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added hypothesis tests to test_feature.py #222 #251

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@uchiha-vivek uchiha-vivek commented Sep 27, 2024

User description

Added Hypothesis tests to test_feature.py
**Issue No : #222 **

**Made two functions for hypothesis testing **

  • test_feature_with_random_data
    _The hypothesis will generate various polygons including regular and irregular polygons .
    Regular Polygons : _If all the polygon sides and interior angles are equal, then they are known as regular polygons. _
    Irregular Polygons : _An irregular polygon does not have all its sides equal and not all the angles are equal in measure. _

  • test_feature_collection_with_random_features
    _Generates random features _

20/20 Test cases passed


PR Type

Tests


Description

  • Added hypothesis-based tests to test_feature.py to enhance test coverage.
  • Implemented polygons, coordinates, and properties strategies for generating random test data.
  • Created test_feature_with_random_data to verify feature creation with random polygons and properties.
  • Created test_feature_collection_with_random_features to verify feature collection creation with random features.

Changes walkthrough 📝

Relevant files
Tests
test_feature.py
Add hypothesis-based tests for feature and feature collection

tests/test_feature.py

  • Added hypothesis strategies for generating random polygons and
    properties.
  • Introduced test_feature_with_random_data for testing features with
    random data.
  • Introduced test_feature_collection_with_random_features for testing
    feature collections.
  • Utilized hypothesis for property-based testing.
  • +53/-0   

    💡 PR-Agent usage: Comment /help "your question" on any pull request to receive relevant information

    Summary by Sourcery

    Add Hypothesis tests to test_feature.py to enhance test coverage by generating random data for features and feature collections.

    Tests:

    • Introduce Hypothesis-based tests in test_feature.py to generate random polygon geometries and properties for testing.
    • Add test_feature_with_random_data to validate feature creation with random polygons and properties.
    • Add test_feature_collection_with_random_features to validate feature collection creation with random features.

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    semanticdiff-com bot commented Sep 27, 2024

    Review changes with SemanticDiff.

    Analyzed 1 of 1 files.

    Filename Status
    ✔️ tests/test_feature.py Analyzed

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    sourcery-ai bot commented Sep 27, 2024

    Reviewer's Guide by Sourcery

    This pull request adds Hypothesis tests to the test_feature.py file, introducing property-based testing for the Feature and FeatureCollection classes. The changes include new composite strategies for generating random polygons, coordinates, and properties, as well as two new test functions that use these strategies to test the Feature and FeatureCollection classes with randomly generated data.

    Sequence Diagram

    sequenceDiagram
        participant Hypothesis
        participant TestFeature
        participant Feature
        participant FeatureCollection
        Hypothesis->>TestFeature: Generate random polygon
        Hypothesis->>TestFeature: Generate random properties
        TestFeature->>Feature: Create Feature with random data
        TestFeature->>Feature: Assert Feature properties
        Hypothesis->>TestFeature: Generate random polygon
        TestFeature->>Feature: Create multiple Features
        TestFeature->>FeatureCollection: Create FeatureCollection
        TestFeature->>FeatureCollection: Assert FeatureCollection properties
    
    Loading

    File-Level Changes

    Change Details Files
    Added Hypothesis library imports and custom composite strategies
    • Imported Hypothesis and related strategies
    • Created a 'polygons' composite strategy to generate random polygon geometries
    • Created a 'coordinates' composite strategy to generate random coordinate lists
    • Created a 'properties' composite strategy to generate random property dictionaries
    tests/test_feature.py
    Implemented new Hypothesis tests for Feature and FeatureCollection classes
    • Added 'test_feature_with_random_data' function to test Feature creation with random polygons and properties
    • Added 'test_feature_collection_with_random_features' function to test FeatureCollection creation with random features
    tests/test_feature.py

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    what-the-diff bot commented Sep 27, 2024

    PR Summary

    • Introduction of Hypothesis-based Testing Functions
      This update has introduced a new approach of testing based on the concept of Hypothesis, which will allow for more efficient generation of data like polygons, coordinates, and other properties.

    • Addition of New Test Cases
      New test cases have been implemented to enhance the system's ability to validate features, especially with the use of random polygon geometries and properties. Moreover, testing of feature collections has been optimized with the introduction of random features.

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    PR Reviewer Guide 🔍

    ⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪
    🧪 PR contains tests
    🔒 No security concerns identified
    ⚡ Key issues to review

    Potential Bug
    The coordinates strategy generates float values with min_value=180 and max_value=180 for longitude, which will always produce 180. This might not provide sufficient test coverage for different longitude values.

    Test Coverage
    The test_feature_collection_with_random_features function only tests with two features. It might be beneficial to test with a variable number of features to ensure the FeatureCollection works correctly with different sizes.

