BuildML

BuildML is a Python library for machine-learning workflows built around a stateful Session. The Session holds the dataset, column roles, train/validation/test membership, fitted preprocessing plans, an optional estimator, and a record of every operation you run. Preprocessing learns from the training partition only; validation and test rows receive frozen transformations. That train-only boundary is enforced in the API.

BuildML 2.4 (2.4.0) is the stable Session 2.x line on PyPI. The public entry point is buildml.Session. For domains, namespaced facades (session.<domain>.*) are the supported public API; flat domain aliases are supported-but-deprecated until BuildML 3.0. See docs/stability.md.

Install

Python 3.10–3.13.

# Session 2.x from PyPI (default)
pip install buildml

# Editable source checkout (recommended for development / proofs)
pip install -e ".[dev]"

# GitHub tip of main
pip install "git+https://github.com/TechLeo-Libraries/BuildML.git"

Legacy 1.x (1.0.9, MIT) remains on PyPI for pin-only installs: pip install "buildml==1.0.9".

Optional extras (scannable)

pip install "buildml[production]"

buildml[production] honesty

Industry domains ship with capability matrices, backend routing, benchmark smokes, and guides. buildml[production] is a best-effort meta-extra: it pulls domain depth plus *-industry adapters. It is not a guarantee that every nested industry wheel installs on every platform.

On Python 3.13 (especially Windows) some nested pins are skipped via environment markers when upstream wheels are missing or broken (LightFM, learn2learn/qpth, giotto-tda, neuralforecast, skope-rules, …). Core sklearn paths and markers that resolve still install. Check each domain’s capability matrix (e.g. session.automl.capability_matrix()) and the proof suite for what actually runs in your environment. For a machine-local inventory of importable industry modules (never a hard fail), run:

python scripts/probe_industry_extras.py

It does not include dashboard, serve, or AI operator extras.

Security notes (bundles + AI)

• Pickle / joblib / torch bundles (opt-in). Checkpoint plans.joblib, pipeline bundles, domain *_plan.joblib, and Torch trainer / TorchScript payloads can execute code on load. Public loaders default to trusted=False and raise ValidationError until you pass trusted=True for artifacts you created or fully trust: for example Session.checkpoint_load(path, trusted=True), session.anomaly.load_bundle(path, trusted=True), Session().predict_from_pipeline(path, frame, trusted=True), or buildml-serve --bundle … --trusted. Prefer JSON sidecars / parquet / Session.checkpoint_load(..., data_only=True) (skips plans without needing trusted) or re-fitting when provenance is unclear. Optional sha256 integrity in manifests detects tampering after save; it does not make a malicious author safe. Residual risk: trusted=True on an attacker-controlled artifact still executes code: untrusted pickle cannot be made safe.
• AI operator. Prompt-injection heuristics in buildml.ai.security are a best-effort layer (NFKC + zero-width / bidi strip, Latin-homoglyph fold, multi-line / base64-ish smuggle patterns, structured InjectionFinding reason codes). Primary controls remain the closed tool registry (runtime register refused), confirm-on-write for mutating tools, and egress levels in buildml.ai.privacy. Residual risk: paraphrase / novel attacks may bypass heuristics: do not treat pattern matching as injection-proof.

A classical workflow

Facades are the supported public API for domains in 2.4.x (session.fairness.*, session.anomaly.*, …). Flat domain aliases still work but emit DeprecationWarning until BuildML 3.0. Classical core is dual on purpose: flat session.fit / session.evaluate / … stay first-class without warnings, and session.classical.* / session.data.* / session.preprocess.* are equivalent paths — not a secondary API. See docs/session-facade-migration.md.

import pandas as pd
from sklearn.linear_model import LogisticRegression

from buildml import Session

frame = pd.DataFrame(
    {
        "age": [21, None, 35, 40, 29, 33, 52, 47],
        "income": [40, 55, 60, 80, 50, 70, 90, 65],
        "approved": [0, 1, 0, 1, 0, 1, 1, 0],
    }
)

session = Session.ingest(frame)
session.set_roles(
    {"age": "feature", "income": "feature", "approved": "target"}
)
session.split(test_size=0.25, stratify=True, random_state=42)

# Fit on train; apply frozen transforms everywhere else.
# Default impute/encode/scale touch feature-role columns only :
# ignore / id / target / group / time / weight stay unmutated
# (pass columns=[...] to force-include).
session.impute(strategy="median")
session.handle_outliers(method="iqr", action="cap")
session.scale(method="standard")
session.fit(LogisticRegression(max_iter=500), task="classification")

result = session.evaluate(partition="test")
print(result.metrics)

When rows are not exchangeable, use group_split, time_split, or inject_split with memberships you designed outside BuildML.

Cross-validation and hyperparameter search draw folds from the training partition only. The Session test holdout is not used for ranking.

from sklearn.tree import DecisionTreeClassifier
from buildml.preprocess import PreprocessRecipe

cv = session.cv_score(
    LogisticRegression(max_iter=500),
    cv=5,
    preprocess=PreprocessRecipe(impute="median", scale="standard"),
)
print(cv.mean_metrics, cv.std_metrics)

search = session.grid_search(
    DecisionTreeClassifier(random_state=0),
    param_grid={"max_depth": [2, 4, 6], "min_samples_leaf": [1, 5]},
    cv=5,
)
print(search.best_params, search.best_score)

Pass a PreprocessRecipe when encoding, binning, feature selection, or outlier fences should be refit inside each fold: on unpoisoned data (no prior Session-global impute/encode/scale). Opt in only with allow_session_global_preprocess=True when you intentionally accept leakage-biased scores, or re-ingest / checkpoint-load unpoisoned data first.

