Score not improving in iterative training using PyKEEN pipeline

I am trying to implement an iterative training process using the PyKEEN library in Python. My goal is to improve the score in each iteration by training the model using the previous iteration's trained model. However, I have encountered an issue where the score does not improve after the first iteration.

from pykeen.pipeline import pipeline
from pykeen.datasets import Nations
from pykeen.models import TransE

dataset = Nations()
initial_model = TransE
model = initial_model
seed = 0
epochs = 10
budget = 4
scores = []

for i in range(budget):
    test = pipeline(
        dataset=dataset,
        model=model,
        training_kwargs=dict(
            num_epochs=epochs,
            use_tqdm_batch=False,
        ),
        random_seed=seed,
        negative_sampler='basic'
    )

    model = test.model

    scores.append(round(test.metric_results.get_metric("hits_at_10"), 3))

print(scores)

In each iteration, I train the model using the PyKEEN pipeline and update the model variable with the trained model from the current iteration. However, the scores remain the same after the first iteration, indicating that the model is not improving.

I suspect that assigning model = test.model in each iteration might be causing the issue. Is there a different approach I should take to ensure that the pipeline trains using the model from the previous iteration?

I tried setting model.training = True, but this had no effect.

I also tried to see whether the model changed at all in subsequent iterations, which it does not.
The initial model is different from subsequent iterations, but all iterations have the same model, even after training. This suggests the model doesn't train at all, which can also be seen in the scores, as these also do not change in subsequent iterations.

I would appreciate any insights or suggestions on how to resolve this issue. Thank you!

I suggest you explicitly save the model and reload it in your loop, pykeen provides a way to save it to a path here

import torch

for i in range(budget):
    test = pipeline(
        dataset=dataset,
        model=model,
        training_kwargs=dict(
            num_epochs=epochs,
            use_tqdm_batch=False,
        ),
        random_seed=seed,
        negative_sampler='basic'
    )

    scores.append(round(test.metric_results.get_metric("hits_at_10"), 3))
    
    test.save_to_directory('./path/to/your/directory')
    model = torch.load('./path/to/your/directory/trained_model.pkl')

print(scores)

I found out the reason it didn't change is due to how I stored the object. Instead of making a copy, it all referenced the same object which caused the results to be the same. I used copy.deepcopy() to solve this.

model = copy.deepcopy(test.model)