diff --git a/pysegcnn/core/graphics.py b/pysegcnn/core/graphics.py
index b8f2e8a7ab313c540f21578ba100ac2549055733..675de171f8572c3e485faa625691f9f4535c6b16 100644
--- a/pysegcnn/core/graphics.py
+++ b/pysegcnn/core/graphics.py
@@ -22,11 +22,13 @@ import logging
# externals
import numpy as np
+import pandas as pd
import torch
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.lines as mlines
+import seaborn as sns
from matplotlib.colors import ListedColormap, BoundaryNorm
from matplotlib.animation import ArtistAnimation
from matplotlib import cm as colormap
@@ -193,7 +195,7 @@ def plot_sample(x, use_bands, labels,
accuracy : `bool`, optional
Whether to calculate the accuracy of the predictions ``y_pred`` with
respect to the ground truth ``y``. The default is `False`
- figsize : `tuple`, optional
+ figsize : `tuple` [`int`], optional
The figure size in centimeters. The default is `(16, 9)`.
bands : `list` [`str`], optional
The bands to build the FCC. The default is `['red', 'green', 'blue']`.
@@ -351,16 +353,11 @@ def plot_confusion_matrix(cm, labels, normalize=True, figsize=(10, 10),
----------
cm : :py:class:`numpy.ndarray`
The confusion matrix.
- labels : `dict` [`int`, `dict`]
- The label dictionary. The keys are the values of the class labels
- in the ground truth ``y``. Each nested `dict` should have keys:
- ``'color'``
- A named color (`str`).
- ``'label'``
- The name of the class label (`str`).
+ labels : `list` [`str`]
+ Names of the classes.
normalize : `bool`, optional
Whether to normalize the confusion matrix. The default is `True`.
- figsize : `tuple`, optional
+ figsize : `tuple` [`int`], optional
The figure size in centimeters. The default is `(10, 10)`.
cmap : `str`, optional
A matplotlib colormap. The default is `'Blues'`.
@@ -384,7 +381,6 @@ def plot_confusion_matrix(cm, labels, normalize=True, figsize=(10, 10),
"""
# number of classes
- labels = [label['label'] for label in labels.values()]
nclasses = len(labels)
# string format to plot values of confusion matrix
@@ -461,7 +457,7 @@ def plot_loss(state_file, figsize=(10, 10), step=5,
state_file : `str` or :py:class:`pathlib.Path`
The model state file. Model state files are stored in
`pysegcnn/main/_models`.
- figsize : `tuple`, optional
+ figsize : `tuple` [`int`], optional
The figure size in centimeters. The default is `(10, 10)`.
step : `int`, optional
The step to label epochs on the x-axis labels. The default is `5`, i.e.
@@ -581,8 +577,8 @@ def plot_class_distribution(ds, figsize=(16, 9), alpha=0.5):
Returns
-------
- cls_df : :py:class:`pandas.DataFrame`
- The class distribution DataFrame.
+ fig : :py:class:`matplotlib.figure.Figure`
+ An instance of :py:class:`matplotlib.figure.Figure`.
"""
# compute class distribution
@@ -683,6 +679,66 @@ def plot_class_distribution(ds, figsize=(16, 9), alpha=0.5):
return fig
+def plot_classification_report(report, labels, figsize=(10, 10), **kwargs):
+ """Plot the :py:func:`sklearn.metrics.classification_report` as heatmap.
+
+ Parameters
+ ----------
+ report : `dict`
+ The dictionary returned by setting ``output_dict=True`` in
+ :py:func:`sklearn.metrics.classification_report`.
+ labels : `list` [`str`]
+ Names of the classes.
+ figsize : `tuple` [`int`], optional
+ The figure size in centimeters. The default is `(10, 10)`.
+ **kwargs :
+ Additional keyword arguments passed to :py:func:`seaborn.heatmap`.
+
+ Returns
+ -------
+ fig : :py:class:`matplotlib.figure.Figure`
+ An instance of :py:class:`matplotlib.figure.Figure`.
