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https://github.com/Steffo99/unimore-bda-6.git
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Fix some leftover bugs
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parent
3d9eeecb2a
commit
4f40aa44b4
3 changed files with 7 additions and 7 deletions
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@ -39,19 +39,19 @@ def main():
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slog.debug("Selected sample_func: %s", sample_func.__name__)
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for SentimentAnalyzer in [
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NLTKSentimentAnalyzer,
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TensorflowCategorySentimentAnalyzer,
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NLTKSentimentAnalyzer,
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]:
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slog = logging.getLogger(f"{__name__}.{sample_func.__name__}.{SentimentAnalyzer.__name__}")
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slog.debug("Selected SentimentAnalyzer: %s", SentimentAnalyzer.__name__)
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for Tokenizer in [
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PottsTokenizer,
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PottsTokenizerWithNegation,
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PlainTokenizer,
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LowercaseTokenizer,
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NLTKWordTokenizer,
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PottsTokenizer,
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PottsTokenizerWithNegation,
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]:
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slog = logging.getLogger(f"{__name__}.{sample_func.__name__}.{SentimentAnalyzer.__name__}.{Tokenizer.__name__}")
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@ -40,7 +40,7 @@ class NLTKSentimentAnalyzer(BaseSentimentAnalyzer):
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Convert the `Text` of a `DataTuple` to a `TokenBag`.
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"""
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count_passage(log, "tokenize_datatuple", 100)
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return self.tokenizer.tokenize_plain(datatuple.text), datatuple.category
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return self.tokenizer.tokenize_and_split_plain(datatuple.text), datatuple.category
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def _add_feature_unigrams(self, dataset: t.Iterator[tuple[TokenBag, Category]]) -> None:
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"""
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@ -71,7 +71,7 @@ class TensorflowSentimentAnalyzer(BaseSentimentAnalyzer, metaclass=abc.ABCMeta):
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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,
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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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@ -177,8 +177,8 @@ class TensorflowCategorySentimentAnalyzer(TensorflowSentimentAnalyzer):
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dataset_func=dataset_func,
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conversion_func=Review.to_tensor_tuple,
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output_signature=(
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tensorflow.TensorSpec(shape=(1,), dtype=tensorflow.string, name="text"),
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tensorflow.TensorSpec(shape=(5,), dtype=tensorflow.float32, name="review_one_hot"),
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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="review_one_hot"),
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),
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)
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