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--- |
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license: mit |
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--- |
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# Dockerfile Commit Classification Model |
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This is a Logistic Regression model enhanced with a rule-based system for multi-label classification of Dockerfile-related commit messages. It combines machine learning with domain-specific rules to achieve accurate categorization. |
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## Files |
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- `logistic_model.joblib`: Trained Logistic Regression model. |
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- `tfidf_vectorizer.joblib`: TF-IDF vectorizer for text preprocessing. |
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- `label_binarizer.joblib`: MultiLabelBinarizer for encoding/decoding labels. |
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## Features |
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- **Hybrid Approach**: Combines machine learning with rule-based adjustments for better classification. |
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- **Dockerfile-Specific Labels**: Categorizes commit messages into predefined classes: |
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- `bug fix` |
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- `code refactoring` |
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- `feature addition` |
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- `maintenance/other` |
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- `Not enough information` |
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- **Multi-Label Support**: Each commit message can belong to multiple categories. |
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## How to Use |
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To use this model, load the files and preprocess your data as follows: |
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```python |
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from joblib import load |
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# Load the model and preprocessing artifacts |
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model = load("logistic_model.joblib") |
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tfidf_vectorizer = load("tfidf_vectorizer.joblib") |
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mlb = load("label_binarizer.joblib") |
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# Example usage |
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new_messages = [ |
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"Fixed an issue with the base image in Dockerfile", |
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"Added multistage builds to reduce image size", |
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"Updated Python version in Dockerfile to 3.10" |
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] |
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X_new_tfidf = tfidf_vectorizer.transform(new_messages) |
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# Predict the labels |
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predictions = model.predict(X_new_tfidf) |
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predicted_labels = mlb.inverse_transform(predictions) |
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# Print results |
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for msg, labels in zip(new_messages, predicted_labels): |
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print(f"Message: {msg}") |
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print(f"Predicted Labels: {', '.join(labels) if labels else 'No labels'}\n") |
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