"""Module for functional boxplots."""
from statsmodels.compat.python import range, zip
from statsmodels.compat.scipy import factorial
from statsmodels.multivariate.pca import PCA
from statsmodels.nonparametric.kernel_density import KDEMultivariate
from statsmodels.graphics.utils import _import_mpl
from collections import OrderedDict
from itertools import combinations
import numpy as np
try:
from scipy.optimize import differential_evolution, brute, fmin
have_de_optim = True
except ImportError:
from scipy.optimize import brute, fmin
have_de_optim = False
from multiprocessing import Pool
import itertools
from . import utils
__all__ = ['hdrboxplot', 'fboxplot', 'rainbowplot', 'banddepth']
class HdrResults(object):
"""Wrap results and pretty print them."""
def __init__(self, kwds):
self.__dict__.update(kwds)
def __repr__(self):
msg = ("HDR boxplot summary:\n"
"-> median:\n{}\n"
"-> 50% HDR (max, min):\n{}\n"
"-> 90% HDR (max, min):\n{}\n"
"-> Extra quantiles (max, min):\n{}\n"
"-> Outliers:\n{}\n"
"-> Outliers indices:\n{}\n"
).format(self.median, self.hdr_50, self.hdr_90,
self.extra_quantiles, self.outliers, self.outliers_idx)
return msg
def _inverse_transform(pca, data):
"""
Inverse transform on PCA.
Use PCA's `project` method by temporary replacing its factors with
`data`.
Parameters
----------
pca : statsmodels Principal Component Analysis instance
The PCA object to use.
data : sequence of ndarrays or 2-D ndarray
The vectors of functions to create a functional boxplot from. If a
sequence of 1-D arrays, these should all be the same size.
The first axis is the function index, the second axis the one along
which the function is defined. So ``data[0, :]`` is the first
functional curve.
Returns
-------
projection : array
nobs by nvar array of the projection onto ncomp factors
"""
factors = pca.factors
pca.factors = data.reshape(-1, factors.shape[1])
projection = pca.project()
pca.factors = factors
return projection
def _curve_constrained(x, idx, sign, band, pca, ks_gaussian):
"""Find out if the curve is within the band.
The curve value at :attr:`idx` for a given PDF is only returned if
within bounds defined by the band. Otherwise, 1E6 is returned.
Parameters
----------
x : float
Curve in reduced space.
idx : int
Index value of the components to compute.
sign : int
Return positive or negative value.
band : list of float
PDF values `[min_pdf, max_pdf]` to be within.
pca : statsmodels Principal Component Analysis instance
The PCA object to use.
ks_gaussian : KDEMultivariate instance
Returns
-------
value : float
Curve value at `idx`.
"""
x = x.reshape(1, -1)
pdf = ks_gaussian.pdf(x)
if band[0] < pdf < band[1]:
value = sign * _inverse_transform(pca, x)[0][idx]
else:
value = 1E6
return value
def _min_max_band(args):
"""
Min and max values at `idx`.
Global optimization to find the extrema per component.
Parameters
----------
args: list
It is a list of an idx and other arguments as a tuple:
idx : int
Index value of the components to compute
The tuple contains:
band : list of float
PDF values `[min_pdf, max_pdf]` to be within.
pca : statsmodels Principal Component Analysis instance
The PCA object to use.
