Interface for a (functional) minibatch generator.
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 5.2k
- Forks
- 916
- Avg merge
- 17h 31m
- Merged PRs (30d)
- 4
Description
I've noticed that in order to create minibatches from training set, the easiest what to achieve this, using mlxtend, is to inherit _IterativeModel and then use _yield_minibatches_idx. Have you thought about providing a more functional interface, i.e. providing a function (generator)?
Then the call would look something like this:
for X_mb, y_mb in minibatch_gen(X_train, y_train):
...
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading _IterativeModel and the _yield_minibatches_idx helper mentioned in the issue to understand the existing minibatch behavior. Define the functional generator's expected inputs, outputs, and batching semantics before locating an appropriate module and tests. Done means the proposed interface is implemented with coverage for its intended behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100