apunkt/ai-privacy-toolkit
1
0
Fork 0
mirror of https://github.com/IBM/ai-privacy-toolkit.git synced 2026-07-05 16:02:10 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Keras regression support

Browse source
This commit is contained in:
abigailt 2022-07-20 14:17:25 +03:00 • committed by abigailgold
parent a7d156660e
commit 77a6e08c8e
4 changed files with 85 additions and 63 deletions
Download patch file Download diff file Expand all files Collapse all files

24
tests/test_model.py
View file

@ -1,7 +1,7 @@
import pytest
import numpy as np
from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType, KerasClassifier, \
from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType, KerasClassifier, KerasRegressor, \
BlackboxClassifierPredictions, BlackboxClassifierPredictFunction, is_one_hot, get_nb_classes
from apt.utils.datasets import ArrayDataset, Data
from apt.utils import dataset_utils
@ -66,6 +66,28 @@ def test_keras_classifier():
assert(0.0 <= score <= 1.0)
def test_keras_regressor():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset_np()
underlying_model = Sequential()
underlying_model.add(Input(shape=(10,)))
underlying_model.add(Dense(100, activation="relu"))
underlying_model.add(Dense(10, activation="relu"))
underlying_model.add(Dense(1))
underlying_model.compile(loss="mean_squared_error", optimizer="adam", metrics=["accuracy"])
model = KerasRegressor(underlying_model)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
score = model.score(test)
def test_blackbox_classifier():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()