From da1ba8b9a59564ac206a25cde82891d5b1937eaa Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Tue, 16 Feb 2021 16:31:11 +0100
Subject: [PATCH] Changed default colormap.
---
pysegcnn/core/graphics.py | 11 ++++++-----
1 file changed, 6 insertions(+), 5 deletions(-)
diff --git a/pysegcnn/core/graphics.py b/pysegcnn/core/graphics.py
index 0130bac..d8b4066 100644
--- a/pysegcnn/core/graphics.py
+++ b/pysegcnn/core/graphics.py
@@ -345,7 +345,7 @@ def plot_sample(x, use_bands, labels,
def plot_confusion_matrix(cm, labels, normalize=True, figsize=(10, 10),
- cmap='viridis', state_file=None, subset=None,
+ cmap='YlGnBu', state_file=None, subset=None,
outpath=os.path.join(HERE, '_graphics/')):
"""Plot the confusion matrix ``cm``.
@@ -360,7 +360,7 @@ def plot_confusion_matrix(cm, labels, normalize=True, figsize=(10, 10),
figsize : `tuple` [`int`], optional
The figure size in centimeters. The default is `(10, 10)`.
cmap : `str`, optional
- A matplotlib colormap. The default is `'viridis'`.
+ A matplotlib colormap. The default is `'YlGnBu'`.
state_file : `str` or `None` or :py:class:`pathlib.Path`, optional
Filename to save the plot to. ``state`` should be an existing model
state file ending with `'.pt'`. The default is `None`, i.e. the plot is
@@ -684,7 +684,7 @@ def plot_class_distribution(ds, figsize=(16, 9), alpha=0.5):
def plot_classification_report(report, labels, figsize=(10, 10),
- cmap='viridis', **kwargs):
+ cmap='YlGnBu', **kwargs):
"""Plot the :py:func:`sklearn.metrics.classification_report` as heatmap.
Parameters
@@ -699,7 +699,7 @@ def plot_classification_report(report, labels, figsize=(10, 10),
figsize : `tuple` [`int`], optional
The figure size in centimeters. The default is `(10, 10)`.
cmap : `str`, optional
- A matplotlib colormap. The default is `'viridis'`.
+ A matplotlib colormap. The default is `'YlGnBu'`.
**kwargs :
Additional keyword arguments passed to :py:func:`seaborn.heatmap`.
@@ -725,7 +725,8 @@ def plot_classification_report(report, labels, figsize=(10, 10),
# plot class wise statistics as heatmap
sns.heatmap(metrics, vmin=0, vmax=1, annot=True, fmt='.2f',
ax=ax, xticklabels=[c.capitalize() for c in metrics.columns],
- yticklabels=[r.capitalize() for r in metrics.index], **kwargs)
+ yticklabels=[r.capitalize() for r in metrics.index], cmap=cmap,
+ **kwargs)
# add a white line separating class-wise and average statistics
ax.plot(np.arange(0, len(metrics.columns) + 1),
--
GitLab