Set the folder property: The folder that this Dataset is in. If not specified, Dataset will appear at the root level. Parameters: folder - the folder value to set. Returns: the Dataset object itself.withLinkedServiceName public Dataset withLinkedServiceName(LinkedServiceReference linkedServiceName...
"""# Find the datasetdataset =Dataset.by_name(dataset_name)# If no dataset found, exit with error messageifdatasetisNone: exit_with_error("Datasetnot found. Unable to archive it.")# If the archive_dir exists we have to ask the user if we should overwriteifos.path.exists(archive_dir):...
except FileNotFoundError: self._download() print(f"Processing {self.name} raw data ...", end=" ") self._process() print("Done") self._save() def _num_classes( self, task: Literal["node", "edge", "graph"] ) -> Union[int, dict[str, int]]: flattened_labels = [] num_class...
ExplanationNotFoundException InitDatasetMissingException InvalidExplanationException MissingEvalDataException MissingExplainException MissingExplanationTypesException MissingPackageException MissingRawTransformationsException NoExperimentNameOrIdException OptionalDependencyMissingException ...
ds = fmri_dataset(args.mri_data, mask=args.mask, add_fa=vol_attr)ifdsisNone:ifargs.dataisNone:raiseRuntimeError('no data source specific')else: ds = hdf2ds(args.data)[0]else:ifargs.dataisnotNone: verbose(1,'ignoring dataset input in favor of other data source -- remove either one...
ignore_not_found Required bool Indicates whether to fail download if some files pointed to by dataset are not found. The default is True. Download will fail if any file download fails for any reason if ignore_not_found is set to False; otherwise a waring will be logged for not found ...
I ran through a similar problem and on further investigation found a warning message with the error log- DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). ...
Error 00278: The dataset, <value>, participates in a <value> but is not registered as branch versioned.
And I found it really different with dataset with simple label true false or yes no. So I don't know how to read this kind confusion matrix. This is my confusion matrix As we can see from this matrix, I Can not understand which one: True Positive False Positive True Negative F...
}/// Not found ?if(i == datasets_standard_.max_size())returnfalse;/// Check for new data or timestampif(datasets_standard_[i].checksum() != _dataset.checksum()) {/// Update Datadatasets_standard_[i].setData(_dataset.data());/// Update Timestampif(_dataset.timestamp().isTimesta...