Comments (2)
Update: I changed the README and the main page to indicate that these algorithms are only available on latest version.
from surprise.
Hi Maher,
Actually these algorithms are only available on the latest version which I haven't published to PyPI (the official python package index), so they're not yet available using pip install surprise
.
To use them you'll need to clone the github repo:
$ git clone [email protected]:NicolasHug/Surprise.git
and install the package:
$ cd surprise
$ python setup.py install
or if you want to use pip:
$ cd surprise
$ pip install .
By default the documentation is for the latest version but you can also see the one for version 1.0.0, which is the version on PyPI.
I'm sorry about all this, it's not clear at all from the README or the project main page that these two algorithms are only available for the latest version. I'll try make it clearer as soon as I can!
Thanks for the feedback! Cheers,
Nicolas
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.