feat: added production association rule module

[mvanwyk]

PR Type

Enhancement, Documentation, Tests

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Description

• Implemented the ProductAssociation class for generating product association rules.
• Added methods to calculate support, confidence, and uplift metrics.
• Included validation for input parameters and data.
• Added comprehensive tests for the ProductAssociation class.
• Documented the module with examples, use cases, and API reference.
• Updated documentation navigation to include the new module.

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Changes walkthrough 📝

	Relevant files
Enhancement	product_association.py Implement product association rules generation module.      pyretailscience/product_association.py Added ProductAssociation class for generating product association rules. Implemented methods to calculate support, confidence, and uplift metrics. Included validation for input parameters and data. +304/-0
Tests	test_product_association.py Add tests for product association rules module.                    tests/test_product_association.py Added tests for ProductAssociation class. Included fixtures for sample data and expected results. Tested various configurations and edge cases. +330/-0
Documentation	analysis_modules.md Document product association rules module.                              docs/analysis_modules.md Documented the product association rules module. Provided examples and use cases. Explained metrics like support, confidence, and uplift. +57/-0    product_association.md Add API reference for product association module.                docs/api/product_association.md Added API reference for ProductAssociation class. +3/-0      product_association.ipynb Add example notebook for product association rules.            docs/examples/product_association.ipynb Created example notebook for product association rules. Demonstrated usage with sample data. Showcased filtering and analysis capabilities. +679/-0  mkdocs.yml Update documentation navigation for product association module. mkdocs.yml Updated navigation to include product association documentation and examples. +2/-0

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Summary by CodeRabbit

• New Features

  • Introduction of a "Product Association Rules" section in the documentation, detailing applications in retail analytics.
  • New documentation file for the product_association module, enhancing user understanding of product associations.
  • Addition of an example notebook demonstrating the practical implementation of product association rules.

• Documentation

  • Expanded navigation structure in documentation to include new sections for product association examples and API references.

• Tests

  • Added a comprehensive suite of unit tests for the ProductAssociation module to ensure functionality and reliability.

[coderabbitai]

Walkthrough

The recent updates introduce a comprehensive framework for product association rules within the retail analytics domain. New documentation and examples enhance understanding of how these rules can optimize sales strategies and customer insights. The ProductAssociation class has been implemented to calculate key metrics like support, confidence, and uplift, supported by tests to ensure reliability. This holistic approach aims to empower retailers with data-driven insights to improve decision-making.

Changes

Files	Change Summary
docs/analysis_modules.md	Added section on "Product Association Rules" detailing functionalities and metrics in retail analytics.
docs/api/product_association.md	New documentation for the product_association module, explaining its purpose and usage.
docs/examples/product_association.ipynb	Introduced a Jupyter notebook demonstrating the practical application of product association rules.
mkdocs.yml	Updated navigation to include new entries for "Product Association" in Examples and Reference sections.
pyretailscience/product_association.py	Implemented the ProductAssociation class to handle product associations and associated metrics.
tests/test_product_association.py	Created unit tests for the ProductAssociation module to ensure functionality and handle edge cases.

Sequence Diagram(s)

sequenceDiagram
    participant Retailer
    participant ProductAssociation
    participant DataFrame
    participant Metrics

    Retailer->>DataFrame: Load transaction data
    Retailer->>ProductAssociation: Initialize with DataFrame
    ProductAssociation->>Metrics: Calculate support, confidence, uplift
    Metrics-->>ProductAssociation: Return calculated metrics
    ProductAssociation-->>Retailer: Provide insights on product associations

Loading

🐇 In the land of retail, where sales are the quest,
A new tool has arrived, it’s simply the best!
With rules of association, insights take flight,
Cross-selling and more, making shopping a delight!
So hop to your data, let metrics unfold,
With every new purchase, let stories be told! 🌟

---

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[qodo-merge-pro]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 PR contains tests

🔒 No security concerns identified

⚡ Key issues to review Possible Bug The method _calc_association uses a complex series of operations and checks that could be simplified or broken down into smaller, more manageable functions. This would improve readability and maintainability. Performance Concern The method _calc_association could potentially handle large datasets inefficiently due to the use of dense operations like toarray() on sparse matrices. Consider optimizing these operations or exploring more efficient data structures.

