Comments (6)
I think you are moving the goalposts. We do not provide guarantees on _fit
, but it does not forbid the user to use it. Making this method a bit more flexible does not change that.
Also, training a model without Cubes + Experiment overhead is exactly why one would consider using the method (e.g. for very dirty prototyping or perhaps for cases not covered by Cubes + Experiment yet).
from topicnet.
off-topic (although not quite): BaseModel has TODO in _fit
's docstring for dataset_trainable
from topicnet.
Did you mean Union
instead of Tuple
? Or am I confused about OR operator in typing
?
from topicnet.
Exactly! The owls are not what they seem. Corrected!
from topicnet.
First, _fit
is "protected" method, meaning we do not guarantee that it should work nice and easy for the user and that everything will work. Meaning, that normally user should not use it to train a model and it exists so we can hook up library components with this method.
Given that we go forward and implement this enhancement we will have to change some of the core architecture: making method "legal" to use makes it so that we have to 1) add a cube information to the fit 2) check that the fit is not overlapping with previous actions 3) train model in a separate thread and save/load it afterwards...
See where it's going? the nice and simple method grows into something that duplicates existing functionality and puts it into the "models" class that we already wanted to "separate" from the training action.
from topicnet.
First, _fit is "protected" method, meaning we do not guarantee that it should work nice and easy for the user and that everything will work
Ok, but it doesn't mean that we shouldn't think about how to make the method better 🙂
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Related Issues (20)
- We need to provide datasets in BOW and Natural-Order forms HOT 1
- Add some kind of Contacts section in README? HOT 5
- Junk stuff accumulates in dataset internals folder
- Dataset_manager - Connection refused HOT 3
- TopicNet's BaseRegularizer can't be used in cube settings?!
- Thetaless sometimes have troubles with modalities HOT 2
- Get document-topic representation HOT 2
- Incorrect link to dataset HOT 1
- Downloading `postnauka` dataset produces error HOT 3
- WinError: Failed to load artm shared library from artm.dll HOT 1
- Making-Decorrelation-and-Topic-Selection-Friends.ipynb Doesn't work in google colab from scratch HOT 3
- Dataset "ruwiki_good" does not want to be downloaded HOT 1
- Custom needs more custom handling HOT 1
- Where are the docs, TopDocs? HOT 1
- Datasets not loading HOT 2
- New Pandas can't work with old Dataset
- Reference HuggingFace Hosted Datasets
- Link to BigARTM Tutorial in Colab
- Wrong "K" in RTL-Wiki download link in demo notebook HOT 2
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