#!/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
def log_values(min_step = -4, counts_per_step = 4):
"""log_values(min_step, counts_per_step) -> log_list
This function computes log-scaled values between :math:`10^{M}` and 1 (including), where :math:`M` is the ``min_ste`` argument, which needs to be a negative integer.
The integral ``counts_per_step`` value defines how many values between two adjacent powers of 10 will be created.
The total number of values will be ``-min_step * counts_per_step + 1``.
**Parameters:**
``min_step`` : int (negative)
The power of 10 that will be the minimum value. E.g., the default ``-4`` will result in the first number to be :math:`10^{-4}` = ``0.00001`` or ``0.01%``
``counts_per_step`` : int (positive)
The number of values that will be put between two adjacent powers of 10.
With the default value ``4`` (and default values of ``min_step``), we will get ``log_list[0] == 1e-4``, ``log_list[4] == 1e-3``, ..., ``log_list[16] == 1``.
**Returns**
``log_list`` : [float]
A list of logarithmically scaled values between :math:`10^{M}` and 1.
"""
import math
return [math.pow(10., i * 1./counts_per_step) for i in range(min_step*counts_per_step,0)] + [1.]
"""Methods to plot error analysis figures such as ROC, precision-recall curve, EPC and DET"""
def roc(negatives, positives, npoints=100, CAR=False, **kwargs):
"""Plots Receiver Operating Characteristic (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 :py:func:`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.
The plot will represent the false-alarm on the horizontal axis and the false-rejection on the vertical axis.
The values for the axis will be computed using :py:func:`bob.measure.roc`.
.. 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.
**Parameters:**
``negatives, positives`` : array_like(1D, float)
The list of negative and positive scores forwarded to :py:func:`bob.measure.roc`
``npoints`` : int
The number of points forwarded to :py:func:`bob.measure.roc`
``CAR`` : bool
If set to ``True``, it will plot the CAR over FAR in using :py:func:`matplotlib.pyplot.semilogx`, otherwise the FAR over FRR linearly using :py:func:`matplotlib.pyplot.plot`.
``kwargs`` : keyword arguments
Extra plotting parameters, which are passed directly to :py:func:`matplotlib.pyplot.plot`.
**Returns:**
The return value is the matplotlib line that was added as defined by :py:func:`matplotlib.pyplot.plot` or :py:func:`matplotlib.pyplot.semilogx`.
"""
from matplotlib import pyplot
from . import roc as calc
out = calc(negatives, positives, npoints)
if not CAR:
return pyplot.plot(100.0*out[0,:], 100.0*out[1,:], **kwargs)
else:
return pyplot.semilogx(100.0*out[0,:], 100.0*(1-out[1,:]), **kwargs)
def roc_for_far(negatives, positives, far_values = log_values(), **kwargs):
"""Plots Receiver Operating Characteristic (ROC) curve for the given list of False Acceptance Rates (FAR).
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 :py:func:`matplotlib.pyplot.semilogx` command. All parameters
passed with exception of the three first parameters of this method will be
directly passed to the plot command.
The plot will represent the False Acceptance Rate (FAR) on the horizontal axis and the Correct Acceptance Rate (CAR) on the vertical axis.
The values for the axis will be computed using :py:func:`bob.measure.roc_for_far`.
.. 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.
**Parameters:**
``negatives, positives`` : array_like(1D, float)
The list of negative and positive scores forwarded to :py:func:`bob.measure.roc`
``far_values`` : [float]
The values for the FAR, where the CAR should be plotted; each value should be in range [0,1].
``kwargs`` : keyword arguments
Extra plotting parameters, which are passed directly to :py:func:`matplotlib.pyplot.plot`.
**Returns:**
The return value is the matplotlib line that was added as defined by :py:func:`matplotlib.pyplot.semilogx`.
"""
from matplotlib import pyplot
from . import roc_for_far as calc
out = calc(negatives, positives, far_values)
return pyplot.semilogx(100.0*out[0,:], 100.0*(1-out[1,:]), **kwargs)
def precision_recall_curve(negatives, positives, npoints=100, **kwargs):
"""Plots Precision-Recall curve.
