Top-k recommender model #663
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Click to view CI ResultsGitHub pull request #663 of commit 4f3e3d708f7bc2ecadbeb83c1620cdf72dab0324, no merge conflicts.
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Commit message: "first pass of top-k recommender"
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[merlin_models] $ /bin/bash /tmp/jenkins6761334082408397487.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 189 items / 42 errors
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Click to view CI ResultsGitHub pull request #663 of commit 378fec819be8f3dce21df53f71c9a040dd8adc04, no merge conflicts.
Running as SYSTEM
Setting status of 378fec819be8f3dce21df53f71c9a040dd8adc04 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1001/console and message: 'Pending'
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Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
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Fetching changes from the remote Git repository
> git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
> git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
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> git config core.sparsecheckout # timeout=10
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Commit message: "fix circular import"
> git rev-list --no-walk 4f3e3d708f7bc2ecadbeb83c1620cdf72dab0324 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins6491777090491700769.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 660 items
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Documentation preview |
| _ = model.fit(ecommerce_data, batch_size=50, epochs=5, steps_per_epoch=1) | ||
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| recommender = model.to_item_recommender(dataset=ecommerce_data, batch_size=50, k=10) | ||
| recommender.compile(run_eagerly=run_eagerly, optimizer="adam") |
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Do we need to set the optimizer of item_recommender if it is just to be used for evaluation and prediction?
| def batch_predict( | ||
| self, dataset: merlin.io.Dataset, batch_size: int, top_k=1, **kwargs | ||
| ) -> merlin.io.Dataset: | ||
| pass |
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Should raise NotImplementedError()
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we can close it since there will be a new PR for top_k_recommender via #718 |
Fixes #622
Goals ⚽
Decouple the top-k local prediction top-k evaluation from the retrieval contrastive learning task.
REfactor retrieval models and add a method to convert to a top-k recommender model :
Ensure the Keras analogy for the top-k recommender where the user can call
ItemRecommender should support different top-k strategies:
Ensure retrieval experiments and CI performance tests are returning same level of performance with the new Retrieval API
Implementation Details 🚧
Testing Details 🔍