create_tensorboard_visualizations¶
-
ampligraph.utils.
create_tensorboard_visualizations
(model, loc, labels=None, write_metadata=True, export_tsv_embeddings=True)¶ Export embeddings to Tensorboard.
This function exports embeddings to disk in a format used by TensorBoard and TensorBoard Embedding Projector. The function exports:
- A number of checkpoint and graph embedding files in the provided location that will allow you to visualize embeddings using Tensorboard. This is generally for use with a local Tensorboard instance.
- a tab-separated file of embeddings
embeddings_projector.tsv
. This is generally used to visualize embeddings by uploading to TensorBoard Embedding Projector. - embeddings metadata (i.e. the embeddings labels from the original knowledge graph), saved to
metadata.tsv
. Such file can be used in TensorBoard or uploaded to TensorBoard Embedding Projector.
The content of
loc
will look like:tensorboard_files/ ├── checkpoint ├── embeddings_projector.tsv ├── graph_embedding.ckpt.data-00000-of-00001 ├── graph_embedding.ckpt.index ├── graph_embedding.ckpt.meta ├── metadata.tsv └── projector_config.pbtxt
Note
A TensorBoard guide is available at this address.
Note
Uploading
embeddings_projector.tsv
andmetadata.tsv
to TensorBoard Embedding Projector will give a result similar to the picture below:Examples
>>> import numpy as np >>> from ampligraph.latent_features import TransE >>> from ampligraph.utils import create_tensorboard_visualizations >>> >>> X = np.array([['a', 'y', 'b'], >>> ['b', 'y', 'a'], >>> ['a', 'y', 'c'], >>> ['c', 'y', 'a'], >>> ['a', 'y', 'd'], >>> ['c', 'y', 'd'], >>> ['b', 'y', 'c'], >>> ['f', 'y', 'e']]) >>> >>> model = TransE(batches_count=1, seed=555, epochs=20, k=10, loss='pairwise', >>> loss_params={'margin':5}) >>> model.fit(X) >>> >>> create_tensorboard_visualizations(model, 'tensorboard_files')
Parameters: - model (EmbeddingModel) – A trained neural knowledge graph embedding model, the model must be an instance of TransE, DistMult, ComplEx, or HolE.
- loc (string) – Directory where the files are written.
- labels (pd.DataFrame) – Label(s) for each embedding point in the Tensorboard visualization. Default behaviour is to use the embeddings labels included in the model.
- export_tsv_embeddings (bool (Default: True) –
If True, will generate a tab-separated file of embeddings at the given path. This is generally used to visualize embeddings by uploading to TensorBoard Embedding Projector.
- write_metadata (bool (Default: True)) – If True will write a file named ‘metadata.tsv’ in the same directory as path.