diff --git a/bob/measure/__init__.py b/bob/measure/__init__.py
index 48159a4da41c1f148d9f88e6c6129338c9a38701..8b05ccd56ca28f495c2925289082521c99a69ca4 100644
--- a/bob/measure/__init__.py
+++ b/bob/measure/__init__.py
@@ -440,9 +440,9 @@ def false_alarm_rate(cmc_scores, threshold):
def eer(negatives, positives, is_sorted=False, also_farfrr=False):
"""Calculates the Equal Error Rate (EER).
- Please note that it is possible that eer != far != frr.
- This function returns (far + frr) / 2 as eer.
- If you also need the far and frr values, set ``also_farfrr`` to ``True``.
+ Please note that it is possible that eer != fpr != fnr.
+ This function returns (fpr + fnr) / 2 as eer.
+ If you also need the fpr and fnr values, set ``also_farfrr`` to ``True``.
Parameters
----------
@@ -459,11 +459,11 @@ def eer(negatives, positives, is_sorted=False, also_farfrr=False):
-------
eer : float
The Equal Error Rate (EER).
- far : float
- The False Accept Rate (FAR). Returned only when ``also_farfrr`` is
+ fpr : float
+ The False Positive Rate (FPR). Returned only when ``also_farfrr`` is
``True``.
- frr : float
- The False Reject Rate (FAR). Returned only when ``also_farfrr`` is
+ fnr : float
+ The False Negative Rate (FNR). Returned only when ``also_farfrr`` is
``True``.
"""
threshold = eer_threshold(negatives, positives, is_sorted)
diff --git a/bob/measure/cpp/error.cpp b/bob/measure/cpp/error.cpp
index facf8114b9c5b989991f6956d0ec639bbb86b14d..d7c7222622a88c552f096fef11bb46653f06f86d 100644
--- a/bob/measure/cpp/error.cpp
+++ b/bob/measure/cpp/error.cpp
@@ -39,13 +39,13 @@ bob::measure::farfrr(const blitz::Array<double, 1> &negatives,
const blitz::Array<double, 1> &positives,
double threshold) {
if (std::isnan(threshold)){
- bob::core::error << "Cannot compute FAR or FRR with threshold NaN.\n";
+ bob::core::error << "Cannot compute FPR (FAR) or FNR (FRR) with threshold NaN.\n";
return std::make_pair(1.,1.);
}
if (!negatives.size())
- throw std::runtime_error("Cannot compute FAR when no negatives are given");
+ throw std::runtime_error("Cannot compute FPR (FAR) when no negatives are given");
if (!positives.size())
- throw std::runtime_error("Cannot compute FRR when no positives are given");
+ throw std::runtime_error("Cannot compute FNR (FRR) when no positives are given");
blitz::sizeType total_negatives = negatives.extent(blitz::firstDim);
blitz::sizeType total_positives = positives.extent(blitz::firstDim);
blitz::sizeType false_accepts = blitz::count(negatives >= threshold);
@@ -395,13 +395,13 @@ double bob::measure::rocch2eer(const blitz::Array<double, 2> &pfa_pmiss) {
/**
* This function computes the ROC coordinates for the given positive and
- * negative values at the given FAR positions.
+ * negative values at the given FPR (FAR) positions.
*
* @param negatives Impostor scores
* @param positives Client scores
- * @param far_list The list of FAR values where the FRR should be calculated
+ * @param far_list The list of FPR (FAR) values where the FNR (FRR) should be calculated
*
- * @return The ROC curve with the FAR in the first row and the FRR in the
+ * @return The ROC curve with the FPR in the first row and the FNR in the
* second.
*/
blitz::Array<double, 2>
diff --git a/bob/measure/cpp/error.h b/bob/measure/cpp/error.h
index f4f6f80ff594e2e04686bbbcd2102e2a00d3ae55..8d9b0b684c0eaf13bc44645c0aca3959dc654505 100644
--- a/bob/measure/cpp/error.h
+++ b/bob/measure/cpp/error.h
@@ -229,7 +229,7 @@ double minimizingThreshold(const blitz::Array<double, 1> &negatives,
/**
* Calculates the threshold that is, as close as possible, to the
* equal-error-rate (EER) given the input data. The EER should be the point
- * where the FAR equals the FRR. Graphically, this would be equivalent to the
+ * where the FPR equals the FNR. Graphically, this would be equivalent to the
* intersection between the R.O.C. (or D.E.T.) curves and the identity.
