betatim

Array API Architecture

Created: 2026-01-07 Last Updated: 2026-01-07

Overview

Scikit-learn's Array API support enables estimators and functions to work with arrays from different libraries (NumPy, CuPy, PyTorch) without modification. This allows computations to run on GPUs when using GPU-backed array libraries.

The implementation follows the Array API Standard, a specification that defines a common API for array manipulation libraries.

betatim

#!/usr/bin/env python3
"""
Ray + RandomForestClassifier with max_calls=1

Demonstrates the impact of max_calls=1 on Ray task execution when using
scikit-learn's RandomForestClassifier.
"""
import time
import ray
from sklearn.datasets import make_classification

betatim

"""
Benchmark: scikit-learn RandomForest vs LightGBM RandomForest

Compares performance across:
- Number of samples (1K, 10K, 100K, 500K)
- Number of features (10, 50, 200)
- Feature types (numerical, categorical, mixed)
- Number of classes (2, 5, 10)

Includes cases optimized for LightGBM's strengths:

betatim

name: tabareana-20251202
channels:
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=2_gnu
  - bzip2=1.0.8=hda65f42_8
  - ca-certificates=2025.11.12=hbd8a1cb_0
  - cuda-cccl_linux-64=13.0.85=ha770c72_0
  - cuda-cudart-dev_linux-64=13.0.96=h376f20c_0

betatim

from __future__ import annotations

import warnings
warnings.simplefilter("error", FutureWarning)
from pathlib import Path
from typing import Any

import pandas as pd

from tabarena.benchmark.experiment import AGModelBagExperiment, ExperimentBatchRunner

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

betatim

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:

1. Run all AUTOMATED checks first and report findings with severity levels

betatim

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

betatim

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'),

betatim

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)