
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

Behersk de nødvendige færdigheder til at designe, implementere og skalere moderne anbefalingsmotorer og transaktionsbaserede associationsmodeller. Gå fra at analysere rå detailkøb ved hjælp af klassiske algoritmiske principper som Apriori og FP-Growth til at opbygge avancerede personaliseringssystemer via kollaborativ filtrering og matrixfaktorisering. Lær at udvikle datapipelines, der driver moderne digitale butiksfacader og øger brugerengagements-målinger.

Opdag, hvordan man finder skjulte relationer i massive transaktionsdatastrukturer. Lær de centrale matematiske rammer, som detailgiganter bruger til at optimere hyldeplacering, lagerstrukturer og produktbundlingsstrategier.

Hvad repræsenterer support-metricen i association rule mining?

Hvilken metric måler bedst styrken af en regel ud over tilfældigheder?

Hvad repræsenterer hver række typisk i en market basket matrix?

Hvad er hovedformålet med Apriori-algoritmen?

Hvordan forbedrer beskæring effektiviteten af Apriori?

Hvilket målepunkt ville du bruge til at identificere regler, der både er hyppige og stærke?

Tag din algoritmiske viden til næste niveau ved at håndtere store virksomhedstransaktionsdata uden hukommelsesnedbrud. Opnå ekspertise i højtydende komprimerede træer og lær, hvordan man eliminerer kommerciel støj fra regelmønstre.

Få praktisk erfaring med at opbygge anbefalingsmotorer, der driver moderne digitale platforme. Lær at se ud over produktgrupperinger for at kortlægge historiske brugeradfærd, vurderinger og delte forbrugsvaner.

Dyk ned i de matematiske grundprincipper for avancerede maskinlæringsmodeller til sparsomme datasæt. Lær, hvordan man afdækker latente egenskaber skjult i multidimensionelle brugerfeedbackdata for nøjagtigt at forudsige manglende interaktioner.

Challenge: Manuelt beregning af associationsregler på eksempelkartoteker

Løsning