import numpy
from .classifiers import (
BoxPlot,
EqualInterval,
FisherJenks,
FisherJenksSampled,
MaximumBreaks,
Quantiles,
StdMean,
UserDefined,
)
__all__ = ["Pooled"]
dispatcher = {
"boxplot": BoxPlot,
"equalinterval": EqualInterval,
"fisherjenks": FisherJenks,
"fisherjenkssampled": FisherJenksSampled,
"quantiles": Quantiles,
"maximumbreaks": MaximumBreaks,
"stdmean": StdMean,
"userdefined": UserDefined,
}
[docs]
class Pooled:
"""Applying global binning across columns.
Parameters
----------
Y : numpy.array
:math:`(n, m)`, values to classify, with :math:`m>1`.
classifier : str (default 'Quantiles')
Name of ``mapclassify.classifier`` to apply.
**kwargs : dict
Additional keyword arguments for classifier.
Attributes
----------
global_classifier : mapclassify.classifiers.MapClassifier
Instance of the pooled classifier defined as the classifier
applied to the union of the columns.
col_classifier : list
Elements are ``MapClassifier`` instances with the pooled classifier
applied to the associated column of ``Y``.
Examples
--------
>>> import mapclassify
>>> import numpy
>>> n = 20
>>> data = numpy.array([numpy.arange(n)+i*n for i in range(1,4)]).T
>>> res = mapclassify.Pooled(data)
>>> res.col_classifiers[0].counts.tolist()
[12, 8, 0, 0, 0]
>>> res.col_classifiers[1].counts.tolist()
[0, 4, 12, 4, 0]
>>> res.col_classifiers[2].counts.tolist()
[0, 0, 0, 8, 12]
>>> res.global_classifier.counts.tolist()
[12, 12, 12, 12, 12]
>>> res.global_classifier.bins == res.col_classifiers[0].bins
array([ True, True, True, True, True])
>>> res.global_classifier.bins
array([31.8, 43.6, 55.4, 67.2, 79. ])
"""
[docs]
def __init__(self, Y, classifier="Quantiles", **kwargs):
method = classifier.lower()
valid_methods = list(dispatcher.keys())
if method not in valid_methods:
raise ValueError(
f"'{classifier}' not a valid classifier. "
f"Currently supported classifiers: {valid_methods}"
)
self.__dict__.update(kwargs)
Y = numpy.asarray(Y)
n, cols = Y.shape
y = numpy.reshape(Y, (-1, 1), order="f")
ymin = y.min()
global_classifier = dispatcher[method](y, **kwargs)
# self.k = global_classifier.k
col_classifiers = []
name = f"Pooled {classifier}"
for c in range(cols):
res = UserDefined(Y[:, c], bins=global_classifier.bins, lowest=ymin)
res.name = name
col_classifiers.append(res)
self.col_classifiers = col_classifiers
self.global_classifier = global_classifier
self._summary()
def _summary(self):
self.classes = self.global_classifier.classes
self.tss = self.global_classifier.tss
self.adcm = self.global_classifier.adcm
self.gadf = self.global_classifier.gadf
def __str__(self):
s = "Pooled Classifier"
rows = [s]
for c in self.col_classifiers:
rows.append(c.table())
return "\n\n".join(rows)
def __repr__(self):
return self.__str__()