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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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85 lines
2.8 KiB
85 lines
2.8 KiB
import abc
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from logging import getLogger
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from typing import Any, Dict, List, Optional, Type, TypeVar
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from pydantic import ValidationError
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from .data import BaseData
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from .exceptions import FileParseException
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from .labels import Label
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from .readers import Builder, Record
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logger = getLogger(__name__)
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T = TypeVar('T')
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class PlainBuilder(Builder):
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def __init__(self, data_class: Type[BaseData]):
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self.data_class = data_class
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def build(self, row: Dict[Any, Any], filename: str, line_num: int) -> Record:
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data = self.data_class.parse(filename=filename)
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yield Record(data=data)
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def build_label(row: Dict[Any, Any], name: str, label_class: Type[Label]) -> List[Label]:
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labels = row[name]
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labels = [labels] if isinstance(labels, (str, int)) else labels
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return [label_class.parse(label) for label in labels]
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def build_data(row: Dict[Any, Any], name: str, data_class: Type[BaseData], filename: str) -> BaseData:
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data = row[name]
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return data_class.parse(text=data, filename=filename)
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class Column(abc.ABC):
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def __init__(self, name: str, value_class: Type[T]):
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self.name = name
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self.value_class = value_class
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@abc.abstractmethod
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def __call__(self, row: Dict[Any, Any], filename: str):
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raise NotImplementedError('Please implement this method in the subclass.')
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class DataColumn(Column):
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def __call__(self, row: Dict[Any, Any], filename: str) -> BaseData:
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return build_data(row, self.name, self.value_class, filename)
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class LabelColumn(Column):
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def __call__(self, row: Dict[Any, Any], filename: str) -> List[Label]:
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return build_label(row, self.name, self.value_class)
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class ColumnBuilder(Builder):
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def __init__(self, data_column: Column, label_columns: Optional[List[Column]] = None):
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self.data_column = data_column
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self.label_columns = label_columns or []
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def build(self, row: Dict[Any, Any], filename: str, line_num: int) -> Record:
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try:
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data = self.data_column(row, filename)
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row.pop(self.data_column.name)
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except KeyError:
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message = f'{self.data_column.name} field does not exist.'
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raise FileParseException(filename, line_num, message)
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except ValidationError:
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message = 'The empty text is not allowed.'
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raise FileParseException(filename, line_num, message)
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labels = []
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for column in self.label_columns:
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try:
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labels.extend(column(row, filename))
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row.pop(column.name)
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except (KeyError, ValidationError, TypeError) as e:
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logger.error('Filename: %s, Line: %s, Data: %s, Error: %s' % (filename, line_num, row, str(e)))
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return Record(data=data, label=labels, line_num=line_num, meta=row)
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