Commit dc941439 authored by Amir MOHAMMADI's avatar Amir MOHAMMADI

Merge branch 'roc' into 'master'

Enable semilogx option in roc curves

Closes #40

See merge request !63
parents b454ba50 2e014dc1
Pipeline #20261 passed with stages
in 24 minutes and 50 seconds
......@@ -36,6 +36,15 @@ def log_values(min_step=-4, counts_per_step=4):
return [math.pow(10., i * 1. / counts_per_step) for i in range(min_step * counts_per_step, 0)] + [1.]
def _semilogx(x, y, **kwargs):
# remove points were x is 0
zero_index = x == 0
x = x[~zero_index]
y = y[~zero_index]
from matplotlib import pyplot
return pyplot.semilogx(x, y, **kwargs)
def roc(negatives, positives, npoints=100, CAR=False, **kwargs):
"""Plots Receiver Operating Characteristic (ROC) curve.
......@@ -91,10 +100,11 @@ def roc(negatives, positives, npoints=100, CAR=False, **kwargs):
if not CAR:
return pyplot.plot(out[0, :], out[1, :], **kwargs)
return pyplot.semilogx(out[0, :],(1 - out[1, :]), **kwargs)
return _semilogx(out[0, :], (1 - out[1, :]), **kwargs)
def roc_for_far(negatives, positives, far_values=log_values(), **kwargs):
def roc_for_far(negatives, positives, far_values=log_values(), CAR=True,
"""Plots the 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
......@@ -127,6 +137,10 @@ def roc_for_far(negatives, positives, far_values=log_values(), **kwargs):
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].
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`.
kwargs (:py:class:`dict`, optional): Extra plotting parameters, which are
passed directly to :py:func:`matplotlib.pyplot.plot`.
......@@ -142,7 +156,10 @@ def roc_for_far(negatives, positives, far_values=log_values(), **kwargs):
from matplotlib import pyplot
from . import roc_for_far as calc
out = calc(negatives, positives, far_values)
return pyplot.semilogx(out[0, :], (1 - out[1, :]), **kwargs)
if not CAR:
return pyplot.plot(out[0, :], out[1, :], **kwargs)
return _semilogx(out[0, :], (1 - out[1, :]), **kwargs)
def precision_recall_curve(negatives, positives, npoints=100, **kwargs):
......@@ -453,7 +470,7 @@ def det_axis(v, **kwargs):
def cmc(cmc_scores, logx=True, **kwargs):
"""Plots the (cumulative) match characteristics and returns the maximum rank.
This function plots a CMC curve using the given CMC scores (:py:class:`list`:
This function plots a CMC curve using the given CMC scores (:py:class:`list`:
A list of tuples, where each tuple contains the
``negative`` and ``positive`` scores for one probe of the database).
......@@ -483,7 +500,7 @@ def cmc(cmc_scores, logx=True, **kwargs):
out = calc(cmc_scores)
if logx:
pyplot.semilogx(range(1, len(out) + 1), out, **kwargs)
_semilogx(range(1, len(out) + 1), out, **kwargs)
pyplot.plot(range(1, len(out) + 1), out, **kwargs)
......@@ -557,6 +574,6 @@ def detection_identification_curve(cmc_scores, far_values=log_values(), rank=1,
# plot curve
if logx:
return pyplot.semilogx(far_values, rates, **kwargs)
return _semilogx(far_values, rates, **kwargs)
return pyplot.plot(far_values, rates, **kwargs)
......@@ -47,12 +47,12 @@ def metrics(ctx, scores, evaluation, **kwargs):
......@@ -392,7 +392,7 @@ def legend_loc_option(dflt='best', **kwargs):
'''Get the legend location of the plot'''
def custom_legend_loc_option(func):
def callback(ctx, param, value):
ctx.meta['legend_loc'] = value.replace('-', ' ')
ctx.meta['legend_loc'] = value.replace('-', ' ') if value else value
return value
return click.option(
'-lc', '--legend-loc', default=dflt, show_default=True,
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