mirror of
https://github.com/Steffo99/unimore-bda-6.git
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278 lines
10 KiB
Python
278 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 CachedDatasetFunc, TextReview, TokenizedReview
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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[[TextReview], tensorflow.Tensor | tuple]
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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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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.string_lookup_layer: tensorflow.keras.layers.StringLookup = tensorflow.keras.layers.StringLookup(max_tokens=TENSORFLOW_MAX_FEATURES.__wrapped__)
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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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@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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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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def dataset_generator():
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for review in dataset_func():
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review: TextReview
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review: TokenizedReview = self.tokenizer.tokenize_review(review)
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tokens: tensorflow.Tensor = self._tokens_to_tensor(review.tokens)
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rating: tensorflow.Tensor = self._rating_to_input(review.rating)
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yield tokens, rating
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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=(
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tensorflow.TensorSpec(shape=(1, None,), dtype=tensorflow.string, name="tokens"),
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self._ratingtensor_shape(),
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),
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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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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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log.debug("Building training dataset...")
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training_set = self._build_dataset(training_dataset_func)
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log.debug("Building validation dataset...")
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validation_set = self._build_dataset(validation_dataset_func)
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log.debug("Building vocabulary...")
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vocabulary = training_set.map(lambda tokens, rating: tokens)
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log.debug("Adapting lookup layer to the vocabulary...")
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self.string_lookup_layer.adapt(vocabulary)
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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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@staticmethod
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def _tokens_to_tensor(tokens: t.Iterator[str]) -> tensorflow.Tensor:
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"""
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Convert an iterator of tokens to a `tensorflow.Tensor`.
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"""
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tensor = tensorflow.convert_to_tensor(
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[list(tokens)],
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dtype=tensorflow.string,
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name="tokens"
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)
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return tensor
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def use(self, text: str) -> float:
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if self.failed:
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raise NotTrainedError("Cannot use a failed model.")
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if not self.trained:
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raise NotTrainedError("Cannot use a non-trained model.")
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tokens = self.tokenizer.tokenize(text)
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tokens = self._tokens_to_tensor(tokens)
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prediction = self.model.predict(tokens, verbose=False)
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prediction = self._prediction_to_rating(prediction)
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return prediction
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@abc.abstractmethod
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def _rating_to_input(self, rating: float) -> tensorflow.Tensor:
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"""
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Convert a review rating to a `tensorflow.Tensor`.
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"""
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raise NotImplementedError()
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@abc.abstractmethod
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def _ratingtensor_shape(self) -> tensorflow.TensorSpec:
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"""
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Returns the shape of the tensor output by `._rating_to_tensor` and accepted as input by `._tensor_to_rating`.
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"""
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raise NotImplementedError()
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@abc.abstractmethod
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def _prediction_to_rating(self, prediction: tensorflow.Tensor) -> float:
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"""
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Convert the results of `tensorflow.keras.Sequential.predict` into a review rating.
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"""
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raise NotImplementedError()
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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_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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self.string_lookup_layer,
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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(0.25),
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tensorflow.keras.layers.GlobalAveragePooling1D(),
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tensorflow.keras.layers.Dropout(0.25),
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tensorflow.keras.layers.Dense(8),
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tensorflow.keras.layers.Dropout(0.25),
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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(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 _rating_to_input(self, rating: float) -> tensorflow.Tensor:
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tensor = tensorflow.convert_to_tensor(
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[[
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1.0 if rating == 1.0 else 0.0,
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1.0 if rating == 2.0 else 0.0,
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1.0 if rating == 3.0 else 0.0,
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1.0 if rating == 4.0 else 0.0,
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1.0 if rating == 5.0 else 0.0,
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]],
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dtype=tensorflow.float32,
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name="rating_one_hot"
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)
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return tensor
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def _ratingtensor_shape(self) -> tensorflow.TensorSpec:
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spec = tensorflow.TensorSpec(shape=(1, 5), dtype=tensorflow.float32, name="rating_one_hot")
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return spec
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def _prediction_to_rating(self, prediction: tensorflow.Tensor) -> float:
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best_prediction = None
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best_prediction_index = None
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for index, prediction in enumerate(iter(prediction[0])):
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if best_prediction is None or prediction > best_prediction:
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best_prediction = prediction
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best_prediction_index = index
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result = float(best_prediction_index) + 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_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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self.string_lookup_layer,
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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(0.25),
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tensorflow.keras.layers.GlobalAveragePooling1D(),
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tensorflow.keras.layers.Dropout(0.25),
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tensorflow.keras.layers.Dense(8),
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tensorflow.keras.layers.Dropout(0.25),
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tensorflow.keras.layers.Dense(1, activation=tensorflow.keras.activations.sigmoid),
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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(clipnorm=1.0),
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loss=tensorflow.keras.losses.MeanAbsoluteError(),
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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 _rating_to_input(self, rating: float) -> tensorflow.Tensor:
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normalized_rating = (rating - 1) / 4
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tensor = tensorflow.convert_to_tensor(
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[normalized_rating],
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dtype=tensorflow.float32,
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name="rating_value"
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)
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return tensor
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def _ratingtensor_shape(self) -> tensorflow.TensorSpec:
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spec = tensorflow.TensorSpec(shape=(1,), dtype=tensorflow.float32, name="rating_value")
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return spec
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def _prediction_to_rating(self, prediction: numpy.array) -> float:
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rating: float = prediction[0, 0]
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rating = 1.0 if rating < 0.5 else 5.0
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return rating
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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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