anomed-challenge 0.0.14

A library aiding to create challenges for the AnoMed competition platform.

Challenge

A library aiding to create challenges for the AnoMed competition platform.

Usage Example

The following example will create a Falcon-based web app that serves the famous iris dataset and uses plain binary accuracy as an evaluation metric. In more detail, these routes are offered (some may have query parameters not mentioned here):

• [GET] / (this displays an "alive message")
• [GET] /data/anonymizer/training (this will serve X_train and y_train)
• [GET] /data/anonymizer/tuning (this will serve X_tune)
• [GET] /data/anonymizer/validation (this will serve X_val)
• [GET] /data/deanonymizer/members (this will serve a subset of X_train and y_train)
• [GET] /data/deanonymizer/non-members (this will serve a subset of X_val and y_val)
• [GET] /data/attack-success-evaluation (this will serve data from the complement of members and also from the complement of non-members).
• [POST] /utility/anonymizer (this will evaluate the quality of an anonymizer's prediction compared to y_tune or y_val.)
• [POST] /utility/deanonymizer (this will evaluate the quality of a denanonymizer's prediction compared to attack-success-evaluation)

import anomed_challenge as anochal
from sklearn import datasets, model_selection

iris = datasets.load_iris()

X = iris.data  # type: ignore
y = iris.target  # type: ignore

X_train, X_other, y_train, y_other = model_selection.train_test_split(
    X, y, test_size=0.3, random_state=42
)
X_tune, X_val, y_tune, y_val = model_selection.train_test_split(
    X_other, y_other, test_size=0.5, random_state=21
)

example_challenge = anochal.SupervisedLearningMIAChallenge(
    training_data=anochal.InMemoryNumpyArrays(X=X_train, y=y_train),
    tuning_data=anochal.InMemoryNumpyArrays(X=X_tune, y=y_tune),
    validation_data=anochal.InMemoryNumpyArrays(X=X_val, y=y_val),
    anonymizer_evaluator=anochal.strict_binary_accuracy,
    MIA_evaluator=anochal.evaluate_MIA,
    MIA_evaluation_dataset_length=5,
)

# This is what GUnicorn expects
application = anochal.supervised_learning_MIA_challenge_server_factory(
    example_challenge
)