Comments (4)
Hi Ken,
I provided an example to read ratings from a pandas dataframe in a previous issue #20.
If there is a strong need to read ratings from different inputs I can try to work on that. May I ask how such use-cases would be useful to you? Also, what would reading ratings from still look like?
Thanks,
Nicolas
from surprise.
Hi thanks for the link.
I'm currently using a custom dataset that takes a random split, then trains the SVD on it. To do this, I'm getting the relevant columns then writing them to disk before providing the file path to the reader. The workflow would be slightly smoother without having to write the data to disk.
I'm currently using a pandas dataframe, so maybe a method where we could load_from_df? I'm not sure what others are using though.
Thanks for the quick response. I'm not desperate for this right now. Just pointing it out as a possible feature request.
from surprise.
I see. I will add an entry to the FAQ following the main lines of #20 to build a dataset from a dataframe. As you can see it all boils down to building the raw_ratings attribute. The dummy reader object is only here to specify the rating scale.
Have you been able to work out a solution for your specific case?
Don't hesitate to come back if you need help.
Nicolas
from surprise.
Yup it's working now. Thanks!
Ken
from surprise.
Related Issues (20)
- build_anti_testset() takes along time and at the end it doesnot work HOT 2
- question - surprise for implicit rating data? HOT 1
- Can Surprise work with PySpark?
- What to do if the dataset I want to read has more than three parameters HOT 1
- A bug when importing data from DataFrame HOT 2
- How do I apply ALS minimization in SVD? HOT 1
- Error: Sample larger than population or is negative? HOT 1
- Issues with running Suprise on M1 mac HOT 1
- trainset do not recommend new products
- Cross-validation kNN wrong results on custom dataset
- Possible memory leakage in SVDpp HOT 1
- GridSearchCV always recommends the first parameter combination as best HOT 2
- Wrong mapping of the raw IDs to the internal IDs
- How to remove NumPy installation in setup.py HOT 4
- Couldn't install Surprise in windows HOT 5
- No timestamp data in trainset HOT 1
- Couldn't install Surprise HOT 5
- How to do kfold crossvalidation on trainset (eg splitting movielens-100k using u1 split. then kfold crossvalidation on u1.base, test on u1.test) HOT 1
- Unexpected RMSE Differences in SVD Models with almost the same Training Data
- Compatibility with Python 3.12 HOT 9
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from surprise.