lobster-ml
Machine learning and deep learning for biological data analysis and framework export.
Installation
# Basic installation
pip install lobster-ml
# With deep learning dependencies (scVI, PyTorch)
pip install lobster-ml[ml]
Agents
| Agent | Description |
|---|---|
machine_learning_expert |
ML specialist for biological data. Feature engineering, data splitting, framework export, and deep learning embeddings. |
Services
| Service | Purpose |
|---|---|
| MLPreparationService | Feature selection, scaling, and train/test/validation splitting |
| MLTranscriptomicsServiceALPHA | Transcriptomics-specific ML workflows (ALPHA) |
| MLProteomicsServiceALPHA | Proteomics-specific ML workflows (ALPHA) |
| scVIEmbeddingService | Deep learning embeddings using scVI for single-cell data |
Features
ML Readiness Assessment
- Evaluate biological datasets for machine learning suitability
- Check sample size, class balance, and feature quality
- Identify potential data leakage and batch effects
- Recommend preprocessing steps before ML pipeline
Feature Engineering
- Highly variable gene selection for dimensionality reduction
- PCA-based feature extraction with variance thresholds
- Marker gene features from differential expression
- Z-score normalization and scaling
Data Splitting
- Stratified train/test/validation splits
- Configurable split ratios (default: 70/15/15)
- Class balance preservation across splits
- Batch-aware splitting to prevent data leakage
Framework Export
- NumPy arrays for scikit-learn workflows
- CSV export for general ML frameworks
- PyTorch tensor datasets with DataLoader support
- TensorFlow NPZ format for Keras models
Deep Learning Embeddings
- scVI integration for variational autoencoder embeddings
- Latent space visualization and clustering
- Transfer learning from pre-trained models
- GPU acceleration when available
Requirements
- Python 3.12+
- lobster-ai >= 1.0.0
- Optional: torch, scvi-tools (for deep learning features)
Tier Requirement
This is a premium agent. Access is controlled at runtime via Lobster AI's tier system.
Documentation
Full documentation: docs.omics-os.com/docs/agents/ml
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for lobster-ml 1.1.427
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| lobster_ml-1.1.427.tar.gz | 136.5 kB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lobster_ml-1.1.427-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 278.6 kB
Release files / lobster_ml-1.1.427.tar.gz
| Download URL | lobster_ml-1.1.427.tar.gz |
|---|---|
| Size | 136.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
a63695c5046c5724b9a74d24d71c4903ca5c159a59e59633455a4bd56d7092d6
|
|
BLAKE2b-256 checksum How to use checksums |
d2d292221882b0f11c2055defe92c21c521dd27653a02241810d288da1d3bfd1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 19, 2026.
Transparency logRelease files / lobster_ml-1.1.427-py3-none-any.whl
| Download URL | lobster_ml-1.1.427-py3-none-any.whl |
|---|---|
| Size | 142.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c7415284f8a99627cfeec84f5c66cd67df6e7e83b8f7a91bc7e576fe63d6772d
|
|
BLAKE2b-256 checksum How to use checksums |
ee8aa2ee9156d9afdd7570fd5086eb714bdda22b533f4377658ca365140a431f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 19, 2026.
Transparency logSecurity for lobster-ml 1.1.427
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 2 files in this release have been uploaded with provenance metadata.
Attestation for lobster_ml-1.1.427.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 19, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
