Source code for pygeoda.clustering.redcap

from ..libgeoda import VecVecDouble, VecDouble
from ..libgeoda import gda_redcap
from ..libgeoda import gda_betweensumofsquare, gda_totalsumofsquare, gda_withinsumofsquare, flat_2dclusters

__author__ = "Xun Li <>, "

[docs]def redcap(k, w, data, method, **kwargs): ''' Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) REDCAP starts from building a spanning tree with 4 different ways (single-linkage, average-linkage, complete-linkage and Ward-linkage). Then, REDCAP provides 2 different ways to prune the tree (First-order and Full-order) to build clusters. In pygeoda, the following methods are provided: * First-order and Single-linkage * Full-order and Single-linkage * Full-order and Complete-linkage * Full-order and Average-linkage * Full-order and Ward-linkage Arguments: k (int): number of clusters w (Weight): An instance of Weight class data (list or dataframe): A list of numeric vectors of selected variable or a data frame of selected variables e.g. guerry[['Crm_prs', 'Literacy']] bound_variable (tuple, optional): A numeric vector of selected bounding variable min_bound (float, optional): a minimum value that the sum value of bounding variable int each cluster should be greater than scale_method (str, optional): One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization). distance_method (str, optional): {"euclidean", "manhattan"} the distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan" random_seed (int,optional): the seed for random number generator. Defaults to 123456789. cpu_threads (int, optional): The number of cpu threads used for parallel computation Returns: dict: A dict with keys {"Clusters", "TotalSS", "Within-clusterSS", "TotalWithin-clusterSS", "Ratio"} ''' min_bound = 0 if 'min_bound' not in kwargs else kwargs['min_bound'] bound_variable = [] if 'bound_variable' not in kwargs else kwargs['bound_variable'] scale_method = "standardize" if "scale_method" not in kwargs else kwargs['scale_method'] distance_method = 'euclidean' if 'distance_method' not in kwargs else kwargs['distance_method'] random_seed = 123456789 if 'random_seed' not in kwargs else kwargs['random_seed'] cpu_threads = 6 if 'cpu_threads' not in kwargs else kwargs['cpu_threads'] if method not in ['firstorder-singlelinkage', 'fullorder-singlelinkage', 'fullorder-averagelinkage', 'fullorder-completelinkage', 'fullorder-wardlinkage']: raise ValueError('The method has to be one of {"firstorder-singlelinkage", "fullorder-completelinkage", "fullorder-averagelinkage","fullorder-singlelinkage", "fullorder-wardlinkage"}') in_data = VecVecDouble() if type(data).__name__ == "DataFrame": data = data.values.transpose().tolist() for d in data: in_data.push_back(d) #in_bound_variable = VecDouble(bound_variable) cluster_ids = gda_redcap(k, w.gda_w, in_data, scale_method, method, distance_method, bound_variable, min_bound, random_seed, cpu_threads) between_ss = gda_betweensumofsquare(cluster_ids, in_data) total_ss = gda_totalsumofsquare(in_data) ratio = between_ss / total_ss within_ss = gda_withinsumofsquare(cluster_ids, in_data) return { "Total sum of squares" : total_ss, "Within-cluster sum of squares" : list(within_ss) + [0]*(len(cluster_ids) - len(within_ss)), "Total within-cluster sum of squares" : between_ss, "The ratio of between to total sum of squares" : ratio, "Clusters" : flat_2dclusters(w.num_obs, cluster_ids), }