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| import numpy as np | |
| import pandas as pd | |
| def generate_scenario() -> dict: | |
| """ | |
| Task 2 (Medium): NaNs and outliers destabilize scaling. | |
| - Data: ~500 rows with columns: age, salary, credit_score, loan_amount, employment_years, target | |
| - Bug: StandardScaler.fit_transform applied to X with NaNs -> ValueError: Input contains NaN | |
| - Also includes extreme outlier salary values (9999999.0) | |
| - Deterministic via seed=42 | |
| """ | |
| rng = np.random.default_rng(42) | |
| n = 500 | |
| age = rng.integers(18, 71, size=n).astype(float) | |
| salary = np.clip(rng.normal(70000, 20000, size=n), 20000, None) | |
| credit_score = np.clip(rng.normal(680, 80, size=n), 300, 850) | |
| loan_amount = np.clip(rng.normal(20000, 10000, size=n), 1000, None) | |
| employment_years = np.clip(rng.normal(8, 5, size=n), 0, 40).astype(float) | |
| # Binary target influenced by features with noise | |
| logits = ( | |
| (age - 40) * -0.03 | |
| + (salary - 70000) / 50000 | |
| + (credit_score - 650) * 0.004 | |
| - (loan_amount - 20000) / 40000 | |
| + (employment_years - 8) * 0.05 | |
| + rng.normal(0, 0.8, size=n) | |
| ) | |
| prob = 1 / (1 + np.exp(-logits)) | |
| target = (prob > 0.5).astype(int) | |
| df = pd.DataFrame( | |
| { | |
| "age": age, | |
| "salary": salary, | |
| "credit_score": credit_score, | |
| "loan_amount": loan_amount, | |
| "employment_years": employment_years, | |
| "target": target, | |
| } | |
| ) | |
| # Inject ~15% rows with NaNs across a subset of columns | |
| nan_rows = rng.choice(np.arange(n), size=int(0.15 * n), replace=False) | |
| for col in ["age", "credit_score", "employment_years"]: | |
| df.loc[nan_rows, col] = np.nan | |
| # Inject ~10 extreme salary outliers | |
| outlier_rows = rng.choice(np.setdiff1d(np.arange(n), nan_rows), size=10, replace=False) | |
| df.loc[outlier_rows, "salary"] = 9_999_999.0 | |
| broken_script = ( | |
| "# Broken: scales before cleaning NaNs/outliers, causing ValueError on NaNs\n" | |
| "from sklearn.preprocessing import StandardScaler\n" | |
| "from sklearn.linear_model import LogisticRegression\n" | |
| "from sklearn.model_selection import train_test_split\n" | |
| "\n" | |
| "X = df[['age','salary','credit_score','loan_amount','employment_years']].copy()\n" | |
| "y = df['target']\n" | |
| "scaler = StandardScaler()\n" | |
| "X_scaled = scaler.fit_transform(X) # ValueError: Input contains NaN\n" | |
| "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n" | |
| "clf = LogisticRegression(max_iter=1000, random_state=42)\n" | |
| "clf.fit(X_train, y_train)\n" | |
| "print('Accuracy:', clf.score(X_test, y_test))\n" | |
| ) | |
| initial_error_log = ( | |
| "Traceback (most recent call last):\n" | |
| " File '<script>', line 1, in <module>\n" | |
| "ValueError: Input contains NaN\n" | |
| ) | |
| task_description = ( | |
| "The dataset contains ~15% NaNs and extreme salary outliers. The script scales before " | |
| "handling missing values, causing StandardScaler to crash with a NaN error." | |
| ) | |
| expected_fix_hint = ( | |
| "Inspect to find NaNs and outlier ranges. Clean with dropna/fillna and clip/quantile capping, " | |
| "then fit the scaler on the cleaned training data after the train_test_split." | |
| ) | |
| return { | |
| "broken_script": broken_script, | |
| "dataframe": df, | |
| "initial_error_log": initial_error_log, | |
| "task_description": task_description, | |
| "expected_fix_hint": expected_fix_hint, | |
| } | |