[qodo-merge-pro]

PR Code Suggestions ✨

Category	Suggestion	Score
Typo	✅ Correct a typo in the documentation text Consider adding a space between 'of' and 'effective' to correct the typo in the text. docs/analysis_modules.md [116] -Marketing and promotions: Association rules can guide the creation ofeffective bundle offers and promotional campaigns. +Marketing and promotions: Association rules can guide the creation of effective bundle offers and promotional campaigns. [Suggestion has been applied] Suggestion importance[1-10]: 10 Why: The suggestion corrects a clear typo, improving the readability and professionalism of the documentation.	10
Enhancement	Add explanations for the columns in the example table to aid reader comprehension Add a brief explanation of the example table columns to enhance understanding for readers unfamiliar with the terms used. docs/analysis_modules.md [144] | product_name_1 | product_name_2 | occurrences_1 | occurrences_2 | cooccurrences | support | confidence | uplift | +<!-- Explanation of columns: + - product_name_1: Name of the first product in the association rule. + - product_name_2: Name of the second product in the association rule. + - occurrences_1: Number of transactions containing the first product. + - occurrences_2: Number of transactions containing the second product. + - cooccurrences: Number of transactions where both products are bought together. + - support: Proportion of transactions with both products over all transactions. + - confidence: Probability of seeing the second product in transactions that contain the first product. + - uplift: Increase in the probability of buying the second product given the first product is bought. --> Apply this suggestion Suggestion importance[1-10]: 9 Why: Adding explanations for the table columns significantly improves the comprehensibility of the example for readers unfamiliar with the terms, enhancing the documentation's utility.	9
	Improve flexibility by using a variable for the file path Replace the hardcoded file path with a variable that can be set at the top of the notebook. This makes the notebook more flexible and easier to use in different environments without modifying the code cells that load data. docs/examples/product_association.ipynb [219] -df = pd.read_parquet("../../data/transactions.parquet") +data_file_path = "../../data/transactions.parquet" # Set the path to the data file at the top of the notebook +df = pd.read_parquet(data_file_path) Apply this suggestion Suggestion importance[1-10]: 7 Why: Using a variable for the file path increases the flexibility and reusability of the notebook, making it easier to adapt to different environments. However, it is a minor enhancement and not crucial for functionality.	7
	Use loops to generate DataFrame to reduce code repetition and enhance clarity Use a loop to generate the DataFrame to avoid repetition and improve code clarity. tests/test_product_association.py [27-37] -return pd.DataFrame({ - "product_1": [ - "beer", "bread", "bread", "bread", "bread", "butter", "butter", "butter", "butter", "diapers", - "eggs", "eggs", "eggs", "eggs", "fruit", "fruit", "fruit", "fruit", "milk", "milk", "milk", - "milk", - ], - "product_2": [ - "diapers", "butter", "eggs", "fruit", "milk", "bread", "eggs", "fruit", "milk", "beer", "bread", - "butter", "fruit", "milk", "bread", "butter", "eggs", "milk", "bread", "butter", "eggs", - "fruit", - ], - ... -}) +products = ["beer", "bread", "butter", "diapers", "eggs", "fruit", "milk"] +data = {"product_1": [], "product_2": []} +for p1 in products: + for p2 in products: + if p1 != p2: + data["product_1"].append(p1) + data["product_2"].append(p2) +return pd.DataFrame(data) Suggestion importance[1-10]: 3 Why: The suggestion to use loops for generating the DataFrame reduces repetition but oversimplifies the data structure, potentially losing the specific test cases intended by the hardcoded values. The original code provides explicit test data which is crucial for testing specific scenarios.	3
Possible bug	Add a check to ensure the group and value columns are not the same to avoid logical errors in processing Consider adding a check to ensure that the value_col and group_col are not the same. This is important because if both columns are the same, it would lead to incorrect calculations of associations, as the same column would be used to identify both the product and the transaction/customer, which is logically incorrect and could lead to misleading results. pyretailscience/product_association.py [132] required_cols = [group_col, value_col] +if group_col == value_col: + raise ValueError("The group column and value column must be different.") Apply this suggestion Suggestion importance[1-10]: 9 Why: This suggestion addresses a potential logical error that could lead to incorrect calculations of associations, which is crucial for the accuracy of the analysis.	9
Robustness	Add error handling to the data loading process Add error handling for the data loading process to manage cases where the file might not exist or is corrupted, enhancing the robustness of the notebook. docs/examples/product_association.ipynb [219] -df = pd.read_parquet("../../data/transactions.parquet") +try: + df = pd.read_parquet("../../data/transactions.parquet") +except Exception as e: + print(f"An error occurred while loading the data: {e}") Apply this suggestion Suggestion importance[1-10]: 9 Why: Adding error handling significantly improves the robustness of the notebook by managing cases where the file might not exist or is corrupted. This is a crucial enhancement for reliability.	9
Maintainability	Encapsulate product association logic into a function for better reusability and testability Consider using a function to encapsulate the logic for generating product association rules, which can then be reused and tested more easily. docs/examples/product_association.ipynb [374-381] -from pyretailscience.product_association import ProductAssociation +def generate_product_association(df): + from pyretailscience.product_association import ProductAssociation + pa = ProductAssociation( + df, + value_col="product_name", + group_col="transaction_id", + ) + return pa.df.head() -pa = ProductAssociation( - df, - value_col="product_name", - group_col="transaction_id", -) -pa.df.head() +# Example usage: +association_df = generate_product_association(df) +print(association_df) Apply this suggestion Suggestion importance[1-10]: 8 Why: Encapsulating the logic into a function improves code maintainability and reusability, making it easier to test and extend. This is a valuable improvement for long-term code management.	8
