Add Drift Detector Benchmarker Prototype #17
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1. Add f1, accuracy scores to CSV 2. Aggregate CSVs 3. Track runtimes for each method 4. All methods now ready for use
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`tip_amount` leaks information to `tip_percent_greater_15`
| df.loc[df['vendorid'] == '2', 'vendorid'] = 2 | ||
| df.loc[df['vendorid'] == '2.0', 'vendorid'] = 2 | ||
| # Correct string type confusion in vendorid, payment_type | ||
| df['vendorid'] = df['vendorid'].astype(float).astype(int) |
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Why astype(float) before astype(int)?
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I needed to massage the type conversions because int("1.0") is invalid but int(float("1.0")) is valid.
yuji3w
changed the title
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The objective of the drift detector benchmarker is to create an easy-to-use framework that benchmarks data drift detection methods. The benchmarked currently uses the NYC taxi dataset.
Key features:
Created benchmarking framework, allowing easy testing of the following drift detectors created in this pr:
Created graphing utility, allowing for plotting inter-week drift on a feature-wise level
Created accessor class and factory, allows for easy access to data frame data.
Created batch loader for loading large date ranges (especially greater than 1 year).