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https://github.com/Steffo99/unimore-bda-6.git
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77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
import abc
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import logging
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import dataclasses
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from ..database import Text, Category, DatasetFunc
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log = logging.getLogger(__name__)
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@dataclasses.dataclass
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class EvaluationResults:
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correct: int
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evaluated: int
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def __repr__(self):
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return f"<EvaluationResults: {self.correct}/{self.evaluated}, {self.correct / self.evaluated * 100:.2f}>"
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def __str__(self):
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return f"{self.correct} / {self.evaluated} - {self.correct / self.evaluated * 100:.2f} %"
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class BaseSentimentAnalyzer(metaclass=abc.ABCMeta):
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"""
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Abstract base class for sentiment analyzers implemented in this project.
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"""
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@abc.abstractmethod
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def train(self, dataset_func: DatasetFunc) -> None:
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"""
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Train the analyzer with the given training dataset.
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"""
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raise NotImplementedError()
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def evaluate(self, dataset_func: DatasetFunc) -> EvaluationResults:
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"""
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Perform a model evaluation by calling repeatedly `.use` on every text of the test dataset and by comparing its resulting category with the expected category.
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Returns a tuple with the number of correct results and the number of evaluated results.
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"""
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evaluated: int = 0
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correct: int = 0
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for review in dataset_func():
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resulting_category = self.use(review.text)
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evaluated += 1
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correct += 1 if resulting_category == review.category else 0
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if not evaluated % 100:
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log.debug("%d evaluated, %d correct, %0.2d %% accuracy", evaluated, correct, correct / evaluated * 100)
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return EvaluationResults(correct=correct, evaluated=evaluated)
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@abc.abstractmethod
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def use(self, text: Text) -> Category:
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"""
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Run the model on the given input.
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"""
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raise NotImplementedError()
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class AlreadyTrainedError(Exception):
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"""
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This model has already been trained and cannot be trained again.
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"""
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class NotTrainedError(Exception):
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"""
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This model has not been trained yet.
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"""
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__all__ = (
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"BaseSentimentAnalyzer",
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"AlreadyTrainedError",
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"NotTrainedError",
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)
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