From 6c7e025b68761f64bc92c2ebd26579ccedc512d2 Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Wed, 31 Mar 2021 14:49:41 +0200
Subject: [PATCH] report the matplotlib backend if scatter fails
---
bob/fusion/base/algorithm/GMM.py | 2 +-
bob/fusion/base/script/boundary.py | 396 +++++++++++++++++------------
bob/fusion/base/script/fuse.py | 7 +-
3 files changed, 236 insertions(+), 169 deletions(-)
diff --git a/bob/fusion/base/algorithm/GMM.py b/bob/fusion/base/algorithm/GMM.py
index 2b96150..82a8f4a 100644
--- a/bob/fusion/base/algorithm/GMM.py
+++ b/bob/fusion/base/algorithm/GMM.py
@@ -76,7 +76,7 @@ class GMM(AlgorithmBob):
if self.gaussians is None:
self.gaussians = array.shape[1] + 1
- logger.warn("Number of Gaussians was None. "
+ logger.warning("Number of Gaussians was None. "
"Using {}.".format(self.gaussians))
# Computes input size
diff --git a/bob/fusion/base/script/boundary.py b/bob/fusion/base/script/boundary.py
index adfcc7e..91fc77d 100644
--- a/bob/fusion/base/script/boundary.py
+++ b/bob/fusion/base/script/boundary.py
@@ -1,7 +1,5 @@
"""Plots the decision boundaries of fusion algorithms.
"""
-from __future__ import print_function, absolute_import, division
-import os
import logging
import click
from bob.extension.scripts.click_helper import verbosity_option
@@ -10,173 +8,243 @@ import numpy as np
from bob.bio.base.score import load_score
from ..tools import (
- get_gza_from_lines_list, check_consistency, get_scores, remove_nan,
- grouping)
+ get_gza_from_lines_list,
+ check_consistency,
+ get_scores,
+ remove_nan,
+ grouping,
+)
from ..algorithm import Algorithm
logger = logging.getLogger(__name__)
-def plot_boundary_decision(algorithm, scores, score_labels, threshold,
- thres_system1=None,
- thres_system2=None,
- do_grouping=False,
- resolution=2000,
- alpha=0.75,
- legends=None,
- i1=0,
- i2=1,
- x_label=None,
- y_label=None,
- **kwargs
- ):
- if legends is None:
- legends = ['Zero Effort Impostor', 'Presentation Attack', 'Genuine']
- markers = ['x', 'o', 's']
-
- if scores.shape[1] > 2:
- raise NotImplementedError(
- "Currently plotting the decision boundary for more than two "
- "systems is not supported.")
-
- import matplotlib.pyplot as plt
- plt.gca() # this is necessary for subplots to work.
-
- X = scores[:, [i1, i2]]
- Y = score_labels
- x_pad = (X[:, i1].max() - X[:, i1].min()) * 0.1
- y_pad = (X[:, i2].max() - X[:, i2].min()) * 0.1
- x_min, x_max = X[:, i1].min() - x_pad, X[:, i1].max() + x_pad
- y_min, y_max = X[:, i2].min() - y_pad, X[:, i2].max() + y_pad
- xx, yy = np.meshgrid(
- np.linspace(x_min, x_max, resolution),
- np.linspace(y_min, y_max, resolution))
-
- contourf = None
- if algorithm is not None:
- temp = np.c_[xx.ravel(), yy.ravel()]
- temp = algorithm.preprocess(temp)
- Z = (algorithm.fuse(temp) > threshold).reshape(xx.shape)
-
- contourf = plt.contour(xx, yy, Z, 1, alpha=1, cmap=plt.cm.gray)
-
- if do_grouping:
- gen = grouping(X[Y == 0, :], **kwargs)
- zei = grouping(X[Y == 1, :], **kwargs)
- atk = grouping(X[Y == 2, :], **kwargs)
- else:
- gen = X[Y == 0, :]
- zei = X[Y == 1, :]
- atk = X[Y == 2, :]
- for i, (X, color) in enumerate(((zei, 'C0'), (atk, 'C1'), (gen, 'C2'))):
- if X.size == 0:
- continue
- plt.scatter(
- X[:, 0], X[:, 1], marker=markers[i], alpha=alpha,
- c=color, label=legends[i])
- plt.legend(bbox_to_anchor=(-0.05, 1.02, 1.05, .102), loc=3,
- ncol=3, mode="expand", borderaxespad=0., fontsize=14)
-
- if thres_system1 is not None:
- plt.axvline(thres_system1, color='red')
- plt.axhline(thres_system2, color='red')
-
- plt.xlim([x_min, x_max])
- plt.ylim([y_min, y_max])
- plt.grid(True)
-
- plt.xlabel(x_label)
- plt.ylabel(y_label)
-
- return contourf
-
-
-@click.command(epilog="""\b
+def plot_boundary_decision(
+ algorithm,
+ scores,
+ score_labels,
+ threshold,
+ thres_system1=None,
+ thres_system2=None,
+ do_grouping=False,
+ resolution=2000,
+ alpha=0.75,
+ legends=None,
+ i1=0,
+ i2=1,
+ x_label=None,
+ y_label=None,
+ **kwargs,
+):
+ if legends is None:
+ legends = ["Zero Effort Impostor", "Presentation Attack", "Genuine"]
+ markers = ["x", "o", "s"]
+
+ if scores.shape[1] > 2:
+ raise NotImplementedError(
+ "Currently plotting the decision boundary for more than two "
+ "systems is not supported."
