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Lottery dataset processor.
Processes lottery number prediction data with mixed row types:
- Lottery rows: 9 columns (user_id, timestamp, 5 numbers, 1 mega)
- Immediate drawing rows: 16 columns (adds trial_num, matches, 5 target numbers, 1 target mega)
"""
from pathlib import Path
from typing import List, Dict, Any, Optional
from datetime import datetime
import pandas as pd
import re
from ..core.base_classes import BaseProcessor
from ..core.config import Config
from ..core.exceptions import ProcessorError
from ..cleaners.encoding_cleaner import EncodingCleaner
from ..cleaners.temporal_parser import TemporalParser
class LotteryProcessor(BaseProcessor):
"""
Processes Lottery data.
Format: Mixed row types in same file
- Lottery rows: user picks 5 numbers (1-47) + 1 mega (1-27)
- Immediate rows: adds trial_num, matches, and 6 target numbers
"""
BATCH_DELIMITER_CLEAN = False # mixed row formats (9 and 16 columns), no delimiter standardization
# File discovery patterns
FILE_PATTERNS = ['lot*.dat']
# Unified output schema
COLUMNS_UNIFIED = [
'user_id', 'timestamp', 'row_type',
'num1', 'num2', 'num3', 'num4', 'num5', 'mega',
'trial_num', 'matches',
'target1', 'target2', 'target3', 'target4', 'target5', 'target_mega',
'file_date', 'source_file', 'source_row_number'
]
def __init__(self, config: Config):
"""
Initialize Lottery processor.
Args:
config: Configuration object
"""
super().__init__(config, 'lottery')
# Initialize cleaners
self.encoding_cleaner = EncodingCleaner(config, self.errata_logger)
self.temporal_parser = TemporalParser(config, self.errata_logger)
# Statistics
self.stats = {
'files_processed': 0,
'files_failed': 0,
'rows_total': 0,
'rows_lottery': 0,
'rows_immediate': 0,
'rows_valid': 0,
'rows_invalid': 0
}
def extract_file_date(self, file_path: Path) -> Optional[datetime]:
"""
Extract date from filename (lotYYMMDD.dat or lotYYMMDDS.dat).
Args:
file_path: Path to file
Returns:
datetime object or None
"""
match = re.search(r'lot(\d{6})', file_path.name)
if not match:
return None
return self.temporal_parser.extract_date_from_filename(file_path.name)
def process_file(self, file_path: Path, pre_cleaned_text: Optional[str] = None) -> Optional[pd.DataFrame]:
"""
Process a single Lottery file.
Args:
file_path: Path to file
pre_cleaned_text: Pre-cleaned text from parallel batch cleaning.
If None, cleans the file inline (backward compat).
Returns:
DataFrame or None if processing fails
"""
try:
# Use pre-cleaned text if available, otherwise clean inline
if pre_cleaned_text is not None:
text = pre_cleaned_text
else:
text = self.encoding_cleaner.clean(file_path)
# DON'T use delimiter cleaner - Lottery has mixed row formats (9 and 16 columns)
# Parse manually to handle variable column counts
from io import StringIO
import csv
reader = csv.reader(StringIO(text), skipinitialspace=True)
rows = list(reader)
if not rows:
self.errata_logger.log_error(
'empty_file',
'File is empty after CSV parsing - entire file omitted',
file_path=str(file_path),
scope='file'
)
return None
# Convert to DataFrame with max columns
df = pd.DataFrame(rows)
if df.empty:
self.errata_logger.log_error(
'empty_file',
'File is empty or has no valid rows - entire file omitted',
file_path=str(file_path),
scope='file'
)
return None
# Detect row types based on column count
# Lottery rows: 8 columns (user, timestamp, 5 nums, mega)
# Immediate rows: 16 columns (adds trial, matches, 5 targets, target_mega)
file_date = self.extract_file_date(file_path)
col_counts = df.notna().sum(axis=1)
is_lottery = col_counts == 8
is_immediate = col_counts == 16
# Count row types
self.stats['rows_lottery'] += is_lottery.sum()
self.stats['rows_immediate'] += is_immediate.sum()
# Create unified DataFrame
result_df = pd.DataFrame(index=df.index)
# Common columns for all rows
result_df['user_id'] = df.iloc[:, 0]
result_df['file_date'] = file_date
# Add audit columns (source file and row numbers)
result_df['source_file'] = file_path.name
result_df['source_row_number'] = range(1, len(result_df) + 1)
# Parse timestamps
result_df['timestamp'] = self.temporal_parser.parse_dates_vectorized(
df.iloc[:, 1], file_path=str(file_path)
)
# Process Lottery rows (8 columns)
result_df.loc[is_lottery, 'row_type'] = 'lottery'
result_df.loc[is_lottery, 'num1'] = self.to_numeric_logged(df.loc[is_lottery].iloc[:, 2], 'num1', str(file_path))
result_df.loc[is_lottery, 'num2'] = self.to_numeric_logged(df.loc[is_lottery].iloc[:, 3], 'num2', str(file_path))
result_df.loc[is_lottery, 'num3'] = self.to_numeric_logged(df.loc[is_lottery].iloc[:, 4], 'num3', str(file_path))
result_df.loc[is_lottery, 'num4'] = self.to_numeric_logged(df.loc[is_lottery].iloc[:, 5], 'num4', str(file_path))
result_df.loc[is_lottery, 'num5'] = self.to_numeric_logged(df.loc[is_lottery].iloc[:, 6], 'num5', str(file_path))
result_df.loc[is_lottery, 'mega'] = self.to_numeric_logged(df.loc[is_lottery].iloc[:, 7], 'mega', str(file_path))
