Added ThresholdSegmentation class

[mvanwyk]

PR Type

Enhancement, Tests

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Description

• Refactored HMLSegmentation to ThresholdSegmentation with enhanced functionality.
• Added input validation for empty DataFrame and mismatched thresholds and segments.
• Introduced HMLSegmentation as a subclass of ThresholdSegmentation with predefined thresholds and segments.
• Implemented comprehensive tests for ThresholdSegmentation and HMLSegmentation classes to ensure correct segmentation and error handling.

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Changes walkthrough 📝

	Relevant files
Enhancement	segmentation.py Refactor and enhance segmentation classes with validation pyretailscience/segmentation.py Renamed HMLSegmentation to ThresholdSegmentation. Added input validation for empty DataFrame and mismatched thresholds and segments. Introduced HMLSegmentation as a subclass of ThresholdSegmentation. Enhanced segmentation logic with user-defined thresholds and segments. +75/-17
Tests	test_segmentation.py Add comprehensive tests for segmentation classes                  tests/test_segmentation.py Added tests for ThresholdSegmentation class. Added tests for HMLSegmentation class. Verified correct segmentation and error handling. +272/-1

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Summary by CodeRabbit

• New Features

  • Introduced a new HMLSegmentation class for streamlined Heavy, Medium, Light, and Zero spenders segmentation.
  • Updated ThresholdSegmentation for customizable user-defined thresholds and segments.

• Bug Fixes

  • Enhanced error handling for empty data scenarios and improved segmentation accuracy.

• Tests

  • Added comprehensive test cases for new and existing segmentation functionalities.

[coderabbitai]

Walkthrough

The ThresholdSegmentation class in pyretailscience/segmentation.py has been improved to allow customer segmentation based on user-defined thresholds and segments, with enhanced error handling. A new HMLSegmentation class, inheriting from ThresholdSegmentation, specializes in categorizing customers into Heavy, Medium, Light, and Zero spenders. Corresponding tests have been added to validate these functionalities.

Changes

Files	Change Summary
pyretailscience/segmentation.py	Revamped ThresholdSegmentation class to support user-defined thresholds and segments, added new HMLSegmentation class.
tests/test_segmentation.py	Added tests for new HMLSegmentation class and updated ThresholdSegmentation tests for enhanced segmentation logic.

Poem

In the code where customers thrive,
Segments now come alive.
With thresholds set and spenders read,
Heavy, Medium, Light now spread.
Zero joins the segmentation spree,
Making data dance with glee.
🐇✨

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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 implementation of ThresholdSegmentation might raise a ValueError if the thresholds do not cover all possible values of the DataFrame. This is handled by checking if any segment_name is null after the segmentation. However, the error message suggests checking thresholds from 0 to 1, which might not be clear since the actual range should depend on the data's distribution. Data Validation: The PR adds checks for empty DataFrame and mismatched thresholds and segments, which are crucial for robustness. However, it might be beneficial to also add a check for the uniqueness of segment IDs or names to prevent potential issues during the mapping process.

[qodo-merge-pro]

PR Code Suggestions ✨

Category	Suggestion	Score
Possible bug	Ensure complete coverage of values by starting thresholds at 0 Ensure that the initial threshold starts from 0 to cover the entire range of values, especially when the first threshold is greater than 0. pyretailscience/segmentation.py [138-139] -if thresholds[0] != 0: +if thresholds and thresholds[0] > 0: q = [0, *thresholds] +else: + q = thresholds Apply this suggestion Suggestion importance[1-10]: 10 Why: This suggestion addresses a potential bug by ensuring that the thresholds cover the entire range of values, which is crucial for accurate segmentation.	10
Possible issue	Add validation to ensure non-empty thresholds list Add a check to ensure that the thresholds list is not empty to prevent runtime errors during segmentation. pyretailscience/segmentation.py [115-117] +if not thresholds: + raise ValueError("Thresholds list cannot be empty.") if len(df) < len(thresholds): msg = f"There are {len(df)} customers, which is less than the number of segment thresholds." raise ValueError(msg) Apply this suggestion Suggestion importance[1-10]: 9 Why: This suggestion adds a crucial validation step to prevent runtime errors, ensuring that the thresholds list is not empty before proceeding with segmentation.	9
Enhancement	Enhance error messaging for clarity on mismatch between thresholds and segments Refactor the error message to include more specific details about the missing thresholds or segments. pyretailscience/segmentation.py [120] -raise ValueError("The number of thresholds must match the number of segments.") +if len(thresholds) != len(segments): + raise ValueError(f"The number of thresholds ({len(thresholds)}) must match the number of segments ({len(segments)}).") Apply this suggestion Suggestion importance[1-10]: 8 Why: The enhanced error message provides more specific details, making it easier for users to understand the cause of the error and fix it.	8
Maintainability	Improve string formatting for consistency and readability Replace the string concatenation with f-string for consistency and improved readability. pyretailscience/segmentation.py [112] -msg = "The dataframe requires the columns " + str(required_cols) + " and they must be non-null" +msg = f"The dataframe requires the columns {required_cols} and they must be non-null" Apply this suggestion Suggestion importance[1-10]: 7 Why: The suggestion improves code readability and consistency by using f-strings, which are more modern and readable than string concatenation.	7

