Source code for bob.measure.plot
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Chakka Murali Mohan, Trainee, IDIAP Research Institute, Switzerland.
# Mon 23 May 2011 14:36:14 CEST
"""Methods to plot error analysis figures such as ROC, EPC and DET"""
[docs]def roc(negatives, positives, npoints=100, CAR=False, **kwargs):
"""Plots Receiver Operating Charactaristic (ROC) curve.
This method will call matplotlib to plot the ROC curve for a system which
contains a particular set of negatives (impostors) and positives (clients)
scores. We use the standard matplotlib.pyplot.plot() command. All parameters
passed with exeception of the three first parameters of this method will be
directly passed to the plot command. If you wish to understand your options,
look here:
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot
The plot will represent the false-alarm on the vertical axis and the
false-rejection on the horizontal axis.
Input arguments:
negatives
a blitz array of negative class scores in float64 format
positives
a blitz array of positive class scores in float64 format
npoints
number of points to use when drawing the ROC curve
CAR
plot CAR over FAR in semilogx (CAR=True) or FAR over FRR linearly (CAR=False, the default)
kwargs
a dictionary of extra plotting parameters, that is passed directly to
matplotlib.pyplot.plot().
.. note::
This function does not initiate and save the figure instance, it only
issues the plotting command. You are the responsible for setting up and
saving the figure as you see fit.
Return value is the matplotlib line that was added as defined by the
matplotlib.pyplot.plot() command.
"""
try:
import matplotlib.pyplot as mpl
except ImportError:
print("Cannot import matplotlib. This package is not essential, but required if you wish to use the plotting functionality.")
raise
from . import roc as calc
out = calc(negatives, positives, npoints)
if not CAR:
return mpl.plot(100.0*out[0,:], 100.0*out[1,:], **kwargs)
else:
return mpl.semilogx(100.0*out[1,:], 100.0*(1-out[0,:]), **kwargs)
[docs]def epc(dev_negatives, dev_positives, test_negatives, test_positives,
npoints=100, **kwargs):
"""Plots Expected Performance Curve (EPC) as defined in the paper:
Bengio, S., Keller, M., Mariéthoz, J. (2004). The Expected Performance Curve.
International Conference on Machine Learning ICML Workshop on ROC Analysis in
Machine Learning, 136(1), 1963–1966. IDIAP RR. Available:
http://eprints.pascal-network.org/archive/00000670/
This method will call matplotlib to plot the EPC curve for a system which
contains a particular set of negatives (impostors) and positives (clients)
for both the development and test sets. We use the standard
matplotlib.pyplot.plot() command. All parameters passed with exeception of
the five first parameters of this method will be directly passed to the plot
command. If you wish to understand your options, look here:
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot
The plot will represent the minimum HTER on the vertical axis and the
cost on the horizontal axis.
Input arguments:
dev_negatives
blitz array of negative class scores on development set in float64 format
dev_positives
blitz array of positive class scores on development set in float64 format
test_negatives
blitz array of negative class scores on test set in float64 format, or a
list of those
test_positives
blitz array of positive class scores on test set in float64 format, or a
list of those
npoints
number of points to use when drawing the EPC curve
kwargs
a dictionary of extra plotting parameters, that is passed directly to
matplotlib.pyplot.plot().
.. note::
This function does not initiate and save the figure instance, it only
issues the plotting commands. You are the responsible for setting up and
saving the figure as you see fit.
Return value is the matplotlib line that was added as defined by the
matplotlib.pyplot.plot() command.
"""
try:
import matplotlib.pyplot as mpl
except ImportError:
print("Cannot import matplotlib. This package is not essential, but required if you wish to use the plotting functionality.")
raise
from . import epc as calc
out = calc(dev_negatives, dev_positives, test_negatives, test_positives,
npoints)
return mpl.plot(out[0,:], 100.0*out[1,:], **kwargs)
[docs]def det(negatives, positives, npoints=100, axisfontsize='x-small', **kwargs):
"""Plots Detection Error Trade-off (DET) curve as defined in the paper:
Martin, A., Doddington, G., Kamm, T., Ordowski, M., & Przybocki, M. (1997).
The DET curve in assessment of detection task performance. Fifth European
Conference on Speech Communication and Technology (pp. 1895-1898). Available:
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.4489&rep=rep1&type=pdf
This method will call matplotlib to plot the DET curve(s) for a system which
contains a particular set of negatives (impostors) and positives (clients)
scores. We use the standard matplotlib.pyplot.plot() command. All parameters
passed with exception of the three first parameters of this method will be
directly passed to the plot command. If you wish to understand your options,
look here:
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot
The plot will represent the false-alarm on the vertical axis and the
false-rejection on the horizontal axis.
This method is strongly inspired by the NIST implementation for Matlab,
called DETware, version 2.1 and available for download at the NIST website:
http://www.itl.nist.gov/iad/mig/tools/
Keyword parameters:
positives
numpy.array of positive class scores in float64 format
negatives
numpy.array of negative class scores in float64 format
npoints
number of points to use when drawing the EPC curve
axisfontsize
the size to be used by x/ytickables to set the font size on the axis
kwargs
a dictionary of extra plotting parameters, that is passed directly to
matplotlib.pyplot.plot().