*/
double eerThreshold(const blitz::Array<double, 1> &negatives,
@@ -252,7 +252,7 @@ double eerRocch(const blitz::Array<double, 1> &negatives,
*
* The value to minimize becomes:
*
- * ER_cost = [cost * FAR] + [(1-cost) * FRR]
+ * ER_cost = [cost * FPR] + [(1-cost) * FNR]
*
* The higher the cost, the higher the importance given to *not* making
* mistakes classifying negatives/noise/impostors.
@@ -271,12 +271,12 @@ inline double minHterThreshold(const blitz::Array<double, 1> &negatives,
}
/**
- * Computes the threshold such that the real FAR is as close as possible
+ * Computes the threshold such that the real FPR is as close as possible
* to the requested far_value.
*
- * @param negatives The impostor scores to be used for computing the FAR
+ * @param negatives The impostor scores to be used for computing the FPR
* @param positives The client scores; ignored by this function
- * @param far_value The FAR value where the threshold should be computed
+ * @param far_value The FPR value where the threshold should be computed
*
* @return The computed threshold
*/
@@ -301,7 +301,7 @@ double frrThreshold(const blitz::Array<double, 1> &negatives,
/**
* Calculates the ROC curve given a set of positive and negative scores and a
* number of desired points. Returns a two-dimensional blitz::Array of
- * doubles that express the X (FRR) and Y (FAR) coordinates in this order.
+ * doubles that express the X (FNR) and Y (FPR) coordinates in this order.
* The points in which the ROC curve are calculated are distributed
* uniformly in the range [min(negatives, positives), max(negatives,
* positives)].
@@ -349,8 +349,8 @@ double rocch2eer(const blitz::Array<double, 2> &pmiss_pfa);
/**
* Calculates the ROC curve given a set of positive and negative scores at
- * the given FAR coordinates. Returns a two-dimensional blitz::Array of
- * doubles that express the X (FAR) and Y (CAR) coordinates in this order.
+ * the given FPR coordinates. Returns a two-dimensional blitz::Array of
+ * doubles that express the X (FPR) and Y (CPR) coordinates in this order.
*/
blitz::Array<double, 2> roc_for_far(const blitz::Array<double, 1> &negatives,
const blitz::Array<double, 1> &positives,
diff --git a/bob/measure/main.cpp b/bob/measure/main.cpp
index 1afcb9c64ed8d4423dc11a84ba33cc3883139040..7c60be6a514ab8a33b49522322cdc62b9bd28e40 100644
--- a/bob/measure/main.cpp
+++ b/bob/measure/main.cpp
@@ -159,7 +159,7 @@ static auto det_doc =
"curve, for which the DET should be "
"evaluated")
.add_return("curve", "array_like(2D, float)",
- "The DET curve, with the FAR in the first and the FRR in "
+ "The DET curve, with the FPR in the first and the FNR in "
"the second row");
static PyObject *det(PyObject *, PyObject *args, PyObject *kwds) {
BOB_TRY
@@ -197,7 +197,7 @@ static auto ppndf_doc =
"By 20.04.2011, you could find such package `here "
"<http://www.itl.nist.gov/iad/mig/tools/>`_.")