	Improve variable naming for better code readability Consider using a more descriptive variable name instead of 'df' to improve code readability and maintainability. docs/analysis_modules.md [136-140] pa = ProductAssociation( - df, + transaction_data, value_col="product_name", group_col="transaction_id", ) Apply this suggestion Suggestion importance[1-10]: 7 Why: Using a more descriptive variable name enhances code readability and maintainability, though it is a minor improvement.	7
	Refactor the _calc_association method to improve readability and maintainability To enhance code readability and maintainability, consider refactoring the large _calc_association method by extracting parts of the logic into smaller, more focused methods. For example, the logic for calculating occurrences and probabilities could be moved into a separate method. pyretailscience/product_association.py [156-213] +def _calc_occurrences_and_probabilities(sparse_matrix, row_count): + occurrences = np.array(sparse_matrix.sum(axis=0)).flatten() + occurence_prob = occurrences / row_count + return occurrences, occurence_prob + def _calc_association( df: pd.DataFrame, value_col: str, group_col: str = "customer_id", target_item: str | None = None, number_of_combinations: Literal[2, 3] = 2, min_occurrences: int = 1, min_cooccurrences: int = 1, min_support: float = 0.0, min_confidence: float = 0.0, min_uplift: float = 0.0, ) -> pd.DataFrame: + # Existing code with calls to the new method where appropriate Apply this suggestion Suggestion importance[1-10]: 6 Why: This suggestion enhances code readability and maintainability by breaking down a large method into smaller, more focused methods, which is beneficial for long-term maintenance.	6
	Improve maintainability by using a fixture for sample data Replace the hardcoded DataFrame creation with a fixture function to improve maintainability and reusability. tests/test_product_association.py [16-20] -return pd.DataFrame({ - "transaction_id": [1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 5], - "product": ["milk", "bread", "fruit", "butter", "eggs", "fruit", "beer", "diapers", - "milk", "bread", "butter", "eggs", "fruit", "bread"], -}) +return self.sample_transactions_df() Apply this suggestion Suggestion importance[1-10]: 4 Why: While using a fixture function can improve maintainability, the suggestion does not provide the implementation of self.sample_transactions_df(), making it unclear how it would be integrated. Additionally, the current hardcoded DataFrame is simple and clear enough for the test context.	4
	Enhance test isolation and reusability by using a helper function for DataFrame creation Refactor the DataFrame creation to use a helper function for generating test data, enhancing test isolation and reusability. tests/test_product_association.py [64-71] -return pd.DataFrame({ - "product_1": [ - ("bread", "butter"), ("bread", "butter"), ("bread", "butter"), ("bread", "eggs"), ("bread", "eggs"), - ("bread", "eggs"), ("bread", "fruit"), ("bread", "fruit"), ("bread", "fruit"), ("bread", "milk"), - ("bread", "milk"), ("bread", "milk"), ("butter", "eggs"), ("butter", "eggs"), ("butter", "eggs"), - ("butter", "fruit"), ("butter", "fruit"), ("butter", "fruit"), ("butter", "milk"), - ("butter", "milk"), ("butter", "milk"), ("eggs", "fruit"), ("eggs", "fruit"), ("eggs", "fruit"), - ("eggs", "milk"), ("eggs", "milk"), ("eggs", "milk"), ("fruit", "milk"), ("fruit", "milk"), - ("fruit", "milk"), - ], - ... -}) +return self.generate_pair_items_df() Suggestion importance[1-10]: 4 Why: Similar to the first suggestion, using a helper function can improve maintainability, but the suggestion lacks the implementation details of self.generate_pair_items_df(). The current hardcoded DataFrame is clear and specific for the test cases.	4
Performance	Use coo_matrix for efficient sparse matrix creation and convert to csr_matrix if necessary To improve the efficiency of the sparse matrix creation, consider using the coo_matrix instead of csr_matrix for the initial creation, as coo_matrix is more efficient for constructing matrices incrementally. This can be converted to csr_matrix afterwards if needed for further operations that require fast row slicing. pyretailscience/product_association.py [231-238] -sparse_matrix = csr_matrix( +from scipy.sparse import coo_matrix +sparse_matrix = coo_matrix( ( [1] * len(unique_combo_df), ( unique_combo_df[group_col].cat.codes, unique_combo_df[value_col].cat.codes, ), ), -) +).tocsr() Apply this suggestion Suggestion importance[1-10]: 7 Why: This suggestion improves performance by using a more efficient matrix construction method, which is beneficial but not critical for correctness.	7
Best practice	Improve variable naming for clarity and maintainability Use more descriptive variable names in the print statements to enhance code readability and maintainability. docs/examples/product_association.ipynb [238-239] -print(f"Number of unique customers: {df['customer_id'].nunique()}") -print(f"Number of unique transactions: {df['transaction_id'].nunique()}") +num_unique_customers = df['customer_id'].nunique() +num_unique_transactions = df['transaction_id'].nunique() +print(f"Number of unique customers: {num_unique_customers}") +print(f"Number of unique transactions: {num_unique_transactions}") Apply this suggestion Suggestion importance[1-10]: 6 Why: Using more descriptive variable names enhances code readability and maintainability. This is a good practice but is a minor improvement in terms of overall impact.	6
	Use list comprehensions for creating DataFrame columns to make the code more concise Use list comprehensions for more concise and Pythonic code when creating DataFrame columns. tests/test_product_association.py [39-40] +num_items = 22 # Adjust as necessary return pd.DataFrame({ - "occurrences_1": [1, 3, 3, 3, 3, 2, 2, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2, 2], - "occurrences_2": [1, 2, 2, 3, 2, 3, 2, 3, 2, 1, 3, 2, 3, 2, 3, 2, 2, 2, 3, 2, 2, 3], + "occurrences_1": [random.randint(1, 3) for _ in range(num_items)], + "occurrences_2": [random.randint(1, 3) for _ in range(num_items)], ... }) Suggestion importance[1-10]: 2 Why: Using list comprehensions with random values does not preserve the specific test cases intended by the hardcoded values. The original explicit values are necessary for ensuring the tests cover the expected scenarios accurately.	2

