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https://github.com/Steffo99/unimore-bda-6.git
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88 lines
2.5 KiB
Python
88 lines
2.5 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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score: float
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def __repr__(self):
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return f"<EvaluationResults: score of {self.score} out of {self.evaluated} evaluated tuples>"
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def __str__(self):
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return f"{self.evaluated} evaluated, {self.correct} correct, {self.correct / self.evaluated * 100:.2} % accuracy, {self.score:.2} score, {self.score / self.evaluated * 100:.2} scoreaccuracy"
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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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score: float = 0.0
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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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score += 1 - (abs(resulting_category - review.category) / 4)
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if not evaluated % 100:
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temp_results = EvaluationResults(correct=correct, evaluated=evaluated, score=score)
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log.debug(f"{temp_results!s}")
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return EvaluationResults(correct=correct, evaluated=evaluated, score=score)
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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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class TrainingFailedError(Exception):
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"""
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The model wasn't able to complete the training and should not be used anymore.
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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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"TrainingFailedError",
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)
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