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https://github.com/Steffo99/unimore-bda-6.git
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113 lines
3.1 KiB
Python
113 lines
3.1 KiB
Python
from __future__ import annotations
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import abc
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import logging
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import dataclasses
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from ..database import CachedDatasetFunc
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from ..tokenizer import BaseTokenizer
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log = logging.getLogger(__name__)
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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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def __init__(self, *, tokenizer: BaseTokenizer):
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self.tokenizer: BaseTokenizer = tokenizer
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def __repr__(self):
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return f"<{self.__class__.__qualname__} with {self.tokenizer} tokenizer>"
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@abc.abstractmethod
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def train(self, training_dataset_func: CachedDatasetFunc, validation_dataset_func: CachedDatasetFunc) -> None:
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"""
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Train the analyzer with the given training and validation datasets.
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"""
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raise NotImplementedError()
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@abc.abstractmethod
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def use(self, text: str) -> float:
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"""
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Run the model on the given input, and return the predicted rating.
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"""
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raise NotImplementedError()
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def evaluate(self, evaluation_dataset_func: CachedDatasetFunc) -> 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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"""
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evaluated: int = 0
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perfect: int = 0
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squared_error: float = 0.0
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for review in evaluation_dataset_func():
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resulting_category = self.use(review.text)
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log.debug("Evaluation step: %d for %s", resulting_category, review)
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evaluated += 1
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try:
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perfect += 1 if resulting_category == review.rating else 0
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squared_error += (resulting_category - review.rating) ** 2
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except ValueError:
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log.warning("Model execution on %s resulted in a NaN value: %s", review, resulting_category)
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return EvaluationResults(perfect=perfect, evaluated=evaluated, mse=squared_error / evaluated)
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@dataclasses.dataclass
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class EvaluationResults:
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"""
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Container for the results of a dataset evaluation.
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"""
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evaluated: int
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"""
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The number of reviews that were evaluated.
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"""
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perfect: int
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"""
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The number of reviews for which the model returned the correct rating.
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"""
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mse: float
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"""
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Mean squared error
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"""
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def __repr__(self):
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return f"<EvaluationResults: {self!s}>"
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def __str__(self):
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return f"Evaluation results:\t{self.evaluated}\tevaluated\t{self.perfect}\tperfect\t{self.perfect / self.evaluated:.2%}\taccuracy\t{self.mse / self.evaluated:.2}\tmean squared error"
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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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