betatim

"""Sweep pipeline `fit` and `predict` runtime against the size of X.

This script times

    StandardScaler -> Nystroem -> LogisticRegression

as a function of how much data it is given, in two independent sweeps:

    fit      GridSearchCV(pipeline, PARAM_GRID, cv=3).fit(X[:n], y[:n]),
             for n from 2_000 to 100_000

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numpy
                         as-is    as numpy   difference     added
  search                 1.18s       1.18s        -0.0%    -164us
  predict               24.2ms      19.1ms       -20.9%    -5.1ms
  serve/call             897us       930us        +3.7%      33us
  score                 10.4ms      10.9ms        +4.8%     493us
  one 10-row predict, step by step:
  scale.transform        111us       135us       +22.2%      24us
  kernel.transform       572us       588us        +2.8%      16us
  clf.predict            206us       202us        -2.2%      -5us

betatim

"""Benchmark storing fitted attributes as NumPy.

Times a PCA + LogisticRegression pipeline with and without the
`store_fitted_as_numpy` configuration option added in
https://github.com/scikit-learn/scikit-learn/pull/34667, for
https://github.com/scikit-learn/scikit-learn/issues/34604.

Needs that branch checked out. Run it with no arguments:

    python bench_store_fitted_as_numpy.py

betatim

name	pr-walkthrough
description	Produce a structured walkthrough of a GitHub PR ordered by causal dependency. Use when the user asks to walk through, analyze, or understand a PR's changes.

PR Walkthrough

Generate a causal-dependency-ordered walkthrough of a GitHub PR and write it to agents/reviews/ in the repository.

This skill is scoped to PRs in the current repository. Cross-repo PRs are out of scope.

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