.. note::
This function does not initiate and save the figure instance, it only
issues the plotting commands. You are the responsible for setting up and
saving the figure as you see fit.
.. note::
If you wish to reset axis zooming, you must use the gaussian scale rather
than the visual marks showed at the plot, which are just there for
displaying purposes. The real axis scale is based on the
bob.measure.ppndf() method. For example, if you wish to set the x and y
axis to display data between 1% and 40% here is the recipe:
.. code-block:: python
import bob
import matplotlib.pyplot as mpl
bob.measure.plot.det(...) #call this as many times as you need
#AFTER you plot the DET curve, just set the axis in this way:
mpl.axis([bob.measure.ppndf(k/100.0) for k in (1, 40, 1, 40)])
We provide a convenient way for you to do the above in this module. So,
optionally, you may use the bob.measure.plot.det_axis() method like this:
.. code-block:: python
import bob
bob.measure.plot.det(...)
# please note we convert percentage values in det_axis()
bob.measure.plot.det_axis([1, 40, 1, 40])
Return value is the matplotlib line that was added as defined by the
matplotlib.pyplot.plot() command.
"""
try:
import matplotlib.pyplot as mpl
except ImportError:
print("Cannot import matplotlib. This package is not essential, but required if you wish to use the plotting functionality.")
raise
# these are some constants required in this method
desiredTicks = [
"0.00001", "0.00002", "0.00005",
"0.0001", "0.0002", "0.0005",
"0.001", "0.002", "0.005",
"0.01", "0.02", "0.05",
"0.1", "0.2", "0.4", "0.6", "0.8", "0.9",
"0.95", "0.98", "0.99",
"0.995", "0.998", "0.999",
"0.9995", "0.9998", "0.9999",
"0.99995", "0.99998", "0.99999"
]
desiredLabels = [
"0.001", "0.002", "0.005",
"0.01", "0.02", "0.05",
"0.1", "0.2", "0.5",
"1", "2", "5",
"10", "20", "40", "60", "80", "90",
"95", "98", "99",
"99.5", "99.8", "99.9",
"99.95", "99.98", "99.99",
"99.995", "99.998", "99.999"
]
# this will actually do the plotting
from . import det as calc
from . import ppndf
out = calc(negatives, positives, npoints)
retval = mpl.plot(out[0,:], out[1,:], **kwargs)
# now the trick: we must plot the tick marks by hand using the PPNDF method
pticks = [ppndf(float(v)) for v in desiredTicks]
ax = mpl.gca() #and finally we set our own tick marks
ax.set_xticks(pticks)
ax.set_xticklabels(desiredLabels, size=axisfontsize)
ax.set_yticks(pticks)
ax.set_yticklabels(desiredLabels, size=axisfontsize)
return retval
[docs]def det_axis(v, **kwargs):
"""Sets the axis in a DET plot.
This method wraps the matplotlib.pyplot.axis() by calling
bob.measure.ppndf() on the values passed by the user so they are meaningful
in a DET plot as performed by bob.measure.plot.det().
Keyword parameters:
v
Python iterable (list or tuple) with the X and Y limits in the order (xmin,
xmax, ymin, ymax). Expected values should be in percentage (between 0 and
100%). If v is not a list or tuple that contains 4 numbers it is passed
without further inspection to matplotlib.pyplot.axis().
kwargs
All remaining arguments will be passed to matplotlib.pyplot.axis() without
further inspection.
Returns whatever matplotlib.pyplot.axis() returns.
"""
import logging
logger = logging.getLogger("bob")
try:
import matplotlib.pyplot as mpl
except ImportError:
print("Cannot import matplotlib. This package is not essential, but required if you wish to use the plotting functionality.")
raise
from . import ppndf
# treat input
try:
tv = list(v) #normal input
if len(tv) != 4: raise IndexError
tv = [ppndf(float(k)/100) for k in tv]
cur = mpl.axis()
# limits must be within bounds
if tv[0] < cur[0]:
logger.warn("Readjusting xmin: the provided value is out of bounds")
tv[0] = cur[0]
if tv[1] > cur[1]:
logger.warn("Readjusting xmax: the provided value is out of bounds")
tv[1] = cur[1]
if tv[2] < cur[2]:
logger.warn("Readjusting ymin: the provided value is out of bounds")
tv[2] = cur[2]
if tv[3] > cur[3]:
logger.warn("Readjusting ymax: the provided value is out of bounds")
tv[3] = cur[3]
except:
tv = v
return mpl.axis(tv, **kwargs)
[docs]def cmc(cmc_scores, logx = True, **kwargs):
"""Plots the (cumulative) match characteristics curve and returns the maximum rank."""
try:
import matplotlib.pyplot as mpl
except ImportError:
print("Cannot import matplotlib. This package is not essential, but required if you wish to use the plotting functionality.")
raise
from . import cmc as calc
out = calc(cmc_scores)
if logx:
mpl.semilogx(range(1, len(out)+1), out * 100, **kwargs)
else:
mpl.plot(range(1, len(out)+1), out * 100, **kwargs)
return len(out)
