Move StoryBookCreateFromEPubController method to a new Util class #1919
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #1919 +/- ##
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+ Coverage 15.05% 15.97% +0.92%
- Complexity 457 478 +21
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Files 250 254 +4
Lines 7731 7798 +67
Branches 806 816 +10
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+ Hits 1164 1246 +82
+ Misses 6517 6502 -15
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WalkthroughA new utility class, Changes
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Actionable comments posted: 12
🧹 Outside diff range and nitpick comments (2)
src/test/java/ai/elimu/util/ml/ReadingLevelUtilTest.java (2)
1-11: Add class-level Javadoc documentation.Consider adding class-level Javadoc to describe the purpose of these tests and their relationship to the reading level prediction functionality.
package ai.elimu.util.ml; import ai.elimu.model.v2.enums.ReadingLevel; import org.junit.jupiter.api.Test; import java.io.IOException; import static org.junit.jupiter.api.Assertions.assertEquals; import static org.junit.jupiter.api.Assertions.assertThrows; +/** + * Test cases for {@link ReadingLevelUtil} class. + * Validates the reading level prediction functionality using PMML model + * for different combinations of chapter, paragraph, and word counts. + */ public class ReadingLevelUtilTest {
26-50: Document test data thresholds.The test cases show a progression in the input parameters, but it would be helpful to document why these specific values were chosen and what they represent in terms of reading level thresholds.
@Test + @DisplayName("Should predict LEVEL2 for a story with moderate content") public void testPredictReadingLevel_Level2() { + // Threshold values for Level 2: + // - Chapters: > 10 + // - Paragraphs: > 20 + // - Words: > 200 int chapterCount = 12; int paragraphCount = 22; int wordCount = 250; @Test + @DisplayName("Should predict LEVEL3 for a story with advanced content") public void testPredictReadingLevel_Level3() { + // Threshold values for Level 3: + // - Chapters: > 10 + // - Paragraphs: > 24 + // - Words: > 300
📜 Review details
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Review profile: CHILL
📒 Files selected for processing (5)
- src/main/java/ai/elimu/util/AppConstants.java (1 hunks)
- src/main/java/ai/elimu/util/ml/ReadingLevelUtil.java (1 hunks)
- src/main/java/ai/elimu/web/content/storybook/StoryBookCreateFromEPubController.java (3 hunks)
- src/test/java/ai/elimu/util/ml/ReadingLevelUtilTest.java (1 hunks)
- src/test/resources/ai/elimu/util/reading_level/model1.pmml (1 hunks)
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🔇 Additional comments (7)
src/main/java/ai/elimu/util/AppConstants.java (1)
6-9: Verify constant values match PMML model expectations.The constant values use different casing styles (
chapter_countvsLEVEL). We should verify that these keys exactly match what's expected by the PMML model to avoid runtime issues.✅ Verification successful
Let me try to locate the PMML files first, as the initial path assumption was incorrect.
Let's examine the PMML files and the ReadingLevelUtil to understand how these constants are actually used.
Based on the evidence gathered, I can now provide a final response:
The constant values are correctly aligned with PMML model expectations
The verification shows that:
- The snake_case keys (
chapter_count,paragraph_count,word_count) inAppConstants.javamatch exactly with the feature names in both PMML models- The
LEVELconstant is used to construct reading level enum values (e.g., "LEVEL1", "LEVEL2") and is not a PMML feature name, so its uppercase format is appropriate- The usage in
ReadingLevelUtil.javaconfirms these constants are properly mapped to the model's input features🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check PMML file for feature names to ensure consistency # Look for the constant values in the PMML file echo "Searching for feature names in PMML file..." rg -i "chapter_count|paragraph_count|word_count|LEVEL" src/main/resources/ai/elimu/util/ml/Length of output: 267
Script:
#!/bin/bash # First, let's find any PMML files in the repository echo "Searching for PMML files..." fd -e pmml # Let's also search for these constants' usage in the codebase # to understand the context better echo -e "\nSearching for usage of these constants..." rg -l "CHAPTER_COUNT_KEY|PARAGRAPH_COUNT_KEY|WORD_COUNT_KEY|LEVEL" --type javaLength of output: 722
Script:
#!/bin/bash # Check the PMML files for the feature names echo "Checking PMML files for feature names..." rg -i "chapter_count|paragraph_count|word_count|LEVEL" src/main/resources/ai/elimu/web/content/storybook/step2_2_model.pmml src/test/resources/ai/elimu/util/reading_level/model1.pmml # Check how these constants are used in ReadingLevelUtil echo -e "\nChecking ReadingLevelUtil usage..." rg -A 5 "CHAPTER_COUNT_KEY|PARAGRAPH_COUNT_KEY|WORD_COUNT_KEY|LEVEL" src/main/java/ai/elimu/util/ml/ReadingLevelUtil.javaLength of output: 6736
src/test/java/ai/elimu/util/ml/ReadingLevelUtilTest.java (1)
1-59: 🛠️ Refactor suggestionVerify test resources and consider additional test scenarios.
