50 lines
1.8 KiB
Plaintext
50 lines
1.8 KiB
Plaintext
# example_model.pkl
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# This is a placeholder file for a pickled machine learning model.
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# In a real-world scenario, this file would contain the serialized representation
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# of a trained machine learning model using the `pickle` library.
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# This model is used by the ai-ethics-validator plugin to demonstrate
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# how to load and use a model for fairness validation.
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# INSTRUCTIONS:
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# 1. Replace this placeholder with your actual trained model.
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# 2. Ensure the model is compatible with the `validate-ethics` command
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# in the plugin. The command expects the model to have a `predict` method
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# that takes input data and returns predictions.
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# 3. Update the `validate_ethics` function in the plugin's main script
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# to correctly load and use your model.
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# 4. Consider using a model that can be easily validated for bias, such as
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# a logistic regression or decision tree.
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# Example of how to create a dummy model (FOR TESTING ONLY):
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# import pickle
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# from sklearn.linear_model import LogisticRegression
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# from sklearn.datasets import make_classification
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#
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# # Generate a synthetic dataset
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# X, y = make_classification(n_samples=100, n_features=2, random_state=42)
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#
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# # Train a logistic regression model
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# model = LogisticRegression(random_state=42)
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# model.fit(X, y)
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#
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# # Save the model to a file
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# with open("example_model.pkl", "wb") as f:
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# pickle.dump(model, f)
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# Placeholder content to prevent errors if the file is not replaced.
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# In a real application, this would be replaced with the pickled model.
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# Replace this with the actual pickled model data.
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class PlaceholderModel:
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def predict(self, data):
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# Placeholder prediction logic
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return [0] * len(data)
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import pickle
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model = PlaceholderModel()
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with open("example_model.pkl", "wb") as f:
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pickle.dump(model, f)
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# END OF FILE |