+ )
+
+ import matplotlib
+ import matplotlib.pyplot as plt
+
+ plt.gca() # this is necessary for subplots to work.
+
+ X = scores[:, [i1, i2]]
+ Y = score_labels
+ x_pad = (X[:, i1].max() - X[:, i1].min()) * 0.1
+ y_pad = (X[:, i2].max() - X[:, i2].min()) * 0.1
+ x_min, x_max = X[:, i1].min() - x_pad, X[:, i1].max() + x_pad
+ y_min, y_max = X[:, i2].min() - y_pad, X[:, i2].max() + y_pad
+ xx, yy = np.meshgrid(
+ np.linspace(x_min, x_max, resolution), np.linspace(y_min, y_max, resolution)
+ )
+
+ contourf = None
+ if algorithm is not None:
+ temp = np.c_[xx.ravel(), yy.ravel()]
+ temp = algorithm.preprocess(temp)
+ Z = (algorithm.fuse(temp) > threshold).reshape(xx.shape)
+
+ contourf = plt.contour(xx, yy, Z, 1, alpha=1, cmap=plt.cm.gray)
+
+ if do_grouping:
+ gen = grouping(X[Y == 0, :], **kwargs)
+ zei = grouping(X[Y == 1, :], **kwargs)
+ atk = grouping(X[Y == 2, :], **kwargs)
+ else:
+ gen = X[Y == 0, :]
+ zei = X[Y == 1, :]
+ atk = X[Y == 2, :]
+ for i, (X, color) in enumerate(((zei, "C0"), (atk, "C1"), (gen, "C2"))):
+ if X.size == 0:
+ continue
+ try:
+ plt.scatter(
+ X[:, 0], X[:, 1], marker=markers[i], alpha=alpha, c=color, label=legends[i]
+ )
+ except Exception as e:
+ raise RuntimeError(f"matplotlib backend: {matplotlib.get_backend()}") from e
+
+ plt.legend(
+ bbox_to_anchor=(-0.05, 1.02, 1.05, 0.102),
+ loc=3,
+ ncol=3,
+ mode="expand",
+ borderaxespad=0.0,
+ fontsize=14,
+ )
+
+ if thres_system1 is not None:
+ plt.axvline(thres_system1, color="red")
+ plt.axhline(thres_system2, color="red")
+
+ plt.xlim([x_min, x_max])
+ plt.ylim([y_min, y_max])
+ plt.grid(True)
+
+ plt.xlabel(x_label)
+ plt.ylabel(y_label)
+
+ return contourf
+
+
+@click.command(
+ epilog="""\b
Examples:
$ bob fusion boundary -vvv {sys1,sys2}/scores-eval -m /path/to/Model.pkl
-""")
-@click.argument('scores', nargs=-1, required=True,
- type=click.Path(exists=True))
-@click.option('-m', '--model-file', required=False,
- help='The path to where the algorithm will be loaded from.')
