Hive_ML.extraction.feature_extraction module#

class Hive_ML.extraction.feature_extraction.NumpyEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)[source]#

Bases: JSONEncoder

Constructor for JSONEncoder, with sensible defaults.

If skipkeys is false, then it is a TypeError to attempt encoding of keys that are not str, int, float or None. If skipkeys is True, such items are simply skipped.

If ensure_ascii is true, the output is guaranteed to be str objects with all incoming non-ASCII characters escaped. If ensure_ascii is false, the output can contain non-ASCII characters.

If check_circular is true, then lists, dicts, and custom encoded objects will be checked for circular references during encoding to prevent an infinite recursion (which would cause an RecursionError). Otherwise, no such check takes place.

If allow_nan is true, then NaN, Infinity, and -Infinity will be encoded as such. This behavior is not JSON specification compliant, but is consistent with most JavaScript based encoders and decoders. Otherwise, it will be a ValueError to encode such floats.

If sort_keys is true, then the output of dictionaries will be sorted by key; this is useful for regression tests to ensure that JSON serializations can be compared on a day-to-day basis.

If indent is a non-negative integer, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0 will only insert newlines. None is the most compact representation.

If specified, separators should be an (item_separator, key_separator) tuple. The default is (’, ‘, ‘: ‘) if indent is None and (‘,’, ‘: ‘) otherwise. To get the most compact JSON representation, you should specify (‘,’, ‘:’) to eliminate whitespace.

If specified, default is a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError.

default(obj)[source]#

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return super().default(o)
Hive_ML.extraction.feature_extraction.extract_features_for_image_and_mask(extractor, image_filename, mask_filename, config_dict, distance_map_filename=None, n_bins=3, logger=None)[source]#

Extract Radiomics for a given image (or list of images), and the corresponding binary mask. If a distance map is specified, extract radiomics at each depth interval, specified by n_bins. image_filename can be a 3D volume, a 4D volume or a list of 3D volumes.

Example

with n_bins=3, Radiomics are extracted at 3 depth interval: 0-33%, 34-66% and 67-100% of the maximum depth.

Parameters:

  • extractor – Radiomics extractor object.

  • image_filename (Union[str, PathLike, List[str]]) – file path of the volume/s from where to extract Radiomics.

  • mask_filename (Union[str, PathLike]) – Segmentation mask file path, used to extract Radiomics only on ROIs.

  • config_dict (Dict[str, Any]) – Configuration dictionary.

  • distance_map_filename (Union[str, PathLike]) – Optional distance map file path, used to extract radiomics at different depth intervals.

  • n_bins (int) – number of intervals to extract depth-wise radiomics.

Return type:

List[Dict[str, Any]]

Returns:

List of extracted features, one element in the list per each 3D volume.

Hive_ML.extraction.feature_extraction.extract_perfusion_feature(perfusion_feature_id, perfusion_map_filename, distance_map_filename, subject, config_dict, n_bins_list=[2])[source]#

Function to extract statistical features (mean, sd, median, max and min) for a given 3D perfusion map at different depth intervals.

Parameters:

  • perfusion_feature_id (str) – Perfusion Feature name.

  • perfusion_map_filename (Union[str, PathLike, List[str]]) – Perfusion map filepath.

  • distance_map_filename (Union[str, PathLike, List[str]]) – Distance map filepath.

  • subject (str) – Subject ID.

  • config_dict (Dict[str, Any]) – Configuration dictionary.

  • n_bins_list (List[int]) – List with different depth intervals to consider in the feature extraction.

Return type:

Dict[str, Any]

Returns:

Dictionary of extracted statistic features at different depth intervals.