datasets/gds_dataset.py
- 2 F541: f-string is missing placeholders
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18 DEFAULT_SPLIT_NAMES = {'train': 'Train', 'val': 'Validation', 'test': 'Test'} -
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30 # since different subsets (e.g., train vs test) might have different transforms -
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64 - transform (function): Any data transformations to be applied to the input x. -
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66 - subset (WILDSSubset): A (potentially subsampled) subset of the WILDSDataset. -
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69 raise ValueError(f"Split {split} not found in dataset's split_dict.") -
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74 split_idx = np.sort(np.random.permutation(split_idx)[:num_to_retain]) -
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80 Convenience function to check that the WILDSDataset is properly configured. -
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87 assert hasattr(self, attr_name), f'WILDSDataset is missing {attr_name}.' -
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92 f'{self.data_dir} does not exist yet. Please generate the dataset first.') -
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100 assert (isinstance(self.y_array, torch.Tensor) or isinstance(self.y_array, list)) -
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101 assert isinstance(self.metadata_array, torch.Tensor), 'metadata_array must be a torch.Tensor' -
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164 'compressed_size' is the approximate size of the compressed dataset in bytes. -
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194 A dictionary mapping splits to integer identifiers (used in split_array), -
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212 An array of integers, with split_array[i] representing what split the i-th data point -
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232 Used for logging and to configure models to produce appropriately-sized output. -
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240 Used for logging and to configure models to produce appropriately-sized output. -
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242 Leave as None if not applicable (e.g., regression or multi-task classification). -
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251 return getattr(self, '_is_classification', (self.n_classes is not None)) -
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263 A list of strings naming each column of the metadata table, e.g., ['hospital', 'y']. -
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271 A Tensor of metadata, with the i-th row representing the metadata associated with -
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272 the i-th data point. The columns correspond to the metadata_fields defined above. -
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279 An optional dictionary that, for each metadata field, contains a list that maps from -
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280 integers (in metadata_array) to a string representing what that integer means. -
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281 This is only used for logging, so that we print out more intelligible metadata values. -
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286 then if metadata_array[i, 0] == 0, the i-th data point belongs to the 'East' hospital -
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301 Note that we only do a version check for datasets where the download_url is set. -
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307 raise ValueError(f'Version {self.version} not supported. Must be in {self.versions_dict.keys()}.') -
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314 data_dir = os.path.join(root_dir, f'{self.dataset_name}_v{self.version}') -
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316 current_major_version, current_minor_version = tuple(map(int, self.version.split('.'))) -
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319 latest_major_version, latest_minor_version = tuple(map(int, self.latest_version.split('.'))) -
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323 f'{self.dataset_name} has been updated to version {self.latest_version}.\n' -
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325 f'We highly recommend updating the dataset by not specifying the older version in the command-line argument or dataset constructor.\n' -
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331 f'{self.dataset_name} has been updated to version {self.latest_version}.\n' -
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342 # If the data_dir exists and does not contain the right RELEASE file, but it is not empty and the download_url is not set, -
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354 f'The {self.dataset_name} dataset could not be found in {data_dir}. {self.dataset_name} cannot be automatically downloaded. Please download it manually.') -
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357 f'The {self.dataset_name} dataset could not be found in {data_dir}. Initialize the dataset with download=True to download the dataset. If you are using the example script, run with --download. This might take some time for large datasets.') -
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362 f'Sorry, {self.dataset_name} cannot be automatically downloaded. Please download it manually.') -
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366 print(f'You can also download the dataset manually at https://wilds.stanford.edu/downloads.') -
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377 print(f"It took {round(download_time_in_minutes, 2)} minutes to download and uncompress the dataset.") -
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380 f"\n{os.path.join(data_dir, 'archive.tar.gz')} may be corrupted. Please try deleting it and rerunning this command.\n") -
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405 def standard_group_eval(metric, grouper, y_pred, y_true, metadata, aggregate=True): -
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409 - grouper (CombinatorialGrouper): Grouper object that converts metadata into groups -
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420 results_str += f"Average {metric.name}: {results[metric.agg_metric_field]:.3f}\n" -
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422 group_results = metric.compute_group_wise(y_pred, y_true, g, grouper.n_groups) -
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433 f"[n = {group_results[metric.group_count_field(group_idx)]:6.0f}]:\t" -
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434 f"{metric.name} = {group_results[metric.group_metric_field(group_idx)]:5.3f}\n") -
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435 results[f'{metric.worst_group_metric_field}'] = group_results[f'{metric.worst_group_metric_field}'] -
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436 results_str += f"Worst-group {metric.name}: {group_results[metric.worst_group_metric_field]:.3f}\n"
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- 1 E712: comparison to False should be 'if cond is False:' or 'if not cond:'