31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
from typing import Dict
|
|
|
|
import pandas as pd
|
|
|
|
from processing.ner.formats import BaseNameFormatter
|
|
|
|
|
|
class ReducedNativeFormatter(BaseNameFormatter):
|
|
def transform(self, row: pd.Series) -> Dict:
|
|
native_parts = self.parse_native_components(row['probable_native'])
|
|
surname = row['probable_surname'] if pd.notna(row['probable_surname']) else ''
|
|
|
|
# Keep only first native component + surname
|
|
reduced_native = native_parts[0] if len(native_parts) > 1 else row['probable_native']
|
|
full_name = f"{reduced_native} {surname}".strip()
|
|
|
|
return {
|
|
'name': full_name,
|
|
'probable_native': reduced_native,
|
|
'identify_name': reduced_native,
|
|
'probable_surname': surname,
|
|
'identify_surname': surname,
|
|
'ner_entities': str(self.create_ner_tags(full_name, [reduced_native], surname)),
|
|
'transformation_type': self.transformation_type,
|
|
**self.compute_derived_attributes(full_name)
|
|
}
|
|
|
|
@property
|
|
def transformation_type(self) -> str:
|
|
return 'reduced_native'
|