bounds : sequence
``(min, max)`` pair for each components
ks_gaussian : KDEMultivariate instance
Returns
-------
band : tuple of float
``(max, min)`` curve values at `idx`
"""
idx, (band, pca, bounds, ks_gaussian, use_brute, seed) = args
if have_de_optim and not use_brute:
max_ = differential_evolution(_curve_constrained, bounds=bounds,
args=(idx, -1, band, pca, ks_gaussian),
maxiter=7, seed=seed).x
min_ = differential_evolution(_curve_constrained, bounds=bounds,
args=(idx, 1, band, pca, ks_gaussian),
maxiter=7, seed=seed).x
else:
max_ = brute(_curve_constrained, ranges=bounds, finish=fmin,
args=(idx, -1, band, pca, ks_gaussian))
min_ = brute(_curve_constrained, ranges=bounds, finish=fmin,
args=(idx, 1, band, pca, ks_gaussian))
band = (_inverse_transform(pca, max_)[0][idx],
_inverse_transform(pca, min_)[0][idx])
return band
[docs]def hdrboxplot(data, ncomp=2, alpha=None, threshold=0.95, bw=None,
xdata=None, labels=None, ax=None, use_brute=False, seed=None):
"""
High Density Region boxplot
Parameters
----------
data : sequence of ndarrays or 2-D ndarray
The vectors of functions to create a functional boxplot from. If a
sequence of 1-D arrays, these should all be the same size.
The first axis is the function index, the second axis the one along
which the function is defined. So ``data[0, :]`` is the first
functional curve.
ncomp : int, optional
Number of components to use. If None, returns the as many as the
smaller of the number of rows or columns in data.
alpha : list of floats between 0 and 1, optional
Extra quantile values to compute. Default is None
threshold : float between 0 and 1, optional
Percentile threshold value for outliers detection. High value means
a lower sensitivity to outliers. Default is `0.95`.
bw: array_like or str, optional
If an array, it is a fixed user-specified bandwidth. If `None`, set to
`normal_reference`. If a string, should be one of:
- normal_reference: normal reference rule of thumb (default)
- cv_ml: cross validation maximum likelihood
- cv_ls: cross validation least squares
xdata : ndarray, optional
The independent variable for the data. If not given, it is assumed to
be an array of integers 0..N-1 with N the length of the vectors in
`data`.
labels : sequence of scalar or str, optional
The labels or identifiers of the curves in `data`. If not given,
outliers are labeled in the plot with array indices.
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being
created.
use_brute : bool
Use the brute force optimizer instead of the default differential
evolution to find the curves. Default is False.
seed : {None, int, np.random.RandomState}
Seed value to pass to scipy.optimize.differential_evolution. Can be an
integer or RandomState instance. If None, then the default RandomState
provided by np.random is used.
Returns
-------
fig : Matplotlib figure instance
If `ax` is None, the created figure. Otherwise the figure to which
`ax` is connected.
hdr_res : HdrResults instance
An `HdrResults` instance with the following attributes:
- 'median', array. Median curve.
- 'hdr_50', array. 50% quantile band. [sup, inf] curves
- 'hdr_90', list of array. 90% quantile band. [sup, inf]
curves.
- 'extra_quantiles', list of array. Extra quantile band.
[sup, inf] curves.
- 'outliers', ndarray. Outlier curves.
Notes
-----
The median curve is the curve with the highest probability on the reduced
space of a Principal Component Analysis (PCA).
Outliers are defined as curves that fall outside the band corresponding
to the quantile given by `threshold`.
The non-outlying region is defined as the band made up of all the
non-outlying curves.
Behind the scene, the dataset is represented as a matrix. Each line
corresponding to a 1D curve. This matrix is then decomposed using Principal
Components Analysis (PCA). This allows to represent the data using a finite
number of modes, or components. This compression process allows to turn the
functional representation into a scalar representation of the matrix. In
other words, you can visualize each curve from its components. Each curve
is thus a point in this reduced space. With 2 components, this is called a
bivariate plot (2D plot).
In this plot, if some points are adjacent (similar components), it means
that back in the original space, the curves are similar. Then, finding the
median curve means finding the higher density region (HDR) in the reduced
space. Moreover, the more you get away from this HDR, the more the curve is
unlikely to be similar to the other curves.
Using a kernel smoothing technique, the probability density function (PDF)
of the multivariate space can be recovered. From this PDF, it is possible
to compute the density probability linked to the cluster of points and plot
its contours.
Finally, using these contours, the different quantiles can be extracted
along with the median curve and the outliers.
