__author__ = "Xun Li <lixun910@gmail.com>"
from ..libgeoda import VecVecDouble, VecDouble
from ..libgeoda import gda_skater
from ..libgeoda import gda_betweensumofsquare, gda_totalsumofsquare, gda_withinsumofsquare, flat_2dclusters
[docs]def skater(k, w, data, **kwargs):
''' Spatial C(K)luster Analysis by Tree Edge Removal
SKATER forms clusters by spatially partitioning data that has similar values for features of interest.
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
Return:
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']
in_data = VecVecDouble()
if type(data).__name__ == "DataFrame":
data = data.values.transpose().tolist()
for d in data:
in_data.push_back(d)
cluster_ids = gda_skater(k, w.gda_w, in_data, scale_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),
}