.add_prototype("value", "ppndf")
- .add_parameter("value", "float", "The value (usually FAR or FRR) for "
+ .add_parameter("value", "float", "The value (usually FPR or FNR) for "
"which the ppndf should be calculated")
.add_return("ppndf", "float",
"The derivative scale of the given value");
@@ -229,7 +229,7 @@ static auto roc_doc =
"max(negatives, positives)]``")
.add_return("curve", "array_like(2D, float)",
"A two-dimensional array of doubles that express the X "
- "(FAR) and Y (FRR) coordinates in this order");
+ "(FPR) and Y (FNR) coordinates in this order");
static PyObject *roc(PyObject *, PyObject *args, PyObject *kwds) {
BOB_TRY
static char **kwlist = roc_doc.kwlist();
@@ -283,8 +283,8 @@ static auto farfrr_doc =
"The output is in form of a tuple of two double-precision real "
"numbers. "
"The numbers range from 0 to 1. "
- "The first element of the pair is the false-accept ratio (FAR), the "
- "second element the false-rejection ratio (FRR).\n\n"
+ "The first element of the pair is the false positive ratio (FPR), the "
+ "second element the false negative ratio (FNR).\n\n"
"The ``threshold`` value does not necessarily have to fall in the "
"range covered by the input scores (negatives and positives "
"altogether), but if it does not, the output will be either (1.0, 0.0) "
@@ -306,9 +306,9 @@ static auto farfrr_doc =
"correctly and incorrectly "
"classified scores")
.add_return("far", "float",
- "The False Accept Rate (FAR) for the given threshold")
+ "The False Positve Rate (FPR) for the given threshold")
.add_return("frr", "float",
- "The False Reject Rate (FRR) for the given threshold");
+ "The False Negative Rate (FNR) for the given threshold");
static PyObject *farfrr(PyObject *, PyObject *args, PyObject *kwds) {
BOB_TRY
char **kwlist = farfrr_doc.kwlist();
@@ -339,7 +339,7 @@ static auto eer_threshold_doc =
"eer_threshold", "Calculates the threshold that is as close as "
"possible to the equal-error-rate (EER) for the given "
"input data",
- "The EER should be the point where the FAR equals the FRR. "
+ "The EER should be the point where the FPR equals the FNR. "
"Graphically, this would be equivalent to the intersection between the "
"ROC (or DET) curves and the identity.\n\n"
".. note::\n\n"
@@ -356,7 +356,7 @@ static auto eer_threshold_doc =
"ascendantly sorted order?")
.add_return("threshold", "float", "The threshold (i.e., as used in "
":py:func:`bob.measure.farfrr`) "
- "where FAR and FRR are as close as "
+ "where FPR and FNR are as close as "
"possible");
static PyObject *eer_threshold(PyObject *, PyObject *args, PyObject *kwds) {
BOB_TRY
@@ -392,8 +392,8 @@ static auto min_weighted_error_rate_threshold_doc =
"false-accepts and false-rejections. "
"This number should be between 0 and 1 and will be clipped to those "
"extremes. "
- "The value to minimize becomes: :math:`ER_{cost} = cost * FAR + "
- "(1-cost) * FRR`. "
+ "The value to minimize becomes: :math:`ER_{cost} = cost * FPR + "
+ "(1-cost) * FNR`. "
"The higher the cost, the higher the importance given to **not** "
"making mistakes classifying negatives/noise/impostors.\n\n"
".. note:: "
@@ -405,8 +405,8 @@ static auto min_weighted_error_rate_threshold_doc =
.add_parameter(
"negatives, positives", "array_like(1D, float)",
"The set of negative and positive scores to compute the threshold")
- .add_parameter("cost", "float", "The relative cost over FAR with "
- "respect to FRR in the threshold "
+ .add_parameter("cost", "float", "The relative cost over FPR with "
+ "respect to FNR in the threshold "
"calculation")
.add_parameter("is_sorted", "bool", "[Default: ``False``] Are both "
"sets of scores already in "
@@ -712,7 +712,7 @@ static PyObject *precision_recall_curve(PyObject *, PyObject *args,
static auto far_threshold_doc =
bob::extension::FunctionDoc(
- "far_threshold", "Computes the threshold such that the real FAR is "
+ "far_threshold", "Computes the threshold such that the real FPR is "
"**at most** the requested ``far_value`` if possible",
"\n\n.. note::\n\n"
" The scores will be sorted internally, requiring the scores to be "
@@ -724,11 +724,11 @@ static auto far_threshold_doc =
"threshold")
.add_parameter(
"negatives", "array_like(1D, float)",
- "The set of negative scores to compute the FAR threshold")
+ "The set of negative scores to compute the FPR threshold")
.add_parameter(
"positives", "array_like(1D, float)",
"Ignored, but needs to be specified -- may be given as ``[]``")