Steps to produce the HDR boxplot include:
1. Compute a multivariate kernel density estimation
2. Compute contour lines for quantiles 90%, 50% and `alpha` %
3. Plot the bivariate plot
4. Compute median curve along with quantiles and outliers curves.
References
----------
[1] R.J. Hyndman and H.L. Shang, "Rainbow Plots, Bagplots, and Boxplots for
Functional Data", vol. 19, pp. 29-45, 2010.
Examples
--------
Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea
surface temperature data.
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
>>> data = sm.datasets.elnino.load(as_pandas=False)
Create a functional boxplot. We see that the years 1982-83 and 1997-98 are
outliers; these are the years where El Nino (a climate pattern
characterized by warming up of the sea surface and higher air pressures)
occurred with unusual intensity.
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> res = sm.graphics.hdrboxplot(data.raw_data[:, 1:],
... labels=data.raw_data[:, 0].astype(int),
... ax=ax)
>>> ax.set_xlabel("Month of the year")
>>> ax.set_ylabel("Sea surface temperature (C)")
>>> ax.set_xticks(np.arange(13, step=3) - 1)
>>> ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"])
>>> ax.set_xlim([-0.2, 11.2])
>>> plt.show()
.. plot:: plots/graphics_functional_hdrboxplot.py
See Also
--------
banddepth, rainbowplot, fboxplot
"""
fig, ax = utils.create_mpl_ax(ax)
if labels is None:
# For use with pandas, get the labels
if hasattr(data, 'index'):
labels = data.index
else:
labels = np.arange(len(data))
data = np.asarray(data)
if xdata is None:
xdata = np.arange(data.shape[1])
n_samples, dim = data.shape
# PCA and bivariate plot
pca = PCA(data, ncomp=ncomp)
data_r = pca.factors
# Create gaussian kernel
ks_gaussian = KDEMultivariate(data_r, bw=bw,
var_type='c' * data_r.shape[1])
# Boundaries of the n-variate space
bounds = np.array([data_r.min(axis=0), data_r.max(axis=0)]).T
# Compute contour line of pvalue linked to a given probability level
if alpha is None:
alpha = [threshold, 0.9, 0.5]
else:
alpha.extend([threshold, 0.9, 0.5])
alpha = list(set(alpha))
alpha.sort(reverse=True)
n_quantiles = len(alpha)
pdf_r = ks_gaussian.pdf(data_r).flatten()
pvalues = [np.percentile(pdf_r, (1 - alpha[i]) * 100,
interpolation='linear')
for i in range(n_quantiles)]
# Find mean, outliers curves
if have_de_optim and not use_brute:
median = differential_evolution(lambda x: - ks_gaussian.pdf(x),
bounds=bounds, maxiter=5, seed=seed).x
else:
median = brute(lambda x: - ks_gaussian.pdf(x),
ranges=bounds, finish=fmin)
outliers_idx = np.where(pdf_r < pvalues[alpha.index(threshold)])[0]
labels_outlier = [labels[i] for i in outliers_idx]
outliers = data[outliers_idx]
# Find HDR given some quantiles
def _band_quantiles(band, use_brute=use_brute, seed=seed):
"""
Find extreme curves for a quantile band.
From the `band` of quantiles, the associated PDF extrema values
are computed. If `min_alpha` is not provided (single quantile value),
`max_pdf` is set to `1E6` in order not to constrain the problem on high
values.
An optimization is performed per component in order to find the min and
max curves. This is done by comparing the PDF value of a given curve
with the band PDF.
Parameters
----------
band : array_like
alpha values ``(max_alpha, min_alpha)`` ex: ``[0.9, 0.5]``
use_brute : bool
Use the brute force optimizer instead of the default differential
evolution to find the curves. Default is False.
seed : {None, int, np.random.RandomState}
Seed value to pass to scipy.optimize.differential_evolution. Can
be an integer or RandomState instance. If None, then the default
RandomState provided by np.random is used.