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    PR Reviewer Guide 🔍

    ⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪
    🧪 PR contains tests
    🔒 No security concerns identified
    ⚡ Key issues to review

    Potential Bug
    The coordinates strategy generates float values with min and max values that may be too restrictive (180 for longitude and 90 for latitude). This could potentially exclude valid coordinate pairs.

    Test Coverage
    The test_feature_with_random_data function doesn't assert the __geo_interface__ of the created feature, which is an important part of the Feature class.

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    ❌ Changes requested. Reviewed everything up to 8f1de59 in 16 seconds

    More details
    • Looked at 76 lines of code in 1 files
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    Workflow ID: wflow_7vFmUU3YIlciRNDf


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    return draw(
    lists(
    tuples(
    floats(min_value=180, max_value=180), floats(min_value=90, max_value=90),
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    The floats strategy in coordinates should have a range for min_value and max_value. Use min_value=-180, max_value=180 for longitude and min_value=-90, max_value=90 for latitude.

    Suggested change
    floats(min_value=180, max_value=180), floats(min_value=90, max_value=90),
    floats(min_value=-180, max_value=180), floats(min_value=-90, max_value=90),

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    Preparing review...

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    Preparing review...

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    codiumai-pr-agent-free bot commented Sep 27, 2024

    PR Code Suggestions ✨

    Latest suggestions up to 8f1de59
    Explore these optional code suggestions:

    CategorySuggestion                                                                                                                                    Score
    Possible bug
    Adjust the range of generated coordinates to ensure valid longitude and latitude values

    Consider using a more precise range for longitude and latitude in the coordinates
    strategy. The current range allows for invalid coordinates (e.g., longitude > 180 or
    latitude > 90). Update the floats calls to use the correct ranges: longitude should
    be between -180 and 180, and latitude should be between -90 and 90.

    tests/test_feature.py [29-40]

     @composite
     def coordinates(draw):
         """Generate a list of coordinates for geometries"""
         return draw(
             lists(
                 tuples(
    -                floats(min_value=180, max_value=180), floats(min_value=90, max_value=90),
    +                floats(min_value=-180, max_value=180), floats(min_value=-90, max_value=90),
                 ),
                 min_size=3,
                 max_size=10,
             ),
         )
    • Apply this suggestion
    Suggestion importance[1-10]: 9

    Why: The suggestion addresses a critical issue by correcting the range of longitude and latitude values, ensuring that generated coordinates are valid. This change is essential for accurate testing and prevents potential bugs related to invalid geographic data.

    9
    Enhancement
    Add assertions to verify the GeoJSON representation of the generated feature

    In the test_feature_with_random_data method, consider adding assertions to check the
    geo_interface of the feature. This will ensure that the GeoJSON representation
    of the feature is correct and consistent with the input data.

    tests/test_feature.py [141-146]

     @given(polygon=polygons(), props=properties())
     def test_feature_with_random_data(self, polygon, props) -> None:
         f = feature.Feature(polygon, props)
         assert isinstance(f, feature.Feature)
         assert f.geometry == polygon
         assert f.properties == props
    +    assert f.__geo_interface__['type'] == 'Feature'
    +    assert f.__geo_interface__['geometry'] == polygon.__geo_interface__
    +    assert f.__geo_interface__['properties'] == props
    • Apply this suggestion
    Suggestion importance[1-10]: 8

    Why: This suggestion improves test coverage by verifying the GeoJSON representation of features, ensuring consistency and correctness of the output. It enhances the reliability of the tests by checking additional aspects of the feature's properties.

    8
    Add assertions to verify the GeoJSON representation of the generated feature collection

    In the test_feature_collection_with_random_features method, consider adding
    assertions to check the geo_interface of the feature collection. This will
    ensure that the GeoJSON representation of the feature collection is correct and
    consistent with the input features.

    tests/test_feature.py [148-155]

     @given(polygon=polygons())
     def test_feature_collection_with_random_features(self, polygon) -> None:
         f1 = feature.Feature(polygon)
         f2 = feature.Feature(polygon)
         fc = feature.FeatureCollection([f1, f2])
         assert isinstance(fc, feature.FeatureCollection)
         assert len(fc) == 2
    +    assert fc.__geo_interface__['type'] == 'FeatureCollection'
    +    assert len(fc.__geo_interface__['features']) == 2
    +    assert all(f['type'] == 'Feature' for f in fc.__geo_interface__['features'])
    • Apply this suggestion
    Suggestion importance[1-10]: 8

    Why: Similar to the previous suggestion, this one adds valuable assertions to check the GeoJSON representation of feature collections. It strengthens the test suite by ensuring that the feature collection's structure and content are as expected.

