# Extending the Interaction Models (Old-Style)¶

Let’s assume you have invented a new interaction model, e.g. this variant of pykeen.models.DistMult

$f(h, r, t) = <h, \sigma(r), t>$

where $$h,r,t \in \mathbb{R}^d$$, and $$\sigma$$ denotes the logistic sigmoid.

## Picking a Base Class¶

From the reference documentation on base models (pykeen.models.base), we can see that pykeen.models.base.EntityRelationEmbeddingModel is a good candidate for a base class since we want to have embeddings for entities and relations.

## Implementing score_hrt()¶

The only implementation we have to provide is of the score_hrt member function:

from pykeen.models.base import EntityRelationEmbeddingModel

class ModifiedDistMult(EntityRelationEmbeddingModel):
def score_hrt(self, hrt_batch):
# Get embeddings
h = self.entity_representations[0](hrt_batch[:, 0])
r = self.relation_representations[0](hrt_batch[:, 1])
t = self.entity_representations[0](hrt_batch[:, 2])
# evaluate interaction function
return h * r.sigmoid() * t


The entity_representations and relation_representations sequences are available for all pykeen.models.base.EntityRelationEmbeddingModel and are lists of length one containing a single instances of a pykeen.nn.Embedding. This may seem like a strange data structure, but it prepares for the much more powerful usages covered by the new-style pykeen.models.ERModel.

The hrt_batch is a long tensor representing the internal indices of the edges. The above example shows a very common way of slicing it to get separate lists of head indices (hrt_batch[:, 0]), relation indices (hrt_batch[:, 1]), and tail indices (hrt_batch[:, 2]). Then, they are passed to the embeddings to look up the actual values. This is vectorized, so the results are also 2-tensors (tensors of embeddings) on which vectorized math can be applied.

## Using a Custom Model with the Pipeline¶

We can use this new model with all available losses, evaluators, training pipelines, inverse triple modeling, via the pykeen.pipeline.pipeline(), since in addition to the names of models (given as strings), it can also take model classes in the model argument.

from pykeen.pipeline import pipeline

pipeline(
model=ModifiedDistMult,
dataset='Nations',
loss='NSSA',
)


If you have a preferred loss function for your model, you can add the loss_default class variable where the value is the loss class.

from pykeen.models.base import EntityRelationEmbeddingModel
from pykeen.losses import NSSALoss

class ModifiedDistMult(EntityRelationEmbeddingModel):
loss_default = NSSALoss

def score_hrt(self, hrt_batch):
h = self.entity_representations[0](hrt_batch[:, 0])
r = self.relation_representations[0](hrt_batch[:, 1])
t = self.entity_representations[0](hrt_batch[:, 2])
return h * r.sigmoid() * t


Now, when using the pipeline, the pykeen.losses.NSSALoss. loss is used by default if none is given. The same kind of modifications can be made to set a default regularizer with regularizer_default.

## Implementing a Custom __init__()¶

Let’s say you modify the previous interaction model to apply a two consecutive linear transformations a and b to the entity embeddings using the torch.nn.Linear module.

$f(h, r, t) = <abh, \sigma(r), abt>$

Each PyKEEN model is a subclass of torch.nn.Module, so you can update the __init__() function. However, there are a couple things to consider:

1. Don’t forget to properly call the super().__init__() and make the base class’s arguments for __init__() available (even if you don’t understand them). This is important for the pipeline to take care of automatically instantiating and running the code you wrote

2. Either before or after super().__init__() (left to your best judgement), you can run any arbitrary code. Just like making normal torch modules, you can set some submodules as attributes of the instance.

3. If your submodules need to be initialized, don’t forget to implement the _reset_parameters_() function. It should call super()._reset_parameters_() function because there are some parameters that could already reset by the base model you have chosen. This function is magically called in a post-init hook, so don’t worry that you don’t call it yourself.

from typing import Optional

import torch.nn

from pykeen.losses import Loss, NSSALoss
from pykeen.models.base import EntityRelationEmbeddingModel
from pykeen.pipeline import pipeline
from pykeen.regularizers import Regularizer
from pykeen.triples import TriplesFactory

class ModifiedLinearDistMult(EntityRelationEmbeddingModel):
loss_default = NSSALoss

def __init__(
self,
hidden_dim: int = 20,  # extra stuff!
**kwargs,  # pass everything else, you neither have to understand nor be able to handle the truth
):
super().__init__(**kwargs)

# Save some extra state information
self.hidden_dim = hidden_dim

# Note that the embedding_dim is available to all EntityRelationEmbeddingModels after init.
self.linear1 = torch.nn.Linear(self.embedding_dim, self.hidden_dim)
self.linear2 = torch.nn.Linear(self.hidden_dim, self.embedding_dim)

def score_hrt(self, hrt_batch):
h = self.entity_representations[0](hrt_batch[:, 0])
r = self.relation_representations[0](hrt_batch[:, 1])
t = self.entity_representations[0](hrt_batch[:, 2])

h = self.linear2(self.linear1(h))
t = self.linear2(self.linear1(t))

return h * r.sigmoid() * t

def _reset_parameters_(self):  # noqa: D102
super()._reset_parameters_()

# weight initialization
torch.nn.init.zeros_(self.linear1.bias)
torch.nn.init.zeros_(self.linear2.bias)
torch.nn.init.xavier_uniform_(self.linear1.weight)
torch.nn.init.xavier_uniform_(self.linear2.weight)


## Adding Custom HPO Default Ranges¶

All subclasses of pykeen.models.base.Model can specify the default ranges or values used during hyper-parameter optimization (HPO). PyKEEN implements a simple dictionary-based configuration that is interpreted by pykeen.hpo.hpo.suggest_kwargs() in the HPO pipeline.

HPO default ranges can be applied to all keyword arguments appearing in the __init__() function of your model by setting a class-level variable called hpo_default.

For example, the hidden_dim can be specified as being on a range between 15 and 50 with the following:

class ModifiedLinearDistMult(EntityRelationEmbeddingModel):
hpo_default = {
'hidden_dim': dict(type=int, low=15, high=50)
}
...


A step size can be imposed with q:

class ModifiedLinearDistMult(EntityRelationEmbeddingModel):
hpo_default = {
'hidden_dim': dict(type=int, low=15, high=50, q=5)
}
...


An alternative scale can be imposed with scale. Right now, the default is linear, and scale can optionally be set to power_two for integers as in:

class ModifiedLinearDistMult(EntityRelationEmbeddingModel):
hpo_default = {
# will uniformly give 2, 4, 8 (left inclusive, right exclusive)
'hidden_dim': dict(type=int, low=2, high=4, scale='power_two')
}
...


Warning

Alternative scales can not currently be used in combination with step size (q).

There are other possibilities for specifying the type as float, categorical, or as bool.

With float, you can’t use the q option nor set the scale to power_two, but the scale can be set to log (see optuna.distributions.LogUniformDistribution).

hpo_default = {
# will uniformly give floats on the range of [1.0, 2.0) (exclusive)
'alpha': dict(type='float', low=1.0, high=2.0),

# will uniformly give 1.0, 2.0, or 4.0 (exclusive)
'beta': dict(type='float', low=1.0, high=8.0, scale='log'),
}


With categorical, you can form a dictionary like the following using type='categorical' and giving a choices entry that contains a sequence of either integers, floats, or strings.

hpo_default = {
'similarity': dict(type='categorical', choices=[...])
}


With bool, you can simply use dict(type=bool) or dict(type='bool').

Note

The HPO rules are subject to change as they are tightly coupled to optuna, which since version 2.0.0 has introduced several new possibilities.