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Preprocessing Pipeline Runner
================================
Central pipeline that runs all enabled preprocessing stages
sequentially and logs each step.
"""
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
import time
import pandas as pd
from preprocessing.text_cleaning import TextCleaningConfig, apply_text_cleaning
from preprocessing.tokenization import (
TokenizationConfig, get_tokenizer, compute_token_stats,
truncate_samples, split_long_samples,
)
from preprocessing.system_prompt import SystemPromptConfig
from preprocessing.dataset_balancing import BalancingConfig, balance_dataset
from preprocessing.quality_filters import QualityFilterConfig, apply_quality_filters
from preprocessing.deduplication import DeduplicationConfig, apply_deduplication
from preprocessing.train_val_split import SplitConfig, split_dataset
from preprocessing.output_formatter import OutputFormatConfig, format_dataset, export_jsonl
from preprocessing.pii_filter import PIIFilterConfig, apply_pii_filter_df
from preprocessing.augmentation import AugmentationConfig, augment_dataset
@dataclass
class PreprocessingConfig:
"""Master configuration for the entire preprocessing pipeline."""
# Column mappings
instruction_col: str = ""
output_col: str = ""
input_col: Optional[str] = None
label_col: Optional[str] = None
# Sub-configs
text_cleaning: TextCleaningConfig = field(default_factory=TextCleaningConfig)
tokenization: TokenizationConfig = field(default_factory=TokenizationConfig)
system_prompt: SystemPromptConfig = field(default_factory=SystemPromptConfig)
balancing: BalancingConfig = field(default_factory=BalancingConfig)
quality_filters: QualityFilterConfig = field(default_factory=QualityFilterConfig)
deduplication: DeduplicationConfig = field(default_factory=DeduplicationConfig)
split: SplitConfig = field(default_factory=SplitConfig)
output_format: OutputFormatConfig = field(default_factory=OutputFormatConfig)
pii_filter: PIIFilterConfig = field(default_factory=PIIFilterConfig)
augmentation: AugmentationConfig = field(default_factory=AugmentationConfig)
@dataclass
class PipelineLog:
"""A single log entry from a pipeline stage."""
stage: str
description: str
rows_before: int
rows_after: int
duration_ms: float
@property
def rows_delta(self) -> int:
return self.rows_after - self.rows_before
class PreprocessingPipeline:
"""
Sequential preprocessing pipeline runner.
Applies all enabled stages and collects logs.
"""
def __init__(self, config: PreprocessingConfig):
self.config = config
self.logs: List[PipelineLog] = []
def _log(self, stage: str, desc: str, before: int, after: int, elapsed: float):
self.logs.append(PipelineLog(
stage=stage,
description=desc,
rows_before=before,
rows_after=after,
duration_ms=round(elapsed * 1000, 1),
))
def run(
self,
df: pd.DataFrame,
progress_callback=None,
) -> Tuple[pd.DataFrame, pd.DataFrame, List[PipelineLog]]:
"""
Run the complete preprocessing pipeline.
Args:
df: Input DataFrame
progress_callback: Optional callable(stage_name, progress_pct) for UI updates
Returns:
(train_df, val_df, logs)
If split is disabled, val_df will be empty.
