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

class TestTask(unittest.TestCase):
    def test_ranked_df_sorted_and_ranked(self):
        import user_code
        importlib.reload(user_code)
        data = {
            "smiles": [
                "CCO",
                "CCCN",
                "CC(=O)O",
                "CCN(CC)CC",
                "CCOC(=O)C"
            ],
            "predicted_activity": [0.23, 0.78, 0.12, 0.56, 0.44]
        }
        df = pd.DataFrame(data)
        result = user_code.rank_candidates(df)
        _dynamic_test(
            self,
            isinstance(result, pd.DataFrame),
            "Returned object is a DataFrame",
            f"Expected a DataFrame, got {type(result)}",
        )
        expected_order = ["CCCN", "CCN(CC)CC", "CCOC(=O)C", "CCO", "CC(=O)O"]
        _dynamic_test(
            self,
            list(result["smiles"]) == expected_order,
            "DataFrame is sorted by predicted_activity descending",
            f"Expected order {expected_order}, got {list(result['smiles'])}",
        )
        expected_ranks = [1, 2, 3, 4, 5]
        _dynamic_test(
            self,
            list(result["rank"]) == expected_ranks,
            "Ranks assigned correctly from 1 to N",
            f"Expected ranks {expected_ranks}, got {list(result['rank'])}",
        )

    def test_input_df_not_modified(self):
        import user_code
        importlib.reload(user_code)
        data = {
            "smiles": ["A", "B", "C"],
            "predicted_activity": [0.5, 0.7, 0.2]
        }
        df = pd.DataFrame(data)
        df_copy = df.copy(deep=True)
        user_code.rank_candidates(df)
        _dynamic_test(
            self,
            df.equals(df_copy),
            "Input DataFrame not modified in-place",
            "Input DataFrame was modified in-place, which is not allowed",
        )

    def test_rank_column_exists(self):
        import user_code
        importlib.reload(user_code)
        data = {
            "smiles": ["X", "Y"],
            "predicted_activity": [1.1, 2.2]
        }
        df = pd.DataFrame(data)
        result = user_code.rank_candidates(df)
        _dynamic_test(
            self,
            "rank" in result.columns,
            "Output DataFrame includes 'rank' column",
            "'rank' column missing from output DataFrame",
        )

    def test_handles_duplicate_scores(self):
        import user_code
        importlib.reload(user_code)
        data = {
            "smiles": ["A", "B", "C"],
            "predicted_activity": [0.5, 0.7, 0.7]
        }
        df = pd.DataFrame(data)
        result = user_code.rank_candidates(df)
        sorted_scores = list(result["predicted_activity"])
        _dynamic_test(
            self,
            sorted_scores == sorted(sorted_scores, reverse=True),
            "DataFrame sorted correctly even with duplicate scores",
            f"Expected descending scores, got {sorted_scores}",
        )
        expected_ranks = [1, 2, 3]
        _dynamic_test(
            self,
            list(result["rank"]) == expected_ranks,
            "Ranks assigned sequentially even with duplicate scores",
            f"Expected ranks {expected_ranks}, got {list(result['rank'])}",
        )

    def test_returns_new_dataframe(self):
        import user_code
        importlib.reload(user_code)
        data = {
            "smiles": ["A", "B", "C"],
            "predicted_activity": [0.5, 0.3, 0.7]
        }
        df = pd.DataFrame(data)
        result = user_code.rank_candidates(df)
        _dynamic_test(
            self,
            result is not df,
            "Function returns a new DataFrame object",
            "Function returned the same DataFrame object (in-place modification)",
        )

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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Explore how molecules are represented digitally and learn to parse and interpret these representations using Python.

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Apply chemoinformatics techniques to screen and rank compound libraries for drug discovery.

Challenge: Screen Compounds Against Target Properties

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Challenge: Screen Compounds Against Target Properties

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