Returns
-------
band_quantiles : list of 1-D array
``(max_quantile, min_quantile)`` (2, n_features)
"""
min_pdf = pvalues[alpha.index(band[0])]
try:
max_pdf = pvalues[alpha.index(band[1])]
except IndexError:
max_pdf = 1E6
band = [min_pdf, max_pdf]
pool = Pool()
data = zip(range(dim), itertools.repeat((band, pca,
bounds, ks_gaussian,
seed, use_brute)))
band_quantiles = pool.map(_min_max_band, data)
pool.terminate()
pool.close()
band_quantiles = list(zip(*band_quantiles))
return band_quantiles
extra_alpha = [i for i in alpha
if 0.5 != i and 0.9 != i and threshold != i]
if len(extra_alpha) > 0:
extra_quantiles = []
for x in extra_alpha:
for y in _band_quantiles([x], use_brute=use_brute, seed=seed):
extra_quantiles.append(y)
else:
extra_quantiles = []
# Inverse transform from n-variate plot to dataset dataset's shape
median = _inverse_transform(pca, median)[0]
hdr_90 = _band_quantiles([0.9, 0.5], use_brute=use_brute, seed=seed)
hdr_50 = _band_quantiles([0.5], use_brute=use_brute, seed=seed)
hdr_res = HdrResults({
"median": median,
"hdr_50": hdr_50,
"hdr_90": hdr_90,
"extra_quantiles": extra_quantiles,
"outliers": outliers,
"outliers_idx": outliers_idx
})
# Plots
ax.plot(np.array([xdata] * n_samples).T, data.T,
c='c', alpha=.1, label=None)
ax.plot(xdata, median, c='k', label='Median')
fill_betweens = []
fill_betweens.append(ax.fill_between(xdata, *hdr_50, color='gray',
alpha=.4, label='50% HDR'))
fill_betweens.append(ax.fill_between(xdata, *hdr_90, color='gray',
alpha=.3, label='90% HDR'))
if len(extra_quantiles) != 0:
ax.plot(np.array([xdata] * len(extra_quantiles)).T,
np.array(extra_quantiles).T,
c='y', ls='-.', alpha=.4, label='Extra quantiles')
if len(outliers) != 0:
for ii, outlier in enumerate(outliers):
if labels_outlier is None:
label = 'Outliers'
else:
label = str(labels_outlier[ii])
ax.plot(xdata, outlier, ls='--', alpha=0.7, label=label)
handles, labels = ax.get_legend_handles_labels()
# Proxy artist for fill_between legend entry
# See http://matplotlib.org/1.3.1/users/legend_guide.html
plt = _import_mpl()
for label, fill_between in zip(['50% HDR', '90% HDR'], fill_betweens):
p = plt.Rectangle((0, 0), 1, 1,
fc=fill_between.get_facecolor()[0])
handles.append(p)
labels.append(label)
by_label = OrderedDict(zip(labels, handles))
if len(outliers) != 0:
by_label.pop('Median')
by_label.pop('50% HDR')
by_label.pop('90% HDR')
ax.legend(by_label.values(), by_label.keys(), loc='best')
return fig, hdr_res
[docs]def fboxplot(data, xdata=None, labels=None, depth=None, method='MBD',
wfactor=1.5, ax=None, plot_opts=None):
"""
Plot functional boxplot.
A functional boxplot is the analog of a boxplot for functional data.
Functional data is any type of data that varies over a continuum, i.e.
curves, probabillity distributions, seasonal data, etc.
The data is first ordered, the order statistic used here is `banddepth`.
Plotted are then the median curve, the envelope of the 50% central region,
the maximum non-outlying envelope and the outlier curves.
Parameters
----------
data : sequence of ndarrays or 2-D ndarray
The vectors of functions to create a functional boxplot from. If a
sequence of 1-D arrays, these should all be the same size.
The first axis is the function index, the second axis the one along
which the function is defined. So ``data[0, :]`` is the first
functional curve.
xdata : ndarray, optional
The independent variable for the data. If not given, it is assumed to
be an array of integers 0..N-1 with N the length of the vectors in
`data`.
labels : sequence of scalar or str, optional
The labels or identifiers of the curves in `data`. If given, outliers
are labeled in the plot.
depth : ndarray, optional
A 1-D array of band depths for `data`, or equivalent order statistic.