    8
    Ensure generated polygons are valid to prevent issues in tests

    In the polygons strategy, consider adding a check to ensure that the generated
    polygon is valid (e.g., no self-intersections, minimum area). This will help prevent
    potential issues when testing with invalid polygons.

    tests/test_feature.py [20-26]

     @composite
     def polygons(draw):
    -    """Generate a polygon geometry"""
    -    """The polygon is closed"""
    +    """Generate a valid polygon geometry"""
         coords = draw(coordinates())
         coords.append(coords[0])
    -    return geometry.Polygon(coords)
    +    polygon = geometry.Polygon(coords)
    +    assume(polygon.is_valid)  # Assuming there's an is_valid method
    +    return polygon
    • Apply this suggestion
    Suggestion importance[1-10]: 7

    Why: The suggestion enhances the robustness of the tests by ensuring that only valid polygons are generated. While it assumes the existence of an is_valid method, the concept is valuable for preventing test failures due to invalid geometries.

    7

    💡 Need additional feedback ? start a PR chat


    Previous suggestions

    Suggestions up to commit 8f1de59
    CategorySuggestion                                                                                                                                    Score
    Enhancement
    Adjust the range of generated coordinates to accurately represent valid latitude and longitude values

    Consider using a more precise range for latitude and longitude in the coordinates
    strategy. Currently, the range for both is set to (-180, 180) for longitude and
    (-90, 90) for latitude, which is not accurate for latitude. Adjust the latitude
    range to be between -90 and 90 degrees.

    tests/test_feature.py [29-40]

     @composite
     def coordinates(draw):
         """Generate a list of coordinates for geometries"""
         return draw(
             lists(
                 tuples(
    -                floats(min_value=180, max_value=180), floats(min_value=90, max_value=90),
    +                floats(min_value=-180, max_value=180), floats(min_value=-90, max_value=90),
                 ),
                 min_size=3,
                 max_size=10,
             ),
         )
    Suggestion importance[1-10]: 9

    Why: The suggestion corrects a critical error in the coordinate generation strategy by setting the correct range for latitude and longitude, which is essential for generating valid geographic data.

    9
    Verify the correct representation of the feature's __geo_interface__

    In the test_feature_with_random_data method, consider adding assertions to check the
    geo_interface of the created feature to ensure it correctly represents the input
    data.

    tests/test_feature.py [141-146]

     @given(polygon=polygons(), props=properties())
     def test_feature_with_random_data(self, polygon, props) -> None:
         f = feature.Feature(polygon, props)
         assert isinstance(f, feature.Feature)
         assert f.geometry == polygon
         assert f.properties == props
    +    assert f.__geo_interface__['type'] == 'Feature'
    +    assert f.__geo_interface__['geometry'] == polygon.__geo_interface__
    +    assert f.__geo_interface__['properties'] == props
    Suggestion importance[1-10]: 8

    Why: The suggestion enhances the test by verifying the __geo_interface__ representation, ensuring that the feature correctly encapsulates the input data, which is crucial for validating the feature's integrity.

    8
    Verify the correct representation of the FeatureCollection's __geo_interface__

    In the test_feature_collection_with_random_features method, consider adding
    assertions to verify the geo_interface of the FeatureCollection and ensure it
    correctly represents all the features.

    tests/test_feature.py [148-155]

     @given(polygon=polygons())
     def test_feature_collection_with_random_features(self, polygon) -> None:
         f1 = feature.Feature(polygon)
         f2 = feature.Feature(polygon)
         fc = feature.FeatureCollection([f1, f2])
         assert isinstance(fc, feature.FeatureCollection)
         assert len(fc) == 2
    +    assert fc.__geo_interface__['type'] == 'FeatureCollection'
    +    assert len(fc.__geo_interface__['features']) == 2
    +    assert all(f['type'] == 'Feature' for f in fc.__geo_interface__['features'])
    Suggestion importance[1-10]: 8

    Why: This suggestion improves the test by checking the __geo_interface__ of the FeatureCollection, ensuring it accurately represents its features, which is important for maintaining data consistency.

    8
    Best practice
    Ensure that generated polygons are valid before using them in tests

    In the polygons strategy, consider adding a check to ensure that the generated
    polygon is valid (e.g., not self-intersecting) before returning it. This will help
    prevent potential issues when testing with invalid polygons.

    tests/test_feature.py [20-26]

     @composite
     def polygons(draw):
    -    """Generate a polygon geometry"""
    -    """The polygon is closed"""
    +    """Generate a valid polygon geometry"""
         coords = draw(coordinates())
         coords.append(coords[0])
    -    return geometry.Polygon(coords)
    +    polygon = geometry.Polygon(coords)
    +    assume(polygon.is_valid)  # Assuming there's an is_valid method
    +    return polygon
    Suggestion importance[1-10]: 7

    Why: Adding a validity check for polygons is a good practice to prevent issues with invalid geometries, although it assumes the existence of an is_valid method, which may not be present.

