search

mljar / mljar-supervised

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
https://mljar.com
MIT License
3.01k stars 401 forks source link

sklearn/metrics/_scorer.py:548: FutureWarning #695

Closed fightpf closed 7 months ago

fightpf commented 7 months ago

When I use Docker Compose for developing the Mercury service, my page displays the following red section hint:

/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_scorer.py:548: FutureWarning: The `needs_threshold` and `needs_proba` parameter are deprecated in version 1.4 and will be removed in 1.6. You can either let `response_method` be `None` or set it to `predict` to preserve the same behaviour.
  warnings.warn(

this is requirement.txt:

django==4.2.3
djangorestframework==3.14.0
django-filter==21.1
markdown==3.3.6
celery>=5.1.2
sqlalchemy==1.4.27
gevent
nbconvert>=7.8.0
ipython_genutils
pyyaml==6.0
django-cors-headers==3.10.1
ipython>=7.30.1
ipykernel>=6.6.0
psutil>=5.8.0
whitenoise>=5.3.0
python-dotenv>=0.19.2
django-drf-filepond==0.4.1
croniter>=1.3.5
pyppeteer==1.0.2
channels[daphne]>=4.0.0
websocket-client>=1.4.2
execnb
ipywidgets==8.0.3 # cant update ipywidgets!
dj-rest-auth[with_social]==3.0.0
boto3==1.26.83
cryptography
pyopenssl>=23.1.1
bleach>=6.0.0
scipy>=1.6.1,<=1.11.4
pyarrow
itables
plotly
mljar-supervised==1.1.2
altair
pandas

how can i fix it?

fightpf commented 7 months ago

Additionally, I discovered that the previously stored AutoML model could not be used. Ultimately, I resolved the issue by appending scikit-learn==1.3.2 to the end.

...
scipy>=1.6.1,<=1.11.4
pyarrow
itables
plotly
mljar-supervised==1.1.2
altair
pandas
scikit-learn==1.3.2
pplonski commented 6 months ago

Fixed warning in #709 - new version 1.1.4 is released on pip.