If not given, it will be calculated through `banddepth`.
method : {'MBD', 'BD2'}, optional
The method to use to calculate the band depth. Default is 'MBD'.
wfactor : float, optional
Factor by which the central 50% region is multiplied to find the outer
region (analog of "whiskers" of a classical boxplot).
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being
created.
plot_opts : dict, optional
A dictionary with plotting options. Any of the following can be
provided, if not present in `plot_opts` the defaults will be used::
- 'cmap_outliers', a Matplotlib LinearSegmentedColormap instance.
- 'c_inner', valid MPL color. Color of the central 50% region
- 'c_outer', valid MPL color. Color of the non-outlying region
- 'c_median', valid MPL color. Color of the median.
- 'lw_outliers', scalar. Linewidth for drawing outlier curves.
- 'lw_median', scalar. Linewidth for drawing the median curve.
- 'draw_nonout', bool. If True, also draw non-outlying curves.
Returns
-------
fig : Matplotlib figure instance
If `ax` is None, the created figure. Otherwise the figure to which
`ax` is connected.
depth : ndarray
1-D array containing the calculated band depths of the curves.
ix_depth : ndarray
1-D array of indices needed to order curves (or `depth`) from most to
least central curve.
ix_outliers : ndarray
1-D array of indices of outlying curves in `data`.
See Also
--------
banddepth, rainbowplot
Notes
-----
The median curve is the curve with the highest band depth.
Outliers are defined as curves that fall outside the band created by
multiplying the central region by `wfactor`. Note that the range over
which they fall outside this band doesn't matter, a single data point
outside the band is enough. If the data is noisy, smoothing may therefore
be required.
The non-outlying region is defined as the band made up of all the
non-outlying curves.
References
----------
[1] Y. Sun and M.G. Genton, "Functional Boxplots", Journal of Computational
and Graphical Statistics, vol. 20, pp. 1-19, 2011.
[2] R.J. Hyndman and H.L. Shang, "Rainbow Plots, Bagplots, and Boxplots for
Functional Data", vol. 19, pp. 29-45, 2010.
Examples
--------
Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea
surface temperature data.
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
>>> data = sm.datasets.elnino.load(as_pandas=False)
Create a functional boxplot. We see that the years 1982-83 and 1997-98 are
outliers; these are the years where El Nino (a climate pattern
characterized by warming up of the sea surface and higher air pressures)
occurred with unusual intensity.
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> res = sm.graphics.fboxplot(data.raw_data[:, 1:], wfactor=2.58,
... labels=data.raw_data[:, 0].astype(int),
... ax=ax)
>>> ax.set_xlabel("Month of the year")
>>> ax.set_ylabel("Sea surface temperature (C)")
>>> ax.set_xticks(np.arange(13, step=3) - 1)
>>> ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"])
>>> ax.set_xlim([-0.2, 11.2])
>>> plt.show()
.. plot:: plots/graphics_functional_fboxplot.py
"""
fig, ax = utils.create_mpl_ax(ax)
plot_opts = {} if plot_opts is None else plot_opts
if plot_opts.get('cmap_outliers') is None:
from matplotlib.cm import rainbow_r
plot_opts['cmap_outliers'] = rainbow_r
data = np.asarray(data)
if xdata is None:
xdata = np.arange(data.shape[1])
# Calculate band depth if required.
if depth is None:
if method not in ['MBD', 'BD2']:
raise ValueError("Unknown value for parameter `method`.")
depth = banddepth(data, method=method)
else:
if depth.size != data.shape[0]:
raise ValueError("Provided `depth` array is not of correct size.")
