From f64532c3aa2c08d9c6723664f2433d94df644c70 Mon Sep 17 00:00:00 2001
From: Nikolai Hartmann <Nikolai.Hartmann@physik.uni-muenchen.de>
Date: Mon, 20 Aug 2018 11:17:47 +0200
Subject: [PATCH] in addition to saving, display plots in interactive mode
---
compare.py | 25 ++++++++++++++-----------
plotting.py | 10 ++++++++++
toolkit.py | 17 +++++++++--------
3 files changed, 33 insertions(+), 19 deletions(-)
diff --git a/compare.py b/compare.py
index 7e4c9a8..98fc4eb 100755
--- a/compare.py
+++ b/compare.py
@@ -8,6 +8,7 @@ import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
from .toolkit import ClassificationProject
+from .plotting import save_show
"""
A few functions to compare different setups
@@ -62,7 +63,7 @@ def overlay_ROC(filename, *projects, **kwargs):
if plot_thresholds:
# to fit right y-axis description
fig.tight_layout()
- fig.savefig(filename)
+ save_show(plt, fig, filename)
plt.close(fig)
def overlay_loss(filename, *projects, **kwargs):
@@ -78,22 +79,24 @@ def overlay_loss(filename, *projects, **kwargs):
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
+ fig, ax = plt.subplots()
+
for p,color in zip(projects,colors):
hist_dict = p.csv_hist
- plt.plot(hist_dict['loss'], linestyle='--', label="Training Loss "+p.name, color=color)
- plt.plot(hist_dict['val_loss'], label="Validation Loss "+p.name, color=color)
+ ax.plot(hist_dict['loss'], linestyle='--', label="Training Loss "+p.name, color=color)
+ ax.plot(hist_dict['val_loss'], label="Validation Loss "+p.name, color=color)
- plt.ylabel('loss')
- plt.xlabel('epoch')
+ ax.set_ylabel('loss')
+ ax.set_xlabel('epoch')
if log:
- plt.yscale("log")
+ ax.set_yscale("log")
if xlim is not None:
- plt.xlim(*xlim)
+ ax.set_xlim(*xlim)
if ylim is not None:
- plt.ylim(*ylim)
- plt.legend(loc='upper right')
- plt.savefig(filename)
- plt.clf()
+ ax.set_ylim(*ylim)
+ ax.legend(loc='upper right')
+ save_show(plt, fig, filename)
+ plt.close(fig)
diff --git a/plotting.py b/plotting.py
index ae25dfc..c8a7425 100644
--- a/plotting.py
+++ b/plotting.py
@@ -20,6 +20,16 @@ logger.addHandler(logging.NullHandler())
Some further plotting functions
"""
+def save_show(plt, fig, filename):
+ "Save a figure and show it in case we are in ipython or jupyter notebook."
+ fig.savefig(filename)
+ try:
+ get_ipython
+ plt.show()
+ except NameError:
+ pass
+
+
def get_mean_event(x, y, class_label):
return [np.mean(x[y==class_label][:,var_index]) for var_index in range(x.shape[1])]
diff --git a/toolkit.py b/toolkit.py
index d366a56..b02ae40 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -42,6 +42,7 @@ from keras import backend as K
import matplotlib.pyplot as plt
from .utils import WeightedRobustScaler, weighted_quantile, poisson_asimov_significance
+from .plotting import save_show
# configure number of cores
# this doesn't seem to work, but at least with these settings keras only uses 4 processes
@@ -901,10 +902,10 @@ class ClassificationProject(object):
logger.info("Create/Update scores for train/test sample")
if do_test:
- self.scores_test = self.predict(self.x_test, mode=mode)
+ self.scores_test = self.predict(self.x_test, mode=mode).reshape(-1)
self._dump_to_hdf5("scores_test")
if do_train:
- self.scores_train = self.predict(self.x_train, mode=mode)
+ self.scores_train = self.predict(self.x_train, mode=mode).reshape(-1)
self._dump_to_hdf5("scores_train")
@@ -1053,7 +1054,7 @@ class ClassificationProject(object):
plot_dir = os.path.join(self.project_dir, "plots")
if not os.path.exists(plot_dir):
os.mkdir(plot_dir)
- fig.savefig(os.path.join(plot_dir, "var_{}.pdf".format(var_index)))
+ save_show(plt, fig, os.path.join(plot_dir, "var_{}.pdf".format(var_index)))
plt.close(fig)
@@ -1063,12 +1064,12 @@ class ClassificationProject(object):
sig = self.w_train_tot[self.y_train == 1]
ax.hist(bkg, bins=bins, range=range, color="b", alpha=0.5)
ax.set_yscale("log")
- fig.savefig(os.path.join(self.project_dir, "eventweights_bkg.pdf"))
+ save_show(plt, fig, os.path.join(self.project_dir, "eventweights_bkg.pdf"))
plt.close(fig)
fig, ax = plt.subplots()
ax.hist(sig, bins=bins, range=range, color="r", alpha=0.5)
ax.set_yscale("log")
- fig.savefig(os.path.join(self.project_dir, "eventweights_sig.pdf"))
+ save_show(plt, fig, os.path.join(self.project_dir, "eventweights_sig.pdf"))
plt.close(fig)
@@ -1147,7 +1148,7 @@ class ClassificationProject(object):
if apply_class_weight:
ax.set_title("Class weights applied")
ax.legend(loc='upper center', framealpha=0.5)
- fig.savefig(os.path.join(self.project_dir, "scores.pdf"))
+ save_show(plt, fig, os.path.join(self.project_dir, "scores.pdf"))
plt.close(fig)
@@ -1203,7 +1204,7 @@ class ClassificationProject(object):
ax.set_xlabel("Cut on NN score")
ax.set_ylabel("Significance")
ax.legend(loc='lower center', framealpha=0.5)
- fig.savefig(os.path.join(self.project_dir, "significances_hist.pdf"))
+ save_show(plt, fig, os.path.join(self.project_dir, "significances_hist.pdf"))
plt.close(fig)
@@ -1276,7 +1277,7 @@ class ClassificationProject(object):
ax.set_xlim(0, 1)
ax2.set_ylabel("Threshold")
ax.legend()
- fig.savefig(os.path.join(self.project_dir, "significances.pdf"))
+ save_show(plt, fig, os.path.join(self.project_dir, "significances.pdf"))
plt.close(fig)
--
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