# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
import contextlib
from itertools import cycle
import matplotlib
import numpy
from sklearn.metrics import auc
from sklearn.metrics import precision_recall_curve as pr_curve
from sklearn.metrics import roc_curve as r_curve
matplotlib.use("agg")
import logging
import matplotlib.pyplot as plt
logger = logging.getLogger(__name__)
@contextlib.contextmanager
def _precision_recall_canvas(title=None):
"""Generates a canvas to draw precision-recall curves.
Works like a context manager, yielding a figure and an axes set in which
the precision-recall curves should be added to. The figure already
contains F1-ISO lines and is preset to a 0-1 square region. Once the
context is finished, ``fig.tight_layout()`` is called.
Parameters
----------
title : :py:class:`str`, Optional
Optional title to add to this plot
Yields
------
figure : matplotlib.figure.Figure
The figure that should be finally returned to the user
axes : matplotlib.figure.Axes
An axis set where to precision-recall plots should be added to
"""
fig, axes1 = plt.subplots(1)
# Names and bounds
axes1.set_xlabel("Recall")
axes1.set_ylabel("Precision")
axes1.set_xlim([0.0, 1.0])
axes1.set_ylim([0.0, 1.0])
if title is not None:
axes1.set_title(title)
axes1.grid(linestyle="--", linewidth=1, color="gray", alpha=0.2)
axes2 = axes1.twinx()
# Annotates plot with F1-score iso-lines
f_scores = numpy.linspace(0.1, 0.9, num=9)
tick_locs = []
tick_labels = []
for f_score in f_scores:
x = numpy.linspace(0.01, 1)
y = f_score * x / (2 * x - f_score)
(l,) = plt.plot(x[y >= 0], y[y >= 0], color="green", alpha=0.1)
tick_locs.append(y[-1])
tick_labels.append("%.1f" % f_score)
axes2.tick_params(axis="y", which="both", pad=0, right=False, left=False)
axes2.set_ylabel("iso-F", color="green", alpha=0.3)
axes2.set_ylim([0.0, 1.0])
axes2.yaxis.set_label_coords(1.015, 0.97)
axes2.set_yticks(tick_locs) # notice these are invisible
for k in axes2.set_yticklabels(tick_labels):
k.set_color("green")
k.set_alpha(0.3)
k.set_size(8)
# we should see some of axes 1 axes
axes1.spines["right"].set_visible(False)
axes1.spines["top"].set_visible(False)
axes1.spines["left"].set_position(("data", -0.015))
axes1.spines["bottom"].set_position(("data", -0.015))
# we shouldn't see any of axes 2 axes
axes2.spines["right"].set_visible(False)
axes2.spines["top"].set_visible(False)
axes2.spines["left"].set_visible(False)
axes2.spines["bottom"].set_visible(False)
# yield execution, lets user draw precision-recall plots, and the legend
# before tighteneing the layout
yield fig, axes1
plt.tight_layout()
def precision_recall_f1iso(data):
"""Creates a precision-recall plot.
This function creates and returns a Matplotlib figure with a
precision-recall plot. The plot will be annotated with F1-score
iso-lines (in which the F1-score maintains the same value).
Parameters
----------
data : dict
A dictionary in which keys are strings defining plot labels and values
are dictionaries with two entries:
* ``df``: :py:class:`pandas.DataFrame`
A dataframe that is produced by our predictor engine containing
the following columns: ``filename``, ``likelihood``,
``ground_truth``.
* ``threshold``: :py:class:`list`
A threshold for each set. Not used here.
Returns
-------
figure : matplotlib.figure.Figure
A matplotlib figure you can save or display (uses an ``agg`` backend)
"""
lines = ["-", "--", "-.", ":"]
colors = [
"#1f77b4",
"#ff7f0e",
"#2ca02c",
"#d62728",
"#9467bd",
"#8c564b",
"#e377c2",
"#7f7f7f",
"#bcbd22",
"#17becf",
]
colorcycler = cycle(colors)
linecycler = cycle(lines)
with _precision_recall_canvas(title=None) as (fig, axes):
legend = []
for name, value in data.items():
df = value["df"]
# plots Recall/Precision curve
prec, recall, _ = pr_curve(df["ground_truth"], df["likelihood"])
_auc = auc(recall, prec)
label = f"{name} (AUC={_auc:.2f})"
color = next(colorcycler)
style = next(linecycler)
(line,) = axes.plot(recall, prec, color=color, linestyle=style)
legend.append((line, label))
if len(label) > 1:
axes.legend(
[k[0] for k in legend],
[k[1] for k in legend],
loc="lower left",
fancybox=True,
framealpha=0.7,
)
return fig
def roc_curve(data, title=None):
"""Creates a ROC plot.
