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analysis.community_analysis

logger_config​

logger​

get_adjacency_matrix​

def get_adjacency_matrix(
        labels: np.ndarray,
        n_clusters: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray]

Calculate the adjacency matrix, transition matrix, and temporal matrix.

Parameters

  • labels (np.ndarray): Array of cluster labels.
  • n_clusters (int): Number of clusters.

Returns

  • Tuple[np.ndarray, np.ndarray, np.ndarray]: Tuple containing: adjacency matrix, transition matrix, and temporal matrix.

get_transition_matrix​

def get_transition_matrix(adjacency_matrix: np.ndarray,
                          threshold: float = 0.0) -> np.ndarray

Compute the transition matrix from the adjacency matrix.

Parameters

  • adjacency_matrix (np.ndarray): Adjacency matrix.
  • threshold (float, optional): Threshold for considering transitions. Defaults to 0.0.

Returns

  • np.ndarray: Transition matrix.

fill_motifs_with_zero_counts​

def fill_motifs_with_zero_counts(unique_motif_labels: np.ndarray,
                                 motif_counts: np.ndarray,
                                 n_clusters: int) -> np.ndarray

Find motifs that never occur in the dataset, and fill the motif_counts array with zeros for those motifs. Example 1:

  • unique_motif_labels = [0, 1, 3, 4]
  • motif_counts = [10, 20, 30, 40],
  • n_clusters = 5
  • the function will return [10, 20, 0, 30, 40]. Example 2:
  • unique_motif_labels = [0, 1, 3, 4]
  • motif_counts = [10, 20, 30, 40],
  • n_clusters = 6
  • the function will return [10, 20, 0, 30, 40, 0].

Parameters

  • unique_motif_labels (np.ndarray): Array of unique motif labels.
  • motif_counts (np.ndarray): Array of motif counts (in number of frames).
  • n_clusters (int): Number of clusters.

Returns

  • np.ndarray: List of motif counts (in number of frame) with 0's for motifs that never happened.

augment_motif_timeseries​

def augment_motif_timeseries(labels: np.ndarray,
                             n_clusters: int) -> Tuple[np.ndarray, np.ndarray]

Augment motif time series by filling zero motifs.

Parameters

  • labels (np.ndarray): Original array of labels.
  • n_clusters (int): Number of clusters.

Returns

  • Tuple[np.ndarray, np.ndarray]: Tuple with:
    • Array of labels augmented with motifs that never occurred, artificially inputed at the end of the original labels array
    • Indices of the motifs that never occurred.

get_motif_labels​

def get_motif_labels(config: dict, sessions: List[str], model_name: str,
                     n_clusters: int,
                     segmentation_algorithm: str) -> np.ndarray

Get motif labels for given files.

Parameters

  • config (dict): Configuration parameters.
  • sessions (List[str]): List of session names.
  • model_name (str): Model name.
  • n_clusters (int): Number of clusters.
  • segmentation_algorithm (str): Which segmentation algorithm to use. Options are 'hmm' or 'kmeans'.

Returns

  • np.ndarray: Array of community labels (integers).

compute_transition_matrices​

def compute_transition_matrices(files: List[str], labels: List[np.ndarray],
                                n_clusters: int) -> List[np.ndarray]

Compute transition matrices for given files and labels.

Parameters

  • files (List[str]): List of file paths.
  • labels (List[np.ndarray]): List of label arrays.
  • n_clusters (int): Number of clusters.

Returns

  • List[np.ndarray]:: List of transition matrices.

create_cohort_community_bag​

def create_cohort_community_bag(
        config: dict, motif_labels: List[np.ndarray],
        trans_mat_full: np.ndarray, cut_tree: int | None, n_clusters: int,
        segmentation_algorithm: Literal["hmm", "kmeans"]) -> list

Create cohort community bag for given motif labels, transition matrix, cut tree, and number of clusters. (markov chain to tree -> community detection)

Parameters

  • config (dict): Configuration parameters.
  • motif_labels (List[np.ndarray]): List of motif label arrays.
  • trans_mat_full (np.ndarray): Full transition matrix.
  • cut_tree (int | None): Cut line for tree.
  • n_clusters (int): Number of clusters.
  • segmentation_algorithm (str): Which segmentation algorithm to use. Options are 'hmm' or 'kmeans'.

Returns

  • List: List of community bags.

get_cohort_community_labels​

def get_cohort_community_labels(
        motif_labels: List[np.ndarray],
        cohort_community_bag: list,
        median_filter_size: int = 7) -> List[np.ndarray]

Transform kmeans/hmm parameterized latent vector motifs into communities. Get cohort community labels for given labels, and community bags.

Parameters

  • labels (List[np.ndarray]): List of label arrays.
  • cohort_community_bag (np.ndarray): List of community bags. Dimensions: (n_communities, n_clusters_in_community)
  • median_filter_size (int, optional): Size of the median filter, in number of frames. Defaults to 7.

Returns

  • List[np.ndarray]: List of cohort community labels for each file.

save_cohort_community_labels_per_file​

def save_cohort_community_labels_per_file(config: dict, sessions: List[str],
                                          model_name: str, n_clusters: int,
                                          segmentation_algorithm: str,
                                          cohort_community_bag: list) -> None

community​

@save_state(model=CommunityFunctionSchema)
def community(config: dict,
              segmentation_algorithm: SegmentationAlgorithms,
              cohort: bool = True,
              cut_tree: int | None = None,
              save_logs: bool = False) -> None

Perform community analysis. Fills in the values in the "community" key of the states.json file. Saves results files at:

  1. If cohort is True:
  • project_name/
    • results/
      • community_cohort/
        • segmentation_algorithm-n_clusters/
          • cohort_community_bag.npy
          • cohort_community_label.npy
          • cohort_segmentation_algorithm_label.npy
          • cohort_transition_matrix.npy
          • hierarchy.pkl
      • file_name/
        • model_name/
          • segmentation_algorithm-n_clusters/
            • community/
              • cohort_community_label_file_name.npy
  1. If cohort is False:
  • project_name/
    • results/
      • file_name/
        • model_name/
          • segmentation_algorithm-n_clusters/
            • community/
              • transition_matrix_file_name.npy
              • community_label_file_name.npy
              • hierarchy_file_name.pkl

Parameters

  • config (dict): Configuration parameters.
  • segmentation_algorithm (SegmentationAlgorithms): Which segmentation algorithm to use. Options are 'hmm' or 'kmeans'.
  • cohort (bool, optional): Flag indicating cohort analysis. Defaults to True.
  • cut_tree (int, optional): Cut line for tree. Defaults to None.
  • save_logs (bool, optional): Flag indicating whether to save logs. Defaults to False.

Returns

  • None