+
+ """
+ # overall accuracy
+ overall_accuracy = report['accuracy']
+
+ # convert classification report to pandas DataFrame
+ report_df = pd.DataFrame(report)
+
+ # create a DataFrame only consisting of the class-wise statistics
+ class_statistics = report_df[labels].transpose()
+
+ # create a DataFrame only consisting of the average metrics
+ avg_metrics = report_df.drop(columns=labels + ['accuracy']).transpose()
+ avg_metrics.support = 1
+
+ # convert support values to relative values
+ class_statistics.support = (class_statistics.support /
+ class_statistics.support.sum())
+
+ # merge dataframes
+ metrics = class_statistics.append(avg_metrics)
+
+ # create a figure
+ fig, ax = plt.subplots(1, 1, figsize=figsize)
+
+ # 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)
+
+ # add a white line separating class-wise and average statistics
+ ax.plot(np.arange(0, len(metrics.columns) + 1),
+ np.tile(len(labels), len(metrics.columns) + 1),
+ color='white', lw=3)
+
+ # set figure title
+ ax.set_title('Overall accuracy: {:.2f}'.format(overall_accuracy), pad=20)
+
+ return fig
+
+
class Animate(object):
"""Easily create animations with :py:mod:`matplotlib`.
diff --git a/pysegcnn/core/trainer.py b/pysegcnn/core/trainer.py
index a2e6170a7f11fadfc99f0fd314412505eeca8de6..a109d7a5999c86494f9e75cf407d032e0eadc7ab 100644
--- a/pysegcnn/core/trainer.py
+++ b/pysegcnn/core/trainer.py
@@ -42,7 +42,7 @@ from pysegcnn.core.dataset import SupportedDatasets
from pysegcnn.core.transforms import Augment
from pysegcnn.core.utils import (item_in_enum, accuracy_function,
reconstruct_scene, check_filename_length,
- array_replace, report2latex)
+ array_replace)
from pysegcnn.core.split import SupportedSplits
from pysegcnn.core.models import (SupportedModels, SupportedOptimizers,
Network)
@@ -50,7 +50,7 @@ from pysegcnn.core.uda import SupportedUdaMethods, CoralLoss, UDA_POSITIONS
from pysegcnn.core.layers import Conv2dSame
from pysegcnn.core.logging import log_conf
from pysegcnn.core.graphics import (plot_loss, plot_confusion_matrix,
- plot_sample)
+ plot_sample, plot_classification_report)
from pysegcnn.core.constants import map_labels
from pysegcnn.main.train_config import HERE
@@ -2061,7 +2061,7 @@ class NetworkInference(BaseConfig):
Path to store plots of model predictions for entire scenes.
perfmc_path : :py:class:`pathlib.Path`
Path to store plots of model performance, e.g. confusion matrix.
- models_path : :py:class:`pathlib.Path`
+ report_path : :py:class:`pathlib.Path`
Path to store the :py:func:`sklearn.metrics.classification_reports`.
models_path : :py:class:`pathlib.Path`
Path to search for model state files ``state_files``.
@@ -2365,6 +2365,18 @@ class NetworkInference(BaseConfig):
return (self.target_labels if self.apply_label_map else
self.source_labels)
+ @property
+ def class_names(self):
+ """Class label names to be predicted.
+
+ Returns
+ -------
+ labels : `list` [`str`]
+ Names of the classes.
+
+ """
+ return [label['label'] for label in self.use_labels.values()]
+
@property
def bands(self):
"""Spectral bands the model was trained with.
@@ -2570,7 +2582,7 @@ class NetworkInference(BaseConfig):
# plot inputs, ground truth and model predictions
_ = plot_sample(inputs.clip(0, 1),
self.bands,
- self.source_labels,
+ self.use_labels,
y=labels,
y_pred={model.__class__.__name__: prdctn},
accuracy=True,
@@ -2585,7 +2597,7 @@ class NetworkInference(BaseConfig):
'_eval.pt'))
def report_name(self, state_file):
- return str(state_file.name).replace(state_file.suffix, '_cr.tex')
+ return str(state_file.name).replace(state_file.suffix, '_cr.png')
def evaluate(self):
"""Evaluate the models on a defined dataset.
@@ -2608,6 +2620,9 @@ class NetworkInference(BaseConfig):
``'cm'``
The confusion matrix of the model, which is only present if
``self.cm=True`` (:py:class:`numpy.ndarray`).
