Optimizing a Model¶
The easiest way to optimize a model is with the pykeen.hpo.hpo_pipeline()
function.
All of the following examples are about getting the best model
when training TransE on the Nations dataset. Each gives a bit
of insight into usage of the hpo_pipeline()
function.
The minimal usage of the hyper-parameter optimization is to specify the
dataset, the model, and how much to run. The following example shows how to
optimize the TransE model on the Nations dataset a given number of times using
the n_trials
argument.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... n_trials=30,
... dataset='Nations',
... model='TransE',
... )
Alternatively, the timeout
can be set. In the following example,
as many trials as possible will be run in 60 seconds.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... timeout=60,
... dataset='Nations',
... model='TransE',
... )
Every model in PyKEEN has default values for its hyper-parameters chosen from the best-reported values in each model’s original paper unless otherwise stated on the model’s reference page. In case hyper-parameters for a model for a specific dataset were not available, we choose the hyper-parameters based on the findings in our large-scale benchmarking [ali2020a].
In addition to reasonable default hyper-parameters, every model in PyKEEN has default “strategies” for optimizing these hyper-parameters which either constitute ranges for integer/floating point numbers or as enumerations for categorical variables and booleans.
While the default values for hyper-parameters are encoded with the python syntax
for default values of the __init__()
function of each model, the ranges/scales can be
found in the class variable pykeen.models.Model.hpo_default
. For
example, the range for TransE’s embedding dimension is set to optimize
between 50 and 350 at increments of 25 in pykeen.models.TransE.hpo_default
.
TransE also has a scoring function norm that will be optimized by a categorical
selection of {1, 2} by default.
Note
These hyper-parameter ranges were chosen as reasonable defaults for the benchmark datasets FB15k-237 / WN18RR. When using different datasets, the ranges might be suboptimal.
All hyper-parameters defined in the hpo_default
of your chosen model will be
optimized by default. If you already have a value that you’re happy with for
one of them, you can specify it with the model_kwargs
attribute. In the
following example, the embedding_dim
for a TransE model is fixed at 200,
while the rest of the parameters will be optimized using the pre-defined HPO strategies in
the model. For TransE, that means that the scoring function norm will be optimized
as 1 or 2.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... model='TransE',
... model_kwargs=dict(
... embedding_dim=200,
... ),
... dataset='Nations',
... n_trials=30,
... )
If you would like to set your own HPO strategy, you can do so with the
model_kwargs_ranges
argument. In the example below, the embeddings are
searched over a larger range (low
and high
), but with a higher step
size (q
), such that 100, 200, 300, 400, and 500 are searched.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_result = hpo_pipeline(
... n_trials=30,
... dataset='Nations',
... model='TransE',
... model_kwargs_ranges=dict(
... embedding_dim=dict(type=int, low=100, high=500, q=100),
... ),
... )
If the given range is not divisible by the step size, then the upper bound will be omitted.
Optimizing the Loss¶
While each model has its own default loss, you can explicitly specify a loss
the same way as in pykeen.pipeline.pipeline()
.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... n_trials=30,
... dataset='Nations',
... model='TransE',
... loss='MarginRankingLoss',
... )
As stated in the documentation for pykeen.pipeline.pipeline()
, each model
specifies its own default loss function in pykeen.models.Model.loss_default
.
For example, the TransE model defines the margin ranking loss as its default in
pykeen.models.TransE.loss_default
.
Each model also specifies default hyper-parameters for the loss function in
pykeen.models.Model.loss_default_kwargs
. For example, DistMultLiteral
explicitly sets the margin to 0.0 in pykeen.models.DistMultLiteral.loss_default_kwargs
.
Unlike the model’s hyper-parameters, the models don’t store the strategies for
optimizing the loss functions’ hyper-parameters. The pre-configured strategies
are stored in the loss function’s class variable pykeen.models.Loss.hpo_default
.
However, similarily to how you would specify model_kwargs_ranges
, you can
specify the loss_kwargs_ranges
explicitly, as in the following example.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... n_trials=30,
... dataset='Nations',
... model='TransE',
... loss='MarginRankingLoss',
... loss_kwargs_ranges=dict(
... margin=dict(type=float, low=1.0, high=2.0),
... ),
... )
Optimizing the Regularizer¶
Every model has a default regularizer (pykeen.models.Model.regularizer_default
)
and default hyper-parameters for the regularizer (pykeen.models.Model.regularizer_default_kwargs
).
Better than the loss is that every regularizer class has a built-in hyper-parameter optimization
strategy just like the model at pykeen.regularizers.Regularizer.hpo_default
.
Therefore, the rules for specifying regularizer
, regularizer_kwargs
, and
regularizer_kwargs_ranges
are the same as for models.
Optimizing the Optimizer¶
Yo dawg, I heard you liked optimization, so we put an optimizer around your
optimizer so you can optimize while you optimize. Since all optimizers used
in PyKEEN come from the PyTorch implementations, they obviously do not have
hpo_defaults
class variables. Instead, every optimizer has a default
optimization strategy stored in pykeen.optimizers.optimizers_hpo_defaults
the same way that the default strategies for losses are stored externally.
Optimizing the Negative Sampler¶
When the stochastic local closed world assumption (sLCWA) training approach is used for training, a negative sampler
(subclass of pykeen.sampling.NegativeSampler
) is chosen.