[coderabbitai]

Actionable comments posted: 0

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between d8c9965 and 448a0dd.

Files selected for processing (2)

• pyretailscience/segmentation.py (2 hunks)
• tests/test_segmentation.py (3 hunks)

Additional comments not posted (25)

pyretailscience/segmentation.py (9)

70-84: Constructor enhancements and parameter validation look good!

The new parameters and enhanced error handling improve the flexibility of the ThresholdSegmentation class. Ensure that all parameters are correctly passed and utilized in the segmentation logic.

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101-103: Validate DataFrame for emptiness.

Good practice to check if the DataFrame is empty before proceeding.

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115-117: Check for sufficient customers relative to thresholds.

Ensuring that the number of customers is not less than the number of thresholds is a good validation step.

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119-121: Ensure thresholds and segments match.

Validating that the number of thresholds matches the number of segments prevents potential segmentation errors.

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123-133: Separate customers with zero spend.

The logic for separating zero spend customers is clear and well-implemented. Ensure that the handling of zero spend customers aligns with the provided options.

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136-140: Ensure thresholds cover all values.

Adding a zero threshold if not present ensures that all values are covered.

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147-151: Check for unsegmented customers.

Raising an error if some customers are not segmented based on thresholds is a good validation step.

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155-155: Combine zero spend customers if needed.

Concatenating the zero spend customers back to the main DataFrame if required is handled well.

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158-186: Constructor correctly initializes superclass with default thresholds and segments.

The HMLSegmentation class simplifies segmentation by providing default parameters for thresholds and segments, which are correctly passed to the superclass.

tests/test_segmentation.py (16)

93-113: Comprehensive test for correct segmentation.

The test ensures that customers are correctly segmented based on given thresholds and segments.

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114-125: Test for single customer segmentation.

The test correctly raises a ValueError for a DataFrame with only one customer, ensuring thresholds and segments are appropriately validated.

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126-170: Test for correct aggregation function.

The test verifies that the correct aggregation function is applied, ensuring flexibility in segmentation criteria.

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171-208: Test for merging segment data back into the original DataFrame.

The test ensures that segment data is correctly merged back, validating the integrity of the original DataFrame.

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209-224: Test for handling duplicate customer ID entries.

The test ensures that duplicate customer IDs are correctly handled, maintaining the DataFrame's integrity.

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225-246: Test for mapping segment names to segment IDs with fixed thresholds.

The test ensures correct mapping of segment names to IDs, validating the consistency of segmentation.

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247-255: Test for incomplete threshold coverage.

The test correctly raises an error when thresholds do not cover all values, ensuring comprehensive segmentation.

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268-282: Test for handling empty DataFrame.

The test correctly raises an error for an empty DataFrame, ensuring required columns are present.

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284-299: Test for excluding zero spend customers.

The test ensures zero spend customers are correctly excluded based on the specified parameter.

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301-317: Test for including zero spend customers with light spenders.

The test ensures zero spend customers are correctly included with light spenders based on the specified parameter.

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319-334: Test for separating zero spend customers.

The test ensures zero spend customers are correctly separated into their own segment based on the specified parameter.

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336-340: Test for missing required columns.

The test correctly raises an error when required columns are missing, ensuring DataFrame integrity.

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342-348: Test for single customer segmentation.

The test correctly raises a ValueError for a DataFrame with only one customer, ensuring thresholds and segments are appropriately validated.

