lizyml-widget 0.8.0

Jupyter Notebook / Google Colab / VS Code Notebooks widget for LizyML

LizyML Widget

Interactive Jupyter widget for LizyML — fit, tune, and run inference on machine learning models without writing code.

Features

• Data Tab — Load a DataFrame, select target, configure columns and cross-validation
• Config Tab — Edit LightGBM hyperparameters, configure tuning search space
• Results Tab — View scores, Plotly plots, feature importance, and inference results
• Config Import/Export — Save and load configurations as YAML
• Python API — Programmatic access to all widget functionality

Requirements

• Python >= 3.10
• Jupyter Notebook, JupyterLab, Google Colab, or VS Code Notebooks
• lizyml >= 0.9.0, < 0.10 (auto-resolved by the [lizyml] extras)

Installation

# Recommended: installs a compatible lizyml automatically
pip install "lizyml-widget[lizyml]"

The [lizyml] extras pin lizyml[plots,tuning,calibration,explain]>=0.9.0,<0.10 so pip / uv / poetry always select a backend version that matches the widget's expected contract.

If you manage lizyml separately, install the widget without extras and pin lizyml yourself:

pip install lizyml-widget
pip install "lizyml[plots,tuning,calibration,explain]>=0.9.0,<0.10"

Widget's LizyMLAdapter validates the installed lizyml version at import time and raises a clear ImportError if the backend is out of range.

Version compatibility

LizyML Widget is tightly coupled to the lizyml ML contract (types, BackendAdapter protocol, tune/fit result shapes), so each minor version of the widget targets a specific lizyml range:

lizyml-widget	lizyml	Highlights
0.8.x	>=0.9.0,<0.10	Re-tune (round progress, boundary expansion, tuning history, w.retune() API)
0.7.x	>=0.7.0,<0.9	Calibration / Search Space default refresh
0.6.x / 0.5.x	>=0.5.0,<0.7	Learning curve metric filter, CV strategy metadata

See docs/VERSION_COMPAT.md for the full matrix, troubleshooting, and upgrade guidance.

Quick Start

import pandas as pd
from lizyml_widget import LizyWidget

df = pd.read_csv("train.csv")
w = LizyWidget()
w.load(df, target="price")
w  # display widget in notebook cell

Programmatic Usage

w = LizyWidget()
w.load(df, target="y").fit()

summary = w.get_fit_summary()
print(summary.metrics)

w.save_model("./model")
w.save_config("config.yaml")

Re-tune (Study Resume + Boundary Expansion)

When the initial Tune run hits a search-space boundary or you simply want more trials, call w.retune() to resume the existing Optuna study in place. The widget reuses the backend model so no history is lost.

w = LizyWidget()
w.load(df, target="y")

# 1. Initial tune (e.g. 50 trials)
w.tune()

# 2. Resume with 30 more trials and let the backend widen boundaries
#    if the best trial lands on an edge.
w.retune(n_trials=30, expand_boundary=True, boundary_threshold=0.05)

summary = w.get_tune_summary()
for r in summary.rounds:
    print(f"Round {r['round']}: {r['n_trials']} trials, "
          f"best={r['best_score_after']}, expanded={r['expanded_dims']}")

The Results tab shows a Re-tune (resume) button inside the Best Params accordion after the initial Tune completes — clicking it runs the same resume flow from the UI, and the round-aware progress bar, Score History chart, and Boundary Expansion panel update in place.

Requires lizyml >= 0.9.0 (auto-resolved by lizyml-widget[lizyml]).

Version

import lizyml_widget
print(lizyml_widget.__version__)

Tutorials

Notebook	Task	Dataset
Regression	Regression	California Housing (sklearn)
Binary Classification	Binary	Breast Cancer Wisconsin (sklearn)
Multiclass Classification	Multiclass	Wine (sklearn)

Supported Environments

• Jupyter Notebook
• JupyterLab
• Google Colab
• VS Code Notebooks

Powered by anywidget for cross-environment compatibility.

Development

# Python
uv sync --all-extras    # installs dev + lizyml dependencies
uv run pytest
uv run ruff check .
uv run mypy src/lizyml_widget/

# TypeScript
cd js
pnpm install
pnpm dev    # watch build
pnpm build  # production build
pnpm lint

Stable Notebook Launch

If VS Code gets stuck reconnecting to an old kernel, prefer launching Jupyter with workspace-local runtime files instead of the default global runtime directory:

./scripts/jupyter-reset.sh
./scripts/jupyter-lab.sh

This keeps runtime/config state under the repository and makes stale kernel/server state easier to clear than relying on Reload Window alone.

License

MIT