"""
self.logs = []
total_stages = 7 # text cleaning, quality, dedup, pii, balancing, augmentation, tokenization
current_stage = 0
def _progress(name):
nonlocal current_stage
current_stage += 1
if progress_callback:
pct = int((current_stage / total_stages) * 100)
progress_callback(name, pct)
cfg = self.config
text_cols = [c for c in [cfg.instruction_col, cfg.output_col, cfg.input_col] if c and c in df.columns]
# ── Stage 1: Text Cleaning ──
t0 = time.time()
before = len(df)
any_cleaning = (
cfg.text_cleaning.remove_html or cfg.text_cleaning.remove_urls or
cfg.text_cleaning.remove_emojis or cfg.text_cleaning.normalize_whitespace or
cfg.text_cleaning.lowercase or cfg.text_cleaning.remove_special_chars or
cfg.text_cleaning.strip_extra_linebreaks
)
if any_cleaning:
df = apply_text_cleaning(df, text_cols, cfg.text_cleaning)
self._log("Text Cleaning", "Applied text cleaning filters", before, len(df), time.time() - t0)
_progress("Text Cleaning")
# ── Stage 2: Quality Filters ──
t0 = time.time()
before = len(df)
has_quality = (
cfg.quality_filters.min_word_count > 0 or
cfg.quality_filters.max_word_count > 0 or
cfg.quality_filters.profanity_filter or
cfg.quality_filters.language_filter or
cfg.quality_filters.remove_low_quality
)
if has_quality and cfg.output_col:
df = apply_quality_filters(df, cfg.output_col, cfg.quality_filters)
self._log("Quality Filters", "Applied quality filters", before, len(df), time.time() - t0)
_progress("Quality Filters")
# ── Stage 3: Deduplication ──
t0 = time.time()
before = len(df)
if cfg.instruction_col and (cfg.deduplication.remove_exact or cfg.deduplication.remove_semantic):
df = apply_deduplication(df, cfg.instruction_col, cfg.deduplication)
self._log("Deduplication", "Removed duplicate samples", before, len(df), time.time() - t0)
_progress("Deduplication")
# ── Stage 4: PII Filtering ──
t0 = time.time()
before = len(df)
has_pii = (
cfg.pii_filter.filter_emails or cfg.pii_filter.filter_phones or
cfg.pii_filter.filter_id_numbers or cfg.pii_filter.filter_api_keys or
cfg.pii_filter.filter_addresses
)
if has_pii:
df = apply_pii_filter_df(df, text_cols, cfg.pii_filter)
self._log("PII Filtering", "Masked PII data", before, len(df), time.time() - t0)
_progress("PII Filtering")
# ── Stage 5: Dataset Balancing ──
t0 = time.time()
before = len(df)
if cfg.balancing.enabled and cfg.balancing.label_column and cfg.balancing.strategy != "none":
df = balance_dataset(df, cfg.balancing.label_column, cfg.balancing.strategy)
self._log("Balancing", "Balanced dataset classes", before, len(df), time.time() - t0)
_progress("Balancing")
# ── Stage 6: Augmentation ──
t0 = time.time()
before = len(df)
if cfg.augmentation.enabled and cfg.instruction_col:
df = augment_dataset(df, cfg.instruction_col, cfg.augmentation)
self._log("Augmentation", "Generated augmented samples", before, len(df), time.time() - t0)
_progress("Augmentation")
# ── Stage 7: Tokenization Controls ──
t0 = time.time()
before = len(df)
if cfg.tokenization.truncate_long or cfg.tokenization.split_long:
try:
tokenizer = get_tokenizer(cfg.tokenization)
is_tiktoken = cfg.tokenization.tokenizer_name == "tiktoken"
for col in text_cols:
if cfg.tokenization.split_long:
df = split_long_samples(
df, col, cfg.tokenization.max_total_tokens,
tokenizer, is_tiktoken, cfg.tokenization.split_overlap,
)
elif cfg.tokenization.truncate_long:
df = truncate_samples(
df, col, cfg.tokenization.max_total_tokens,
tokenizer, is_tiktoken,
)
except ImportError:
pass # tokenizer not available
self._log("Tokenization", "Applied tokenization controls", before, len(df), time.time() - t0)
_progress("Tokenization")
# ── Split ──
train_df, val_df = split_dataset(df, cfg.split)
return train_df, val_df, self.logs
def get_safe_preset() -> PreprocessingConfig:
"""Return a sensible 'safe preset' configuration for common use cases."""
return PreprocessingConfig(
text_cleaning=TextCleaningConfig(
remove_html=True,
remove_urls=True,
remove_emojis=False,
normalize_whitespace=True,
lowercase=False,
remove_special_chars=False,
strip_extra_linebreaks=True,
),
quality_filters=QualityFilterConfig(
min_word_count=3,
max_word_count=0,
profanity_filter=False,
language_filter=False,
remove_low_quality=True,
min_quality_length=20,
),
deduplication=DeduplicationConfig(
remove_exact=True,
remove_semantic=False,
),
pii_filter=PIIFilterConfig(
filter_emails=True,
filter_phones=True,
filter_id_numbers=True,
filter_api_keys=True,
filter_addresses=False,
),
split=SplitConfig(
enabled=True,
train_ratio=0.9,
random_seed=42,
shuffle=True,
),
output_format=OutputFormatConfig(
format_type="openai_chat",
),
system_prompt=SystemPromptConfig(
system_prompt="You are a helpful AI assistant.",
prepend_to_all=True,
),
)
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