121 lines
4.8 KiB
Python
121 lines
4.8 KiB
Python
import random
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from typing import List
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import logging
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import numpy as np
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import pandas as pd
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from tqdm import tqdm
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from core.config import PipelineConfig
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from core.utils import get_data_file_path
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from core.utils.data_loader import OPTIMIZED_DTYPES, DataLoader
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from processing.ner.formats.connectors_format import ConnectorFormatter
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from processing.ner.formats.extended_surname_format import ExtendedSurnameFormatter
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from processing.ner.formats.native_only_format import NativeOnlyFormatter
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from processing.ner.formats.original_format import OriginalFormatter
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from processing.ner.formats.position_flipped_format import PositionFlippedFormatter
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from processing.ner.formats.reduced_native_format import ReducedNativeFormatter
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class NEREngineering:
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"""
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Feature engineering for NER dataset to prevent position-based learning
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and encourage sequence characteristic learning.
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"""
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def __init__(self, config: PipelineConfig):
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self.config = config
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self.data_loader = DataLoader(config)
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self.connectors = ["wa", "ya", "ka", "ba", "la"]
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self.additional_surnames = [
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"jean",
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"paul",
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"marie",
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"joseph",
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"pierre",
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"claude",
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"andre",
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"michel",
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"robert",
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]
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random.seed(self.config.data.random_seed)
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np.random.seed(self.config.data.random_seed)
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# Initialize format classes
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self.formatters = {
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"original": OriginalFormatter(self.connectors, self.additional_surnames),
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"native_only": NativeOnlyFormatter(self.connectors, self.additional_surnames),
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"position_flipped": PositionFlippedFormatter(self.connectors, self.additional_surnames),
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"reduced_native": ReducedNativeFormatter(self.connectors, self.additional_surnames),
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"connector_added": ConnectorFormatter(self.connectors, self.additional_surnames),
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"extended_surname": ExtendedSurnameFormatter(self.connectors, self.additional_surnames),
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}
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def load_data(self) -> pd.DataFrame:
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"""Load and filter NER-tagged data from CSV file"""
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filepath = get_data_file_path(self.config.data.output_files["featured"], self.config)
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df = self.data_loader.load_csv_complete(filepath)
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# Filter only NER-tagged rows
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ner_data = df[df["ner_tagged"] == 1].copy()
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logging.info(f"Loaded {len(ner_data)} NER-tagged records from {len(df)} total records")
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return ner_data
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def compute(self) -> None:
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logging.info("Applying feature engineering transformations...")
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input_filepath = get_data_file_path(self.config.data.output_files["featured"], self.config)
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output_filepath = get_data_file_path(
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self.config.data.output_files["engineered"], self.config
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)
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df = self.data_loader.load_csv_complete(input_filepath)
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ner_df = df[df["ner_tagged"] == 1].copy()
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logging.info(f"Loaded {len(ner_df)} NER-tagged records from {len(df)} total records")
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del df # No need to keep in memory
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ner_df = ner_df.sample(frac=1, random_state=self.config.data.random_seed).reset_index(
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drop=True
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)
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total_rows = len(ner_df)
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# Calculate split points
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split_25_1 = int(total_rows * 0.25)
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split_25_2 = int(total_rows * 0.50)
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split_25_3 = int(total_rows * 0.75)
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split_10_1 = int(total_rows * 0.85)
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split_10_2 = int(total_rows * 0.95)
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# Define transformation groups
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groups = [
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(0, split_25_1, "original"), # First 25%: original format
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(split_25_1, split_25_2, "native_only"), # Second 25%: remove surname
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(split_25_2, split_25_3, "position_flipped"), # Third 25%: flip positions
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(split_25_3, split_10_1, "reduced_native"), # Fourth 10%: reduce native components
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(split_10_1, split_10_2, "connector_added"), # Fifth 10%: add connectors
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(split_10_2, total_rows, "extended_surname"), # Last 5%: extend surnames
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]
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for start, end, trans_type in groups:
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logging.info(f"Group {trans_type}: {start} to {end} ({end - start} rows)")
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# Process each group
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rows = []
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for start, end, formatter_key in groups:
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formatter = self.formatters[formatter_key]
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for idx in tqdm(range(start, end), desc=f"Processing {formatter_key}"):
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row = ner_df.iloc[idx]
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transformed = formatter.transform(row)
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# Keep original columns and add transformed ones
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new_row = row.to_dict()
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new_row.update(transformed)
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rows.append(new_row)
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self.data_loader.save_csv(pd.DataFrame(rows), output_filepath)
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logging.info(f"Engineered dataset saved to {output_filepath}")
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