from ..libgeoda import gda_quantilelisa, gda_multiquantilelisa
from ..libgeoda import VecBool, VecInt, VecVecDouble, VecVecBool, VecDouble
from .lisa import lisa
__author__ = "Xun Li <lixun910@gmail.com>, Hang Zhang <zhanghanggis@163.com>"
'''
Changes:
1/21/2021 update quantile_lisa; add multiquantile_lisa
2/11/2021 update local_quantilelisa, local_multiquantilelisa
'''
[docs]def local_quantilelisa(w, data, k, q, **kwargs):
"""Quantile LISA Statistics
The function to apply quantile LISA statistics
Args:
w (Weight): A spatial Weights object
data (tuple/list/pandas.Series): A list of numeric values of selected variable
k (int): The number of quantiles, range[1, n-1]
q (int): The index of selected quantile for lisa, range[0, k-1]
undefs (list, optional): A list of boolean values to indicate which value is undefined or null
permutations (int, optional): The number of permutations for the LISA computation
permutation_method (str, optional): The permutation method used for the LISA computation. Options are {'complete', 'lookup-table'}. Default is 'complete'.
significance_cutoff (float, optional): A cutoff value for significance p-values to filter not-significant clusters
cpu_threads (int, optional): The number of cpu threads used for parallel LISA computation
seed (int, optional): The seed for random number generator
Returns:
lisa: An instance of lisa class represents the results of quantile lisa
"""
if w == None:
raise ValueError("Weights is None.")
if data is None:
raise ValueError("The data from selected variable is empty.")
if k == None or k < 1:
raise ValueError("The value of k needs to be greater than 1 ")
if q == None or q < 0:
raise ValueError("The value of q needs to be greater than 0")
elif q > k:
raise ValueError("The value of q needs to be smaller than or equal to k")
undefs = VecBool() if 'undefs' not in kwargs else kwargs['undefs']
significance_cutoff = 0.05 if 'significance_cutoff' not in kwargs else kwargs['significance_cutoff']
permutations = 999 if 'permutations' not in kwargs else kwargs['permutations']
permutation_method = 'complete' if 'permutation_method' not in kwargs else kwargs['permutation_method']
cpu_threads = 6 if 'cpu_threads' not in kwargs else kwargs['cpu_threads']
seed = 123456789 if 'seed' not in kwargs else kwargs['seed']
lisa_obj = gda_quantilelisa(w.gda_w, k, q, list(data), list(undefs), significance_cutoff, cpu_threads, permutations, permutation_method, seed)
return lisa(lisa_obj)
[docs]def local_multiquantilelisa(w, data, k, q, **kwargs):
"""Multivariate Quantile LISA Statistics
The function to apply multivariate quantile LISA statistics
Args:
w (Weight): A spatial Weights object
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']]
k (tuple): A tuple of "k" (int) values indicate the number of quantiles for each variable
q (tuple): A tuple of "q" (int) values indicate which quantile or interval for each variable used in local join count statistics
permutations (int, optional): The number of permutations for the LISA computation
permutation_method (str, optional): The permutation method used for the LISA computation. Options are {'complete', 'lookup-table'}. Default is 'complete'.
significance_cutoff (float, optional): A cutoff value for significance p-values to filter not-significant clusters
cpu_threads (int, optional): The number of cpu threads used for parallel LISA computation
seed (int, optional): The seed for random number generator
Returns:
lisa: An instance of lisa class represents the results of quantile lisa
"""
undefs = VecVecBool() if 'undefs' not in kwargs else kwargs['undefs']
significance_cutoff = 0.05 if 'significance_cutoff' not in kwargs else kwargs['significance_cutoff']
permutations = 999 if 'permutations' not in kwargs else kwargs['permutations']
permutation_method = 'complete' if 'permutation_method' not in kwargs else kwargs['permutation_method']
cpu_threads = 6 if 'cpu_threads' not in kwargs else kwargs['cpu_threads']
seed = 123456789 if 'seed' not in kwargs else kwargs['seed']
if w == None:
raise ValueError("Weights is None.")
if len(data) == 0:
raise ValueError("The input data can not be empty.")
if len(data) != len(k) or len(k) != len(q):
raise ValueError("The size of k, q and data are not matched.")
if type(data).__name__ == "DataFrame":
data = data.values.transpose().tolist()
lisa_obj = gda_multiquantilelisa(w.gda_w, k, q, data, undefs, significance_cutoff, cpu_threads, permutations, permutation_method, seed)
return lisa(lisa_obj)