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classifiers.py
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classifiers.py
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"""
A module of classification schemes for choropleth mapping.
"""
import copy
import functools
import warnings
import numpy as np
import scipy.stats as stats
from sklearn.cluster import KMeans as KMEANS
__author__ = "Sergio J. Rey"
__all__ = [
"MapClassifier",
"quantile",
"BoxPlot",
"EqualInterval",
"FisherJenks",
"FisherJenksSampled",
"JenksCaspall",
"JenksCaspallForced",
"JenksCaspallSampled",
"HeadTailBreaks",
"MaxP",
"MaximumBreaks",
"NaturalBreaks",
"Quantiles",
"Percentiles",
"StdMean",
"UserDefined",
"gadf",
"KClassifiers",
"CLASSIFIERS",
]
CLASSIFIERS = (
"BoxPlot",
"EqualInterval",
"FisherJenks",
"FisherJenksSampled",
"HeadTailBreaks",
"JenksCaspall",
"JenksCaspallForced",
"JenksCaspallSampled",
"MaxP",
"MaximumBreaks",
"NaturalBreaks",
"Quantiles",
"Percentiles",
"StdMean",
"UserDefined",
)
K = 5 # default number of classes in any map scheme with this as an argument
SEEDRANGE = 1000000 # range for drawing random ints from for Natural Breaks
FMT = "{:.2f}"
try:
from numba import njit
HAS_NUMBA = True
except ImportError:
HAS_NUMBA = False
def njit(type, cache):
def decorator_njit(func):
@functools.wraps(func)
def wrapper_decorator(*args, **kwargs):
return func(*args, **kwargs)
return wrapper_decorator
return decorator_njit
def _format_intervals(mc, fmt="{:.0f}"):
"""
Helper methods to format legend intervals.
Parameters
----------
mc: MapClassifier
fmt: str (default '{:.0f}')
Specification of formatting for legend.
Returns
-------
tuple:
edges : list
:math:`k` strings for class intervals.
max_width : int
Length of largest interval string.
lower_open : bool
True: lower bound of first interval is open.
False: lower bound of first interval is closed.
Notes
-----
For some classifiers, it is possible that the upper bound of the first
interval is less than the minimum value of the attribute that is being
classified. In these cases ``lower_open=True`` and the lower bound of the
interval is set to ``numpy.NINF```.
"""
lowest = mc.y.min()
if hasattr(mc, "lowest"):
if mc.lowest is not None:
lowest = mc.lowest
lower_open = False
if lowest > mc.bins[0]:
lowest = np.NINF
lower_open = True
edges = [lowest]
edges.extend(mc.bins)
edges = [fmt.format(edge) for edge in edges]
max_width = max([len(edge) for edge in edges])
return edges, max_width, lower_open
def _get_mpl_labels(mc, fmt="{:.1f}"):
"""
Helper method to format legend intervals for matplotlib (and geopandas).
Parameters
----------
mc : MapClassifier
fmt : str (default '{:.1f}')
Specification of formatting for legend.
Returns
-------
intervals : list
:math:`k` strings for class intervals.
"""
edges, max_width, lower_open = _format_intervals(mc, fmt)
k = len(edges) - 1
left = ["["]
if lower_open:
left = ["("]
left.extend("(" * k)
right = "]" * (k + 1)
lower = ["{:>{width}}".format(edges[i], width=max_width) for i in range(k)]
upper = ["{:>{width}}".format(edges[i], width=max_width) for i in range(1, k + 1)]
lower = [_l + r for _l, r in zip(left, lower)]
upper = [_l + r for _l, r in zip(upper, right)]
intervals = [_l + ", " + r for _l, r in zip(lower, upper)]
return intervals
def _get_table(mc, fmt="{:.2f}"):
"""
Helper function to generate tabular classification report.
Parameters
----------
mc: MapClassifier
fmt: str (default '{:.2f}')
specification of formatting for legend.
Returns
-------
table : str
Formatted table of classification results.