- .add_parameter("far_value", "float", "[Default: ``0.001``] The FAR "
+ .add_parameter("far_value", "float", "[Default: ``0.001``] The FPR "
"value, for which the threshold "
"should be computed")
.add_parameter("is_sorted", "bool",
@@ -738,7 +738,7 @@ static auto far_threshold_doc =
"will require more memory")
.add_return(
"threshold", "float",
- "The threshold such that the real FAR is at most ``far_value``");
+ "The threshold such that the real FPR is at most ``far_value``");
static PyObject *far_threshold(PyObject *, PyObject *args, PyObject *kwds) {
BOB_TRY
static char **kwlist = far_threshold_doc.kwlist();
@@ -768,7 +768,7 @@ static PyObject *far_threshold(PyObject *, PyObject *args, PyObject *kwds) {
static auto frr_threshold_doc =
bob::extension::FunctionDoc(
- "frr_threshold", "Computes the threshold such that the real FRR is "
+ "frr_threshold", "Computes the threshold such that the real FNR is "
"**at most** the requested ``frr_value`` if possible",
"\n\n.. note::\n\n"
" The scores will be sorted internally, requiring the scores to be "
@@ -783,8 +783,8 @@ static auto frr_threshold_doc =
"Ignored, but needs to be specified -- may be given as ``[]``")
.add_parameter(
"positives", "array_like(1D, float)",
- "The set of positive scores to compute the FRR threshold")
- .add_parameter("frr_value", "float", "[Default: ``0.001``] The FRR "
+ "The set of positive scores to compute the FNR threshold")
+ .add_parameter("frr_value", "float", "[Default: ``0.001``] The FNR "
"value, for which the threshold "
"should be computed")
.add_parameter("is_sorted", "bool",
@@ -868,8 +868,8 @@ static auto rocch_doc =
"negatives, positives", "array_like(1D, float)",
"The set of negative and positive scores to compute the curve")
.add_return("curve", "array_like(2D, float)",
- "The ROC curve, with the first row containing the FAR, and "
- "the second row containing the FRR");
+ "The ROC curve, with the first row containing the FPR, and "
+ "the second row containing the FNR");
static PyObject *rocch(PyObject *, PyObject *args, PyObject *kwds) {
BOB_TRY
/* Parses input arguments in a single shot */
@@ -925,8 +925,8 @@ static PyObject *rocch2eer(PyObject *, PyObject *args, PyObject *kwds) {
static auto roc_for_far_doc =
bob::extension::FunctionDoc(
"roc_for_far", "Calculates the ROC curve for a given set of positive "
- "and negative scores and the FAR values, for which the "
- "FRR should be computed",
+ "and negative scores and the FPR values, for which the "
+ "FNR should be computed",
".. note::\n\n"
" The scores will be sorted internally, requiring the scores to be "
"copied.\n"
@@ -938,15 +938,15 @@ static auto roc_for_far_doc =
"The set of negative and positive scores to compute the curve")
.add_parameter(
"far_list", "array_like(1D, float)",
- "A list of FAR values, for which the FRR values should be computed")
+ "A list of FPR values, for which the FNR values should be computed")
.add_parameter("is_sorted", "bool",
"[Default: ``False``] Set this to ``True`` if both sets "
"of scores are already sorted in ascending order. If "
"``False``, scores will be sorted internally, which "
"will require more memory")
.add_return("curve", "array_like(2D, float)",
- "The ROC curve, which holds a copy of the given FAR values "
- "in row 0, and the corresponding FRR values in row 1");
+ "The ROC curve, which holds a copy of the given FPR values "
+ "in row 0, and the corresponding FNR values in row 1");
static PyObject *roc_for_far(PyObject *, PyObject *args, PyObject *kwds) {
BOB_TRY
/* Parses input arguments in a single shot */
diff --git a/bob/measure/plot.py b/bob/measure/plot.py
index 25ac3537061cbc6cc4c9520c615d35a846fd6849..a59159a11ed90da43daa0463ee9c836dd29e927d 100644
--- a/bob/measure/plot.py
+++ b/bob/measure/plot.py
@@ -80,9 +80,9 @@ def roc(negatives, positives, npoints=100, CAR=False, **kwargs):
npoints (:py:class:`int`, optional): The number of points for the plot. See
(:py:func:`bob.measure.roc`)
- CAR (:py:class:`bool`, optional): 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`.
+ CAR (:py:class:`bool`, optional): If set to ``True``, it will plot the CPR
+ (CAR) over FPR in using :py:func:`matplotlib.pyplot.semilogx`, otherwise the
+ FPR over FNR linearly using :py:func:`matplotlib.pyplot.plot`.
kwargs (:py:class:`dict`, optional): Extra plotting parameters, which are
passed directly to :py:func:`matplotlib.pyplot.plot`.
