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| """ | |
| Task 2 — Medium: Fix Formats + Remove Duplicates | |
| Objective: Standardise phone & date formats and drop duplicate rows. | |
| Score: weighted average of format_score (0.7) + dupe_score (0.3) | |
| """ | |
| import re | |
| import pandas as pd | |
| from server.data_generator import generate_task2_datasets | |
| TASK_ID = 2 | |
| MAX_STEPS = 30 | |
| DESCRIPTION = ( | |
| "Task 2 (Medium) — Fix Formats and Remove Duplicates\n" | |
| "You have a product catalog with:\n" | |
| " • Phone numbers in mixed formats (need: NNN-NNN-NNNN)\n" | |
| " • Dates in mixed formats (need: YYYY-MM-DD)\n" | |
| " • Duplicate rows (~15)\n\n" | |
| "Available operations:\n" | |
| " fix_format — column: 'phone' | 'listed_date'\n" | |
| " drop_duplicates — no column needed\n\n" | |
| "Example actions:\n" | |
| ' {"operation": "fix_format", "column": "phone"}\n' | |
| ' {"operation": "fix_format", "column": "listed_date"}\n' | |
| ' {"operation": "drop_duplicates"}' | |
| ) | |
| PHONE_RE = re.compile(r"^\d{3}-\d{3}-\d{4}$") | |
| DATE_RE = re.compile(r"^\d{4}-\d{2}-\d{2}$") | |
| def load(): | |
| dirty, clean = generate_task2_datasets() | |
| original_phone_issues = int((~dirty["phone"].str.match(PHONE_RE)).sum()) | |
| original_date_issues = int((~dirty["listed_date"].apply( | |
| lambda x: bool(DATE_RE.match(str(x))) if pd.notna(x) else False | |
| )).sum()) | |
| original_dupes = len(dirty) - len(dirty.drop_duplicates()) | |
| meta = { | |
| "orig_phone": original_phone_issues, | |
| "orig_date": original_date_issues, | |
| "orig_dupes": original_dupes, | |
| } | |
| return dirty.copy(), clean, meta | |
| def score(current_df, meta: dict) -> float: | |
| phone_issues = int((~current_df["phone"].str.match(PHONE_RE)).sum()) | |
| date_issues = int((~current_df["listed_date"].apply( | |
| lambda x: bool(DATE_RE.match(str(x))) if pd.notna(x) else False | |
| )).sum()) | |
| dupes = len(current_df) - len(current_df.drop_duplicates()) | |
| phone_score = 1.0 - phone_issues / max(meta["orig_phone"], 1) | |
| date_score = 1.0 - date_issues / max(meta["orig_date"], 1) | |
| dupe_score = 1.0 - dupes / max(meta["orig_dupes"], 1) | |
| combined = 0.35 * phone_score + 0.35 * date_score + 0.30 * dupe_score | |
| return round(max(0.0, min(1.0, combined)), 4) | |
| def count_errors(current_df, meta: dict) -> int: | |
| phone_issues = int((~current_df["phone"].str.match(PHONE_RE)).sum()) | |
| date_issues = int((~current_df["listed_date"].apply( | |
| lambda x: bool(DATE_RE.match(str(x))) if pd.notna(x) else False | |
| )).sum()) | |
| dupes = len(current_df) - len(current_df.drop_duplicates()) | |
| return phone_issues + date_issues + dupes | |