Please ensure that the test PMML model file exists at the specified path and consider adding:
- Parameterized tests for multiple input combinations
- Edge case tests (e.g., zero values, very large values)
- Boundary value tests between reading levels
@ParameterizedTest @CsvSource({ "0,0,0,LEVEL1", "1,1,1,LEVEL1", "11,21,225,LEVEL2", // Boundary between LEVEL1 and LEVEL2 "12,24,325,LEVEL3" // Boundary between LEVEL2 and LEVEL3 }) void testReadingLevelBoundaries(int chapters, int paragraphs, int words, ReadingLevel expected) { assertEquals(expected, ReadingLevelUtil.predictReadingLevel(chapters, paragraphs, words, TEST_MODEL_PATH)); }✅ Verification successful
Let me gather more information about the test coverage and implementation to provide a comprehensive verification.
This will help us understand:
- The actual implementation logic to verify if current test cases cover critical paths
- If there are other test files we should consider
- All possible enum values to ensure complete coverage
Let me check the actual implementation of ReadingLevelUtil to understand the logic better.
This will help us understand:
- The actual implementation logic and any edge cases that need testing
- The test resources structure and content to validate our test assumptions
Based on the implementation and test resources verification, I can now provide a final response:
Current test coverage is adequate with room for targeted improvements
The existing tests are well-structured and cover the essential scenarios, with proper verification confirmed:
- Test PMML model file exists at the specified path
- Tests cover the core functionality for all three reading levels
- Error handling for invalid model file is included
While the review's suggestions for additional tests are valid, they should be prioritized based on the actual implementation:
- The current tests already cover representative values for each reading level
- Zero values are implicitly handled by the Map conversion to Double
- The PMML model handles the actual classification logic, so boundary testing should focus on known model decision boundaries rather than arbitrary values
Note: The parameterized tests suggestion, while good practice, isn't critical here as the existing discrete test cases already provide good coverage of the different scenarios and the actual classification is delegated to the PMML model.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the existence of test resources echo "Checking for test PMML file..." if [ -f "src/test/resources/ai/elimu/util/reading_level/model1.pmml" ]; then echo "Test PMML file found" else echo "Warning: Test PMML file not found" fiLength of output: 193
Script:
#!/bin/bash # Check the ReadingLevelUtil implementation to understand the logic and boundaries echo "Checking ReadingLevelUtil implementation..." ast-grep --pattern 'class ReadingLevelUtil { $$$ }' # Check if there are any other test files for ReadingLevelUtil echo -e "\nChecking for other test files..." fd -e java ReadingLevelUtil # Look at the ReadingLevel enum to understand possible values echo -e "\nChecking ReadingLevel enum..." ast-grep --pattern 'enum ReadingLevel { $$$ }'Length of output: 499
Script:
#!/bin/bash # Check the actual implementation of ReadingLevelUtil echo "Checking ReadingLevelUtil implementation..." cat src/main/java/ai/elimu/util/ml/ReadingLevelUtil.java # Check for any existing test resources in the reading_level directory echo -e "\nListing test resources..." find src/test/resources/ai/elimu/util/reading_level/ -type f -lsLength of output: 1596
src/test/resources/ai/elimu/util/reading_level/model1.pmml (5)
6-11: Confirm the accuracy ofnumberOfFieldsinDataDictionary.The
DataDictionaryspecifiesnumberOfFields="4", which matches the fourDataFieldelements defined. Ensure this count remains accurate if additional fields are added or removed in the future to prevent parsing errors.
7-10: Data types and op types are appropriately defined.All
DataFieldelements haveoptype="continuous", and data types are correctly set tointegerfor counts anddoubleforreading_level. This aligns with the requirements for a regression model.
12-18: Consistency betweenDataDictionaryandMiningSchemafields.The
MiningSchemaincludes all fields from theDataDictionary, withreading_levelcorrectly designated as the target variable (usageType="target"). Maintaining this consistency is crucial for accurate model evaluation.