-@click.option('-t', '--threshold', type=click.FLOAT, required=False,
- help='The threshold to classify scores after fusion. Usually '
- 'calculated from fused development set.')
-@click.option('-g', '--group', type=click.INT, default=0, show_default=True,
- help='If given scores will be grouped into N samples.')
-@click.option('-G', '--grouping', type=click.Choice(('random', 'kmeans')),
- default='kmeans', show_default=True,
- help='The gouping algorithm to be used.')
-@click.option('-o', '--output', default='scatter.pdf',
- show_default=True, type=click.Path(writable=True),
- help='The path to the saved plot.')
-@click.option('-X', '--x-label', default='Recognition scores',
- show_default=True, help='The label for the first system.')
-@click.option('-Y', '--y-label', default='PAD scores', show_default=True,
- help='The label for the second system.')
-@click.option('--skip-check', is_flag=True, show_default=True,
- help='If True, it will skip checking for the consistency '
- 'between scores.')
+"""
+)
+@click.argument("scores", nargs=-1, required=True, type=click.Path(exists=True))
+@click.option(
+ "-m",
+ "--model-file",
+ required=False,
+ help="The path to where the algorithm will be loaded from.",
+)
+@click.option(
+ "-t",
+ "--threshold",
+ type=click.FLOAT,
+ required=False,
+ help="The threshold to classify scores after fusion. Usually "
+ "calculated from fused development set.",
+)
+@click.option(
+ "-g",
+ "--group",
+ type=click.INT,
+ default=0,
+ show_default=True,
+ help="If given scores will be grouped into N samples.",
+)
+@click.option(
+ "-G",
+ "--grouping",
+ type=click.Choice(("random", "kmeans")),
+ default="kmeans",
+ show_default=True,
+ help="The gouping algorithm to be used.",
+)
+@click.option(
+ "-o",
+ "--output",
+ default="scatter.pdf",
+ show_default=True,
+ type=click.Path(writable=True),
+ help="The path to the saved plot.",
+)
+@click.option(
+ "-X",
+ "--x-label",
+ default="Recognition scores",
+ show_default=True,
+ help="The label for the first system.",
+)
+@click.option(
+ "-Y",
+ "--y-label",
+ default="PAD scores",
+ show_default=True,
+ help="The label for the second system.",
+)
+@click.option(
+ "--skip-check",
+ is_flag=True,
+ show_default=True,
+ help="If True, it will skip checking for the consistency " "between scores.",
+)
@verbosity_option()
-def boundary(scores, model_file, threshold, group, grouping, output, x_label,
- y_label, skip_check, **kwargs):
- """Plots the decision boundaries of fusion algorithms.
-
- The script takes several scores (usually eval scores) from different
- biometric and pad systems and a trained algorithm and plots the decision
- boundary.
-
- You need to provide two score files from two systems. System 1 will be
- plotted on the x-axis.
- """
- # load the algorithm
- algorithm = None
- if model_file:
- algorithm = Algorithm().load(model_file)
- assert threshold is not None, "threshold must be provided with the model"
-
- # load the scores
- score_lines_list_eval = [load_score(path) for path in scores]
-
- # genuine, zero effort impostor, and attack list
- idx1, gen_le, zei_le, atk_le = get_gza_from_lines_list(
- score_lines_list_eval)
-
- # check if score lines are consistent
- if not skip_check:
- check_consistency(gen_le, zei_le, atk_le)
-
- # concatenate the scores and create the labels
- scores = get_scores(gen_le, zei_le, atk_le)
- score_labels = np.zeros((scores.shape[0],))
- gensize = gen_le[0].shape[0]
- zeisize = zei_le[0].shape[0]
- score_labels[:gensize] = 0
- score_labels[gensize: gensize + zeisize] = 1
- score_labels[gensize + zeisize:] = 2
- found_nan, nan_idx, scores = remove_nan(scores, False)
- score_labels = score_labels[~nan_idx]
-
- if found_nan:
- logger.warn('{} nan values were removed.'.format(np.sum(nan_idx)))
-
- # plot the decision boundary
- do_grouping = True
- if group < 1:
- do_grouping = False
-
- import matplotlib
- if not hasattr(matplotlib, 'backends'):
- matplotlib.use('pdf')
- import matplotlib.pyplot as plt
- plot_boundary_decision(
- algorithm, scores, score_labels, threshold,
- do_grouping=do_grouping,
- npoints=group,
- seed=0,
- gformat=grouping,
- x_label=x_label,
- y_label=y_label,
- )
- plt.savefig(output, transparent=True)
- plt.close()
+def boundary(
+ scores,
+ model_file,
+ threshold,
+ group,
+ grouping,
+ output,
+ x_label,
+ y_label,
+ skip_check,
+ **kwargs,
+):
+ """Plots the decision boundaries of fusion algorithms.
