README.md

metadata

language: en
license: mit
library_name: scikit-learn
tags:
  - python
  - package-compatibility
  - prediction
  - scikit-learn
  - tabular-classification
metrics:
  - accuracy
  - f1
model-index:
  - name: pycompat-predictor
    results:
      - task:
          type: tabular-classification
          name: Package Compatibility Prediction
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9708
          - name: F1 Score
            type: f1
            value: 0.97

PyCompat — Python Package Compatibility Predictor

AI model that predicts whether a Python package version is compatible with a given system (OS, Python version, platform) and recommends the best compatible versions.

Model Details

• Model Type: Random Forest (compatibility) + Gradient Boosting (error type)
• Training Data: 5484 compatibility test records
• Packages: 198 unique packages
• Python Versions: 3.10, 3.11, 3.12, 3.9
• Platforms: darwin_x86_64

Performance

Model	Accuracy	F1 Score
Compatibility	0.9708	0.97
Error Type	0.9836	0.9826

Usage

from pycompat_model import PyCompatModel

# Load model
model = PyCompatModel.load("./model")

# Single prediction
result = model.predict("boto3", "1.42.49", "3.12", "darwin_x86_64")
print(result)
# {'is_compatible': True, 'confidence': 0.9977, 'predicted_error_type': 'none', ...}

# Get recommendations
recs = model.recommend("alembic", "3.9")
for r in recs:
    status = "✅" if r["is_compatible"] else "❌"
    print(f"  v{r['version']} {status} ({r['confidence']:.0%})")

# Batch prediction
results = model.predict_batch([
    {"package": "boto3", "version": "1.42.49", "python_version": "3.12"},
    {"package": "alembic", "version": "1.18.4", "python_version": "3.9"},
])

Error Types Predicted

Error Type	Description
none	Fully compatible
no_wheel	No compatible wheel/distribution found
import_error	Installs but fails to import
abi_mismatch	ABI incompatibility with dependencies
build_error	Failed to build from source
timeout	Network timeout during install

Training

from pycompat_model import PyCompatModel

model = PyCompatModel.train_from_data("data.json")
model.save("./model")