@@ -107,7 +107,7 @@ def roc(negatives, positives, npoints=100, CAR=False, **kwargs):
def roc_for_far(negatives, positives, far_values=log_values(), CAR=True,
**kwargs):
- """Plots the ROC curve for the given list of False Acceptance Rates (FAR).
+ """Plots the ROC curve for the given list of False Positive 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)
@@ -115,8 +115,8 @@ def roc_for_far(negatives, positives, far_values=log_values(), CAR=True,
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
+ The plot will represent the False Positive Rate (FPR) on the horizontal
+ axis and the Correct Positive Rate (CPR) on the vertical axis. The values
for the axis will be computed using :py:func:`bob.measure.roc_for_far`.
.. note::
@@ -136,12 +136,12 @@ def roc_for_far(negatives, positives, far_values=log_values(), CAR=True,
"positive" (signal, class) samples of your classifier. See
(:py:func:`bob.measure.roc`)
- far_values (:py:class:`list`, optional): The values for the FAR, where the
- CAR should be plotted; each value should be in range [0,1].
+ far_values (:py:class:`list`, optional): The values for the FPR, where the
+ CPR (CAR) should be plotted; each value should be in range [0,1].
- CAR (:py:class:`bool`, optional): 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`.
+ CAR (:py:class:`bool`, optional): If set to ``True``, it will plot the CPR
+ (CAR) over FPR in using :py:func:`matplotlib.pyplot.semilogx`, otherwise the
+ FPR over FNR linearly using :py:func:`matplotlib.pyplot.plot`.
kwargs (:py:class:`dict`, optional): Extra plotting parameters, which are
passed directly to :py:func:`matplotlib.pyplot.plot`.
@@ -510,14 +510,14 @@ def cmc(cmc_scores, logx=True, **kwargs):
def detection_identification_curve(cmc_scores, far_values=log_values(), rank=1, logx=True, **kwargs):
- """Plots the Detection & Identification curve over the FAR
+ """Plots the Detection & Identification curve over the FPR
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 detection and identification curve first computes FPR 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
@@ -534,8 +534,8 @@ def detection_identification_curve(cmc_scores, far_values=log_values(), rank=1,
rank (:py:class:`int`, optional): The rank for which the curve should be
plotted
- far_values (:py:class:`list`, optional): The values for the FAR, where the
- CAR should be plotted; each value should be in range [0,1].
+ far_values (:py:class:`list`, optional): The values for the FPR (FAR), where the
+ CPR (CAR) should be plotted; each value should be in range [0,1].
logx (:py:class:`bool`, optional): If set (the default), plots the rank
axis in logarithmic scale using :py:func:`matplotlib.pyplot.semilogx` or
diff --git a/bob/measure/script/commands.py b/bob/measure/script/commands.py
index 653dc39a545ee43be7bd6fb3d407c9dd3be9254d..a20d6d947f1474257609068cb6f27f28e2117d02 100644
--- a/bob/measure/script/commands.py
+++ b/bob/measure/script/commands.py
@@ -17,7 +17,8 @@ CRITERIA = ('eer', 'min-hter', 'far')
criteria=CRITERIA, score_format=SCORE_FORMAT,
hter_note=' ',
command='bob measure metrics'),
- criteria=CRITERIA)
+ criteria=CRITERIA,
+ far_name="FPR")
def metrics(ctx, scores, evaluation, **kwargs):
process = figure.Metrics(ctx, scores, evaluation, load.split)
process.run()
@@ -25,7 +26,7 @@ def metrics(ctx, scores, evaluation, **kwargs):
@common_options.roc_command(
common_options.ROC_HELP.format(
- score_format=SCORE_FORMAT, command='bob measure roc'))
+ score_format=SCORE_FORMAT, command='bob measure roc'), far_name="FPR")
def roc(ctx, scores, evaluation, **kwargs):
process = figure.Roc(ctx, scores, evaluation, load.split)
process.run()
@@ -33,7 +34,7 @@ def roc(ctx, scores, evaluation, **kwargs):
