Hello, I am trying to replicate the demo notebook but using a different open source da

ADBench with custom data about adbench HOT 1 OPEN

minqi824 commented on August 16, 2024 1

ADBench with custom data

from adbench.

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Minqi824 commented on August 16, 2024

I sincerely apologize for my late reply. We have released the latest version of ADBench, which now support the implementation of either customized dataset or algorithm. After installing ADBench and upgrading to its latest version, you can run AD algorithms (or your customized algorithm) on the customized dataset. See the following codes:

# customized model on ADBench's datasets
from adbench.run import RunPipeline
from adbench.baseline.Customized.run import Customized

# notice that you should specify the corresponding category of your customized AD algorithm
# for example, here we use Logistic Regression as customized clf, which belongs to the supervised algorithm
# for your own algorithm, you can realize the same usage as other baselines by modifying the fit.py, model.py, and run.py files in the adbench/baseline/Customized
pipeline = RunPipeline(suffix='ADBench', parallel='supervise', realistic_synthetic_mode=None, noise_type=None)
results = pipeline.run(clf=Customized)

# customized model on customized dataset
import numpy as np
dataset = {}
dataset['X'] = np.random.randn(1000, 20)
dataset['y'] = np.random.choice([0, 1], 1000)
results = pipeline.run(dataset=dataset, clf=Customized)