This function creates and returns a Matplotlib figure with a
ROC plot.
Parameters
----------
data : dict
A dictionary in which keys are strings defining plot labels and values
are dictionaries with two entries:
* ``df``: :py:class:`pandas.DataFrame`
A dataframe that is produced by our predictor engine containing
the following columns: ``filename``, ``likelihood``,
``ground_truth``.
* ``threshold``: :py:class:`list`
A threshold for each set. Not used here.
Returns
-------
figure : matplotlib.figure.Figure
A matplotlib figure you can save or display (uses an ``agg`` backend)
"""
fig, axes = plt.subplots(1)
# Names and bounds
axes.set_xlabel("1 - specificity")
axes.set_ylabel("Sensitivity")
axes.set_xlim([0.0, 1.0])
axes.set_ylim([0.0, 1.0])
# we should see some of axes 1 axes
axes.spines["right"].set_visible(False)
axes.spines["top"].set_visible(False)
axes.spines["left"].set_position(("data", -0.015))
axes.spines["bottom"].set_position(("data", -0.015))
if title is not None:
axes.set_title(title)
axes.grid(linestyle="--", linewidth=1, color="gray", alpha=0.2)
plt.tight_layout()
lines = ["-", "--", "-.", ":"]
colors = [
"#1f77b4",
"#ff7f0e",
"#2ca02c",
"#d62728",
"#9467bd",
"#8c564b",
"#e377c2",
"#7f7f7f",
"#bcbd22",
"#17becf",
]
colorcycler = cycle(colors)
linecycler = cycle(lines)
legend = []
for name, value in data.items():
df = value["df"]
# plots roc curve
fpr, tpr, _ = r_curve(df["ground_truth"], df["likelihood"])
_auc = auc(fpr, tpr)
label = f"{name} (AUC={_auc:.2f})"
color = next(colorcycler)
style = next(linecycler)
(line,) = axes.plot(fpr, tpr, color=color, linestyle=style)
legend.append((line, label))
if len(label) > 1:
axes.legend(
[k[0] for k in legend],
[k[1] for k in legend],
loc="lower right",
fancybox=True,
framealpha=0.7,
)
return fig
def relevance_analysis_plot(data, title=None):
"""Create an histogram plot to show the relative importance of features.
Parameters
----------
data : :py:class:`list`
The list of values (one for each feature)
Returns
-------
figure : matplotlib.figure.Figure
A matplotlib figure you can save or display (uses an ``agg`` backend)
"""
fig, axes = plt.subplots(1, 1, figsize=(6, 6))
# Names and bounds
axes.set_xlabel("Features")
axes.set_ylabel("Importance")
# we should see some of axes 1 axes
axes.spines["right"].set_visible(False)
axes.spines["top"].set_visible(False)
if title is not None:
axes.set_title(title)
# 818C2E = likely
# F2921D = could be
# 8C3503 = unlikely
labels = [
"Cardiomegaly",
"Emphysema",
"Pleural effusion",
"Hernia",
"Infiltration",
"Mass",
"Nodule",
"Atelectasis",
"Pneumothorax",
"Pleural thickening",
"Pneumonia",
"Fibrosis",
"Edema",
"Consolidation",
]
bars = axes.bar(labels, data, color="#8C3503")
bars[2].set_color("#818C2E")
bars[4].set_color("#818C2E")
bars[10].set_color("#818C2E")
bars[5].set_color("#F2921D")
bars[6].set_color("#F2921D")
bars[7].set_color("#F2921D")
bars[11].set_color("#F2921D")
bars[13].set_color("#F2921D")
for tick in axes.get_xticklabels():
tick.set_rotation(90)
fig.tight_layout()
return fig