@common_options.det_command(
common_options.DET_HELP.format(
- score_format=SCORE_FORMAT, command='bob measure det'))
+ score_format=SCORE_FORMAT, command='bob measure det'), far_name="FPR")
def det(ctx, scores, evaluation, **kwargs):
process = figure.Det(ctx, scores, evaluation, load.split)
process.run()
@@ -49,7 +50,7 @@ def epc(ctx, scores, **kwargs):
@common_options.hist_command(
common_options.HIST_HELP.format(
- score_format=SCORE_FORMAT, command='bob measure hist'))
+ score_format=SCORE_FORMAT, command='bob measure hist'), far_name="FPR")
def hist(ctx, scores, evaluation, **kwargs):
process = figure.Hist(ctx, scores, evaluation, load.split)
process.run()
@@ -58,7 +59,7 @@ def hist(ctx, scores, evaluation, **kwargs):
@common_options.evaluate_command(
common_options.EVALUATE_HELP.format(
score_format=SCORE_FORMAT, command='bob measure evaluate'),
- criteria=CRITERIA)
+ criteria=CRITERIA, far_name="FPR")
def evaluate(ctx, scores, evaluation, **kwargs):
common_options.evaluate_flow(
ctx, scores, evaluation, metrics, roc, det, epc, hist, **kwargs)
@@ -69,7 +70,7 @@ def evaluate(ctx, scores, evaluation, **kwargs):
names='FtA, FAR, FRR, FMR, FMNR, HTER',
criteria=CRITERIA, score_format=SCORE_FORMAT,
command='bob measure multi-metrics'),
- criteria=CRITERIA)
+ criteria=CRITERIA, far_name="FPR")
def multi_metrics(ctx, scores, evaluation, protocols_number, **kwargs):
ctx.meta['min_arg'] = protocols_number * (2 if evaluation else 1)
process = figure.MultiMetrics(ctx, scores, evaluation, load.split)
diff --git a/bob/measure/script/common_options.py b/bob/measure/script/common_options.py
index 55f3ccc3db2efaf3c84a92d99f8a5158f64a3bff..f4c1f0f355dfb86a0c5460cb6a552ceff956a4ad 100644
--- a/bob/measure/script/common_options.py
+++ b/bob/measure/script/common_options.py
@@ -220,16 +220,16 @@ def subplot_option(dflt=111, **kwargs):
def cost_option(**kwargs):
- '''Get option to get cost for FAR'''
+ '''Get option to get cost for FPR'''
def custom_cost_option(func):
def callback(ctx, param, value):
if value < 0 or value > 1:
- raise click.BadParameter("Cost for FAR must be betwen 0 and 1")
+ raise click.BadParameter("Cost for FPR must be betwen 0 and 1")
ctx.meta['cost'] = value
return value
return click.option(
'-C', '--cost', type=float, default=0.99, show_default=True,
- help='Cost for FAR in minDCF',
+ help='Cost for FPR in minDCF',
callback=callback, **kwargs)(func)
return custom_cost_option
@@ -388,34 +388,40 @@ def decimal_option(dflt=1, **kwargs):
return custom_decimal_option
-def far_option(**kwargs):
+def far_option(far_name="FAR", **kwargs):
'''Get option to get far value'''
def custom_far_option(func):
def callback(ctx, param, value):
if value is not None and (value > 1 or value < 0):
- raise click.BadParameter("FAR value should be between 0 and 1")
+ raise click.BadParameter(
+ "{} value should be between 0 and 1".format(far_name)
+ )
ctx.meta['far_value'] = value
return value
return click.option(
- '-f', '--far-value', type=click.FLOAT, default=None,
- help='The FAR value for which to compute threshold. This option '
- 'must be used alongside `--criterion far`.',
+ '-f', '--{}-value'.format(far_name.lower()), 'far_value',
+ type=click.FLOAT, default=None,
+ help='The {} value for which to compute threshold. This option '
+ 'must be used alongside `--criterion far`.'.format(far_name),
callback=callback, show_default=True, **kwargs)(func)
return custom_far_option
-def min_far_option(dflt=1e-4, **kwargs):
+def min_far_option(far_name="FAR",dflt=1e-4, **kwargs):
'''Get option to get min far value'''
def custom_min_far_option(func):
def callback(ctx, param, value):
if value is not None and (value > 1 or value < 0):
- raise click.BadParameter("FAR value should be between 0 and 1")
