mirror of
https://github.com/Steffo99/unimore-bda-6.git
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269 lines
10 KiB
Python
269 lines
10 KiB
Python
import abc
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import typing as t
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import numpy
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import tensorflow
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import logging
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from ..database import Text, Category, CachedDatasetFunc, Review
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from ..config import TENSORFLOW_EMBEDDING_SIZE, TENSORFLOW_MAX_FEATURES, TENSORFLOW_EPOCHS
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from ..tokenizer import BaseTokenizer
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from .base import BaseSentimentAnalyzer, AlreadyTrainedError, NotTrainedError, TrainingFailedError
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log = logging.getLogger(__name__)
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if len(tensorflow.config.list_physical_devices(device_type="GPU")) == 0:
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log.warning("Tensorflow reports no GPU acceleration available.")
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else:
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log.debug("Tensorflow successfully found GPU acceleration!")
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ConversionFunc = t.Callable[[Review], tensorflow.Tensor | tuple]
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def build_dataset(dataset_func: CachedDatasetFunc, conversion_func: ConversionFunc, output_signature: tensorflow.TensorSpec | tuple) -> tensorflow.data.Dataset:
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"""
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Convert a `CachedDatasetFunc` to a `tensorflow.data.Dataset`.
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"""
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def dataset_generator():
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for review in dataset_func():
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yield conversion_func(review)
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log.debug("Creating dataset...")
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dataset = tensorflow.data.Dataset.from_generator(
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dataset_generator,
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output_signature=output_signature,
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)
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log.debug("Caching dataset...")
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dataset = dataset.cache()
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log.debug("Configuring dataset prefetch...")
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dataset = dataset.prefetch(buffer_size=tensorflow.data.AUTOTUNE)
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return dataset
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class TensorflowSentimentAnalyzer(BaseSentimentAnalyzer, metaclass=abc.ABCMeta):
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"""
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Base class for a sentiment analyzer using `tensorflow`.
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"""
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def __init__(self, *, tokenizer: BaseTokenizer):
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if not tokenizer.supports_tensorflow():
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raise TypeError("Tokenizer does not support Tensorflow")
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super().__init__(tokenizer=tokenizer)
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self.trained: bool = False
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self.failed: bool = False
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self.tokenizer: BaseTokenizer = tokenizer
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self.text_vectorization_layer: tensorflow.keras.layers.TextVectorization = self._build_text_vectorization_layer()
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self.model: tensorflow.keras.Sequential = self._build_model()
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self.history: tensorflow.keras.callbacks.History | None = None
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def _build_text_vectorization_layer(self) -> tensorflow.keras.layers.TextVectorization:
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"""
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Create a `tensorflow`-compatible `TextVectorization` layer.
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"""
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log.debug("Creating TextVectorization layer...")
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layer = tensorflow.keras.layers.TextVectorization(
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standardize=self.tokenizer.tokenize_tensorflow_and_expand_dims,
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max_tokens=TENSORFLOW_MAX_FEATURES.__wrapped__
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)
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log.debug("Created TextVectorization layer: %s", layer)
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return layer
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@abc.abstractmethod
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def _build_model(self) -> tensorflow.keras.Sequential:
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"""
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Create the `tensorflow.keras.Sequential` model that should be executed by this sentiment analyzer.
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"""
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raise NotImplementedError()
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@abc.abstractmethod
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def _build_dataset(self, dataset_func: CachedDatasetFunc) -> tensorflow.data.Dataset:
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"""
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Create a `tensorflow.data.Dataset` from the given `CachedDatasetFunc`.
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"""
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raise NotImplementedError()
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def _adapt_textvectorization(self, dataset: tensorflow.data.Dataset) -> None:
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"""
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Adapt the `.text_vectorization_layer` to the given dataset.
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"""
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log.debug("Preparing dataset to adapt %s...", self.text_vectorization_layer)
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dataset = dataset.map(lambda text, category: text)
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log.debug("Adapting %s...", self.text_vectorization_layer)
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self.text_vectorization_layer.adapt(dataset)
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def _vectorize_dataset(self, dataset: tensorflow.data.Dataset) -> tensorflow.data.Dataset:
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"""
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Apply the `.text_vectorization_layer` to the text in the dataset.
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"""
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def vectorize_entry(text, category):
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return self.text_vectorization_layer(text), category
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log.debug("Vectorizing dataset: %s", dataset)
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dataset = dataset.map(vectorize_entry)
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log.debug("Vectorized dataset: %s", dataset)
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return dataset
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def train(self, training_dataset_func: CachedDatasetFunc, validation_dataset_func: CachedDatasetFunc) -> None:
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if self.failed:
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log.error("Tried to train a failed model.")
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raise AlreadyTrainedError("Cannot re-train a failed model.")
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if self.trained:
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log.error("Tried to train an already trained model.")
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raise AlreadyTrainedError("Cannot re-train an already trained model.")
