
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

Beheers de vaardigheden die nodig zijn om moderne aanbevelingssystemen en transactionele associatiemodellen te ontwerpen, implementeren en schalen. Ga van het analyseren van ruwe retailtransacties met klassieke algoritmische principes zoals Apriori en FP-Growth naar het bouwen van geavanceerde personalisatiesystemen via collaborative filtering en matrixfactorisatie. Leer hoe je datapijplijnen ontwikkelt die de ontdekking van moderne digitale winkelomgevingen aandrijven en gebruikersbetrokkenheidsstatistieken stimuleren.

Ontdek hoe verborgen relaties binnen enorme transactionele datastructuren kunnen worden gevonden. Leer de kern van wiskundige raamwerken die door retailreuzen worden gebruikt om schapindeling, voorraadstructuren en productbundelstrategieën te optimaliseren.

Wat geeft de support-metriek weer bij het ontdekken van associatieregels?

Welke metriek meet het beste de sterkte van een regel boven toeval?

Wat stelt elke rij in een market basket-matrix doorgaans voor?

Wat is het belangrijkste doel van het Apriori-algoritme?

Hoe verhoogt pruning de efficiëntie van Apriori?

Welke metriek zou je gebruiken om regels te identificeren die zowel frequent als sterk zijn?

Breng je algoritmische kennis naar een hoger niveau door zware zakelijke transactiegegevens te verwerken zonder geheugenproblemen. Beheers hoogpresterende gecomprimeerde bomen en ontdek hoe commerciële ruis uit regelpatronen kan worden geëlimineerd.

Doe praktische ervaring op met het bouwen van aanbevelingssystemen die moderne digitale platforms aandrijven. Leer hoe u verder kunt kijken dan productgroeperingen om historische gebruikersgedragingen, beoordelingen en gedeelde consumentengewoonten in kaart te brengen.

Verdiep u in de wiskundige basisprincipes van geavanceerde machine learning-modellen voor schaarse datasets. Ontdek hoe latente kenmerken in multidimensionale gebruikersfeedbackgegevens kunnen worden blootgelegd om ontbrekende interacties nauwkeurig te voorspellen.

Challenge: Handmatig Berekenen van Associatieregels op Voorbeeldboekhoudingen

Oplossing