# Inner area is 25%-75% region of band-depth ordered curves.
ix_depth = np.argsort(depth)[::-1]
median_curve = data[ix_depth[0], :]
ix_IQR = data.shape[0] // 2
lower = data[ix_depth[0:ix_IQR], :].min(axis=0)
upper = data[ix_depth[0:ix_IQR], :].max(axis=0)
# Determine region for outlier detection
inner_median = np.median(data[ix_depth[0:ix_IQR], :], axis=0)
lower_fence = inner_median - (inner_median - lower) * wfactor
upper_fence = inner_median + (upper - inner_median) * wfactor
# Find outliers.
ix_outliers = []
ix_nonout = []
for ii in range(data.shape[0]):
if (np.any(data[ii, :] > upper_fence) or
np.any(data[ii, :] < lower_fence)):
ix_outliers.append(ii)
else:
ix_nonout.append(ii)
ix_outliers = np.asarray(ix_outliers)
# Plot envelope of all non-outlying data
lower_nonout = data[ix_nonout, :].min(axis=0)
upper_nonout = data[ix_nonout, :].max(axis=0)
ax.fill_between(xdata, lower_nonout, upper_nonout,
color=plot_opts.get('c_outer', (0.75, 0.75, 0.75)))
# Plot central 50% region
ax.fill_between(xdata, lower, upper,
color=plot_opts.get('c_inner', (0.5, 0.5, 0.5)))
# Plot median curve
ax.plot(xdata, median_curve, color=plot_opts.get('c_median', 'k'),
lw=plot_opts.get('lw_median', 2))
# Plot outliers
cmap = plot_opts.get('cmap_outliers')
for ii, ix in enumerate(ix_outliers):
label = str(labels[ix]) if labels is not None else None
ax.plot(xdata, data[ix, :],
color=cmap(float(ii) / (len(ix_outliers)-1)), label=label,
lw=plot_opts.get('lw_outliers', 1))
if plot_opts.get('draw_nonout', False):
for ix in ix_nonout:
ax.plot(xdata, data[ix, :], 'k-', lw=0.5)
if labels is not None:
ax.legend()
return fig, depth, ix_depth, ix_outliers
[docs]def rainbowplot(data, xdata=None, depth=None, method='MBD', ax=None,
cmap=None):
"""
Create a rainbow plot for a set of curves.
A rainbow plot contains line plots of all curves in the dataset, colored in
order of functional depth. The median curve is shown in black.
Parameters
----------
data : sequence of ndarrays or 2-D ndarray
The vectors of functions to create a functional boxplot from. If a
sequence of 1-D arrays, these should all be the same size.
The first axis is the function index, the second axis the one along
which the function is defined. So ``data[0, :]`` is the first
functional curve.
xdata : ndarray, optional
The independent variable for the data. If not given, it is assumed to
be an array of integers 0..N-1 with N the length of the vectors in
`data`.
depth : ndarray, optional
A 1-D array of band depths for `data`, or equivalent order statistic.
If not given, it will be calculated through `banddepth`.
method : {'MBD', 'BD2'}, optional
The method to use to calculate the band depth. Default is 'MBD'.
ax : Matplotlib AxesSubplot instance, optional
If given, this subplot is used to plot in instead of a new figure being
created.
cmap : Matplotlib LinearSegmentedColormap instance, optional
The colormap used to color curves with. Default is a rainbow colormap,
with red used for the most central and purple for the least central
curves.
Returns
-------
fig : Matplotlib figure instance
If `ax` is None, the created figure. Otherwise the figure to which
`ax` is connected.
See Also
--------
banddepth, fboxplot
References
----------
[1] R.J. Hyndman and H.L. Shang, "Rainbow Plots, Bagplots, and Boxplots for
Functional Data", vol. 19, pp. 29-25, 2010.
Examples
--------
Load the El Nino dataset. Consists of 60 years worth of Pacific Ocean sea
surface temperature data.
