Test reorg #308
Test reorg #308
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Important Review skippedAuto incremental reviews are disabled on this repository. Please check the settings in the CodeRabbit UI or the You can disable this status message by setting the WalkthroughThe recent changes enhance the testing frameworks for several components of the Changes
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Files selected for processing (8)
- Makefile (1 hunks)
- tests/testthat/test-caretEnsemble.R (4 hunks)
- tests/testthat/test-caretList.R (3 hunks)
- tests/testthat/test-caretPredict.R (2 hunks)
- tests/testthat/test-caretStack.R (5 hunks)
- tests/testthat/test-classSelection.R (2 hunks)
- tests/testthat/test-multiclass.R (9 hunks)
- tests/testthat/test-permutationImportance.R (5 hunks)
Files skipped from review due to trivial changes (1)
- Makefile
Additional comments not posted (54)
tests/testthat/test-classSelection.R (3)
4-12: Data preparation for multiclass classification looks good.The
irisdataset is appropriately loaded and partitioned for training and testing multiclass classification scenarios.
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18-74: Multiclass validation updates are appropriate.The function
runBinaryLevelValidationhas been updated to handle three levels, which is suitable for multiclass classification using theirisdataset.
78-95: Test cases for multiclass validation are comprehensive.The tests cover the validation of excluded classes and target class selection, ensuring robustness in multiclass scenarios.
tests/testthat/test-multiclass.R (4)
1-12: Data handling and model specification improvements are sound.The addition of
tuneLengthand the use of theBostondataset broaden the scope and robustness of the tests.
18-20: Prediction assertions are precise and clear.The use of
expect_s3_classensures accurate class type verification for predictions.
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65-100: Handling of class names with underscores is well-tested.The modifications to the
iris_moddataset and related tests ensure that class naming variations are correctly handled.
110-112: Increased accuracy threshold enhances model performance standards.Raising the threshold to 0.95 ensures stricter validation of model accuracy.
tests/testthat/test-caretPredict.R (4)
11-25: Ensemble model setup enhances testing capabilities.The creation of
ens.regandens.classusingcaretEnsembleallows for comprehensive evaluation of ensemble methods.
35-38: Helper function for data table structure is well-designed.The
expect_data_table_structurefunction streamlines validation and promotes code reuse.
106-153: Error handling inisClassifierAndValidateis robust.The enhanced validation logic and error handling ensure comprehensive checks for various model configurations.
159-191: Validation of excluded class levels is comprehensive.The tests ensure robust input validation and error handling for excluded class levels.
tests/testthat/test-permutationImportance.R (11)
4-14: LGTM: Explicit use ofstats::rnorm.The explicit use of
stats::rnormenhances clarity about the function's origin.
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16-23:
LGTM: Well-structured model training function.The use of
set.seedensures reproducibility, andcaret::trainis used correctly.
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25-39:
LGTM: Comprehensive importance score checks.The function effectively validates the integrity of importance scores with comprehensive expectations.
42-55: LGTM: Comprehensive classifier identification tests.The test covers both
trainandcaretEnsemblemodels effectively.
57-71: LGTM: Thorough permutation importance tests for regression and classification.The test is well-structured and leverages helper functions for clarity and code reuse.
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73-95:
LGTM: Comprehensive multiclass classification tests.The test covers multiclass classification scenarios effectively using a synthetic dataset.
98-114: LGTM: Single feature edge case tests.The test effectively covers edge cases for datasets with a single feature.
117-134: LGTM: Constant feature edge case tests.The test comprehensively covers scenarios with constant features.
136-145: LGTM: Perfect predictor tests.The test effectively covers the perfect predictor scenario.
148-153: LGTM: Multiclass classification tests using iris dataset.The test effectively utilizes the iris dataset to validate multiclass classification functionality.
154-213: LGTM: Comprehensive edge case tests for permutation importance.The test covers a wide range of edge cases, ensuring robustness.
tests/testthat/test-caretList.R (7)
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1-26:
LGTM: Well-organized setup section.The setup ensures reproducibility with
set.seedand usesutils::datafor clarity.