Artifacts and inspection

session.checkpoint_save("artifacts/checkpoint")
restored = Session.checkpoint_load("artifacts/checkpoint")

session.save_pipeline("artifacts/pipeline", evaluate_partition="test")
loaded = Session.ingest(frame).load_pipeline("artifacts/pipeline")
loaded.apply_preprocess_plans()

before = session.explain("scale", moment="before")
steps = session.workflow()
walkthrough = session.walkthrough(export_html="artifacts/workflow.html")

Checkpoints store data workflow state. Model and pipeline bundles store fitted estimators and preprocess plans: they do not embed each other. workflow() marks operations as done / available / blocked / skipped from API prerequisites; it does not judge domain fit.

Learning while you work

Every operation and concept is written for three reading levels. beginner is the default and assumes no prior machine-learning vocabulary: plain-language summary, an analogy, the steps in order, what each parameter means in practice, the pitfalls, and a glossary of the terms the answer itself used.

before = session.explain("split")          # level="beginner" by default
print(before.beginner.plain_summary)
print(before.beginner.analogy)
for step in before.beginner.steps:
    print("-", step)
for knob in before.beginner.key_parameters:
    print(knob.name, "→", knob.plain_meaning, "|", knob.typical_choice)

session.explain("split", moment="after")   # what it did, in this session
session.explain("split", level="advanced") # same facts, no hand-holding

explain is about this session right now: what is missing, what changed, how to read the result. When the question is conceptual instead, use learn, which takes a concept key, an operation name, or whatever word tripped you up:

session.learn()                    # where to start, in reading order
session.learn("leakage")           # a term → the concept that teaches it
session.learn("stratified")        # spelling/punctuation is forgiving
brief = session.learn("data-splitting")
[note.key for note in brief.read_first]   # read these before this one
[note.key for note in brief.read_next]    # read these once it lands

The same material backs Session.explain, Session.learn, workflow(), walkthrough(), and the AI operator's explain_operation / learn_concept tools, so no surface can drift from another. Teaching content explains ideas and BuildML's contract; it does not inspect your data or certify that a choice is appropriate for it.

EDA and reports

session.eda(export_html="artifacts/eda.html")

# pip install "buildml[dashboard]"
handle = session.eda_app(port=8765)
# handle.url -> http://127.0.0.1:8765/

session.evaluate(
    partition="test",
    include_plots=True,
    export_html="artifacts/evaluation.html",
)

Reports surface screening evidence. They do not establish causality, fairness, or deployment readiness on their own. Deeper classical topics (target encoding, PCA, calibration, learning curves, permutation importance) live in the classical quickstart and workflow guide.

Domains at a glance

Core import buildml stays light (numpy, pandas, scikit-learn). Domain methods attach to the same Session; classical fit / evaluate stay unchanged. Each refined domain exposes an honest capability matrix reporting which backends are installed.

Torch covers tabular MLP, text/sequence, and multimodal fusion; speech (buildml[speech]) is ASR + finetune-lite: not Whisper-scale FM training from scratch. RAG defaults to lexical hashing; semantic embeddings and grounded session.rag.generate are first-class when extras resolve. NLP models a text column that lives on the dataset: document classification and analysis, distinct from RAG retrieval and from Torch fine-tuning. The AI operator defaults to propose→confirm→execute: not unconstrained agency.

Full guide index: guides/README.md.

Proof suite

End-to-end evidence that Session domains work with honest splits and holdout metrics lives under proofs/: not smoke tests.

# Full harness from repo root
python -m proofs._lib.run_all --tier all

# Single project (synthetic or real public)
python proofs/loan-approval-classical/script.py
python proofs/breast-cancer-classical/script.py

Install domain extras as needed before running (editable install preferred):

pip install -e ".[tda,rl,rag,recommenders-industry,automl-industry]"
# richer backends when wheels resolve:
#   implicit → movie-recs ALS
#   sentence-transformers → support-kb-rag dense embeddings
#   flaml / autogluon.tabular → churn-automl / ledger industry AutoML

TDA prefers pip install -e ".[tda]". Gymnasium / SB3 deepen the IL+RL path via buildml[rl] / buildml[rl-industry] when wheels resolve. See proofs/README.md for the inventory, Tier C interpretation, and re-run instructions.

Alpha status

This is pre-release software. Bundle schema version strings, report layouts, and method signatures may change before a stable 2.x cut. See docs/stability.md for the public-surface freeze policy.

Shipped: observational fairness disparity reports (session.fairness.evaluate) and optional SHAP attribution (explain_shap via buildml[shap]). Still out of scope: out-of-core sklearn training, legal fairness certification, and unconstrained LLM agency. Speech ASR defaults to a CI-safe stub backend; backend="transformers" is optional and not Whisper-scale FM training.

See CHANGELOG.md for release notes and guides/glossary.md for BuildML terminology: session.learn(term) covers general machine-learning vocabulary.

Documentation

• Proof suite: Tier A/B/C inventory, harness, Tier C interpretation
• Guides: quickstarts (each major domain links its proof) and glossary
• Concepts: roles, partitions, train-fitted plans
• Workflow guide: ordering, leakage, diagnostics
• Sphinx docs: installation, features, API reference, legacy boundary
• Changelog: release notes

BuildML 1.x legacy boundary

BuildML 1.x (SupervisedLearning and the old module layout) lives under buildml/_legacy/ for reference only. It is not imported from the 2.x package root. There is no compatibility shim that re-exports 1.x APIs from import buildml.

Author and license

Leonard Onyiriuba: LinkedIn · leonard.c.onyiriuba@gmail.com

Issues: GitHub

Apache License 2.0.