+ raise click.BadParameter(
+ "{} value should be between 0 and 1".format(far_name)
+ )
ctx.meta['min_far_value'] = value
return value
return click.option(
- '-M', '--min-far-value', type=click.FLOAT, default=dflt,
- help='Select the minimum FAR value used in ROC and DET plots; '
- 'should be a power of 10.',
+ '-M', '--min-{}-value'.format(far_name.lower()), 'min_far_value',
+ type=click.FLOAT, default=dflt,
+ help='Select the minimum {} value used in ROC and DET plots; '
+ 'should be a power of 10.'.format(far_name),
callback=callback, show_default=True, **kwargs)(func)
return custom_min_far_option
@@ -585,7 +591,8 @@ def style_option(**kwargs):
return custom_style_option
-def metrics_command(docstring, criteria=('eer', 'min-hter', 'far')):
+def metrics_command(docstring, criteria=('eer', 'min-hter', 'far'),
+ far_name="FAR"):
def custom_metrics_command(func):
func.__doc__ = docstring
@@ -596,7 +603,7 @@ def metrics_command(docstring, criteria=('eer', 'min-hter', 'far')):
@output_log_metric_option()
@criterion_option(criteria)
@thresholds_option()
- @far_option()
+ @far_option(far_name=far_name)
@legends_option()
@open_file_mode_option()
@verbosity_option()
@@ -632,7 +639,7 @@ METRICS_HELP = """Prints a table that contains {names} for a given
"""
-def roc_command(docstring):
+def roc_command(docstring, far_name="FAR"):
def custom_roc_command(func):
func.__doc__ = docstring
@@ -648,7 +655,7 @@ def roc_command(docstring):
@semilogx_option(True)
@lines_at_option()
@axes_val_option()
- @min_far_option()
+ @min_far_option(far_name=far_name)
@x_rotation_option()
@x_label_option()
@y_label_option()
@@ -687,7 +694,7 @@ ROC_HELP = """Plot ROC (receiver operating characteristic) curve.
"""
-def det_command(docstring):
+def det_command(docstring, far_name="FAR"):
def custom_det_command(func):
func.__doc__ = docstring
@@ -701,7 +708,7 @@ def det_command(docstring):
@sep_dev_eval_option()
@eval_option()
@axes_val_option(dflt='0.01,95,0.01,95')
- @min_far_option()
+ @min_far_option(far_name=far_name)
@x_rotation_option(dflt=45)
@x_label_option()
@y_label_option()
@@ -785,7 +792,7 @@ EPC_HELP = """Plot EPC (expected performance curve).
"""
-def hist_command(docstring):
+def hist_command(docstring, far_name="FAR"):
def custom_hist_command(func):
func.__doc__ = docstring
@@ -799,7 +806,7 @@ def hist_command(docstring):
@no_legend_option()
@legend_ncols_option()
@criterion_option()
- @far_option()
+ @far_option(far_name=far_name)
@no_line_option()
@thresholds_option()
@subplot_option()
@@ -841,7 +848,8 @@ HIST_HELP = """ Plots histograms of positive and negatives along with threshold
"""
-def evaluate_command(docstring, criteria=('eer', 'min-hter', 'far')):
+def evaluate_command(docstring, criteria=('eer', 'min-hter', 'far'),
+ far_name="FAR"):
def custom_evaluate_command(func):
func.__doc__ = docstring
@@ -852,7 +860,7 @@ def evaluate_command(docstring, criteria=('eer', 'min-hter', 'far')):
@table_option()
@eval_option()
@criterion_option(criteria)
- @far_option()
+ @far_option(far_name=far_name)
@output_log_metric_option()
@output_plot_file_option(default_out='eval_plots.pdf')
@lines_at_option()
@@ -955,7 +963,8 @@ def n_protocols_option(required=True, **kwargs):
return custom_n_protocols_option
-def multi_metrics_command(docstring, criteria=('eer', 'min-hter', 'far')):
+def multi_metrics_command(docstring, criteria=('eer', 'min-hter', 'far'),
+ far_name="FAR"):
def custom_metrics_command(func):
func.__doc__ = docstring
@@ -967,7 +976,7 @@ def multi_metrics_command(docstring, criteria=('eer', 'min-hter', 'far')):
@output_log_metric_option()
@criterion_option(criteria)
@thresholds_option()
- @far_option()
+ @far_option(far_name=far_name)
@legends_option()
@open_file_mode_option()
@verbosity_option()