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
>>> data = sm.datasets.elnino.load(as_pandas=False)
Create a rainbow plot:
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> res = sm.graphics.rainbowplot(data.raw_data[:, 1:], ax=ax)
>>> ax.set_xlabel("Month of the year")
>>> ax.set_ylabel("Sea surface temperature (C)")
>>> ax.set_xticks(np.arange(13, step=3) - 1)
>>> ax.set_xticklabels(["", "Mar", "Jun", "Sep", "Dec"])
>>> ax.set_xlim([-0.2, 11.2])
>>> plt.show()
.. plot:: plots/graphics_functional_rainbowplot.py
"""
fig, ax = utils.create_mpl_ax(ax)
if cmap is None:
from matplotlib.cm import rainbow_r
cmap = rainbow_r
data = np.asarray(data)
if xdata is None:
xdata = np.arange(data.shape[1])
# Calculate band depth if required.
if depth is None:
if method not in ['MBD', 'BD2']:
raise ValueError("Unknown value for parameter `method`.")
depth = banddepth(data, method=method)
else:
if depth.size != data.shape[0]:
raise ValueError("Provided `depth` array is not of correct size.")
ix_depth = np.argsort(depth)[::-1]
# Plot all curves, colored by depth
num_curves = data.shape[0]
for ii in range(num_curves):
ax.plot(xdata, data[ix_depth[ii], :], c=cmap(ii / (num_curves - 1.)))
# Plot the median curve
median_curve = data[ix_depth[0], :]
ax.plot(xdata, median_curve, 'k-', lw=2)
return fig
[docs]def banddepth(data, method='MBD'):
"""
Calculate the band depth for a set of functional curves.
Band depth is an order statistic for functional data (see `fboxplot`), with
a higher band depth indicating larger "centrality". In analog to scalar
data, the functional curve with highest band depth is called the median
curve, and the band made up from the first N/2 of N curves is the 50%
central region.
Parameters
----------
data : ndarray
The vectors of functions to create a functional boxplot from.
The first axis is the function index, the second axis the one along
which the function is defined. So ``data[0, :]`` is the first
functional curve.
method : {'MBD', 'BD2'}, optional
Whether to use the original band depth (with J=2) of [1]_ or the
modified band depth. See Notes for details.
Returns
-------
depth : ndarray
Depth values for functional curves.
Notes
-----
Functional band depth as an order statistic for functional data was
proposed in [1]_ and applied to functional boxplots and bagplots in [2]_.
The method 'BD2' checks for each curve whether it lies completely inside
bands constructed from two curves. All permutations of two curves in the
set of curves are used, and the band depth is normalized to one. Due to
the complete curve having to fall within the band, this method yields a lot
of ties.
The method 'MBD' is similar to 'BD2', but checks the fraction of the curve
falling within the bands. It therefore generates very few ties.
References
----------
.. [1] S. Lopez-Pintado and J. Romo, "On the Concept of Depth for
Functional Data", Journal of the American Statistical Association,
vol. 104, pp. 718-734, 2009.
.. [2] Y. Sun and M.G. Genton, "Functional Boxplots", Journal of
Computational and Graphical Statistics, vol. 20, pp. 1-19, 2011.
"""
def _band2(x1, x2, curve):
xb = np.vstack([x1, x2])
if np.any(curve < xb.min(axis=0)) or np.any(curve > xb.max(axis=0)):
res = 0
else:
res = 1
return res
def _band_mod(x1, x2, curve):
xb = np.vstack([x1, x2])
res = np.logical_and(curve >= xb.min(axis=0),
curve <= xb.max(axis=0))
return np.sum(res) / float(res.size)
if method == 'BD2':
band = _band2
elif method == 'MBD':
band = _band_mod
else:
raise ValueError("Unknown input value for parameter `method`.")
num = data.shape[0]
ix = np.arange(num)
depth = []
for ii in range(num):
res = 0
for ix1, ix2 in combinations(ix, 2):
res += band(data[ix1, :], data[ix2, :], data[ii, :])
# Normalize by number of combinations to get band depth
normfactor = factorial(num) / 2. / factorial(num - 2)
depth.append(float(res) / normfactor)
return np.asarray(depth)
