gridmaster 0.2.0

A lightweight multi-stage grid search AutoML framework for classifiers. GridMaster v0.2.0: Log-scale search + plot customization features

GridMaster: A Lightweight Multi-Stage Grid Search AutoML Framework for Classifiers

GridMaster is a Python module built for data scientists who want flexible and interpretable model selection and tuning on structured classification datasets. It automates the hyperparameter tuning process using coarse-to-fine grid search and supports model evaluation, visualization, export, and reproducibility.

⚠️ GridMaster is currently designed for classification tasks only, and does not yet support regressors.

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🔍 Supported Models

Model Name	Backend Module	Notes
Logistic Regression	sklearn.linear_model	Great interpretability via coefficients
Random Forest	sklearn.ensemble	Robust baseline, provides feature importance
XGBoost	xgboost	Highly accurate, supports regularization
LightGBM	lightgbm	Fast, efficient gradient boosting
CatBoost	catboost	Handles categorical features natively

❌ DecisionTreeClassifier is intentionally excluded due to its high variance and limited generalization ability. Use ensemble variants instead.

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🚀 Installation

pip install scikit-learn xgboost lightgbm catboost pandas numpy matplotlib joblib

Then clone the repository:

git clone https://github.com/wins-wang/GridMaster.git

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⚙️ How GridMaster Works

from gridmaster import GridMaster
gm = GridMaster(models=[...], X_train=..., y_train=...)

You can then call:

• gm.coarse_search(...)
• gm.fine_search(...)
• gm.multi_stage_search(...)
• gm.compare_best_models(...)
• Visualization + export + import...

All with built-in output redirection for noisy models.

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🔁 Recommended Workflow

gm.multi_stage_search("xgboost", scoring="roc_auc")
gm.compare_best_models(X_test, y_test, metrics=["f1", "roc_auc"])
gm.plot_cv_score_curve("xgboost")
gm.export_model_package("xgboost")

Use suppress_output=False if you want to see model logs.

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📘 Demo Usage: Breast Cancer Classifier

# demo_usage.ipynb

from gridmaster import GridMaster
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import pandas as pd
import os
import warnings

warnings.filterwarnings("ignore")

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

gm = GridMaster(
    models=["logistic", "random_forest", "xgboost", "lightgbm", "catboost"],
    X_train=X_train,
    y_train=y_train
)

for model in ["logistic", "random_forest", "xgboost", "lightgbm", "catboost"]:
    gm.multi_stage_search(model, scoring="f1")

best_model, scores = gm.compare_best_models(X_test, y_test, metrics=["accuracy", "f1", "roc_auc"])
print("Best model:", best_model)
print(scores)

# Visualize all
for model in ["logistic", "random_forest", "xgboost", "lightgbm", "catboost"]:
    gm.plot_cv_score_curve(model)
    gm.plot_confusion_matrix(model, X_test, y_test)
    if model == "logistic":
        gm.plot_model_coefficients(model)
    else:
        gm.plot_feature_importance(model)

# Export and reload
os.makedirs("outputs", exist_ok=True)
gm.export_all_models(folder_path="outputs")
gm.import_all_models(folder_path="outputs")

# Final report
final_model = gm.results[best_model]["best_model"]
X_test_df = pd.DataFrame(X_test, columns=X_train.columns)
y_pred = final_model.predict(X_test_df)
print(classification_report(y_test, y_pred))

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📦 Packaging Info

• Author: Winston Wang
• GitHub: wins-wang
• Email: [e-mail]

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📜 License

MIT License. See LICENSE.