Each has a strategy stored in pykeen.sampling.NegativeSampler.hpo_default
.
Like models and regularizers, the rules are the same for specifying negative_sampler
,
negative_sampler_kwargs
, and negative_sampler_kwargs_ranges
.
Optimizing Everything Else¶
Without loss of generality, the following arguments to pykeen.pipeline.pipeline()
have corresponding *_kwargs and *_kwargs_ranges:
training_loop
(only kwargs, not kwargs_ranges)evaluator
evaluation
Early Stopping¶
Early stopping can be baked directly into the optuna
optimization.
The important keys are stopper='early'
and stopper_kwargs
.
When using early stopping, the hpo_pipeline()
automatically takes
care of adding appropriate callbacks to interface with optuna
.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... n_trials=30,
... dataset='Nations',
... model='TransE',
... stopper='early',
... stopper_kwargs=dict(frequency=5, patience=2, relative_delta=0.002),
... )
These stopper kwargs were chosen to make the example run faster. You will likely want to use different ones.
Optimizing Optuna¶
By default, optuna
uses the Tree-structured Parzen Estimator (TPE)
estimator (optuna.samplers.TPESampler
), which is a probabilistic
approach.
To emulate most hyper-parameter optimizations that have used random
sampling, use optuna.samplers.RandomSampler
like in:
>>> from pykeen.hpo import hpo_pipeline
>>> from optuna.samplers import RandomSampler
>>> hpo_pipeline_result = hpo_pipeline(
... n_trials=30,
... sampler=RandomSampler,
... dataset='Nations',
... model='TransE',
... )
Alternatively, the strings "tpe"
or "random"
can be used so you
don’t have to import optuna
in your script.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... n_trials=30,
... sampler='random',
... dataset='Nations',
... model='TransE',
... )
While optuna.samplers.RandomSampler
doesn’t (currently) take
any arguments, the sampler_kwargs
parameter can be used to pass
arguments by keyword to the instantiation of
optuna.samplers.TPESampler
like in:
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... n_trials=30,
... sampler='tpe',
... sampler_kwargs=dict(prior_weight=1.1),
... dataset='Nations',
... model='TransE',
... )
Full Examples¶
The examples above have shown the permutation of one setting at a time. This section has some more complete examples.
The following example sets the optimizer, loss, training, negative sampling, evaluation, and early stopping settings.
>>> from pykeen.hpo import hpo_pipeline
>>> hpo_pipeline_result = hpo_pipeline(
... n_trials=30,
... dataset='Nations',
... model='TransE',
... model_kwargs=dict(embedding_dim=20, scoring_fct_norm=1),
... optimizer='SGD',
... optimizer_kwargs=dict(lr=0.01),
... loss='marginranking',
... loss_kwargs=dict(margin=1),
... training_loop='slcwa',
... training_kwargs=dict(num_epochs=100, batch_size=128),
... negative_sampler='basic',
... negative_sampler_kwargs=dict(num_negs_per_pos=1),
... evaluator_kwargs=dict(filtered=True),
... evaluation_kwargs=dict(batch_size=128),
... stopper='early',
... stopper_kwargs=dict(frequency=5, patience=2, relative_delta=0.002),
... )
If you have the configuration as a dictionary:
>>> from pykeen.hpo import hpo_pipeline_from_config
>>> config = {
... 'optuna': dict(
... n_trials=30,
... ),
... 'pipeline': dict(
... dataset='Nations',
... model='TransE',
... model_kwargs=dict(embedding_dim=20, scoring_fct_norm=1),
... optimizer='SGD',
... optimizer_kwargs=dict(lr=0.01),
... loss='marginranking',
... loss_kwargs=dict(margin=1),
... training_loop='slcwa',
... training_kwargs=dict(num_epochs=100, batch_size=128),
... negative_sampler='basic',
... negative_sampler_kwargs=dict(num_negs_per_pos=1),
... evaluator_kwargs=dict(filtered=True),
... evaluation_kwargs=dict(batch_size=128),
... stopper='early',
... stopper_kwargs=dict(frequency=5, patience=2, relative_delta=0.002),
... )
... }
... hpo_pipeline_result = hpo_pipeline_from_config(config)
If you have a configuration (in the same format) in a JSON file:
>>> import json
>>> config = {
... 'optuna': dict(
... n_trials=30,
... ),
... 'pipeline': dict(
... dataset='Nations',
... model='TransE',
... model_kwargs=dict(embedding_dim=20, scoring_fct_norm=1),
... optimizer='SGD',
... optimizer_kwargs=dict(lr=0.01),
... loss='marginranking',
... loss_kwargs=dict(margin=1),
... training_loop='slcwa',
... training_kwargs=dict(num_epochs=100, batch_size=128),
... negative_sampler='basic',
... negative_sampler_kwargs=dict(num_negs_per_pos=1),
... evaluator_kwargs=dict(filtered=True),
... evaluation_kwargs=dict(batch_size=128),
... stopper='early',
... stopper_kwargs=dict(frequency=5, patience=2, relative_delta=0.002),
... )
... }
... with open('config.json', 'w') as file:
... json.dump(config, file, indent=2)
... hpo_pipeline_result = hpo_pipeline_from_path('config.json')