pkl

evaluate:
  task: link prediction
  file_name: <wn18.test>
  filter_files:
    - <wn18.train>
    - <wn18.valid>
    - <wn18.test>
  # fast_mode: 3000

I set num_epoch as 0, and set resume as True. And I find that if resume is set as False or not set, the evaluation result is totally wrong (seems to initialize the embeddings again). So I think it is very important to set resume as True if you just want to reload saved embedding model and evaluate on test set without training again. PS: My graphvite version is v0.2.2.

[KiddoZhu]

Yes this is resolved in v0.2.1+. Sorry we forget to close the issue.