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

class TestTask(unittest.TestCase):
    def setUp(self):
        self.pd = __import__('pandas')
        self.df = self.pd.DataFrame({
            "Asset_A": [0.01, 0.02, -0.005, 0.015, 0.007],
            "Asset_B": [0.008, 0.018, -0.004, 0.013, 0.009],
            "Market": [0.009, 0.019, -0.0045, 0.014, 0.008]
        })

    def test_function_exists_and_returns_array(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'predict_asset_returns', None)
        _dynamic_test(
            self,
            callable(func),
            "Function 'predict_asset_returns' is defined.",
            "Function 'predict_asset_returns' is not defined.",
        )
        result = func(self.df) if callable(func) else None
        _dynamic_test(
            self,
            result is not None and isinstance(result, (np.ndarray, list)),
            "Function returns a numpy array or list.",
            f"Returned value is not a numpy array or list: {result}",
        )

    def test_predictions_are_close_to_expected(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'predict_asset_returns', None)
        result = func(self.df) if callable(func) else None
        # Calculate expected using solution
        from sklearn.linear_model import LinearRegression
        X = self.df[["Asset_B", "Market"]].values
        y = self.df["Asset_A"].values
        model = LinearRegression()
        model.fit(X, y)
        expected = model.predict(X)
        _dynamic_test(
            self,
            result is not None and np.allclose(result, expected, atol=1e-8),
            "Predictions are correct and match LinearRegression output.",
            f"Predictions do not match expected values.\\nExpected: {expected}\\nGot: {result}",
        )

    def test_works_with_different_data(self):
        import user_code
        importlib.reload(user_code)
        func = getattr(user_code, 'predict_asset_returns', None)
        # New test data
        df2 = self.pd.DataFrame({
            "Asset_A": [0.02, 0.03, 0.01, 0.025, 0.012],
            "Asset_B": [0.015, 0.025, 0.005, 0.018, 0.011],
            "Market": [0.012, 0.022, 0.004, 0.017, 0.013]
        })
        result = func(df2) if callable(func) else None
        from sklearn.linear_model import LinearRegression
        X = df2[["Asset_B", "Market"]].values
        y = df2["Asset_A"].values
        model = LinearRegression()
        model.fit(X, y)
        expected = model.predict(X)
        _dynamic_test(
            self,
            result is not None and np.allclose(result, expected, atol=1e-8),
            "Function works with new data and returns correct predictions.",
            f"Predictions do not match for new data.\\nExpected: {expected}\\nGot: {result}",
        )

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: Predict Asset Returns with Linear Regression

Solução

Challenge: Predict Asset Returns with Linear Regression

Solução