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350-358: Test for immutability of input DataFrame.

The test ensures the input DataFrame is not altered, maintaining data integrity.

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359-372: Test for alternate value column.

The test ensures correct segmentation when an alternate value column is used.

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Line range hint 256-266:
Test for handling empty DataFrame with errors.

The test correctly raises an error when the DataFrame is missing a required column, ensuring required columns are present.

[coderabbitai]

Actionable comments posted: 0

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between 448a0dd and fa6d7d7.

Files selected for processing (2)

• pyretailscience/segmentation.py (2 hunks)
• tests/test_segmentation.py (3 hunks)

Files skipped from review as they are similar to previous changes (1)

• pyretailscience/segmentation.py

Additional comments not posted (21)

tests/test_segmentation.py (21)

93-94: Review class TestThresholdSegmentation

The class TestThresholdSegmentation is introduced to cover the new ThresholdSegmentation class.

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96-113: Review method test_correct_segmentation

The method test_correct_segmentation correctly verifies that customers are segmented based on the provided thresholds and segments.

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114-125: Review method test_single_customer

The method test_single_customer correctly verifies that a ValueError is raised when attempting to segment a single customer.

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126-165: Review method test_correct_aggregation_function

The method test_correct_aggregation_function correctly verifies that the aggregation function is applied and the segmentation is accurate.

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166-203: Review method test_correctly_checks_segment_data

The method test_correctly_checks_segment_data correctly verifies that segment data is merged back into the original DataFrame accurately.

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204-219: Review method test_handles_dataframe_with_duplicate_customer_id_entries

The method test_handles_dataframe_with_duplicate_customer_id_entries correctly verifies that the segmentation handles duplicate customer IDs.

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220-241: Review method test_correctly_maps_segment_names_to_segment_ids_with_fixed_thresholds

The method test_correctly_maps_segment_names_to_segment_ids_with_fixed_thresholds correctly verifies that segment names and IDs are mapped accurately.

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242-250: Review method test_thresholds_not_unique

The method test_thresholds_not_unique correctly verifies that a ValueError is raised when the thresholds are not unique.

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251-259: Review method test_thresholds_too_few_segments

The method test_thresholds_too_few_segments correctly verifies that a ValueError is raised when the number of segments does not match the number of thresholds.

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265-277: Review method test_thresholds_too_too_few_thresholds

The method test_thresholds_too_too_few_thresholds correctly verifies that a ValueError is raised when the number of thresholds does not match the number of segments.

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291-292: Review class TestHMLSegmentation

The class TestHMLSegmentation is introduced to cover the new HMLSegmentation class.

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299-305: Review method test_no_transactions

The method test_no_transactions correctly verifies that a ValueError is raised when there are no transactions.

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307-323: Review method test_handles_zero_spend_customers_are_excluded_in_result

The method test_handles_zero_spend_customers_are_excluded_in_result correctly verifies that zero spend customers are excluded from the segmentation results when zero_value_customers is set to "exclude".

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325-340: Review method test_handles_zero_spend_customers_include_with_light

The method test_handles_zero_spend_customers_include_with_light correctly verifies that zero spend customers are included in the "Light" segment when zero_value_customers is set to "include_with_light".

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342-357: Review method test_handles_zero_spend_customers_separate_segment

The method test_handles_zero_spend_customers_separate_segment correctly verifies that zero spend customers are placed in a separate segment when zero_value_customers is set to "separate_segment".

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359-363: Review method test_raises_value_error_if_required_columns_missing

The method test_raises_value_error_if_required_columns_missing correctly verifies that a ValueError is raised when required columns are missing.

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365-371: Review method test_segments_customer_single

The method test_segments_customer_single correctly verifies that a ValueError is raised when the DataFrame contains only one customer.

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373-381: Review method test_input_dataframe_not_changed

The method test_input_dataframe_not_changed correctly verifies that the original DataFrame remains unchanged after segmentation.

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382-395: Review method test_alternate_value_col

The method test_alternate_value_col correctly verifies that the segmentation works with an alternate value column.

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278-279: Review class TestSegTransactionStats

The class TestSegTransactionStats contains tests for the SegTransactionStats class.

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Line range hint 278-289: Review method test_handles_empty_dataframe_with_errors

The method test_handles_empty_dataframe_with_errors correctly verifies that a ValueError is raised when the DataFrame is missing a required column.