Hello, I am wondering why this PR (scikit-learn/scikit-learn#18758) doesn't show up at the top here:
Is it because I had submitted it a long time ago, but my recent changes are considered updates?
pytest sklearnand see the following. Is this ok, or is there something wrong with my build:
SKIPPED  sklearn/utils/tests/test_validation.py:1374: could not import 'pandas': No module named 'pandas' ==== 355 failed, 19625 passed, 1443 skipped, 117 xfailed, 37 xpassed, 3371 warnings in 2380.84s (0:39:40) ==== (sklearn-dev)
Deleted this message from the /dev/ channel. Copying and pasting here:
I am Bhavya Bhardwaj (https://github.com/Bhavya1705). I am a student of Electronics and Communication at Amrita Vishwa Vidyapeetham, India. My thanks to you and the team for sklearn. I have been try to make some contributions to the scikit-learn library - scikit-learn/scikit-learn#5516. I have made the code, and the necessary changes to the init file and test files, in addition to the _classification file. This is the links to my commits - scikit-learn/scikit-learn#20861, as you will see, there are many mistakes, that I have made, Any help that you can render to me would be much appreciated and would be a wonderful learning experience.
Hi. I am trying to develop my own Estimator based on TransformerMixin and BaseEstimator. To make sure I am doing things right I have added a test to my project :
import MyEstimator from sklearn.utils.estimator_checks import check_estimator def test () : me = MyEstimator(**params) check_estimator(me)
If I run the test, I get the following error message :
AssertionError: The error message should contain one of the following patterns: 0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* is required.
I don't understand how I am supposed to take care of that. I am even more surprise because my fit_transorm method uses self._validate_data at the beginning. I would expect that function to take care of case like these. Could someone help me with that issue ?