[coderabbitai]

Actionable comments posted: 0

Review details

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Review profile: CHILL

Commits

Files that changed from the base of the PR and between 4db1168 and c65b6d7.

Files selected for processing (6)

• docs/analysis_modules.md (1 hunks)
• docs/api/product_association.md (1 hunks)
• docs/examples/product_association.ipynb (1 hunks)
• mkdocs.yml (1 hunks)
• pyretailscience/product_association.py (1 hunks)
• tests/test_product_association.py (1 hunks)

Files skipped from review due to trivial changes (1)

• docs/api/product_association.md

Additional comments not posted (36)

mkdocs.yml (2)

18-18: Add new example notebook to navigation.

The new entry for the "Product Association" example notebook is correctly added under the "Examples" section.

---

26-26: Add new API reference to navigation.

The new entry for the "Product Association" API reference is correctly added under the "Reference" section.

docs/analysis_modules.md (4)

98-105: Clear and informative introduction.

The introduction to the "Product Association Rules" section is clear and informative, explaining the purpose and utility of product association rules in retail analytics.

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107-122: Comprehensive list of applications.

The list of applications for product association rules is comprehensive, covering various aspects of retail business operations.

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124-129: Detailed explanation of metrics.

The explanations of the metrics (support, confidence, uplift) are detailed and clear, helping users understand their significance in analyzing product relationships.

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133-142: Practical example provided.

The example code snippet demonstrates practical usage of the ProductAssociation class, which is helpful for users to understand how to apply the module.

tests/test_product_association.py (19)

1-2: Add module description.

The module docstring provides a brief description of the tests for the ProductAssociation module.

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12-20: Good use of fixtures for test data.

The transactions_df fixture provides a sample DataFrame for testing, which is a good practice for reusability and readability.

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23-55: Expected results fixture is well-defined.

The expected_results_single_items_df fixture provides the expected results for single item association analysis, which is essential for validating the test outcomes.