"""
intervals = _get_mpl_labels(mc, fmt)
interval_width = len(intervals[0])
counts = list(map(str, mc.counts))
count_width = max([len(count) for count in counts])
count_width = max(count_width, len("count"))
interval_width = max(interval_width, len("interval"))
header = f"{'Interval' : ^{interval_width}}"
header += " " * 3 + f"{'Count' : >{count_width}}"
title = mc.name
header += "\n" + "-" * len(header)
table = [title, "", header]
for i, interval in enumerate(intervals):
row = f"{interval} | {counts[i] : >{count_width}}"
table.append(row)
return "\n".join(table)
def head_tail_breaks(values, cuts):
"""Head tail breaks helper function."""
values = np.array(values)
mean = np.mean(values)
if len(cuts) > 0 and cuts[-1] == mean: # this fixes floating point from GH#117
return cuts
cuts.append(mean)
if len(set(values)) > 1:
return head_tail_breaks(values[values > mean], cuts)
return cuts
def quantile(y, k=4):
"""
Calculates the quantiles for an array.
Parameters
----------
y : numpy.array
:math:`(n,1)`, values to classify.
k : int (default 4)
Number of quantiles.
Returns
-------
q : numpy.array
:math:`(n,1)`, quantile values.
Notes
-----
If there are enough ties that the quantile values repeat, we collapse to
pseudo quantiles in which case the number of classes will be less than ``k``.
Examples
--------
>>> import mapclassify
>>> import numpy
>>> x = numpy.arange(1000)
>>> mapclassify.classifiers.quantile(x)
array([249.75, 499.5 , 749.25, 999. ])
>>> mapclassify.classifiers.quantile(x, k=3)
array([333., 666., 999.])
"""
w = 100.0 / k
p = np.arange(w, 100 + w, w)
if p[-1] > 100.0:
p[-1] = 100.0
q = np.array([stats.scoreatpercentile(y, pct) for pct in p])
q = np.unique(q)
k_q = len(q)
if k_q < k:
warnings.warn(
f"Not enough unique values in array to form {k} classes. "
f"Setting k to {k_q}.",
UserWarning,
)
return q
def binC(y, bins):
"""
Bin categorical/qualitative data.
Parameters
----------
y : numpy.array
:math:`(n,q)`, categorical values.
bins : numpy.array
:math:`(k,1)`, unique values associated with each bin.
Return
------
b : numpy.array
:math:`(n,q)` bin membership, values between ``0`` and ``k-1``.
Examples
--------
>>> import numpy
>>> import mapclassify
>>> numpy.random.seed(1)
>>> x = numpy.random.randint(2, 8, (10, 3))
>>> bins = list(range(2, 8))
>>> x
array([[7, 5, 6],
[2, 3, 5],
[7, 2, 2],
[3, 6, 7],
[6, 3, 4],
[6, 7, 4],
[6, 5, 6],
[4, 6, 7],
[4, 6, 3],
[3, 2, 7]])
>>> y = mapclassify.classifiers.binC(x, bins)
>>> y
array([[5, 3, 4],
[0, 1, 3],
[5, 0, 0],
[1, 4, 5],
[4, 1, 2],
[4, 5, 2],
[4, 3, 4],
[2, 4, 5],
[2, 4, 1],
[1, 0, 5]])
"""
if np.ndim(y) == 1:
k = 1
n = np.shape(y)[0]
else:
n, k = np.shape(y)
b = np.zeros((n, k), dtype="int")
for i, bin in enumerate(bins):
b[np.nonzero(y == bin)] = i
# check for non-binned items and warn if needed
vals = set(y.flatten())
for val in vals:
if val not in bins:
warnings.warn(f"\nValue not in bin: {val}\nBins: {bins}", UserWarning)
return b
def bin(y, bins):
"""
Bin interval/ratio data.
Parameters
----------
y : numpy.array
:math:`(n,q)`, values to bin.
bins : numpy.array
:math:`(k,1)`, upper bounds of each bin (monotonic).
Returns
-------
b : numpy.array
:math:`(n,q)`, values of values between ``0`` and ``k-1``.
