From cc9c16c4309a0d6dfe7fc2c4882d71e324f1be7d Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Fri, 5 Feb 2021 17:13:06 +0100
Subject: [PATCH] Debugged classification report logging.
---
pysegcnn/core/trainer.py | 26 ++++++++++++++------------
pysegcnn/core/utils.py | 4 ++--
2 files changed, 16 insertions(+), 14 deletions(-)
diff --git a/pysegcnn/core/trainer.py b/pysegcnn/core/trainer.py
index d2bad54..7df3bca 100644
--- a/pysegcnn/core/trainer.py
+++ b/pysegcnn/core/trainer.py
@@ -2618,6 +2618,9 @@ class NetworkInference(BaseConfig):
dictConfig(log_conf(log.log_file))
log.init_log('Evaluating model: {}.'.format(state))
+ # classification report labels
+ cr_labels = [v['label'] for _, v in self.source_labels.items()]
+
# check whether model was already evaluated
if self.eval_file(state).exists():
LOGGER.info('Found existing model evaluation: {}.'
@@ -2661,15 +2664,14 @@ class NetworkInference(BaseConfig):
output['y_pred'] = y_pred
# calculate classification report from sklearn
+ report_name = self.report_path.joinpath(self.report_name(state))
LOGGER.info('Calculating classification report: {}'
- .format(self.report_path.joinpath(
- self.report_name(state))))
+ .format(report_name))
# export report to Latex table
- report = classification_report(y_true, y_pred, target_names=[
- v['label'] for _, v in self.source_labels.items()])
- report2latex(report, filename=self.report_path.joinpath(
- self.report_name(state)))
+ report = classification_report(
+ y_true, y_pred, target_names=cr_labels, output_dict=True)
+ report2latex(report, filename=report_name)
# check whether to calculate confusion matrix
if self.cm:
@@ -2712,15 +2714,15 @@ class NetworkInference(BaseConfig):
['{}'.format(mstate.name) for mstate in self.state_files]))
# calculate classification report from sklearn
+ report_name = self.report_path.joinpath(
+ self.report_name(base_name.replace(fold_number, 'kfold')))
LOGGER.info('Calculating classification report: {}'
- .format(self.report_path.joinpath(self.report_name(
- base_name.replace(fold_number, 'kfold')))))
+ .format(report_name))
# export aggregated report to Latex table
- report = classification_report(y_true, y_pred, target_names=[
- v['label'] for _, v in self.source_labels.items()])
- report2latex(report, filename=self.report_path.joinpath(
- self.report_name(base_name.replace(fold_number, 'kfold'))))
+ report = classification_report(
+ y_true, y_pred, target_names=cr_labels, output_dict=True)
+ report2latex(report, filename=report_name)
# chech whether to compute the aggregated confusion matrix
if self.cm:
diff --git a/pysegcnn/core/utils.py b/pysegcnn/core/utils.py
index caa3959..65419ad 100644
--- a/pysegcnn/core/utils.py
+++ b/pysegcnn/core/utils.py
@@ -2649,7 +2649,7 @@ def report2latex(classification_report, filename=None):
"""
# convert to pandas DataFrame and export to latex
- df = pd.DataFrame.from_dict(classification_report)
+ df = pd.DataFrame(classification_report).transpose()
# check if output filename exists
if filename is not None:
@@ -2658,4 +2658,4 @@ def report2latex(classification_report, filename=None):
filename.parent().mkdir(exist_ok=True, parents=True)
# export to latex
- df.to_latex(buf=filename)
+ df.to_latex(buf=str(filename))
--
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