#!/usr/bin/env python
# encoding: utf-8
# The MIT License (MIT)
# Copyright (c) 2016 CNRS
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# AUTHORS
# Hervé BREDIN - http://herve.niderb.fr
import warnings
import numpy as np
from pyannote.metrics.binary_classification import det_curve
from pyannote.metrics.binary_classification import precision_recall_curve
import matplotlib
with warnings.catch_warnings():
warnings.simplefilter("ignore")
matplotlib.use('Agg')
import matplotlib.pyplot as plt
[docs]def plot_distributions(y_true, scores, save_to, xlim=None, nbins=100, ymax=3., dpi=150):
"""Scores distributions
This function will create (and overwrite) the following files:
- {save_to}.scores.png
- {save_to}.scores.eps
Parameters
----------
y_true : (n_samples, ) array-like
Boolean reference.
scores : (n_samples, ) array-like
Predicted score.
save_to : str
Files path prefix
"""
plt.figure(figsize=(12, 12))
if xlim is None:
xlim = (np.min(scores), np.max(scores))
bins = np.linspace(xlim[0], xlim[1], nbins)
plt.hist(scores[y_true], bins=bins, color='g', alpha=0.5, normed=True)
plt.hist(scores[~y_true], bins=bins, color='r', alpha=0.5, normed=True)
# TODO heuristic to estimate ymax from nbins and xlim
plt.ylim(0, ymax)
plt.tight_layout()
plt.savefig(save_to + '.scores.png', dpi=dpi)
plt.savefig(save_to + '.scores.eps')
plt.close()
return True
[docs]def plot_det_curve(y_true, scores, save_to,
distances=False, dpi=150):
"""DET curve
This function will create (and overwrite) the following files:
- {save_to}.det.png
- {save_to}.det.eps
- {save_to}.det.txt
Parameters
----------
y_true : (n_samples, ) array-like
Boolean reference.
scores : (n_samples, ) array-like
Predicted score.
save_to : str
Files path prefix.
distances : boolean, optional
When True, indicate that `scores` are actually `distances`
dpi : int, optional
Resolution of .png file. Defaults to 150.
Returns
-------
eer : float
Equal error rate
"""
fpr, fnr, thresholds, eer = det_curve(y_true, scores, distances=distances)
# plot DET curve
plt.figure(figsize=(12, 12))
plt.loglog(fpr, fnr, 'b')
plt.loglog([eer], [eer], 'bo')
plt.xlabel('False Positive Rate')
plt.ylabel('False Negative Rate')
plt.xlim(1e-2, 1.)
plt.ylim(1e-2, 1.)
plt.grid(True)
plt.tight_layout()
plt.savefig(save_to + '.det.png', dpi=dpi)
plt.savefig(save_to + '.det.eps')
plt.close()
# save DET curve in text file
txt = save_to + '.det.txt'
line = '{t:.6f} {fp:.6f} {fn:.6f}\n'
with open(txt, 'w') as f:
for i, (t, fp, fn) in enumerate(zip(thresholds, fpr, fnr)):
f.write(line.format(t=t, fp=fp, fn=fn))
return eer
[docs]def plot_precision_recall_curve(y_true, scores, save_to,
distances=False, dpi=150):
"""Precision/recall curve
This function will create (and overwrite) the following files:
- {save_to}.precision_recall.png
- {save_to}.precision_recall.eps
- {save_to}.precision_recall.txt
Parameters
----------
y_true : (n_samples, ) array-like
Boolean reference.
scores : (n_samples, ) array-like
Predicted score.
save_to : str
Files path prefix.
distances : boolean, optional
When True, indicate that `scores` are actually `distances`
dpi : int, optional
Resolution of .png file. Defaults to 150.
Returns
-------
auc : float
Area under precision/recall curve
"""
precision, recall, thresholds, auc = precision_recall_curve(
y_true, scores, distances=distances)
# plot P/R curve
plt.figure(figsize=(12, 12))
plt.plot(recall, precision, 'b')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.tight_layout()
plt.savefig(save_to + '.precision_recall.png', dpi=dpi)
plt.savefig(save_to + '.precision_recall.eps')
plt.close()
# save P/R curve in text file
txt = save_to + '.precision_recall.txt'
line = '{t:.6f} {p:.6f} {r:.6f}\n'
with open(txt, 'w') as f:
for i, (t, p, r) in enumerate(zip(thresholds, precision, recall)):
f.write(line.format(t=t, p=p, r=r))
return auc