Examples
--------
>>> import numpy
>>> import mapclassify
>>> numpy.random.seed(1)
>>> x = numpy.random.randint(2, 20, (10, 3))
>>> bins = [10, 15, 20]
>>> b = mapclassify.classifiers.bin(x, bins)
>>> x
array([[ 7, 13, 14],
[10, 11, 13],
[ 7, 17, 2],
[18, 3, 14],
[ 9, 15, 8],
[ 7, 13, 12],
[16, 6, 11],
[19, 2, 15],
[11, 11, 9],
[ 3, 2, 19]])
>>> b
array([[0, 1, 1],
[0, 1, 1],
[0, 2, 0],
[2, 0, 1],
[0, 1, 0],
[0, 1, 1],
[2, 0, 1],
[2, 0, 1],
[1, 1, 0],
[0, 0, 2]])
"""
if np.ndim(y) == 1:
k = 1
n = np.shape(y)[0]
else:
n, k = np.shape(y)
b = np.zeros((n, k), dtype="int")
i = len(bins)
if type(bins) != list:
bins = bins.tolist()
binsc = copy.copy(bins)
while binsc:
i -= 1
c = binsc.pop(-1)
b[np.nonzero(y <= c)] = i
return b
def bin1d(x, bins):
"""
Place values of a 1-d array into bins and determine
counts of values in each bin.
Parameters
----------
x : numpy.array
:math:`(n, 1)`, values to bin.
bins : numpy.array
:math:`(k,1)`, upper bounds of each bin (monotonic).
Returns
-------
binIds : numpy.array
1-d array of integer bin IDs.
counts : int
Number of elements of ``x`` falling in each bin.
Examples
--------
>>> import numpy
>>> import mapclassify
>>> x = numpy.arange(100, dtype = "float")
>>> bins = [25, 74, 100]
>>> binIds, counts = mapclassify.classifiers.bin1d(x, bins)
>>> binIds
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
>>> list(counts)
[26, 49, 25]
"""
left = [-float("inf")]
left.extend(bins[0:-1])
right = bins
cuts = list(zip(left, right))
k = len(bins)
binIds = np.zeros(x.shape, dtype="int")
while cuts:
k -= 1
l, r = cuts.pop(-1)
binIds += (x > l) * (x <= r) * k
counts = np.bincount(binIds, minlength=len(bins))
return (binIds, counts)
def load_example():
"""
Helper function for doc tests
"""
from .datasets import calemp
return calemp.load()
def _kmeans(y, k=5, n_init=10):
"""
Helper function to do k-means in one dimension.
Parameters
----------
y : numpy.array
:math:`(n,1)`, values to classify.
k : int, (default 5)
Number of classes to form.
n_init : int, (default 10)
Number of initial solutions. Best of initial results is returned.
"""
y = y * 1.0 # KMEANS needs float or double dtype
y.shape = (-1, 1)
result = KMEANS(n_clusters=k, init="k-means++", n_init=n_init).fit(y)
class_ids = result.labels_
centroids = result.cluster_centers_
binning = []
for c in range(k):
values = y[class_ids == c]
binning.append([values.max(), len(values)])
binning = np.array(binning)
binning = binning[binning[:, 0].argsort()]
cuts = binning[:, 0]
y_cent = np.zeros_like(y)
for c in range(k):
y_cent[class_ids == c] = centroids[c]
diffs = y - y_cent
diffs *= diffs
return class_ids, cuts, diffs.sum(), centroids
def natural_breaks(values, k=5, init=10):
"""
Natural breaks helper function. Jenks natural breaks is k-means in one dimension.
Parameters
----------
values : numpy.array
:math:`(n, 1)` values to bin.
k : int, (default 5)
Number of classes.
init: int, (default 10)
Number of different solutions to obtain using different centroids.
Best solution is returned.
"""
values = np.array(values)
uv = np.unique(values)
uvk = len(uv)
if uvk < k:
warnings.warn(
f"Not enough unique values in array to form {k} classes. "
f"Setting k to {uvk}.",
UserWarning,
)
k = uvk
kres = _kmeans(values, k, n_init=init)
sids = kres[-1] # centroids
fit = kres[-2]
class_ids = kres[0]
cuts = kres[1]
return (sids, class_ids, fit, cuts)
@njit("f8[:](f8[:], u2)", cache=True)
def _fisher_jenks_means(values, classes=5):
"""
Jenks Optimal (Natural Breaks) algorithm implemented in Python.
Notes
-----
The original Python code comes from here:
http://danieljlewis.org/2010/06/07/jenks-natural-breaks-algorithm-in-python/
and is based on a JAVA and Fortran code available here:
https://stat.ethz.ch/pipermail/r-sig-geo/2006-March/000811.html
Returns class breaks such that classes are internally homogeneous while
assuring heterogeneity among classes.
