In many real-world datasets, you often encounter a class imbalance problem—where one class (the majority) vastly outnumbers the other (the minority). This imbalance can bias models towards predicting the majority class, reducing predictive accuracy for the minority class. One common solution is **undersampling**, where you randomly reduce the number of samples in the majority class to match the count of the minority class. This challenge will give you hands-on practice with this technique. You will receive a DataFrame containing a categorical target column with two classes. Your goal is to return a new DataFrame where both classes are present in equal numbers, achieved by randomly undersampling the majority class.


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

class TestTask(unittest.TestCase):
    def setUp(self):
        if "user_code" in sys.modules:
            del sys.modules["user_code"]
        import user_code
        importlib.reload(user_code)
        self.undersample_majority_class = getattr(user_code, 'undersample_majority_class', None)

    def test_balanced_result(self):
        import user_code
        data = {
            "feature": list(range(12)),
            "target":  ["A"] * 8 + ["B"] * 4
        }
        df = pd.DataFrame(data)
        balanced = self.undersample_majority_class(df, "target") if self.undersample_majority_class else None
        _dynamic_test(
            self,
            balanced is not None and set(balanced['target'].value_counts()) == {4},
            "Returned DataFrame contains both classes in equal numbers",
            f"Expected both classes to have 4 samples, got counts: {None if balanced is None else balanced['target'].value_counts().to_dict()}"
        )

    def test_minority_preserved(self):
        import user_code
        data = {
            "feature": list(range(8)),
            "target":  ["A"] * 2 + ["B"] * 6
        }
        df = pd.DataFrame(data)
        balanced = self.undersample_majority_class(df, "target") if self.undersample_majority_class else None
        minority_rows = df[df["target"] == "A"]
        if balanced is not None:
            for idx, row in minority_rows.iterrows():
                match = ((balanced["feature"] == row["feature"]) & (balanced["target"] == row["target"]))
                _dynamic_test(
                    self,
                    match.any(),
                    "All original minority class samples are preserved",
                    f"Minority sample {row.to_dict()} not found in balanced DataFrame"
                )
        else:
            _dynamic_test(self, False, "", "Function returned None")

    def test_majority_count(self):
        import user_code
        data = {
            "feature": list(range(10)),
            "target":  ["X"] * 7 + ["Y"] * 3
        }
        df = pd.DataFrame(data)
        balanced = self.undersample_majority_class(df, "target") if self.undersample_majority_class else None
        if balanced is not None:
            counts = balanced['target'].value_counts()
            _dynamic_test(
                self,
                counts["X"] == counts["Y"] == 3,
                "Majority class is undersampled to match minority class count",
                f"Expected 3 samples for each class, got: {counts.to_dict()}"
            )
        else:
            _dynamic_test(self, False, "", "Function returned None")

    def test_already_balanced(self):
        import user_code
        data = {
            "feature": list(range(6)),
            "target":  ["A", "A", "A", "B", "B", "B"]
        }
        df = pd.DataFrame(data)
        balanced = self.undersample_majority_class(df, "target") if self.undersample_majority_class else None
        if balanced is not None:
            counts = balanced['target'].value_counts()
            _dynamic_test(
                self,
                counts["A"] == counts["B"] == 3 and len(balanced) == 6,
                "Function works when DataFrame is already balanced",
                f"Expected 3 samples for each class, got: {counts.to_dict()}"
            )
        else:
            _dynamic_test(self, False, "", "Function returned None")

    def test_different_labels(self):
        import user_code
        data = {
            "feature": list(range(9)),
            "target":  [1, 1, 1, 1, 0, 0, 0, 0, 0]
        }
        df = pd.DataFrame(data)
        balanced = self.undersample_majority_class(df, "target") if self.undersample_majority_class else None
        if balanced is not None:
            counts = balanced['target'].value_counts()
            _dynamic_test(
                self,
                set(counts) == {4},
                "Function works for any two-class DataFrame, regardless of class labels",
                f"Expected both classes to have 4 samples, got: {counts.to_dict()}"
            )
        else:
            _dynamic_test(self, False, "", "Function returned None")

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

A practical, hands-on course for aspiring data scientists ready to tackle real-world large data challenges. Learn to efficiently process, sample, and analyze massive datasets using Python and essential libraries. Each section features engaging video explanations and interactive challenges to build your expertise.

Learn foundational strategies for handling datasets too large to fit in memory, including chunking and streaming techniques.

Explore methods to balance and sample large datasets, including oversampling and undersampling.

Learn to use the polars library for fast, memory-efficient data processing.

Challenge: Apply Undersampling

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