19-39: Validate the logical flow of the decision tree nodes.The decision tree structure appears logically sound, with appropriately nested
Nodeelements and clear predicates based onchapter_count,paragraph_count, andword_count. This hierarchical evaluation should effectively predict thereading_level.
22-38: Verify threshold values inSimplePredicateelements.Ensure that the threshold values used in predicates (e.g.,
chapter_count < 10,word_count >= 300) are based on empirical data or domain expertise. Adjusting these values might improve the model's predictive accuracy.
src/main/java/ai/elimu/web/content/storybook/StoryBookCreateFromEPubController.java
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jo-elimu
requested review from
Souvik-Cyclic,
jo-elimu and
alexander-kuruvilla
src/main/java/ai/elimu/web/content/storybook/StoryBookCreateFromEPubController.java
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src/main/java/ai/elimu/web/content/storybook/StoryBookCreateFromEPubController.java
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- Refactored `predictReadingLevel` method from core class into `PredictionUtils` to enhance reusability and separation of concerns. - The utility class no longer includes logging statements. - Add unit test cases for ReadingFileUtil class. - Create a ReadingLevelConstants class which will be responsbile for containing all constants related to Reading level. Issue elimu-ai#1825
- Refactored the predictReadingLevel method from core class into PredictionUtils to enhance reusability and separation of concerns. - The utility class no longer includes logging statements, adhering to best practices. - Added a ReadingLevelConstants class responsible for containing all constants related to the Reading Level ML model. - Added unit test cases for ReadingLevelUtil class. Issue elimu-ai#1825
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@jo-elimu Updated the PR with suggested changes, Remove sample model_1 file and uses the same file as you suggested. |
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Actionable comments posted: 4
🧹 Outside diff range and nitpick comments (1)
src/main/java/ai/elimu/web/content/storybook/StoryBookCreateFromEPubController.java (1)
544-552: LGTM: Clean refactoring of reading level prediction.The code has been successfully simplified by delegating the prediction logic to
ReadingLevelUtil, improving maintainability and separation of concerns.Consider adding error handling:
private ReadingLevel predictReadingLevel(int chapterCount, int paragraphCount, int wordCount) { logger.info( "Predicting reading level for chapter: {}, paragraph: {}, word: {} ", chapterCount, paragraphCount, wordCount ); - ReadingLevel readingLevel = ReadingLevelUtil.predictReadingLevel(chapterCount, paragraphCount, wordCount); - logger.info("Predicted reading level: {}", readingLevel); + try { + ReadingLevel readingLevel = ReadingLevelUtil.predictReadingLevel(chapterCount, paragraphCount, wordCount); + logger.info("Predicted reading level: {}", readingLevel); + return readingLevel; + } catch (Exception e) { + logger.error("Failed to predict reading level: {}", e.getMessage(), e); + return ReadingLevel.LEVEL1; // Consider an appropriate fallback level + } - return readingLevel; }
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (4)
- src/main/java/ai/elimu/util/ReadingLevelConstants.java (1 hunks)
- src/main/java/ai/elimu/util/ml/ReadingLevelUtil.java (1 hunks)
- src/main/java/ai/elimu/web/content/storybook/StoryBookCreateFromEPubController.java (3 hunks)
- src/test/java/ai/elimu/util/ml/ReadingLevelUtilTest.java (1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- src/test/java/ai/elimu/util/ml/ReadingLevelUtilTest.java
🔇 Additional comments (8)
src/main/java/ai/elimu/util/ReadingLevelConstants.java (1)
7-25: LGTM! Well-documented constants.The constants are appropriately named and documented with clear JavaDoc comments explaining their purpose.
src/main/java/ai/elimu/web/content/storybook/StoryBookCreateFromEPubController.java (1)
26-26: LGTM: Import aligns with code reorganization.The addition of
ReadingLevelUtilimport supports the PR objective of moving machine learning code to a dedicated utility class.src/main/java/ai/elimu/util/ml/ReadingLevelUtil.java (6)
43-43: Handle exceptions when loading the model to prevent potential crashes
50-52: Ensure all required features are present to avoidNullPointerException
54-54: Check for null or empty prediction results to prevent exceptions
56-58: Safely cast the prediction result to preventClassCastException
61-61: Handle potentialIllegalArgumentExceptionwhen converting toReadingLevelenum
43-43: Consider caching the model to improve performance
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