parse package, extract module¶
The extract module parses extract.config.yml files.
Here’s a rough outline of how it works.
parseextract.py contains the following functions: - validate_spect_params - validate_segment_params - _validate_feature_group_and_convert_to_list - _validate_todo_list_dict - validate_yaml
validate_yaml¶
validate_yaml is the main function; it gets called by parse.
_validate_todo_list_dict¶
_validate_todo_list_dict is called by validate_yaml when it finds a key todo_list whose value is a list of dicts.
_validate_feature_group_and_convert_to_list¶
This function validates feature groups, which are validated differently than feature lists. Feature lists are validated by making sure every element in the list is a string found within a valid features list, which is a concatenation of all the features listend in feature_groups.yml in the parse module.
The parsing of a feature_group key is a little more complicated. The first step is to make sure the group or groups appear in the dictionary of valid feature groups in hvc/parse/feature_groups.yml’. The keys of the dictionary of valid feature groups are the valid feature group names, and the values of the dictionary are the actual lists of features. If each `str in `feature_group1 is s a valid feature group, then the list of features is taken from the dictionary
of valid feature groups. The list is then validated by comparing it to the list of all features in features.yml.
This is to make sure the developer didn’t make a typo. If feature_group is a list of feature group names, then feature_list will consist of all features from all groups in the list. A vector of the same length as the new feature list has values that indicate which feature group each element in the new feature list belongs to. This vector is named feature_list_group_ID. A dict named ftr_group_dict is also returned,
where each key is a name of a feature group and its
corresponding value is the ID number given to that feature group. Using this dict and the identity array, hvc/parse/select can pull the correct features out of a feature array given a feature group name.
Example: ```Python >>> ftr_tuple = _validate_feature_group_and_convert_to_list(feature_group=[‘knn’,’svm’])
>>> ftr_tuple[0]
[‘duration group’,’preceding syllable duration’ … ] # and so on
>>> ftr_tuple[1]
np.ndarray([0,0,0,0,0,1,1,1,1,1]) # some array with one of two ID numbers
>>> len(ftr_typle[0]) = ftr_tuple[1].shape[-1]
True
>>> ftr_tuple[2]
If a feature_list was passed to this function along with feature_group, the features from the feature groups are appended to the feature_list, and in the feature_group_ID vector, the original features from the original feature list have a value of None.