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Blackbox predict method (#43)

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* Support output probabilities
* Support black box classifier with predict method
* Update requirements (security alert #1)
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abigailgold 2022-06-30 18:23:53 +03:00 • committed by GitHub
parent bb224cd3dd
commit c6eb553a9f
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5 changed files with 159 additions and 50 deletions
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tests/test_model.py
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@ -1,6 +1,8 @@
import pytest
import numpy as np
from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType, KerasClassifier, BlackboxClassifier
from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType, KerasClassifier, \
BlackboxClassifierPredictions, BlackboxClassifierPredictFunction
from apt.utils.datasets import ArrayDataset, Data
from apt.utils import dataset_utils
@ -67,7 +69,7 @@ def test_blackbox_classifier():
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
data = Data(train, test)
model = BlackboxClassifier(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
@ -80,7 +82,7 @@ def test_blackbox_classifier_no_test():
train = ArrayDataset(x_train, y_train)
data = Data(train)
model = BlackboxClassifier(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
pred = model.predict(train)
assert(pred.shape[0] == x_train.shape[0])
@ -93,7 +95,7 @@ def test_blackbox_classifier_no_train():
test = ArrayDataset(x_test, y_test)
data = Data(test=test)
model = BlackboxClassifier(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
pred = model.predict(test)
assert(pred.shape[0] == x_test.shape[0])
@ -107,7 +109,7 @@ def test_blackbox_classifier_no_test_y():
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test)
data = Data(train, test)
model = BlackboxClassifier(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
pred = model.predict(train)
assert(pred.shape[0] == x_train.shape[0])
@ -129,7 +131,7 @@ def test_blackbox_classifier_no_train_y():
train = ArrayDataset(x_train)
test = ArrayDataset(x_test, y_test)
data = Data(train, test)
model = BlackboxClassifier(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
@ -145,3 +147,38 @@ def test_blackbox_classifier_no_train_y():
assert(unable_to_predict_train,True)
def test_blackbox_classifier_probabilities():
(x_train, _), (_, _) = dataset_utils.get_iris_dataset_np()
y_train = np.array([[0.23, 0.56, 0.21] for i in range(105)])
train = ArrayDataset(x_train, y_train)
data = Data(train)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_PROBABILITIES)
pred = model.predict(train)
assert (pred.shape[0] == x_train.shape[0])
assert (0.0 < pred).all()
assert (pred < 1.0).all()
score = model.score(train)
assert (0.0 <= score <= 1.0)
def test_blackbox_classifier_predict():
def predict(x):
return [0.23, 0.56, 0.21]
(x_train, y_train), (_, _) = dataset_utils.get_iris_dataset_np()
train = ArrayDataset(x_train, y_train)
model = BlackboxClassifierPredictFunction(predict, ModelOutputType.CLASSIFIER_PROBABILITIES, (4,), 3)
pred = model.predict(train)
assert (pred.shape[0] == x_train.shape[0])
assert (0.0 < pred).all()
assert (pred < 1.0).all()
score = model.score(train)
assert (0.0 <= score <= 1.0)