This method will call ``matplotlib`` to plot the precision-recall curve for a system which
contains a particular set of ``negatives`` (impostors) and ``positives`` (clients)
scores. We use the standard :py:func:`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.
.. 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.
**Parameters:**
``negatives, positives`` : array_like(1D, float)
The list of negative and positive scores forwarded to :py:func:`bob.measure.precision_recall_curve`
``npoints`` : int
The number of points forwarded to :py:func:`bob.measure.precision_recall_curve`
``kwargs`` : keyword arguments
Extra plotting parameters, which are passed directly to :py:func:`matplotlib.pyplot.plot`.
**Returns:**
The return value is the ``matplotlib`` line that was added as defined by :py:func:`matplotlib.pyplot.plot`.
"""
from matplotlib import pyplot
from . import precision_recall_curve as calc
out = calc(negatives, positives, npoints)
return pyplot.plot(100.0*out[0,:], 100.0*out[1,:], **kwargs)
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
:py:func:`matplotlib.pyplot.plot` command. All parameters passed with exception of
the five first parameters of this method will be directly passed to the plot
command.
The plot will represent the minimum HTER on the vertical axis and the cost on the horizontal axis.
.. 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.
**Parameters:**
``dev_negatives, dev_positvies, test_negatives, test_positives`` : array_like(1D, float)
See :py:func:bob.measure.epc` for details
``npoints`` : int
See :py:func:bob.measure.epc` for details
``kwargs`` : keyword arguments
Extra plotting parameters, which are passed directly to :py:func:`matplotlib.pyplot.plot`.
**Returns:**
The return value is the ``matplotlib`` line that was added as defined by :py:func:`matplotlib.pyplot.plot`.
"""
from matplotlib import pyplot
from . import epc as calc
out = calc(dev_negatives, dev_positives, test_negatives, test_positives, npoints)
return pyplot.plot(out[0,:], 100.0*out[1,:], **kwargs)
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 :py:func:`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.
The plot will represent the false-alarm on the horizontal axis and the
false-rejection on the vertical 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/
.. 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 :py:func:`bob.measure.ppndf`.
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.measure
from matplotlib import pyplot
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:
pyplot.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 :py:func:`bob.measure.plot.det_axis` method like this:
.. code-block:: python
import bob.measure
bob.measure.plot.det(...)
# please note we convert percentage values in det_axis()
bob.measure.plot.det_axis([1, 40, 1, 40])
**Parameters:**
``negatives, positives`` : array_like(1D, float)
The list of negative and positive scores forwarded to :py:func:`bob.measure.det`
``npoints`` : int
The number of points forwarded to :py:func:`bob.measure.det`
``axisfontsize`` : str
The size to be used by x/y-tick-labels to set the font size on the axis
``kwargs`` : keyword arguments
Extra plotting parameters, which are passed directly to :py:func:`matplotlib.pyplot.plot`.
**Returns:**
The return value is the ``matplotlib`` line that was added as defined by :py:func:`matplotlib.pyplot.plot`.
"""
# 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 matplotlib import pyplot
from . import det as calc
from . import ppndf
out = calc(negatives, positives, npoints)
retval = pyplot.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 = pyplot.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
def det_axis(v, **kwargs):
"""Sets the axis in a DET plot.
This method wraps the :py:func:`matplotlib.pyplot.axis` by calling
:py:func:`bob.measure.ppndf` on the values passed by the user so they are meaningful
in a DET plot as performed by :py:func:`bob.measure.plot.det`.
**Parameters:**
``v`` : (int, int, int, int)
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 :py:func:`matplotlib.pyplot.axis`.
``kwargs`` : keyword arguments
Extra parameters, which are passed directly to :py:func:`matplotlib.pyplot.axis`.
**Returns:**
Returns whatever :py:func:`matplotlib.pyplot.axis` returns.
