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ModelOutputType is now a Flag instead of regular enum. Combinations of the base flags are provided for all of the previous output types for convenience. All checks in the code now use the basic flags and not the complex types.

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Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailt 2024-07-03 13:29:37 +03:00
parent 2895b40f05
commit 367cae679b
10 changed files with 126 additions and 100 deletions
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4
tests/test_anonymizer.py
View file

@ -13,7 +13,7 @@ from torch.nn import functional
from scipy.special import expit
from apt.utils.datasets.datasets import PytorchData
from apt.utils.models import ModelOutputType
from apt.utils.models import CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS
from apt.utils.models.pytorch_model import PyTorchClassifier
from apt.anonymization import Anonymize
from apt.utils.dataset_utils import get_iris_dataset_np, get_adult_dataset_pd, get_nursery_dataset_pd
@ -237,7 +237,7 @@ def test_anonymize_pytorch_multi_label_binary():
optimizer = optim.RMSprop(model.parameters(), lr=0.01)
art_model = PyTorchClassifier(model=model,
output_type=ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
output_type=CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),

45
tests/test_minimizer.py
View file

@ -24,8 +24,9 @@ from apt.utils.models.pytorch_model import PyTorchClassifier
from apt.minimization import GeneralizeToRepresentative
from apt.utils.dataset_utils import get_iris_dataset_np, get_adult_dataset_pd, get_german_credit_dataset_pd
from apt.utils.datasets import ArrayDataset
from apt.utils.models import SklearnClassifier, ModelOutputType, SklearnRegressor, KerasClassifier
from apt.utils.models import SklearnClassifier, SklearnRegressor, KerasClassifier, \
CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL, \
CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS, CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS
tf.compat.v1.disable_eager_execution()
@ -219,7 +220,7 @@ def test_minimizer_params(cells):
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
expected_generalizations = {'categories': {}, 'category_representatives': {},
@ -261,7 +262,7 @@ def test_minimizer_params_not_transform(cells):
samples = ArrayDataset(x, y, features)
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
gen = GeneralizeToRepresentative(model, cells=cells, generalize_using_transform=False)
@ -273,7 +274,7 @@ def test_minimizer_fit(data_two_features):
x, y, features, _ = data_two_features
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
ad = ArrayDataset(x)
predictions = model.predict(ad)
@ -303,7 +304,7 @@ def test_minimizer_ncp(data_two_features):
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
ad = ArrayDataset(x)
ad1 = ArrayDataset(x1, features_names=features)
@ -346,7 +347,7 @@ def test_minimizer_ncp_categorical(data_four_features):
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(encoded, y))
ad = ArrayDataset(x)
ad1 = ArrayDataset(x1)
@ -386,7 +387,7 @@ def test_minimizer_fit_not_transform(data_two_features):
x, y, features, x1 = data_two_features
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
ad = ArrayDataset(x)
predictions = model.predict(ad)
@ -416,7 +417,7 @@ def test_minimizer_fit_pandas(data_four_features):
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(encoded, y))
predictions = model.predict(ArrayDataset(encoded))
if predictions.shape[1] > 1:
@ -454,7 +455,7 @@ def test_minimizer_params_categorical(cells_categorical):
preprocessor, encoded = create_encoder(numeric_features, categorical_features, x)
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(encoded, y))
predictions = model.predict(ArrayDataset(encoded))
if predictions.shape[1] > 1:
@ -478,7 +479,7 @@ def test_minimizer_fit_qi(data_three_features):
qi = ['age', 'weight']
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
ad = ArrayDataset(x)
predictions = model.predict(ad)
@ -512,7 +513,7 @@ def test_minimizer_fit_pandas_qi(data_five_features):
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(encoded, y))
predictions = model.predict(ArrayDataset(encoded))
if predictions.shape[1] > 1:
@ -547,7 +548,7 @@ def test_minimize_ndarray_iris():
qi = ['sepal length (cm)', 'petal length (cm)']
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model.fit(ArrayDataset(x_train, y_train))
predictions = model.predict(ArrayDataset(x_train))
if predictions.shape[1] > 1:
@ -590,7 +591,7 @@ def test_minimize_pandas_adult():
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(encoded, y_train))
predictions = model.predict(ArrayDataset(encoded))
if predictions.shape[1] > 1:
@ -646,7 +647,7 @@ def test_german_credit_pandas():
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(encoded, y_train))
predictions = model.predict(ArrayDataset(encoded))
if predictions.shape[1] > 1:
@ -764,7 +765,7 @@ def test_x_y():
qi = [0, 2]
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
ad = ArrayDataset(x)
predictions = model.predict(ad)
@ -804,7 +805,7 @@ def test_x_y_features_names():
qi = ['age', 'weight']
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
ad = ArrayDataset(x)
predictions = model.predict(ad)
@ -1206,7 +1207,7 @@ def test_keras_model():
base_est.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model = KerasClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = KerasClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(x, y))
ad = ArrayDataset(x_test)
predictions = model.predict(ad)
@ -1274,7 +1275,7 @@ def test_minimizer_pytorch(data_three_features):
optimizer = optim.Adam(base_est.parameters(), lr=0.01)
model = PyTorchClassifier(model=base_est,
output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
output_type=CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(3,),
@ -1316,7 +1317,7 @@ def test_minimizer_pytorch_iris():
optimizer = optim.Adam(base_est.parameters(), lr=0.01)
model = PyTorchClassifier(model=base_est,
output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
output_type=CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(4,),
@ -1387,7 +1388,7 @@ def test_minimizer_pytorch_multi_label_binary():
optimizer = optim.RMSprop(orig_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=orig_model,
output_type=ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
output_type=CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),
@ -1444,7 +1445,7 @@ def test_errors():
y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = SklearnClassifier(base_est, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model.fit(ArrayDataset(X, y))
ad = ArrayDataset(X)
predictions = model.predict(ad)

