In this challenge, you will practice handling class imbalance in a large dataset by applying **oversampling**. You are provided with a `pandas` DataFrame that contains a target column with imbalanced classes. Your goal is to create a new DataFrame in which the minority class is oversampled so that both classes have the same number of rows. This technique is useful in scenarios where you want to prevent models from being biased toward the majority class.


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

class TestTask(unittest.TestCase):
    def setUp(self):
        self.user_code = importlib.import_module("user_code")
        self.data = {
            "feature1": [1, 2, 3, 4, 5, 6],
            "feature2": [10, 20, 30, 40, 50, 60],
            "target": ["A", "A", "A", "B", "B", "B"]
        }
        self.df = pd.DataFrame(self.data).iloc[:-1]  # Remove one B

    def test_columns_preserved(self):
        importlib.reload(self.user_code)
        balanced_df = self.user_code.oversample_minority(self.df, "target")
        _dynamic_test(
            self,
            set(balanced_df.columns) == set(self.df.columns),
            "Returned DataFrame contains the same columns as input DataFrame.",
            f"Returned DataFrame columns {balanced_df.columns.tolist()} do not match input columns {self.df.columns.tolist()}"
        )

    def test_balanced_class_counts(self):
        importlib.reload(self.user_code)
        balanced_df = self.user_code.oversample_minority(self.df, "target")
        counts = balanced_df["target"].value_counts()
        _dynamic_test(
            self,
            len(set(counts.values)) == 1 and counts["A"] == counts["B"],
            "Each class in the target column is balanced (equal row counts).",
            f"Class counts are not balanced: {counts.to_dict()}"
        )

    def test_total_row_count(self):
        importlib.reload(self.user_code)
        majority_count = self.df["target"].value_counts().max()
        balanced_df = self.user_code.oversample_minority(self.df, "target")
        expected_total = 2 * majority_count
        _dynamic_test(
            self,
            balanced_df.shape[0] == expected_total,
            "Total number of rows is twice the original majority class count.",
            f"Expected {expected_total} rows, got {balanced_df.shape[0]}"
        )

    def test_no_column_alteration(self):
        importlib.reload(self.user_code)
        balanced_df = self.user_code.oversample_minority(self.df, "target")
        _dynamic_test(
            self,
            all(col in balanced_df.columns for col in self.df.columns) and all(col in self.df.columns for col in balanced_df.columns),
            "No columns were dropped or altered in the returned DataFrame.",
            f"Returned columns: {balanced_df.columns.tolist()}, original columns: {self.df.columns.tolist()}"
        )

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 Oversampling

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