Comments (2)
The only bug I have seen so far is in the TrainingPlan of the first regressor model dataset = pd.read_csv(self.dataset_path, delimiter=',')
that should be dataset = pd.read_csv(self.dataset_path, delimiter=';')
The order of the models is misleading because the testing part should be run with the first model, but when we run the notebook cell by cell, we are testing the second model (which is a SGDclassifier) with the data of the first model, hence the matrix multiplication error you have spotted. Possible actions to change the notebook are discussed internally with the team and will be updated in this issue
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Fixed in PR #911
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Related Issues (20)
- Reorganize existing end2end tests in 'tests/end2end/' directory
- Building `gui` container fails because of missing `fedbiomed-node` environment HOT 2
- Remove old end-to-end tests: Github actions and test scripts
- Add a way to execute cells / distinguish cells in notebooks for end2end tests HOT 1
- Continue #1091 - GUI container execution fails
- Add more debug messages into transport module
- Design proposal for structured datasets
- Node fails during training with "gRPC already finished" error message
- Researcher component needs better memory management HOT 2
- [BUG]: cannot load a `nifti` image in the medical folder dataset of Fed-BioMed if it contains dots (`.`) in its name HOT 1
- [BUG]: `clean_researcher.py` does not clean `Researcher` well HOT 1
- Redesign/Refactor `MPCController` in order to have more generic implementation HOT 1
- Create a class to implement key aggrement of diffie-hellman HOT 1
- Add new class inherits from `BaseSecaggSetup` for LOM implementation for node
- Redesign database for secagg to add LOM secagg context HOT 1
- Add new manager called SecaggDhManager where LOM context is managed.
- Refactor current LOM PoC HOT 4
- Add end2end tests documentation to website documentation
- Design how message exchange between nodes works on the node side application layer HOT 1
- Implement message exchange between nodes on the node side application layer
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