result_df.loc[is_lottery, 'trial_num'] = None
result_df.loc[is_lottery, 'matches'] = None
result_df.loc[is_lottery, 'target1'] = None
result_df.loc[is_lottery, 'target2'] = None
result_df.loc[is_lottery, 'target3'] = None
result_df.loc[is_lottery, 'target4'] = None
result_df.loc[is_lottery, 'target5'] = None
result_df.loc[is_lottery, 'target_mega'] = None
# Process Immediate rows (16 columns)
result_df.loc[is_immediate, 'row_type'] = 'immediate'
result_df.loc[is_immediate, 'num1'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 2], 'num1', str(file_path))
result_df.loc[is_immediate, 'num2'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 3], 'num2', str(file_path))
result_df.loc[is_immediate, 'num3'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 4], 'num3', str(file_path))
result_df.loc[is_immediate, 'num4'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 5], 'num4', str(file_path))
result_df.loc[is_immediate, 'num5'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 6], 'num5', str(file_path))
result_df.loc[is_immediate, 'mega'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 7], 'mega', str(file_path))
result_df.loc[is_immediate, 'trial_num'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 8], 'trial_num', str(file_path))
result_df.loc[is_immediate, 'matches'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 9], 'matches', str(file_path))
result_df.loc[is_immediate, 'target1'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 10], 'target1', str(file_path))
result_df.loc[is_immediate, 'target2'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 11], 'target2', str(file_path))
result_df.loc[is_immediate, 'target3'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 12], 'target3', str(file_path))
result_df.loc[is_immediate, 'target4'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 13], 'target4', str(file_path))
result_df.loc[is_immediate, 'target5'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 14], 'target5', str(file_path))
result_df.loc[is_immediate, 'target_mega'] = self.to_numeric_logged(df.loc[is_immediate].iloc[:, 15], 'target_mega', str(file_path))
# Validate and clean
result_df = self.validate_dataframe(result_df, file_path)
# Ensure all unified columns exist
for col in self.COLUMNS_UNIFIED:
if col not in result_df.columns:
result_df[col] = None
# Reorder to unified schema
result_df = result_df[self.COLUMNS_UNIFIED]
self.stats['files_processed'] += 1
self.stats['rows_valid'] += len(result_df)
return result_df
except Exception as e:
import traceback
self.errata_logger.log_error(
'file_processing_failed',
f'{type(e).__name__}: {e} - entire file omitted',
file_path=str(file_path),
scope='file',
context={'traceback': traceback.format_exc()},
)
return None
def validate_dataframe(self, df: pd.DataFrame, file_path: Path) -> pd.DataFrame:
"""
Validate and clean DataFrame.
Args:
df: Input DataFrame
file_path: Source file path
Returns:
Cleaned DataFrame
"""
initial_count = len(df)
valid_mask = pd.Series([True] * len(df), index=df.index)
# Validate user_id length
if 'user_id' in df.columns:
long_users = df['user_id'].str.len() > 64
if long_users.any():
valid_mask &= ~long_users
self.errata_logger.log_error(
'user_id_too_long',
f'{long_users.sum()} rows with user_id > 64 chars',
file_path=str(file_path),
scope='row'
)
# Validate lottery numbers (1-47)
for col in ['num1', 'num2', 'num3', 'num4', 'num5', 'target1', 'target2', 'target3', 'target4', 'target5']:
if col in df.columns:
df[col] = self.to_numeric_logged(df[col], col, str(file_path))
invalid = (df[col].notna()) & ((df[col] < 1) | (df[col] > 47))
if invalid.any():
valid_mask &= ~invalid
self.errata_logger.log_error(
f'{col}_out_of_range',
f'{invalid.sum()} rows with {col} not in 1-47',
file_path=str(file_path),
scope='row'
)
# Validate mega numbers (1-27)
for col in ['mega', 'target_mega']:
if col in df.columns:
df[col] = self.to_numeric_logged(df[col], col, str(file_path))
invalid = (df[col].notna()) & ((df[col] < 1) | (df[col] > 27))
if invalid.any():
valid_mask &= ~invalid
self.errata_logger.log_error(
f'{col}_out_of_range',
f'{invalid.sum()} rows with {col} not in 1-27',
file_path=str(file_path),
scope='row'
)
# Validate matches (0-6)
if 'matches' in df.columns:
df['matches'] = self.to_numeric_logged(df['matches'], 'matches', str(file_path))
invalid = (df['matches'].notna()) & ((df['matches'] < 0) | (df['matches'] > 6))
if invalid.any():
valid_mask &= ~invalid
self.errata_logger.log_error(
'matches_out_of_range',
f'{invalid.sum()} rows with matches not in 0-6',
file_path=str(file_path),
scope='row'
)
# Filter to valid rows
df_valid = df[valid_mask].copy()
invalid_count = initial_count - len(df_valid)
if invalid_count > 0:
self.errata_logger.log_file_summary(
str(file_path),
'partial',
initial_count,
len(df_valid),
invalid_count
)
return df_valid
def get_stats(self) -> Dict[str, Any]:
"""Get processing statistics."""
return self.stats.copy()
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