41
tests/test_model.py
View file

@ -2,7 +2,12 @@ import pytest
import numpy as np
from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType, KerasClassifier, KerasRegressor, \
BlackboxClassifierPredictions, BlackboxClassifierPredictFunction, is_one_hot, get_nb_classes, XGBoostClassifier
BlackboxClassifierPredictions, BlackboxClassifierPredictFunction, is_one_hot, get_nb_classes, XGBoostClassifier, \
CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL, CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES, \
CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES, CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS, \
CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS, CLASSIFIER_MULTI_OUTPUT_CATEGORICAL, \
CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES, CLASSIFIER_MULTI_OUTPUT_CLASS_PROBABILITIES, \
CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS, CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS
from apt.utils.datasets import ArrayDataset, Data, DatasetWithPredictions
from apt.utils import dataset_utils
@ -24,7 +29,7 @@ tf.compat.v1.disable_eager_execution()
def test_sklearn_classifier():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
underlying_model = RandomForestClassifier()
model = SklearnClassifier(underlying_model, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = SklearnClassifier(underlying_model, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
model.fit(train)
@ -81,7 +86,7 @@ def test_keras_classifier():
underlying_model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model = KerasClassifier(underlying_model, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = KerasClassifier(underlying_model, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
@ -119,7 +124,7 @@ def test_xgboost_classifier():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset_np()
underlying_model = XGBClassifier()
underlying_model.fit(x_train, y_train)
model = XGBoostClassifier(underlying_model, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL,
model = XGBoostClassifier(underlying_model, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL,
input_shape=(4,), nb_classes=3)
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
@ -138,7 +143,7 @@ def test_blackbox_classifier():
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = BlackboxClassifierPredictions(data, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
@ -154,7 +159,7 @@ def test_blackbox_classifier_predictions():
train = DatasetWithPredictions(y_train, x_train)
test = DatasetWithPredictions(y_test, x_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = BlackboxClassifierPredictions(data, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
assert model.model_type is None
@ -169,7 +174,7 @@ def test_blackbox_classifier_predictions_y():
train = DatasetWithPredictions(y_train, x_train, y_train)
test = DatasetWithPredictions(y_test, x_test, y_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = BlackboxClassifierPredictions(data, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
@ -189,7 +194,7 @@ def test_blackbox_classifier_predictions_multi_label_cat():
train = DatasetWithPredictions(y_train, x_train, y_train)
test = DatasetWithPredictions(y_test, x_test, y_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CATEGORICAL)
model = BlackboxClassifierPredictions(data, CLASSIFIER_MULTI_OUTPUT_CATEGORICAL)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
@ -217,7 +222,7 @@ def test_blackbox_classifier_predictions_multi_label_binary():
train = DatasetWithPredictions(pred_train, x_train, y_train)
test = DatasetWithPredictions(pred_test, x_test, y_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES)
model = BlackboxClassifierPredictions(data, CLASSIFIER_MULTI_OUTPUT_BINARY_PROBABILITIES)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
@ -243,7 +248,7 @@ def test_blackbox_classifier_no_test():
train = ArrayDataset(x_train, y_train)
data = Data(train)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = BlackboxClassifierPredictions(data, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
pred = model.predict(train)
assert (pred.shape[0] == x_train.shape[0])
@ -260,7 +265,7 @@ def test_blackbox_classifier_no_train():
test = ArrayDataset(x_test, y_test)
data = Data(test=test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = BlackboxClassifierPredictions(data, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
@ -278,7 +283,7 @@ def test_blackbox_classifier_no_test_y():
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = BlackboxClassifierPredictions(data, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
pred = model.predict(train)
assert (pred.shape[0] == x_train.shape[0])
@ -301,7 +306,7 @@ def test_blackbox_classifier_no_train_y():
train = ArrayDataset(x_train)
test = ArrayDataset(x_test, y_test)
data = Data(train, test)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
model = BlackboxClassifierPredictions(data, CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL)
pred = model.predict(test)
assert (pred.shape[0] == x_test.shape[0])
@ -325,7 +330,7 @@ def test_blackbox_classifier_probabilities():
train = ArrayDataset(x_train, y_train)
data = Data(train)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
model = BlackboxClassifierPredictions(data, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES)
pred = model.predict(train)
assert (pred.shape[0] == x_train.shape[0])
assert (0.0 < pred).all()
@ -345,7 +350,7 @@ def test_blackbox_classifier_multi_label_probabilities():
train = ArrayDataset(x_train, y_train)
data = Data(train)
model = BlackboxClassifierPredictions(data, ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_PROBABILITIES)
model = BlackboxClassifierPredictions(data, CLASSIFIER_MULTI_OUTPUT_CLASS_PROBABILITIES)
pred = model.predict(train)
assert (pred.shape[0] == x_train.shape[0])
assert (0.0 < pred).all()
@ -361,7 +366,7 @@ def test_blackbox_classifier_predict():
train = ArrayDataset(x_train, y_train)
model = BlackboxClassifierPredictFunction(predict, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES,
model = BlackboxClassifierPredictFunction(predict, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES,
(4,), 3)
pred = model.predict(train)
assert (pred.shape[0] == x_train.shape[0])
@ -381,7 +386,7 @@ def test_blackbox_classifier_predict_scalar():
train = ArrayDataset(x_train, y_train)
model = BlackboxClassifierPredictFunction(predict, ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES,
model = BlackboxClassifierPredictFunction(predict, CLASSIFIER_SINGLE_OUTPUT_CLASS_PROBABILITIES,
(4,), 3)
pred = model.predict(train)
assert (pred.shape[0] == x_train.shape[0])
@ -400,7 +405,7 @@ def test_is_one_hot():
def test_get_nb_classes():
(_, y_train), (_, y_test) = dataset_utils.get_iris_dataset_np()
output_type = ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL
output_type = CLASSIFIER_SINGLE_OUTPUT_CATEGORICAL
# shape: (x,) - not 1-hot
nb_classes_test = get_nb_classes(y_test, output_type)
nb_classes_train = get_nb_classes(y_train, output_type)