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training_set = self._build_dataset(training_dataset_func)
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validation_set = self._build_dataset(validation_dataset_func)
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self._adapt_textvectorization(training_set)
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training_set = self._vectorize_dataset(training_set)
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validation_set = self._vectorize_dataset(validation_set)
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log.info("Training: %s", self.model)
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self.history: tensorflow.keras.callbacks.History | None = self.model.fit(
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training_set,
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validation_data=validation_set,
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epochs=TENSORFLOW_EPOCHS.__wrapped__,
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callbacks=[
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tensorflow.keras.callbacks.TerminateOnNaN()
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],
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)
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if len(self.history.epoch) < TENSORFLOW_EPOCHS.__wrapped__:
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log.error("Model %s training failed: only %d epochs computed", self.model, len(self.history.epoch))
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self.failed = True
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raise TrainingFailedError()
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else:
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log.info("Model %s training succeeded!", self.model)
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self.trained = True
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@abc.abstractmethod
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def _translate_prediction(self, a: numpy.array) -> Category:
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"""
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Convert the results of `tensorflow.keras.Sequential.predict` into a `.Category`.
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"""
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raise NotImplementedError()
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def use(self, text: Text) -> Category:
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if self.failed:
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log.error("Tried to use a failed model.")
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raise NotTrainedError("Cannot use a failed model.")
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if not self.trained:
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log.error("Tried to use a non-trained model.")
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raise NotTrainedError("Cannot use a non-trained model.")
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vector = self.text_vectorization_layer(text)
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prediction = self.model.predict(vector, verbose=False)
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return self._translate_prediction(prediction)
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class TensorflowCategorySentimentAnalyzer(TensorflowSentimentAnalyzer):
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"""
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A `tensorflow`-based sentiment analyzer that considers each star rating as a separate category.
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"""
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def _build_dataset(self, dataset_func: CachedDatasetFunc) -> tensorflow.data.Dataset:
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return build_dataset(
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dataset_func=dataset_func,
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conversion_func=Review.to_tensor_tuple_category,
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output_signature=(
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tensorflow.TensorSpec(shape=(), dtype=tensorflow.string, name="text"),
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tensorflow.TensorSpec(shape=(1, 5,), dtype=tensorflow.float32, name="category_one_hot"),
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),
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)
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def _build_model(self) -> tensorflow.keras.Sequential:
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log.debug("Creating sequential categorizer model...")
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model = tensorflow.keras.Sequential([
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tensorflow.keras.layers.Embedding(
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input_dim=TENSORFLOW_MAX_FEATURES.__wrapped__ + 1,
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output_dim=TENSORFLOW_EMBEDDING_SIZE.__wrapped__,
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),
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tensorflow.keras.layers.Dropout(0.10),
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tensorflow.keras.layers.GlobalAveragePooling1D(),
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tensorflow.keras.layers.Dropout(0.10),
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tensorflow.keras.layers.Dense(5, activation="softmax"),
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])
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log.debug("Compiling model: %s", model)
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model.compile(
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optimizer=tensorflow.keras.optimizers.Adam(global_clipnorm=1.0),
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loss=tensorflow.keras.losses.CategoricalCrossentropy(),
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metrics=[
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tensorflow.keras.metrics.CategoricalAccuracy(),
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]
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)
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log.debug("Compiled model: %s", model)
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return model
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def _translate_prediction(self, a: numpy.array) -> Category:
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max_i = None
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max_p = None
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for i, p in enumerate(iter(a[0])):
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if max_p is None or p > max_p:
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max_i = i
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max_p = p
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result = float(max_i) + 1.0
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return result
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class TensorflowPolarSentimentAnalyzer(TensorflowSentimentAnalyzer):
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"""
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A `tensorflow`-based sentiment analyzer that uses the floating point value rating to get as close as possible to the correct category.
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"""
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def _build_dataset(self, dataset_func: CachedDatasetFunc) -> tensorflow.data.Dataset:
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return build_dataset(
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dataset_func=dataset_func,
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conversion_func=Review.to_tensor_tuple_normvalue,
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output_signature=(
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tensorflow.TensorSpec(shape=(), dtype=tensorflow.string, name="text"),
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tensorflow.TensorSpec(shape=(1,), dtype=tensorflow.float32, name="category"),
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),
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)
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def _build_model(self) -> tensorflow.keras.Sequential:
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log.debug("Creating sequential categorizer model...")
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model = tensorflow.keras.Sequential([
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tensorflow.keras.layers.Embedding(
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input_dim=TENSORFLOW_MAX_FEATURES.__wrapped__ + 1,
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output_dim=TENSORFLOW_EMBEDDING_SIZE.__wrapped__,
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),
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tensorflow.keras.layers.Dropout(0.10),
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tensorflow.keras.layers.GlobalAveragePooling1D(),
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tensorflow.keras.layers.Dropout(0.10),
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tensorflow.keras.layers.Dense(1),
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])
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log.debug("Compiling model: %s", model)
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model.compile(
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optimizer=tensorflow.keras.optimizers.Adadelta(global_clipnorm=1.0),
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loss=tensorflow.keras.losses.MeanSquaredError(),
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metrics=[
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tensorflow.keras.metrics.MeanAbsoluteError(),
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]
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)
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log.debug("Compiled model: %s", model)
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return model
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def _translate_prediction(self, a: numpy.array) -> Category:
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return (a[0, 0] + 0.5) * 5
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__all__ = (
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"TensorflowSentimentAnalyzer",
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"TensorflowCategorySentimentAnalyzer",
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"TensorflowPolarSentimentAnalyzer",
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)
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