scitex-ml 0.1.0

SciTeX ML — machine learning, classification, training utilities

scitex-ml

Reproducible classical and deep machine-learning utilities for scientific research.

Full Documentation · pip install scitex-ml

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Problem and Solution

#	Problem	Solution
1	Boilerplate around scikit-learn — every paper re-implements Classifier factories, train/eval loops, classification reports, ROC/PR plots.	Classifier + ClassificationReporter — thin factory over scikit-learn estimators that snaps directly into a reporter for cross-validation aware metrics, confusion matrices, and figure export.
2	Time-series CV done by hand — researchers re-derive blocking / sliding-window / calendar splitters per project, often with off-by-one bugs.	Time-series CV splitters — TimeSeriesStratifiedSplit, TimeSeriesBlockingSplit, TimeSeriesSlidingWindowSplit, TimeSeriesCalendarSplit ship with consistent APIs and tested edge cases.
3	Training-loop ergonomics — EarlyStopping, learning-curve logging, optimiser shortcuts, multi-task losses are all glue code that drifts between repos.	First-class training utilities — EarlyStopping, LearningCurveLogger, MultiTaskLoss, get_optimizer / set_optimizer, vendored Ranger.
4	Heavy ML deps mixed with LLM SDKs — installing one pulls all of scikit-learn, torch, openai, anthropic.	Split package — generative-AI lives in scitex-genai; scitex-ml keeps the classical / deep-ML stack and nothing else.

Installation

pip install scitex-ml          # core
pip install scitex-ml[heavy]   # + torch / catboost / optuna / pytorch_pretrained_vit
pip install scitex-ml[mcp]     # + fastmcp
pip install scitex-ml[all]     # everything

Through the umbrella: pip install scitex[ml]. Requires Python ≥ 3.10.

Quick Start

import scitex_ml
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
X_tr, X_te, y_tr, y_te = train_test_split(X, y, random_state=0, stratify=y)

# Classifier — factory over scikit-learn estimators.
clf = scitex_ml.Classifier("LogisticRegression")
clf.fit(X_tr, y_tr)
print(f"test accuracy: {clf.score(X_te, y_te):.3f}")

# ClassificationReporter — metric tracking + figure export.
reporter = scitex_ml.ClassificationReporter(save_dir="./results")
reporter.calc_metrics(y_te, clf.predict(X_te), clf.predict_proba(X_te))
reporter.summarize()
reporter.save()

For a runnable walk-through see examples/01_classification.ipynb.

Demo

A complete classification + reporting walk-through (Iris, train/test split, Classifier("LogisticRegression"), ClassificationReporter metric persistence) lives in examples/01_classification.ipynb.

flowchart LR
    Data[load_iris<br/>train_test_split] --> Clf[scitex_ml.Classifier&quot;LogisticRegression&quot;]
    Clf -->|fit / score| Pred[y_pred / y_proba]
    Pred --> Reporter[scitex_ml.ClassificationReporter]
    Reporter -->|calc_metrics| Metrics[bacc · ROC-AUC · Conf-Mat]
    Reporter -->|save| Artefacts[results/<br/>metrics.csv · roc.png · pr.png]

A second examples/example_classifier.py runs the same flow as a script so it can be wired into tests/examples/test_example_classifier.py for CI smoke coverage.

Architecture

scitex-ml sits in the middle layer of the SciTeX ecosystem:

scitex-python (umbrella)
    └── scitex.ml ── thin sys.modules-aliasing shim
                     └── scitex_ml (this package)
                           ├── classification/   Classifier, ClassificationReporter,
                           │                     time-series CV splitters
                           ├── training/         EarlyStopping, LearningCurveLogger
                           ├── loss/             MultiTaskLoss + regularisers
                           ├── optim/            get_optimizer / set_optimizer + Ranger
                           ├── metrics/          calc_bacc, calc_conf_mat, calc_roc_auc
                           ├── clustering/       PCA + UMAP wrappers
                           ├── feature_extraction/  ViT embeddings
                           ├── feature_selection/   univariate / multivariate
                           ├── plt/              ROC / PR / learning-curve / conf-mat plots
                           ├── sampling/         undersampling helpers
                           ├── sklearn/          scikit-learn integration helpers
                           └── sk/               sktime compatibility

Cross-package dependencies are minimal: scitex-logging, scitex-io, scitex-plt, scitex-repro, scitex-types. Heavy deps (torch, catboost, optuna, pytorch_pretrained_vit) live behind the [heavy] extra and are gated by _AVAILABLE flags in the source so a pip install scitex-ml install without [heavy] still imports cleanly.

4 Interfaces

Python API ⭐⭐⭐ (primary)

import scitex_ml

# Time-series cross-validation
from scitex_ml.classification import TimeSeriesStratifiedSplit
splitter = TimeSeriesStratifiedSplit(n_splits=5)

# Training utilities
stopper = scitex_ml.EarlyStopping(patience=10, direction="minimize")
logger = scitex_ml.LearningCurveLogger()

# Multi-task loss + optimiser
mtl = scitex_ml.MultiTaskLoss(are_regression=[False, False])
optimizer = scitex_ml.set_optimizer(model, "adam", lr=1e-3)

# Metrics
result = scitex_ml.metrics.calc_bacc(y_true, y_pred)
cm = scitex_ml.metrics.calc_conf_mat(y_true, y_pred)

Full API reference

CLI ⭐ — none

scitex-ml ships no dedicated CLI. ML workflows are composed in Python and run via the umbrella scitex CLI / @scitex.session decorator.

MCP ⭐ — none

No MCP server in this package. The umbrella scitex CLI surfaces ML-adjacent MCP tools (e.g. scitex stats, scitex plt).

Skills ⭐⭐

Skill index for AI agents lives at src/scitex_ml/_skills/scitex-ml/SKILL.md. Sub-skills cover classification, training, loss, optim, clustering, metrics, sampling, feature-selection.

Full skills directory

Part of SciTeX

scitex-ml is part of SciTeX. Install via the umbrella with pip install scitex[ml] to use as scitex.ml (Python).

import scitex

scitex.ml.Classifier  # same object as scitex_ml.Classifier
scitex.ml.classification.TimeSeriesStratifiedSplit  # deep paths resolve via the umbrella shim

scitex.ml delegates to scitex_ml — they share the same API.

The SciTeX system follows the Four Freedoms for Research below, inspired by the Free Software Definition:

Four Freedoms for Research

0. The freedom to run your research anywhere — your machine, your terms.
1. The freedom to study how every step works — from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 — because we believe research infrastructure deserves the same freedoms as the software it runs on.

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