
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib

class TestTask(unittest.TestCase):
    def setUp(self):
        self.transactions = [
            ["milk", "bread", "butter"],
            ["bread", "butter"],
            ["milk", "bread"],
            ["milk", "eggs"],
            ["bread", "eggs"],
            ["milk", "bread", "butter", "eggs"]
        ]
        self.module = importlib.import_module("user_code")

    def test_support_milk_bread(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        _dynamic_test(
            self,
            callable(func),
            "Function calculate_association_metrics exists",
            "Function calculate_association_metrics does not exist or is not callable."
        )
        result = func(self.transactions, "milk", "bread")
        _dynamic_test(
            self,
            result is not None and isinstance(result, dict),
            "Function returns a dictionary result",
            f"Function did not return a dict: {result}"
        )
        expected_support = 3 / 6  # 3 transactions contain both milk and bread
        actual_support = result.get("support", -1)
        _dynamic_test(
            self,
            abs(actual_support - expected_support) < 1e-6,
            "Correct support for ('milk', 'bread')",
            f"Expected support {expected_support}, got {actual_support}"
        )

    def test_confidence_milk_bread(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        result = func(self.transactions, "milk", "bread")
        expected_confidence = 3 / 4  # 4 transactions contain milk; 3 of those also contain bread
        actual_confidence = result.get("confidence", -1)
        _dynamic_test(
            self,
            abs(actual_confidence - expected_confidence) < 1e-6,
            "Correct confidence for ('milk', 'bread')",
            f"Expected confidence {expected_confidence}, got {actual_confidence}"
        )

    def test_lift_milk_bread(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        result = func(self.transactions, "milk", "bread")
        support_b = 5 / 6  # bread appears in 5 transactions
        expected_confidence = 3 / 4
        expected_lift = expected_confidence / support_b
        actual_lift = result.get("lift", -1)
        _dynamic_test(
            self,
            abs(actual_lift - expected_lift) < 1e-6,
            "Correct lift for ('milk', 'bread')",
            f"Expected lift {expected_lift}, got {actual_lift}"
        )

    def test_support_bread_butter(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        result = func(self.transactions, "bread", "butter")
        expected_support = 3 / 6  # 3 transactions contain both bread and butter
        actual_support = result.get("support", -1)
        _dynamic_test(
            self,
            abs(actual_support - expected_support) < 1e-6,
            "Correct support for ('bread', 'butter')",
            f"Expected support {expected_support}, got {actual_support}"
        )

    def test_confidence_bread_butter(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        result = func(self.transactions, "bread", "butter")
        expected_confidence = 3 / 5  # bread in 5 transactions, 3 of those also butter
        actual_confidence = result.get("confidence", -1)
        _dynamic_test(
            self,
            abs(actual_confidence - expected_confidence) < 1e-6,
            "Correct confidence for ('bread', 'butter')",
            f"Expected confidence {expected_confidence}, got {actual_confidence}"
        )

    def test_lift_bread_butter(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        result = func(self.transactions, "bread", "butter")
        support_b = 3 / 6  # butter appears in 3 transactions
        expected_confidence = 3 / 5
        expected_lift = expected_confidence / support_b
        actual_lift = result.get("lift", -1)
        _dynamic_test(
            self,
            abs(actual_lift - expected_lift) < 1e-6,
            "Correct lift for ('bread', 'butter')",
            f"Expected lift {expected_lift}, got {actual_lift}"
        )

    def test_never_appear_together(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        result = func(self.transactions, "milk", "cheese")
        _dynamic_test(
            self,
            result is not None and isinstance(result, dict),
            "Function returns a dictionary result for never-appear-together case",
            f"Function did not return a dict: {result}"
        )
        _dynamic_test(
            self,
            result["support"] == 0,
            "Support is zero when items never appear together",
            f"Expected support 0, got {result['support']}"
        )
        _dynamic_test(
            self,
            result["confidence"] == 0,
            "Confidence is zero when items never appear together",
            f"Expected confidence 0, got {result['confidence']}"
        )

    def test_item_never_appears(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        result = func(self.transactions, "cheese", "butter")
        _dynamic_test(
            self,
            result is not None and isinstance(result, dict),
            "Function returns a dictionary result for never-appear-item case",
            f"Function did not return a dict: {result}"
        )
        _dynamic_test(
            self,
            result["support"] == 0,
            "Support is zero when item_a never appears",
            f"Expected support 0, got {result['support']}"
        )
        _dynamic_test(
            self,
            result["confidence"] == 0,
            "Confidence is zero when item_a never appears",
            f"Expected confidence 0, got {result['confidence']}"
        )
        _dynamic_test(
            self,
            result["lift"] == 0,
            "Lift is zero when item_a never appears",
            f"Expected lift 0, got {result['lift']}"
        )

    def test_both_items_never_appear(self):
        importlib.reload(self.module)
        func = getattr(self.module, "calculate_association_metrics", None)
        result = func(self.transactions, "apples", "oranges")
        _dynamic_test(
            self,
            result is not None and isinstance(result, dict),
            "Function returns a dictionary result for both items never appearing",
            f"Function did not return a dict: {result}"
        )
        _dynamic_test(
            self,
            result["support"] == 0,
            "Support is zero when both items never appear",
            f"Expected support 0, got {result['support']}"
        )
        _dynamic_test(
            self,
            result["confidence"] == 0,
            "Confidence is zero when both items never appear",
            f"Expected confidence 0, got {result['confidence']}"
        )
        _dynamic_test(
            self,
            result["lift"] == 0,
            "Lift is zero when both items never appear",
            f"Expected lift 0, got {result['lift']}"
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

Master the skills needed to design, implement, and scale modern recommendation engines and transactional association models. Go from analyzing raw retail checkouts using classic algorithmic principles like Apriori and FP-Growth to building advanced personalization systems via collaborative filtering and matrix factorization. Learn how to engineer data pipelines that power modern digital storefront discovery layers and drive user engagement metrics.

Discover how to find hidden relationships within massive transactional data structures. Learn the core mathematical frameworks retail giants use to optimize shelf placement, inventory structures, and product bundling strategies.

What does the support metric represent in association rule mining?

Which metric best measures the strength of a rule beyond random chance?

In a market basket matrix, what does each row typically represent?

What is the main purpose of the Apriori algorithm?

How does pruning improve the efficiency of Apriori?

Which metric would you use to identify rules that are both frequent and strong?

Take your algorithmic knowledge to the next level by handling heavy corporate transaction data without memory crashes. Master high-performance compressed trees and discover how to eliminate commercial noise from rule patterns.

Get hands-on experience building the recommendation engines that power modern digital platforms. Learn how to look past product groupings to map historical user behaviors, ratings, and shared consumer habits.

Dive into the mathematical fundamentals of advanced machine learning models for sparse datasets. Learn how to uncover latent features hidden inside multi-dimensional user feedback data to accurately predict missing interactions.

Challenge: Manually Calculating Association Rules on Sample Ledgers

Solution