betatim

import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_validate
from skrub import tabular_pipeline
from skrub.datasets import fetch_employee_salaries


def main():
    print("Loading employee salaries dataset ...")
    data = fetch_employee_salaries()

betatim

#!/usr/bin/env python3
"""
Employee Salary Prediction
==========================
Predict current annual salary for Montgomery County employees
using a scikit-learn pipeline with mixed feature types.
"""

try:
    import cuml.accel

betatim

AGENTS Instruction

This file contains is additional guidance for AI agents and other AI editors.

---

Core Principles

These principles reduce common LLM coding mistakes. Apply them to every task.

betatim

"""
Pipeline(StandardScaler, PCA, RandomForest) with GridSearchCV
=============================================================

GridSearchCV over an all-proxy Pipeline on the full Forest Cover Type
dataset (581K samples, 54 features, 7 classes).

Pipeline: StandardScaler -> PCA -> RandomForestClassifier

All three steps are cuml.accel proxies, so the GridSearchCV patch

betatim

"""
Benchmark: numpy-to-cupy (CPU-to-GPU) transfer times on this machine.

Target: GPU 1 (NVIDIA RTX A6000, 48 GB, PCIe Gen4 x16 slot)
"""

import time
import statistics
import numpy as np
import cupy as cp

betatim

import tarfile
import time
import urllib.request
from collections import OrderedDict
from pathlib import Path

import numpy as np
import scipy.io
import scipy.sparse
from sklearn.decomposition import TruncatedSVD

betatim

AGENTS Instruction

This project is called foobar. Its goal is to provide ...

This file contains is additional guidance for AI agents and other AI editors.

---

Core Principles

betatim

"""
Test: Validate that cuml native estimators can be converted to ONNX
via as_sklearn() -> skl2onnx -> onnxruntime.

Unlike cuml.accel proxies (which skl2onnx recognizes directly), native cuml
estimators must first be converted to sklearn via as_sklearn() before
skl2onnx.convert_sklearn() will accept them.

Run without cuml.accel:
    python test_onnx_as_sklearn.py

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