
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

Beherrschen Sie die Fähigkeiten, die erforderlich sind, um moderne Empfehlungssysteme und transaktionale Assoziationsmodelle zu entwerfen, zu implementieren und zu skalieren. Analysieren Sie Rohdaten aus Einzelhandels-Transaktionen mit klassischen algorithmischen Prinzipien wie Apriori und FP-Growth und entwickeln Sie fortschrittliche Personalisierungssysteme mittels kollaborativem Filtern und Matrixfaktorisierung. Lernen Sie, wie man Datenpipelines entwickelt, die moderne digitale Storefront-Entdeckungsschichten antreiben und Nutzerbindungsmetriken verbessern.

Entdecken Sie, wie verborgene Zusammenhänge in umfangreichen transaktionalen Datenstrukturen aufgedeckt werden können. Lernen Sie die grundlegenden mathematischen Rahmenwerke kennen, die von Einzelhandelsriesen zur Optimierung der Regalplatzierung, der Lagerstrukturen und der Produktbündelungsstrategien verwendet werden.

Was stellt die Support-Metrik im Association Rule Mining dar?

Welche Kennzahl misst am besten die Stärke einer Regel über den Zufall hinaus?

Was stellt in einer Market Basket Matrix typischerweise jede Zeile dar?

Was ist der Hauptzweck des Apriori-Algorithmus?

Wie verbessert das Pruning die Effizienz von Apriori?

Welchen Messwert würden Sie verwenden, um Regeln zu identifizieren, die sowohl häufig als auch stark sind?

Bringen Sie Ihr algorithmisches Wissen auf die nächste Stufe, indem Sie umfangreiche Unternehmens-Transaktionsdaten ohne Speicherabstürze verarbeiten. Beherrschen Sie hochleistungsfähige komprimierte Bäume und entdecken Sie Methoden zur Eliminierung von kommerziellem Rauschen aus Regelmustern.

Sammeln Sie praktische Erfahrungen beim Aufbau von Empfehlungssystemen, die moderne digitale Plattformen antreiben. Lernen Sie, über Produktgruppierungen hinauszublicken, um historische Nutzerverhalten, Bewertungen und gemeinsame Konsumgewohnheiten abzubilden.

Tauchen Sie ein in die mathematischen Grundlagen fortgeschrittener Machine-Learning-Modelle für spärliche Datensätze. Erfahren Sie, wie latente Merkmale in mehrdimensionalen Benutzerfeedback-Daten aufgedeckt werden können, um fehlende Interaktionen präzise vorherzusagen.

Challenge: Manuelles Berechnen von Assoziationsregeln Anhand von Musterkonten

Lösung