Generate embeddings of new points

Hello, I have two questions: 1- When we use .fit to generate the embeddings, how to save the model and use it to create the embeddings of new entities of the KG? 2- Before when I used this library, I did not get any error for the label_predicates, while now it returns an unexpected keyword argument! kg = KG("dataset.xml", label_predicates=[rdflib.URIRef(x) for x in label_predicates])

[bsteenwi]

Hmm ok fair point, seems like a bug indeed…

I’ve investigated this problem a little more in-depth together with @GillesVandewiele and we concluded the following: Our walk hashing procedure within pyrdf2vec is as follows:

vertex.name if i == 0 or i % 2 == 1 or self.md5_bytes is None else hash...

More in general, we do not hash the first node within a walk as this node is in most cases the root node (those for which you want to generate an embedding).

we check if the root node is within the vw vocab during the transform function

if not all([entity in self._model.wv for entity in entities]):
    raise ValueError(
                "The entities must have been provided to fit() first "
                "before they can be transformed into a numerical vector."
            )

But with the with_reverse option set to true, the walks are constructed differently:

if self.with_reverse:
            return [
                r_walk[:-1] + walk
                for walk in fct_search(kg, entity)
                for r_walk in fct_search(kg, entity, is_reverse=True)
            ]

Here you can see that the reverse paths are prefixed to the normal paths, resulting in a node at index 0 different from the root node. The root node will now be hashed somewhere within this path, and cannot be found in the wv vocab in its original form.

As @GillesVandewiele suggested, we might better hash all subjects and predicates within pyrdf2vec and check if the hashed root nodes are available in de wv vocab.

[bsteenwi]

import os

import pandas as pd
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC

from pyrdf2vec import RDF2VecTransformer
from pyrdf2vec.embedders import Word2Vec
from pyrdf2vec.graphs import KG
from pyrdf2vec.walkers import RandomWalker


RANDOM_STATE = 22
if __name__ == '__main__':
    db_kg = KG("https://dbpedia.org/sparql", skip_verify=True)
    print(db_kg)

    db_transformer = RDF2VecTransformer(walkers=[RandomWalker(1, None, n_jobs=2, random_state=RANDOM_STATE)], embedder=Word2Vec(size = 100), verbose=1)
    db_entities = ['http://dbpedia.org/resource/Belgium','http://dbpedia.org/resource/France']
    db_walk_embeddings, _ = db_transformer.fit_transform(
    db_kg,
    db_entities,
    )

    db_transformer.save("model")


    db_transformer = RDF2VecTransformer(walkers=[RandomWalker(1, None, n_jobs=2, random_state=RANDOM_STATE)], embedder=Word2Vec(size = 100)).load("model")

    input_entity = ['http://dbpedia.org/resource/Hallstatt_culture']
    db_transformer.fit_transform(
        db_kg,
        input_entity,
        is_update=True,
    )

This works on my system without errors (pyrdf2vec version 0.2.3) (depth is 1 to speed up this test)