DistMult

class ampligraph.latent_features.DistMult(k=100, eta=2, epochs=100, batches_count=100, seed=0, embedding_model_params={'normalize_ent_emb': False}, optimizer='adagrad', optimizer_params={'lr': 0.1}, loss='nll', loss_params={}, regularizer=None, regularizer_params={}, model_checkpoint_path='saved_model/', verbose=False, **kwargs)

The DistMult model

The model as described in [YYH+14].

\[f_{DistMult}=\langle \mathbf{r}_p, \mathbf{e}_s, \mathbf{e}_o \rangle\]

Examples

>>> import numpy as np
>>> from ampligraph.latent_features import DistMult
>>> model = DistMult(batches_count=1, seed=555, epochs=20, k=10, loss='pairwise', loss_params={'margin':5})
>>> 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.fit(X)
>>> model.predict(np.array([['f', 'y', 'e'], ['b', 'y', 'd']]))
[3.29703, -3.543957]
>>> model.get_embeddings(['f','e'], type='entity')
array([[-0.7101061 , -0.35752687,  0.5337027 , -0.612499  , -0.34532365,
-0.7219143 , -0.07083285,  0.19323194,  1.0108972 ,  0.42850104],
[-1.2280471 , -0.22018537,  0.17179069,  0.757755  , -0.05845603,
 0.94373196, -0.14994079, -0.929564  ,  1.0907435 ,  0.20400602]],
dtype=float32)

Methods

__init__([k, eta, epochs, batches_count, …]) Initialize an EmbeddingModel
fit(X[, early_stopping, early_stopping_params]) Train an DistMult.
get_embeddings(entities[, type]) Get the embeddings of entities or relations.
predict(X[, from_idx, get_ranks]) Predict the score of triples using a trained embedding model.
__init__(k=100, eta=2, epochs=100, batches_count=100, seed=0, embedding_model_params={'normalize_ent_emb': False}, optimizer='adagrad', optimizer_params={'lr': 0.1}, loss='nll', loss_params={}, regularizer=None, regularizer_params={}, model_checkpoint_path='saved_model/', verbose=False, **kwargs)

Initialize an EmbeddingModel

Also creates a new Tensorflow session for training.
Parameters:
  • k (int) – Embedding space dimensionality
  • eta (int) – The number of negatives that must be generated at runtime during training for each positive.
  • epochs (int) – The iterations of the training loop.
  • batches_count (int) – The number of batches in which the training set must be split during the training loop.
  • seed (int) – The seed used by the internal random numbers generator.
  • embedding_model_params (dict) –

    DistMult-specific hyperparams:

    • normalize_ent_emb - Flag to indicate whether to normalize entity embeddings after each batch update (default:False)
  • optimizer (string) – The optimizer used to minimize the loss function. Choose between sgd, adagrad, adam, momentum.
  • optimizer_params (dict) –

    Parameters values specific to the optimizer. Currently supported:

    • lr - learning rate (used by all the optimizers)
    • momentum - learning momentum (used by momentum optimizer)
  • loss (string) –

    The type of loss function to use during training.

    • pairwise the model will use pairwise margin-based loss function.
    • nll the model will use negative loss likelihood.
    • absolute_margin the model will use absolute margin likelihood.
    • self_adversarial the model will use adversarial sampling loss function.
  • loss_params (dict) –

    Parameters dictionary specific to the loss.

    (Refer documentation of specific loss functions for more details)

  • regularizer (string) –

    The regularization strategy to use with the loss function.

    • LP the model will use L1, L2 or L3 based on the value passed to param p.
    • None the model will not use any regularizer
  • regularizer_params (dict) –

    Parameters dictionary specific to the regularizer.

    (Refer documentation of regularizer for more details)

  • model_checkpoint_path (string) – Path to save the model.
  • verbose (bool) – Verbose mode
  • kwargs (dict) – Additional inputs, if any
fit(X, early_stopping=False, early_stopping_params={})

Train an DistMult.

The model is trained on a training set X using the training protocol described in [TWR+16].
Parameters:
  • X (ndarray, shape [n, 3]) – The training triples
  • early_stopping (bool) – Flag to enable early stopping(default:False)
  • early_stopping_params (dictionary) –

    Dictionary of parameters for early stopping. Following keys are supported:

    • x_valid: ndarray, shape [n, 3] : Validation set to be used for early stopping.
    • criteria: string : criteria for early stopping hits10, hits3, hits1 or mrr (default).
    • x_filter: ndarray, shape [n, 3] : Filter to be used (no filter by default).
    • burn_in: int : Number of epochs to pass before kicking in early stopping (default: 100).
    • check_interval: int : Early stopping interval after burn-in (default:10).
    • stop_interval: int : Stop if criteria is performing worse over n consecutive checks (default: 3).
get_embeddings(entities, type='entity')

Get the embeddings of entities or relations.

Parameters:
  • entities (array-like, dtype=int, shape=[n]) – The entities (or relations) of interest. Element of the vector must be the original string literals, and not internal IDs.
  • type (string) – If ‘entity’, will consider input as KG entities. If relation, they will be treated as KG predicates.
Returns:

embeddings – An array of k-dimensional embeddings.
Return type:

ndarray, shape [n, k]
predict(X, from_idx=False, get_ranks=False)

Predict the score of triples using a trained embedding model.

The function returns raw scores generated by the model. To obtain probability estimates, use a logistic sigmoid.
Parameters:
  • X (ndarray, shape [n, 3]) – The triples to score.
  • from_idx (bool) – If True, will skip conversion to internal IDs. (default: False).
  • get_ranks (bool) – Flag to compute ranks by scoring against corruptions (default: False).
Returns:

  • scores_predict (ndarray, shape [n]) – The predicted scores for input triples X.
  • rank (ndarray, shape [n]) – Rank of the triple