+
+ The script takes several scores (usually eval scores) from different
+ biometric and pad systems and a trained algorithm and plots the decision
+ boundary.
+
+ You need to provide two score files from two systems. System 1 will be
+ plotted on the x-axis.
+ """
+ # load the algorithm
+ algorithm = None
+ if model_file:
+ algorithm = Algorithm().load(model_file)
+ assert threshold is not None, "threshold must be provided with the model"
+
+ # load the scores
+ score_lines_list_eval = [load_score(path) for path in scores]
+
+ # genuine, zero effort impostor, and attack list
+ idx1, gen_le, zei_le, atk_le = get_gza_from_lines_list(score_lines_list_eval)
+
+ # check if score lines are consistent
+ if not skip_check:
+ check_consistency(gen_le, zei_le, atk_le)
+
+ # concatenate the scores and create the labels
+ scores = get_scores(gen_le, zei_le, atk_le)
+ score_labels = np.zeros((scores.shape[0],))
+ gensize = gen_le[0].shape[0]
+ zeisize = zei_le[0].shape[0]
+ score_labels[:gensize] = 0
+ score_labels[gensize : gensize + zeisize] = 1
+ score_labels[gensize + zeisize :] = 2
+ found_nan, nan_idx, scores = remove_nan(scores, False)
+ score_labels = score_labels[~nan_idx]
+
+ if found_nan:
+ logger.warn("{} nan values were removed.".format(np.sum(nan_idx)))
+
+ # plot the decision boundary
+ do_grouping = True
+ if group < 1:
+ do_grouping = False
+
+ import matplotlib.pyplot as plt
+
+ plot_boundary_decision(
+ algorithm,
+ scores,
+ score_labels,
+ threshold,
+ do_grouping=do_grouping,
+ npoints=group,
+ seed=0,
+ gformat=grouping,
+ x_label=x_label,
+ y_label=y_label,
+ )
+ plt.savefig(output, transparent=True)
+ plt.close()
diff --git a/bob/fusion/base/script/fuse.py b/bob/fusion/base/script/fuse.py
index ecb808b..8ad23f3 100644
--- a/bob/fusion/base/script/fuse.py
+++ b/bob/fusion/base/script/fuse.py
@@ -8,7 +8,6 @@ from bob.extension.scripts.click_helper import (
import os
import numpy as np
-from bob.io.base import create_directories_safe
from bob.bio.base.score import load_score, dump_score
from bob.bio.base import utils
@@ -45,7 +44,7 @@ kwargs: %s
def save_fused_scores(save_path, fused_scores, score_lines):
score_lines['score'] = fused_scores
gen, zei, atk, _, _, _ = get_2negatives_1positive(score_lines)
- create_directories_safe(os.path.dirname(save_path))
+ os.makedirs(os.path.dirname(save_path), exist_ok=True)
dump_score(save_path, score_lines)
dump_score(save_path + '-licit', np.append(gen, zei))
dump_score(save_path + '-spoof', np.append(gen, atk))
@@ -190,7 +189,7 @@ def fuse(scores, algorithm, groups, output_dir, model_file, skip_check, force,
click.MissingParameter
If the algorithm is not provided.
"""
- create_directories_safe(output_dir)
+ os.makedirs(output_dir, exist_ok=True)
if not model_file:
do_training = True
model_file = os.path.join(output_dir, 'Model.pkl')
@@ -303,7 +302,7 @@ def fuse(scores, algorithm, groups, output_dir, model_file, skip_check, force,
scores_eval_lines = scores_eval_lines[~nan_eval]
if found_nan:
- logger.warn('Some nan values were removed.')
+ logger.warning('Some nan values were removed.')
routine_fusion(
algorithm, model_file, scores_train_lines, scores_train,
--
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