Tim Head betatim

GitHub PR Activity Tracker

A Python script that tracks Pull Request activity for a specific user over a configurable time period using the PyGithub library.

Features

Tracks PRs where the user:
- Created the PR
- Added comments
Submitted reviews

AI-Assisted PR Review Checklist for Scikit-learn

Purpose: This checklist is optimized for AI assistants (like Cursor) to perform automated PR reviews. It separates automatable checks from those requiring human judgment, provides specific patterns to detect, and includes commands to run.

How to Use This Checklist

For AI Agents:

Run all AUTOMATED checks first and report findings with severity levels

Summary of Issues

Classification Metrics Sparse Support Bug (Issue #32036): A bug where classification metrics in scikit-learn claim sparse matrix support in docstrings but raise an error when used with sparse inputs. The issue is reliably reproducible with provided code steps, expected (support) vs. actual behavior (TypeError), and environment details in the traceback. No major missing elements. Link
RandomizedSearchCV Feature Request (Issue #32032): A proposal to add weights for controlling the probability of selecting items in a list of parameter distributions, useful for complex pipelines with interdependent hyperparameters. This is a feature enhancement, not a bug, and includes clear examples and rationale. Link
CI Failure on Linux Build (Issue #32022): Reported CI failure on a specific build configuration, with a reference to logs but no detailed steps to rep

Building for bare metal

Checkout https://github.com/rapidsai/gpu-xb-ai:

git clone https://github.com/rapidsai/gpu-xb-ai

Create a conda environment from conda/environments/gpu-xb-ai-legate-all.yaml:

conda env create -f conda/environments/gpu-xb-ai-legate-all.yaml

	import numpy as np
	import logging
	import time

	# Configure logging
	logging.basicConfig(
	level=logging.INFO,
	format='%(asctime)s - %(levelname)s - %(message)s',
	handlers=[
	logging.FileHandler('calculations.log'),

	import sklearn
	import numpy as np
	import torch


	sklearn.set_config(array_api_dispatch=True)

	def my_code(X, cdist=False):
	if cdist:
	dist = torch.cdist(X, X, p=2)

	conda-forge/osx-arm64 Using cache
	conda-forge/noarch Using cache


	Looking for: ['python=3.9', 'numpy=1.19.5', 'blas', 'scipy=1.6.0', 'cython=3.0.10', 'joblib', 'threadpoolctl', 'matplotlib=3.3.4', 'pandas=1.1.5', 'pyamg', "pytest[version='<8']", 'pytest-xdist', 'pillow', 'pip', 'ninja', 'meson-python', 'scikit-image=0.17.2', 'seaborn', 'memory_profiler', 'compilers', 'sphinx=6.0.0', 'sphinx-gallery=0.15.0', 'sphinx-copybutton=0.5.2', 'numpydoc=1.2.0', 'sphinx-prompt=1.3.0', 'plotly=5.14.0', 'polars=0.19.12', 'pooch', 'pip']


	Could not solve for environment specs
	The following packages are incompatible
	├─ numpy 1.19.5** is installable with the potential options

	==================================================================================== short test summary info ====================================================================================
	FAILED sklearn/model_selection/tests/test_split.py::test_array_api_train_test_split[True-None-cupy.array_api-None-None] - ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
	FAILED sklearn/model_selection/tests/test_split.py::test_array_api_train_test_split[True-stratify1-cupy-None-None] - ValueError: kind can only be None or 'stable'
	FAILED sklearn/model_selection/tests/test_split.py::test_array_api_train_test_split[True-stratify1-cupy.array_api-None-None] - ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2.
	FAILED sklearn/model_selection/tests/test_split.py::test_array_api_train_test_split[False-None-cupy.array_api-None-None] - ValueError: The truth value of an array with

	from dask_mpi import initialize
	from dask import distributed


	def dask_info():
	distributed.print("woah i'm running!")
	distributed.print("ncores:", client.ncores())
	distributed.print()
	distributed.print(client.scheduler_info())

	[215/275] Linking CXX shared library libcuml++.so
	FAILED: libcuml++.so
	: && /datasets/thead/mambaforge/envs/cuml-dev-23.12-dgx15/bin/x86_64-conda-linux-gnu-c++ -fPIC -fvisibility-inlines-hidden -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /datasets/thead/mambaforge/envs/cuml-dev-23.12-dgx15/include -I/datasets/thead/mambaforge/envs/cuml-dev-23.12-dgx15/targets/x86_64-linux/include -L/datasets/thead/mambaforge/envs/cuml-dev-23.12-dgx15/targets/x86_64-linux/lib -L/datasets/thead/mambaforge/envs/cuml-dev-23.12-dgx15/targets/x86_64-linux/lib/stubs -O3 -DNDEBUG -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,--allow-shlib-undefined -Wl,-rpath,/datasets/thead/mambaforge/envs/cuml-dev-23.12-dgx15/lib -Wl,-rpath-link,/datasets/thead/mambaforge/envs/cuml-dev-23.12-dgx15/lib -L/datasets/thead/mambaforge/envs/cuml-dev-23.12-dgx15/lib -L/datasets/thead/mamb