
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import pandas as pd
import numpy as np

class TestTask(unittest.TestCase):
    def setUp(self):
        # Prepare the expected DataFrame for cumulative returns
        self.data = {
            "Month": ["2023-01", "2023-02", "2023-03", "2023-04", "2023-05", "2023-06"],
            "Asset_A": [0.01, -0.02, 0.03, 0.02, 0.01, 0.04],
            "Asset_B": [0.02, 0.01, 0.00, -0.01, 0.03, 0.02],
            "Asset_C": [0.00, 0.03, -0.01, 0.01, 0.02, 0.03],
            "Portfolio": [0.013, 0.005, 0.01, 0.007, 0.019, 0.03],
        }
        self.df = pd.DataFrame(self.data)
        # Calculate expected cumulative returns
        for col in ["Asset_A", "Asset_B", "Asset_C", "Portfolio"]:
            self.df[f"cum_{col}"] = (1 + self.df[col]).cumprod()

    def test_cumulative_returns(self):
        import user_code
        importlib.reload(user_code)
        # Check that cumulative columns exist and are correct
        for col in ["Asset_A", "Asset_B", "Asset_C", "Portfolio"]:
            user_df = getattr(user_code, 'df', None)
            _dynamic_test(
                self,
                isinstance(user_df, pd.DataFrame),
                f"DataFrame 'df' exists.",
                f"DataFrame 'df' not found or wrong type."
            )
            user_cum = user_df.get(f"cum_{col}", None)
            expected_cum = self.df[f"cum_{col}"]
            _dynamic_test(
                self,
                user_cum is not None and np.allclose(user_cum.values, expected_cum.values, atol=1e-6),
                f"Cumulative returns for {col} are correct.",
                f"Cumulative returns for {col} are incorrect. Expected {expected_cum.values}, got {user_cum.values if user_cum is not None else None}."
            )

    def test_portfolio_outperforms_periods(self):
        import user_code
        importlib.reload(user_code)
        user_df = getattr(user_code, 'df', None)
        _dynamic_test(
            self,
            isinstance(user_df, pd.DataFrame),
            f"DataFrame 'df' exists.",
            f"DataFrame 'df' not found or wrong type."
        )
        # Find periods where portfolio outperforms all assets
        expected_outperform = (
            self.df["cum_Portfolio"] > self.df[["cum_Asset_A", "cum_Asset_B", "cum_Asset_C"]].max(axis=1)
        )
        user_outperform = (
            user_df["cum_Portfolio"] > user_df[["cum_Asset_A", "cum_Asset_B", "cum_Asset_C"]].max(axis=1)
        )
        _dynamic_test(
            self,
            np.all(expected_outperform.values == user_outperform.values),
            "Outperform periods are correctly identified.",
            f"Expected outperform periods {expected_outperform.values}, got {user_outperform.values}."
        )

    def test_plot_lines_exist(self):
        import user_code
        import matplotlib
        import matplotlib.pyplot as plt
        importlib.reload(user_code)
        # After running user_code, check current figure
        fig = plt.gcf()
        ax = plt.gca()
        lines = ax.get_lines()
        labels = [l.get_label() for l in lines]
        for label in ["Asset A", "Asset B", "Asset C", "Portfolio"]:
            _dynamic_test(
                self,
                label in labels,
                f"Plot includes line for {label}.",
                f"Plot missing line for {label}."
            )
        # Check title
        title = ax.get_title()
        _dynamic_test(
            self,
            "portfolio" in title.lower() and "cumulative" in title.lower(),
            "Plot title is descriptive.",
            f"Plot title '{title}' does not reference portfolio and cumulative returns."
        )

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

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Challenge: Plot Portfolio and Asset Growth

Lösung