16
tests/test_pytorch.py
View file

@ -6,7 +6,9 @@ from scipy.special import expit
from art.utils import check_and_transform_label_format
from apt.utils.datasets.datasets import PytorchData
from apt.utils.models import ModelOutputType
from apt.utils.models import CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS, CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS, \
CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES, CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS, \
CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS
from apt.utils.models.pytorch_model import PyTorchClassifier
from art.utils import load_nursery
from apt.utils import dataset_utils
@ -128,7 +130,7 @@ def test_pytorch_nursery_state_dict():
optimizer = optim.Adam(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
output_type=CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),
@ -161,7 +163,7 @@ def test_pytorch_nursery_save_entire_model():
optimizer = optim.Adam(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
output_type=CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),
@ -201,7 +203,7 @@ def test_pytorch_predictions_single_label_binary():
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model, output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS,
model = PyTorchClassifier(model=inner_model, output_type=CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS,
loss=criterion,
optimizer=optimizer, input_shape=(4,),
nb_classes=2)
@ -238,7 +240,7 @@ def test_pytorch_predictions_single_label_binary_prob():
optimizer = optim.Adam(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES,
output_type=CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES,
loss=criterion,
optimizer=optimizer, input_shape=(4,),
nb_classes=2)
@ -307,7 +309,7 @@ def test_pytorch_predictions_multi_label_cat():
optimizer.step()
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS,
output_type=CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),
@ -348,7 +350,7 @@ def test_pytorch_predictions_multi_label_binary():
optimizer = optim.RMSprop(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
output_type=CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),