"""
import logging
logger = logging.getLogger("bob.measure")
from matplotlib import pyplot
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 = pyplot.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 pyplot.axis(tv, **kwargs)
def cmc(cmc_scores, logx = True, **kwargs):
"""Plots the (cumulative) match characteristics curve and returns the maximum rank.
This function plots a CMC curve using the given CMC scores, which can be read from the our score files using the :py:func:`bob.measure.load.cmc_four_column` or :py:func:`bob.measure.load.cmc_five_column` methods.
The structure of the ``cmc_scores`` parameter is relatively complex.
It contains a list of pairs of lists.
For each probe object, a pair of list negative and positive scores is required.
**Parameters:**
``cmc_scores`` : [(array_like(1D, float), array_like(1D, float))]
See :py:func:`bob.measure.cmc`
``logx`` : bool
Plot the rank axis in logarithmic scale using :py:func:`matplotlib.pyplot.semilogx` or in linear scale using :py:func:`matplotlib.pyplot.plot`? (Default: ``True``)
``kwargs`` : keyword arguments
Extra plotting parameters, which are passed directly to :py:func:`matplotlib.pyplot.plot` or :py:func:`matplotlib.pyplot.semilogx`.
**Returns:**
The number of classes (clients) in the given scores.
"""
from matplotlib import pyplot
from . import cmc as calc
out = calc(cmc_scores)
if logx:
pyplot.semilogx(range(1, len(out)+1), out * 100, **kwargs)
else:
pyplot.plot(range(1, len(out)+1), out * 100, **kwargs)
return len(out)
def detection_identification_curve(cmc_scores, far_values = log_values(), rank = 1, logx = True, **kwargs):
"""Plots the Detection & Identification curve over the FAR for the given FAR values.
This curve is designed to be used in an open set identification protocol, and defined in Chapter 14.1 of [LiJain2005]_.
It requires to have at least one open set probe item, i.e., with no corresponding gallery, such that the positives for that pair are ``None``.
The detection and identification curve first computes FAR thresholds based on the out-of-set probe scores (negative scores).
For each probe item, the **maximum** negative score is used.
Then, it plots the detection and identification rates for those thresholds, which are based on the in-set probe scores only.
See [LiJain2005]_ for more details.
**Parameters:**
``cmc_scores`` : [(array_like(1D, float), array_like(1D, float))]
See :py:func:`bob.measure.detection_identification_rate`
``far_values`` : [float]
The values for the FAR, where the CAR should be plotted; each value should be in range [0,1].
``rank`` : int or ``None``
The rank for which the curve should be plotted, 1 by default.
``logx`` : bool
Plot the FAR axis in logarithmic scale using :py:func:`matplotlib.pyplot.semilogx` or in linear scale using :py:func:`matplotlib.pyplot.plot`? (Default: ``True``)
``kwargs`` : keyword arguments
Extra plotting parameters, which are passed directly to :py:func:`matplotlib.pyplot.plot` or :py:func:`matplotlib.pyplot.semilogx`.
**Returns:**
The return value is the ``matplotlib`` line that was added as defined by :py:func:`matplotlib.pyplot.plot`.
.. [LiJain2005] **Stan Li and Anil K. Jain**, *Handbook of Face Recognition*, Springer, 2005
"""
import numpy
from matplotlib import pyplot
from . import far_threshold, detection_identification_rate
# for each probe, for which no positives exists, get the highest negative score; and sort them to compute the FAR thresholds
negatives = sorted(max(neg) for neg,pos in cmc_scores if (pos is None or not numpy.array(pos).size) and neg is not None)
if not negatives:
raise ValueError("There need to be at least one pair with only negative scores")
# compute thresholds based on FAR values
thresholds = [far_threshold(negatives, [], v, True) for v in far_values]
# compute detection and identification rate based on the thresholds for the given rank
rates = [100.*detection_identification_rate(cmc_scores, t, rank) for t in thresholds]
# plot curve
if logx:
return pyplot.semilogx(far_values, rates, **kwargs)
else:
return pyplot.plot(far_values, rates, **kwargs)