# RandomBaseline¶

class ampligraph.latent_features.RandomBaseline(seed=0)

Random baseline

A dummy model that assigns a pseudo-random score included between 0 and 1, drawn from a uniform distribution.

The model is useful whenever you need to compare the performance of another model on a custom knowledge graph, and no other baseline is available.

Note

Although the model still requires invoking the fit() method, no actual training will be carried out.

Examples

>>> import numpy as np
>>> from ampligraph.latent_features import RandomBaseline
>>> model = RandomBaseline()
>>> 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']]))
[0.5488135039273248, 0.7151893663724195]


Methods

 __init__([seed]) Initialize the model fit(X) Train the random model predict(X[, from_idx]) Assign random scores to candidate triples and then ranks them
__init__(seed=0)

Initialize the model

Parameters: seed (int) – The seed used by the internal random numbers generator.
fit(X)

Train the random model

Parameters: X (ndarray, shape [n, 3]) – The training triples
predict(X, from_idx=False)

Assign random scores to candidate triples and then ranks them

Parameters: X (ndarray, shape [n, 3]) – The triples to score. from_idx (bool) – If True, will skip conversion to internal IDs. (default: False). scores – The predicted scores for input triples X. ndarray, shape [n]