"""
n_data = len(values)
mat1 = np.zeros((n_data + 1, classes + 1), dtype=np.int32)
mat2 = np.zeros((n_data + 1, classes + 1), dtype=np.float32)
mat1[1, 1:] = 1
mat2[2:, 1:] = np.inf
v = np.float32(0)
for _l in range(2, len(values) + 1):
s1 = np.float32(0)
s2 = np.float32(0)
w = np.float32(0)
for m in range(1, _l + 1):
i3 = _l - m + 1
val = np.float32(values[i3 - 1])
s2 += val * val
s1 += val
w += np.float32(1)
v = s2 - (s1 * s1) / w
i4 = i3 - 1
if i4 != 0:
for j in range(2, classes + 1):
if mat2[_l, j] >= (v + mat2[i4, j - 1]):
mat1[_l, j] = i3
mat2[_l, j] = v + mat2[i4, j - 1]
mat1[_l, 1] = 1
mat2[_l, 1] = v
k = len(values)
kclass = np.zeros(classes + 1, dtype=values.dtype)
kclass[classes] = values[len(values) - 1]
kclass[0] = values[0]
for countNum in range(classes, 1, -1):
pivot = mat1[k, countNum]
id = int(pivot - 2)
kclass[countNum - 1] = values[id]
k = int(pivot - 1)
return np.delete(kclass, 0)
class MapClassifier(object):
r"""
Abstract class for all map classifications :cite:`Slocum_2009`
For an array :math:`y` of :math:`n` values, a map classifier places each
value :math:`y_i` into one of :math:`k` mutually exclusive and exhaustive
classes. Each classifer defines the classes based on different criteria,
but in all cases the following hold for the classifiers in PySAL:
.. math:: C_j^l < y_i \le C_j^u \ \forall i \in C_j
where :math:`C_j` denotes class :math:`j` which has lower bound
:math:`C_j^l` and upper bound :math:`C_j^u`.
Map Classifiers Supported
* :class:`mapclassify.classifiers.BoxPlot`
* :class:`mapclassify.classifiers.EqualInterval`
* :class:`mapclassify.classifiers.FisherJenks`
* :class:`mapclassify.classifiers.FisherJenksSampled`
* :class:`mapclassify.classifiers.HeadTailBreaks`
* :class:`mapclassify.classifiers.JenksCaspall`
* :class:`mapclassify.classifiers.JenksCaspallForced`
* :class:`mapclassify.classifiers.JenksCaspallSampled`
* :class:`mapclassify.classifiers.MaxP`
* :class:`mapclassify.classifiers.MaximumBreaks`
* :class:`mapclassify.classifiers.NaturalBreaks`
* :class:`mapclassify.classifiers.Quantiles`
* :class:`mapclassify.classifiers.Percentiles`
* :class:`mapclassify.classifiers.StdMean`
* :class:`mapclassify.classifiers.UserDefined`
In addition to the classifiers, there are several utility functions that
can be used to evaluate the properties of a specific classifier,
or for automatic selection of a classifier and number of classes.
* :func:`mapclassify.classifiers.gadf`
* :class:`mapclassify.classifiers.K_classifiers`
"""
def __init__(self, y):
y = np.asarray(y).flatten()
self.name = "Map Classifier"
self.fmt = FMT
self.y = y
self._classify()
self._summary()
def get_fmt(self):
return self._fmt
def set_fmt(self, fmt):
self._fmt = fmt
fmt = property(get_fmt, set_fmt)
def _summary(self):
yb = self.yb
self.classes = [np.nonzero(yb == c)[0].tolist() for c in range(self.k)]
self.tss = self.get_tss()
self.adcm = self.get_adcm()
self.gadf = self.get_gadf()
def _classify(self):
self._set_bins()
self.yb, self.counts = bin1d(self.y, self.bins)
def _update(self, data, *args, **kwargs):
"""
The only thing that *should* happen in this function is
1. input sanitization for pandas
2. classification/reclassification.
Using their ``__init__`` methods, all classifiers can re-classify given
different input parameters or additional data.
If you've got a cleverer updating equation other than the intial estimation
equation, remove the call to ``self.__init__`` below and replace it with
the updating function.