    7

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    Hey @uchiha-vivek - I've reviewed your changes - here's some feedback:

    Overall Comments:

    • The coordinate generation strategy allows for invalid coordinates (longitude > 180 or latitude > 90). Please adjust the floats() calls in the coordinates composite strategy to ensure valid geographic coordinates.
    • Consider adding more complex test scenarios that combine different aspects of Feature and FeatureCollection classes to ensure they work well together.
    Here's what I looked at during the review
    • 🟢 General issues: all looks good
    • 🟢 Security: all looks good
    • 🟡 Testing: 3 issues found
    • 🟢 Complexity: all looks good
    • 🟢 Documentation: all looks good

    Sourcery is free for open source - if you like our reviews please consider sharing them ✨
    Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.

    @composite
    def coordinates(draw):
    """Generate a list of coordinates for geometries"""
    return draw(
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    suggestion (testing): Consider testing coordinates outside the valid range

    The current implementation limits latitude and longitude to their maximum valid ranges. It might be useful to test how the Feature class handles invalid coordinates (e.g., latitudes > 90 or < -90, longitudes > 180 or < -180) to ensure proper error handling or validation.

    @composite
    def coordinates(draw):
        """Generate a list of coordinates for geometries, including invalid ones"""
        return draw(
            lists(
                tuples(
                    floats(min_value=-200, max_value=200), floats(min_value=-100, max_value=100),
                ),
                min_size=3,
                max_size=10,
            ),
        )
    

    Comment on lines +141 to +146
    @given(polygon=polygons(), props=properties())
    def test_feature_with_random_data(self, polygon, props) -> None:
    f = feature.Feature(polygon, props)
    assert isinstance(f, feature.Feature)
    assert f.geometry == polygon
    assert f.properties == props
    Copy link

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    The reason will be displayed to describe this comment to others. Learn more.

    suggestion (testing): Add assertions for Feature's geo_interface

    The test_feature_with_random_data function could benefit from additional assertions to verify the correctness of the Feature's geo_interface attribute. This would ensure that the GeoJSON representation of the Feature is correctly generated for various input combinations.

    @given(polygon=polygons(), props=properties())
    def test_feature_with_random_data(self, polygon, props) -> None:
        f = feature.Feature(polygon, props)
        assert isinstance(f, feature.Feature)
        assert f.geometry == polygon
        assert f.properties == props
        assert f.__geo_interface__ == {
            "type": "Feature",
            "geometry": polygon.__geo_interface__,
            "properties": props
        }
    

    Comment on lines +148 to +154
    @given(polygon=polygons())
    def test_feature_collection_with_random_features(self, polygon) -> None:
    f1 = feature.Feature(polygon)
    f2 = feature.Feature(polygon)
    fc = feature.FeatureCollection([f1, f2])
    assert isinstance(fc, feature.FeatureCollection)
    assert len(fc) == 2
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    suggestion (testing): Extend FeatureCollection test with varying number of features

    The current test for FeatureCollection always uses two features. Consider parameterizing the test to create FeatureCollections with a varying number of features, including edge cases like empty collections and large collections.

    Suggested change
    @given(polygon=polygons())
    def test_feature_collection_with_random_features(self, polygon) -> None:
    f1 = feature.Feature(polygon)
    f2 = feature.Feature(polygon)
    fc = feature.FeatureCollection([f1, f2])
    assert isinstance(fc, feature.FeatureCollection)
    assert len(fc) == 2
    @given(polygon=polygons(), num_features=st.integers(min_value=0, max_value=100))
    def test_feature_collection_with_random_features(self, polygon, num_features) -> None:
    features = [feature.Feature(polygon) for _ in range(num_features)]
    fc = feature.FeatureCollection(features)
    assert isinstance(fc, feature.FeatureCollection)
    assert len(fc) == num_features

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    codecov bot commented Sep 27, 2024

    Codecov Report

    All modified and coverable lines are covered by tests ✅

    Project coverage is 100.00%. Comparing base (5b3f9ed) to head (a1526ef).

    Additional details and impacted files
    @@            Coverage Diff            @@
    ##           develop      #251   +/-   ##
    =========================================
      Coverage   100.00%   100.00%           
    =========================================
      Files           32        32           
      Lines         2695      2728   +33     
      Branches        85        85           
    =========================================
    + Hits          2695      2728   +33     

    ☔ View full report in Codecov by Sentry.
    📢 Have feedback on the report? Share it here.

    @cleder
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    cleder commented Sep 28, 2024

    Please add a new file tests/hypothesis/test_features.py for the hypothesis test.

    @cleder cleder linked an issue Oct 2, 2024 that may be closed by this pull request
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    Add Hypothesis tests for Feature and FeatureCollection
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