Syoy commited on
Commit
9151572
1 Parent(s): 1d77548

added lof scores computed with dev train and add train data separately.

Browse files
Files changed (2) hide show
  1. data/scores.csv +0 -0
  2. dcase23-task2-enriched.py +10 -0
data/scores.csv ADDED
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dcase23-task2-enriched.py CHANGED
@@ -367,6 +367,10 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
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  "d2v": datasets.Value("string"),
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  "d3p": datasets.Value("string"),
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  "d3v": datasets.Value("string"),
 
 
 
 
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  }
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  if self.config.embeddings_urls is not None:
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  features.update({
@@ -419,6 +423,7 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
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  "audio_files": dl_manager.iter_archive(audio_path[split]),
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  "embeddings": embeddings[split],
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  "metadata_file": dl_manager.download_and_extract(self.config.data_urls["metadata"]) if self.config.data_urls["metadata"] is not None else None,
 
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  "is_streaming": dl_manager.is_streaming,
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  },
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  ) for split in split_type if split in self.config.splits
@@ -431,11 +436,14 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
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  audio_files: Optional[Iterable],
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  embeddings: Optional[Dict],
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  metadata_file: Optional[str],
 
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  is_streaming: Optional[bool],
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  ):
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  """Yields examples."""
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  if metadata_file is not None:
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  metadata = pd.read_csv(metadata_file)
 
 
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  data_fields = list(self._info().features.keys())
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  id_ = 0
@@ -450,6 +458,8 @@ class DCASE2023Task2Dataset(datasets.GeneratorBasedBuilder):
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  result = {field: None for field in data_fields}
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  if metadata_file is not None:
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  result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict())
 
 
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  for emb_key in embeddings.keys():
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  result[emb_key] = np.asarray(embeddings[emb_key][lookup]).squeeze().tolist()
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  result["path"] = path
 
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  "d2v": datasets.Value("string"),
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  "d3p": datasets.Value("string"),
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  "d3v": datasets.Value("string"),
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+ "dev_train_lof_anomaly": datasets.Value("int64"),
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+ "dev_train_lof_anomaly_score": datasets.Value("float32"),
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+ "add_train_lof_anomaly": datasets.Value("int64"),
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+ "add_train_lof_anomaly_score": datasets.Value("float32"),
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  }
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  if self.config.embeddings_urls is not None:
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  features.update({
 
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  "audio_files": dl_manager.iter_archive(audio_path[split]),
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  "embeddings": embeddings[split],
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  "metadata_file": dl_manager.download_and_extract(self.config.data_urls["metadata"]) if self.config.data_urls["metadata"] is not None else None,
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+ "scores_file": dl_manager.download_and_extract("data/scores.csv"),
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  "is_streaming": dl_manager.is_streaming,
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  },
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  ) for split in split_type if split in self.config.splits
 
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  audio_files: Optional[Iterable],
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  embeddings: Optional[Dict],
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  metadata_file: Optional[str],
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+ scores_file: Optional[str],
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  is_streaming: Optional[bool],
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  ):
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  """Yields examples."""
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  if metadata_file is not None:
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  metadata = pd.read_csv(metadata_file)
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+ if scores_file is not None:
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+ scores = pd.read_csv(scores_file)
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  data_fields = list(self._info().features.keys())
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  id_ = 0
 
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  result = {field: None for field in data_fields}
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  if metadata_file is not None:
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  result.update(metadata[metadata["path"] == lookup].T.squeeze().to_dict())
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+ if scores is not None:
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+ result.update(scores[scores["path"] == lookup].T.squeeze().to_dict())
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  for emb_key in embeddings.keys():
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  result[emb_key] = np.asarray(embeddings[emb_key][lookup]).squeeze().tolist()
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  result["path"] = path