"""
if data is not None:
data = np.asarray(data).flatten()
data = np.append(data.flatten(), self.y)
else:
data = self.y
self.__init__(data, *args, **kwargs)
@classmethod
def make(cls, *args, **kwargs):
"""
Configure and create a classifier that will consume data and produce
classifications, given the configuration options specified by this
function.
Note that this implements a *partial application* of the relevant class
constructor. ``make`` creates a function that returns classifications; it
does not actually do the classification.
If you want to classify data directly, use the appropriate class
constructor, like ``Quantiles``, ``Max_Breaks``, etc.
If you *have* a classifier object, but want to find which bins new data
falls into, use ``find_bin``.
Parameters
----------
*args : required positional arguments
All positional arguments required by the classifier,
**excluding** the input data.
rolling : bool
A boolean configuring the outputted classifier to use
a rolling classifier rather than a new classifier for
each input. If ``rolling``, this adds the current data to
all of the previous data in the classifier, and
rebalances the bins, like a running median computation.
return_object : bool
Return the classifier object (or not).
return_bins : bool
Return the bins/breaks (or not).
return_counts : bool
Return the histogram of objects falling into each bin (or not).
Returns
-------
A function that consumes data and returns their bins (and object,
bins/breaks, or counts, if requested).
Notes
-----
This is most useful when you want to run a classifier many times
with a given configuration, such as when classifying many columns of an
array or dataframe using the same configuration.
Examples
--------
>>> import libpysal
>>> import mapclassify
>>> import geopandas
>>> import numpy
>>> import pandas
>>> df = geopandas.read_file(libpysal.examples.get_path("columbus.dbf"))
>>> classifier = mapclassify.Quantiles.make(k=9)
>>> cl = df[["HOVAL", "CRIME", "INC"]].apply(classifier)
>>> cl["HOVAL"].values[:10]
array([8, 7, 2, 4, 1, 3, 8, 5, 7, 8])
>>> cl["CRIME"].values[:10]
array([0, 1, 3, 4, 6, 2, 0, 5, 3, 4])
>>> cl["INC"].values[:10]
array([7, 8, 5, 0, 3, 5, 0, 3, 6, 4])
>>> data = [
... numpy.linspace(3,8,num=10),
... numpy.linspace(10, 0, num=10),
... numpy.linspace(-5, 15, num=10)
... ]
>>> data = pandas.DataFrame(data).T
>>> data
0 1 2
0 3.000000 10.000000 -5.000000
1 3.555556 8.888889 -2.777778
2 4.111111 7.777778 -0.555556
3 4.666667 6.666667 1.666667
4 5.222222 5.555556 3.888889
5 5.777778 4.444444 6.111111
6 6.333333 3.333333 8.333333
7 6.888889 2.222222 10.555556
8 7.444444 1.111111 12.777778
9 8.000000 0.000000 15.000000
>>> data.apply(mapclassify.Quantiles.make(rolling=True))
0 1 2
0 0 4 0
1 0 4 0
2 1 4 0
3 1 3 0
4 2 2 1
5 2 1 2
6 3 1 4
7 3 0 4
8 4 0 4
9 4 0 4
>>> dbf = libpysal.io.open(libpysal.examples.get_path("baltim.dbf"))
>>> data = dbf.by_col_array("PRICE", "LOTSZ", "SQFT")
>>> my_bins = [1, 10, 20, 40, 80]
>>> cl = [mapclassify.UserDefined.make(bins=my_bins)(a) for a in data.T]
>>> len(cl)
3
>>> cl[0][:10]
array([4, 5, 5, 5, 4, 4, 5, 4, 4, 5])
"""
# only flag overrides return flag
to_annotate = copy.deepcopy(kwargs)
return_object = kwargs.pop("return_object", False)
return_bins = kwargs.pop("return_bins", False)
return_counts = kwargs.pop("return_counts", False)
rolling = kwargs.pop("rolling", False)
if rolling:
# just initialize a fake classifier
data = list(range(10))
cls_instance = cls(data, *args, **kwargs)
# and empty it, since we'll be using the update
cls_instance.y = np.array([])
else:
cls_instance = None
# wrap init in a closure to make a consumer.
# Qc Na: "Objects/Closures are poor man's Closures/Objects"
def classifier(data, cls_instance=cls_instance):
if rolling:
cls_instance.update(data, inplace=True, **kwargs)
yb = cls_instance.find_bin(data)
else:
cls_instance = cls(data, *args, **kwargs)
yb = cls_instance.yb
outs = [yb, None, None, None]
outs[1] = cls_instance if return_object else None
outs[2] = cls_instance.bins if return_bins else None
outs[3] = cls_instance.counts if return_counts else None
outs = [a for a in outs if a is not None]
if len(outs) == 1:
return outs[0]
else:
return outs
# for debugging/jic, keep around the kwargs.
# in future, we might want to make this a thin class, so that we can
# set a custom repr. Call the class `Binner` or something, that's a
# pre-configured Classifier that just consumes data, bins it, &
# possibly updates the bins.
classifier._options = to_annotate
return classifier
def update(self, y=None, inplace=False, **kwargs):
"""
Add data or change classification parameters.
Parameters
----------
y : numpy.array (default None)
:math:`(n,1)`, array of data to classify.
inplace : bool (default False)
Whether to conduct the update in place or to return a
copy estimated from the additional specifications.
**kwargs : dict
Additional parameters that are passed to the ``__init__`` function
of the class. For documentation, check the class constructor.
"""
kwargs.update({"k": kwargs.pop("k", self.k)})
if inplace:
self._update(y, **kwargs)
else:
new = copy.deepcopy(self)
new._update(y, **kwargs)
return new
def __str__(self):
return self.table()
def __repr__(self):
return self.table()
def table(self):
fmt = self.fmt
return _get_table(self, fmt=fmt)
def __call__(self, *args, **kwargs):
"""
This will allow the classifier to be called like it's a function.
Whether or not we want to make this be ``find_bin`` or ``update`` is a
design decision.
I like this as ``find_bin``, since a classifier's job should be to classify
the data given to it using the rules estimated from the ``_classify()``.
function.
"""
return self.find_bin(*args)
def get_tss(self):
"""Returns sum of squares over all class means."""
tss = 0
for class_def in self.classes:
if len(class_def) > 0:
yc = self.y[class_def]
css = yc - yc.mean()
css *= css
tss += sum(css)
return tss
def _set_bins(self):
pass
def get_adcm(self):
"""
Absolute deviation around class median (*ADCM*).
Calculates the absolute deviations of each observation about its class
median as a measure of fit for the classification method.
Returns sum of *ADCM* over all classes.
"""
adcm = 0
for class_def in self.classes:
if len(class_def) > 0:
yc = self.y[class_def]
yc_med = np.median(yc)
ycd = np.abs(yc - yc_med)
adcm += sum(ycd)
return adcm
def get_gadf(self):
"""Goodness of absolute deviation of fit."""
adam = (np.abs(self.y - np.median(self.y))).sum()
if adam == 0: # array is invariant
gadf = 1
else:
gadf = 1 - self.adcm / adam
return gadf
def find_bin(self, x):
"""
Sort input or inputs according to the current bin estimate.
Parameters
----------
x : numpy.array, int, float
A value or array of values to fit within the estimated bins.
Returns
-------
right : numpy.array, int
A bin index or array of bin indices that classify the
input into one of the classifiers' bins.
Notes
-----
This differs from similar functionality in
``numpy.digitize(x, classi.bins, right=True)``.
This will always provide the closest bin, so data "outside" the classifier,
above and below the max/min breaks, will be classified into the nearest bin.
``numpy.digitize`` returns :math:`k+1` for data greater than the greatest bin,
but retains 0 for data below the lowest bin.
"""
x = np.asarray(x).flatten()
right = np.digitize(x, self.bins, right=True)
if right.max() == len(self.bins):
right[right == len(self.bins)] = len(self.bins) - 1
return right
def get_legend_classes(self, fmt=FMT):
"""
Format the strings for the classes on the legend.
Parameters
----------
fmt : str (default '{:.2f}')
Formatting specification.
Returns
-------
classes : list
:math:`k` strings with class interval definitions.
"""
return _get_mpl_labels(self, fmt)
def plot(
self,
gdf,
border_color="lightgray",
border_width=0.10,
title=None,
legend=False,
cmap="YlGnBu",
axis_on=True,
legend_kwds={"loc": "lower right", "fmt": FMT},
file_name=None,
dpi=600,
ax=None,
):
"""
Plot a mapclassifier object.