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58-102: Expected results for pair items are well-defined.

The expected_results_pair_items_df fixture provides the expected results for pair items association analysis, ensuring comprehensive test coverage.

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104-113: Test for single item associations.

The test for calculating association rules for single items is correctly implemented, ensuring the functionality works as expected.

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114-131: Test for target single item associations.

The test for calculating association rules for a target single item is correctly implemented, validating the specific functionality.

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132-142: Test for pair item associations.

The test for calculating association rules for pairs of items is correctly implemented, ensuring the functionality works as expected.

---

143-158: Test for target pair item associations.

The test for calculating association rules for target pairs of items is correctly implemented, validating the specific functionality.

---

160-177: Test for minimum occurrences.

The test for calculating association rules with a minimum occurrences level is correctly implemented, ensuring the functionality works as expected.

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179-195: Test for minimum cooccurrences.

The test for calculating association rules with a minimum cooccurrences level is correctly implemented, ensuring the functionality works as expected.

---

197-213: Test for minimum support.

The test for calculating association rules with a minimum support level is correctly implemented, ensuring the functionality works as expected.

---

215-231: Test for minimum confidence.

The test for calculating association rules with a minimum confidence level is correctly implemented, ensuring the functionality works as expected.

---

233-249: Test for minimum uplift.

The test for calculating association rules with a minimum uplift level is correctly implemented, ensuring the functionality works as expected.

---

251-266: Test for invalid number of combinations.

The test for handling invalid number of combinations is correctly implemented, ensuring proper error handling.

---

268-277: Test for invalid minimum occurrences.

The test for handling invalid minimum occurrences is correctly implemented, ensuring proper error handling.

---

278-287: Test for invalid minimum cooccurrences.

The test for handling invalid minimum cooccurrences is correctly implemented, ensuring proper error handling.

---

288-304: Test for invalid minimum support range.

The test for handling invalid minimum support range is correctly implemented, ensuring proper error handling.

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305-321: Test for invalid minimum confidence range.

The test for handling invalid minimum confidence range is correctly implemented, ensuring proper error handling.

---

322-330: Test for invalid minimum uplift range.

The test for handling invalid minimum uplift range is correctly implemented, ensuring proper error handling.

pyretailscience/product_association.py (5)

91-99: Good use of argument validation.

The validation logic ensures that the input arguments are within valid ranges, which helps prevent runtime errors.

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132-140: Effective use of custom contract for validation.

The use of CustomContract to validate the input DataFrame ensures that the required columns are present and non-null, which is crucial for the association analysis.

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214-225: Comprehensive argument validation.

The validation logic ensures that the input arguments are within valid ranges, which helps prevent runtime errors.

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227-239: Efficient use of sparse matrix operations.

The use of sparse matrix operations helps handle large datasets efficiently, which is crucial for performance in large-scale retail analytics.

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292-302: Robust filtering of results.

The filtering logic ensures that only meaningful association rules are included in the results, based on the specified minimum thresholds for occurrences, cooccurrences, support, confidence, and uplift.

docs/examples/product_association.ipynb (6)

1-41: Well-written overview.

The markdown cell provides a comprehensive overview of the product association module and its applications in retail analytics.

---

44-221: Correct dataset loading and display.

The code cell correctly loads the sample dataset and displays the first few rows.

---

224-240: Correct calculation of unique customers and transactions.

The code cell correctly calculates and prints the number of unique customers and transactions in the dataset.

---

243-247: Concise introduction to the example.

The markdown cell effectively introduces the example for generating product association rules.

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250-382: Correct demonstration of ProductAssociation class.

The code cell correctly demonstrates the use of the ProductAssociation class to generate association rules for the entire dataset.

---

392-516: Correct demonstration of ProductAssociation class with a specific target item.

The code cell correctly demonstrates the use of the ProductAssociation class to generate association rules for a specific item.

[codecov]

Codecov Report

Attention: Patch coverage is 88.70968% with 7 lines in your changes missing coverage. Please review.

Files	Patch %	Lines
pyretailscience/product_association.py	88.70%	6 Missing and 1 partial ⚠️

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Files	Coverage Δ
pyretailscience/product_association.py	88.70% <88.70%> (ø)

... and 8 files with indirect coverage changes

[qodo-merge-pro]

Suggestion: Correct a typo in the documentation text [Typo, importance: 10]

Suggested change

	4. Marketing and promotions: Association rules can guide the creation ofeffective bundle offers and promotional
	Marketing and promotions: Association rules can guide the creation of effective bundle offers and promotional campaigns.