eval on generate_corruption...

[snash4]

Hi,
I am trying to generate negative samples using generate_corruptions_for_eval, since it returns a tensor, and when I try to convert it to bumpy using tensor.eval(), it throws exception of out of memory.
I tried it on even small sample, but it has same exception.

Please suggest the solution.
Thanks.

[tabacof]

Hi @snash4, you can try adapting the snippet below to your uses:

import types
from functools import partial
import tensorflow as tf
from sklearn.utils import check_random_state
from tqdm import tqdm
from ampligraph.datasets import AmpligraphDatasetAdapter, NumpyDatasetAdapter
from ampligraph.evaluation import generate_corruptions_for_fit, to_idx, generate_corruptions_for_eval, \
    hits_at_n_score, mrr_score

def generate_corruptions(self, X_pos, batches_count, epochs):
    try:
        tf.reset_default_graph()
        self.rnd = check_random_state(self.seed)
        tf.random.set_random_seed(self.seed)

        self._load_model_from_trained_params()

        dataset_handle = NumpyDatasetAdapter()
        dataset_handle.use_mappings(self.rel_to_idx, self.ent_to_idx)

        dataset_handle.set_data(X_pos, "pos")

        batch_size_pos = int(np.ceil(dataset_handle.get_size("pos") / batches_count))

        gen_fn = partial(dataset_handle.get_next_train_batch, batch_size=batch_size_pos, dataset_type="pos")
        dataset = tf.data.Dataset.from_generator(gen_fn,
                                                 output_types=tf.int32,
                                                 output_shapes=(None, 3))
        dataset = dataset.repeat().prefetch(1)
        dataset_iter = tf.data.make_one_shot_iterator(dataset)

        x_pos_tf = dataset_iter.get_next()

        e_s, e_p, e_o = self._lookup_embeddings(x_pos_tf)
        scores_pos = self._fn(e_s, e_p, e_o)

        x_neg_tf = generate_corruptions_for_fit(x_pos_tf,
                                                entities_list=None,
                                                eta=1,
                                                corrupt_side='s+o',
                                                entities_size=0,
                                                rnd=self.seed)

        e_s_neg, e_p_neg, e_o_neg = self._lookup_embeddings(x_neg_tf)
        scores = self._fn(e_s_neg, e_p_neg, e_o_neg)

        epoch_iterator_with_progress = tqdm(range(1, epochs + 1), disable=(not self.verbose), unit='epoch')

        scores_list = []
        with tf.Session(config=self.tf_config) as sess:
            sess.run(tf.global_variables_initializer())
            for _ in epoch_iterator_with_progress:
                losses = []
                for batch in range(batches_count):
                    scores_list.append(sess.run(scores))

        dataset_handle.cleanup()
        return np.concatenate(scores_list)
    
    except Exception as e:
        dataset_handle.cleanup()
        raise e

model.generate_corruptions = types.MethodType(generate_corruptions, model)

So, what I am doing above is adding a new method to some model object (e.g. a TransE model) that scores newly generated corruptions.

In your case, you don't need the model scores themselves, so you can delete all lines related to the embeddings and to the score. Now, you only need to actually change the output of the function, fromsess.run(scores) to something like sess.run(x_neg_tf). You can control the memory utilisation by changing the batches_count argument.

If you face any problems, please do say so. If you need interactive help, visit us at our Slack channel.

[snash4]

Hi tabacof,
Thanks a lot for the code. It worked after a few modifications.
While generating the negative triplets, I noticed two things:

1. Repetitions that is some negative triplets are generated multiple times. Well this is not a problem.
2. It generates positive triplets as negative triplets. How does the model behave during in these cases as it sees same triplets are positive as well as negative.

One more thing, I couldn't understand is that why the negative samples are generated per batch?

Thanks.

[lukostaz]

@snash4 can I go ahead and close this?