28-54: LGTM: Comprehensive model specification tests.The test effectively covers valid and invalid scenarios for
caretModelSpec,tuneCheck, andmethodCheck.
59-103: LGTM: Thorough S3 method tests for caretList.The test effectively covers the expected functionality of S3 methods, including target extraction and list operations.
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107-142:
LGTM: Comprehensive prediction tests for caretList.The test effectively covers prediction functionality for classification and regression models, including edge cases.
146-219: LGTM: Robust caretList tests across various scenarios.The test covers a wide range of scenarios, including handling missing data and imbalanced datasets.
222-235: LGTM: Plot and summary method tests for caretList.The test effectively validates the expected outputs of
plotandsummarymethods.
237-256: LGTM: Comprehensive support tests for combined model types in caretList.The test ensures that
caretListsupports regression, binary, and multiclass models effectively.tests/testthat/test-caretEnsemble.R (10)
1-9: LGTM: Well-organized setup section for caretEnsemble.The setup ensures reproducibility with
set.seedand usesutils::datafor clarity.
36-67: LGTM: Efficient encapsulation of prediction test logic.The
test_predictionsfunction effectively reduces redundancy by encapsulating common test logic.
69-79: LGTM: Residual extraction tests for regression models.The test effectively validates the expected behavior of residual extraction.
81-92: LGTM: Comprehensive ensembling and prediction tests.The test ensures that ensembling works as expected for regression and classification models.
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95-132:
LGTM: Ensembling tests for models with differing predictors.The test effectively covers scenarios with varying model predictors.
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140-180:
LGTM: Comprehensive tests for custom model ensembles.The test ensures that custom models are handled correctly, including error scenarios.
192-229: LGTM: Variable importance and plotting tests for caretEnsemble.The test effectively validates the expected outputs of variable importance and plotting methods.
232-245: LGTM: Autoplot tests for caretEnsemble using ggplot2.The test ensures that
autoplotworks as expected for different scenarios.
247-255: LGTM: Summary method tests for caretEnsemble.The test effectively validates the expected outputs of the summary method.
257-275: LGTM: Prediction tests with and without standard errors and weights.The test ensures that prediction functionality is robust across different options.
tests/testthat/test-caretStack.R (15)
36-38: Helper functionexpect_all_finiteis well-implemented.The function effectively checks for finite values across data columns using
vapply.
44-50: Test case "caretStack creates valid ensemble models" is comprehensive.This test case effectively verifies the creation and basic functionalities of ensemble models.
53-74: Test case "predict works for classification and regression ensembles" is robust.The use of a parameter grid ensures comprehensive testing of prediction scenarios.
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79-92: Test case "Classification predictions return correct output" is effective.The test validates the structure and correctness of classification predictions.
95-99: Test case "Regression predictions return correct output" is well-structured.The test ensures regression predictions return expected named outputs.
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102-108: Test case "Predictions are reproducible" ensures reliability.The use of a set seed to verify reproducibility is well-executed.
111-121: Test case "caretStack handles missing data correctly" is crucial.The test ensures that the model handles NA values appropriately.
124-129: Test case "caretStack handles multiclass problems" is effective.The test ensures correct handling of multiclass predictions.
132-147: Test case "caretStack works with different stacking algorithms" is versatile.The test ensures robustness across different stacking methods.
153-158: Test case "caretStack handles custom preprocessing" is well-implemented.The test ensures that custom preprocessing steps are correctly handled.
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164-173: Test case "caretStack handles custom performance function" is comprehensive.The test ensures integration of custom performance metrics.
179-213: Test case "caretStack handles new data correctly" ensures adaptability.The test verifies accurate predictions with new data inputs.
217-225: Test case "caretStack coerces lists to caretLists" is well-structured.The test validates the coercion process with appropriate warnings.
228-232: Test case "caretStack errors if new_X is provided but not new_y" enforces data integrity.The test ensures that both
new_Xandnew_yare required together.
235-239: Test case "caretStack errors if new_y is provided but not new_X" complements previous checks.The test reinforces the requirement for both
new_Xandnew_y.
Test now run in about 30 seconds, down from about 90 seconds. So 1/3 of the runtime.