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continue setting up ci #3

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merged 12 commits into from
Jul 9, 2024
Merged

continue setting up ci #3

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69c0d13
_
jeromedockes Jul 9, 2024
c8c60f8
swig
jeromedockes Jul 9, 2024
3ea5926
swig as requirement of build system
jeromedockes Jul 9, 2024
32529e5
_
jeromedockes Jul 9, 2024
b4e4f4f
_
jeromedockes Jul 9, 2024
6c4f61a
_
jeromedockes Jul 9, 2024
7a46402
_
jeromedockes Jul 9, 2024
e775fbd
no gpu :(
jeromedockes Jul 9, 2024
65033d9
Revert "_"
jeromedockes Jul 9, 2024
4b49ec2
_
jeromedockes Jul 9, 2024
27a49b1
fail-fast
jeromedockes Jul 9, 2024
4a4e1fa
device=cpu in resnet tests
jeromedockes Jul 9, 2024
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device=cpu in resnet tests
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@jeromedockes
jeromedockes committed Jul 9, 2024
commit 4a4e1fa92bc73c71cfee68254558b1ac588a3cdb
18 changes: 9 additions & 9 deletions tests/test_resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,9 +26,9 @@ def test_numerical_data(n_classes, resnet_or_mlp):

# Train the classifier
if resnet_or_mlp == "resnet":
clf = Resnet_RTDL_D_Classifier()
clf = Resnet_RTDL_D_Classifier(device="cpu")
elif resnet_or_mlp == "mlp":
clf = MLP_RTDL_D_Classifier()
clf = MLP_RTDL_D_Classifier(device="cpu")
clf.fit(X_train, y_train, cat_features=[False] * 20) # Assuming no categorical features

# Predict and evaluate
Expand All @@ -51,9 +51,9 @@ def test_categorical_data(n_classes, resnet_or_mlp):

# Train the classifier with categorical feature
if resnet_or_mlp == "resnet":
clf = Resnet_RTDL_D_Classifier()
clf = Resnet_RTDL_D_Classifier(device="cpu")
elif resnet_or_mlp == "mlp":
clf = MLP_RTDL_D_Classifier()
clf = MLP_RTDL_D_Classifier(device="cpu")
clf.fit(X_train, y_train, cat_features=[False] * 20 + [True])

# Predict and evaluate
Expand All @@ -80,9 +80,9 @@ def test_regressor_numerical_categorical(tranformed_target, resnet_or_mlp):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=43)

if resnet_or_mlp == "resnet":
regressor = Resnet_RTDL_D_Regressor(transformed_target=tranformed_target, random_state=41)
regressor = Resnet_RTDL_D_Regressor(transformed_target=tranformed_target, random_state=41, device="cpu")
elif resnet_or_mlp == "mlp":
regressor = MLP_RTDL_D_Regressor(transformed_target=tranformed_target, random_state=41)
regressor = MLP_RTDL_D_Regressor(transformed_target=tranformed_target, random_state=41, device="cpu")
regressor.fit(X_train, y_train, cat_features=cat_features)
predictions = regressor.predict(X_test)

Expand All @@ -98,9 +98,9 @@ def test_regressor_numerical_categorical(tranformed_target, resnet_or_mlp):

def create_model(regression, resnet_or_mlp, **kwargs):
if resnet_or_mlp == "resnet":
model = Resnet_RTDL_D_Regressor(**kwargs) if regression else Resnet_RTDL_D_Classifier(**kwargs)
model = Resnet_RTDL_D_Regressor(device="cpu", **kwargs) if regression else Resnet_RTDL_D_Classifier(device="cpu", **kwargs)
elif resnet_or_mlp == "mlp":
model = MLP_RTDL_D_Regressor(**kwargs) if regression else MLP_RTDL_D_Classifier(**kwargs)
model = MLP_RTDL_D_Regressor(device="cpu", **kwargs) if regression else MLP_RTDL_D_Classifier(device="cpu", **kwargs)
return model


Expand Down Expand Up @@ -205,4 +205,4 @@ def test_high_cardinality(seed, resnet_or_mlp):
# assert np.isfinite(history[:, 'valid_loss']).any()
# predictions = model.predict(X)
# assert not np.allclose(predictions, np.mean(y[100:])), "Predictions should not be the mean of the training set"
# assert model.alg_interface_.sub_split_interfaces[0].model.predict_mean == False
# assert model.alg_interface_.sub_split_interfaces[0].model.predict_mean == False
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