+ ``report``
+ The classification report dictionary as returned by
+ :py:func:`sklearn.metrics.classification_report`.
"""
# iterate over the models to evaluate
@@ -2627,76 +2642,81 @@ class NetworkInference(BaseConfig):
# load existing model evaluation
if not self.overwrite:
inference[state.stem] = torch.load(self.eval_file(state))
- continue
else:
# overwrite existing model evaluation
LOGGER.info('Overwriting model evaluation: {}.'
.format(self.eval_file(state)))
self.eval_file(state).unlink()
- # plot loss and accuracy
- plot_loss(check_filename_length(state), outpath=self.perfmc_path)
-
- # load the target dataset to evaluate the model on
- self.trg_ds = self.load_dataset(
- state, implicit=self.implicit, test=self.test,
- domain=self.domain)
-
- # load the source dataset the model was trained on
- self.src_ds = self.load_dataset(state, test=None)
-
- # load the pretrained model
- model, _ = Network.load_pretrained_model(state)
-
- # evaluate the model on the target dataset
- output = self.predict(model)
-
- # merge predictions of the different samples
- y_true = np.asarray([v['y_true'].flatten() for _, v
- in output.items()]).flatten()
- y_pred = np.asarray([v['y_pred'].flatten() for _, v
- in output.items()]).flatten()
-
- # predictions and ground truth of the entire target dataset
- output['y_true'] = y_true
- output['y_pred'] = y_pred
-
- # classification report labels
- cr_labels = [v['label'] for _, v in self.source_labels.items()]
-
- # calculate classification report from sklearn
- report_name = self.report_path.joinpath(self.report_name(state))
- LOGGER.info('Calculating classification report: {}'
- .format(report_name))
-
- # export report to Latex table
- 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:
+ else:
- # calculate confusion matrix
- LOGGER.info('Computing confusion matrix ...')
- conf_mat = confusion_matrix(y_true, y_pred)
+ # plot loss and accuracy
+ plot_loss(check_filename_length(state),
+ outpath=self.perfmc_path)
- # add confusion matrix to model output
- output['cm'] = conf_mat
+ # load the target dataset to evaluate the model on
+ self.trg_ds = self.load_dataset(
+ state, implicit=self.implicit, test=self.test,
+ domain=self.domain)
- # plot confusion matrix
- plot_confusion_matrix(
- conf_mat, self.source_labels, state_file=state,
- subset=self.trg_ds.name,
- outpath=self.perfmc_path)
+ # load the source dataset the model was trained on
+ self.src_ds = self.load_dataset(state, test=None)
- # save model predictions to file
- LOGGER.info('Saving model evaluation: {}'
- .format(self.eval_file(state)))
- torch.save(output, self.eval_file(state))
+ # load the pretrained model
+ model, _ = Network.load_pretrained_model(state)
+
+ # evaluate the model on the target dataset
+ output = self.predict(model)
+
+ # merge predictions of the different samples
+ y_true = np.asarray([v['y_true'].flatten() for _, v
+ in output.items()]).flatten()
+ y_pred = np.asarray([v['y_pred'].flatten() for _, v
+ in output.items()]).flatten()
+
+ # predictions and ground truth of the entire target dataset
+ output['y_true'] = y_true
+ 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(report_name))
+ report = classification_report(
+ y_true, y_pred, target_names=self.class_names,
+ output_dict=True)
+
+ # store report in output dictionary
+ output['report'] = report
+
+ # plot classification report
+ fig = plot_classification_report(report, self.class_names)
+ fig.savefig(report_name, dpi=300, bbox_inches='tight')
+
+ # check whether to calculate confusion matrix
+ if self.cm:
+
+ # calculate confusion matrix
+ LOGGER.info('Computing confusion matrix ...')
+ conf_mat = confusion_matrix(y_true, y_pred)
+
+ # add confusion matrix to model output
+ output['cm'] = conf_mat
+
+ # plot confusion matrix
+ plot_confusion_matrix(
+ conf_mat, self.class_names, state_file=state,
+ subset=self.trg_ds.name,
+ outpath=self.perfmc_path)
+
+ # save model predictions to file
+ LOGGER.info('Saving model evaluation: {}'
+ .format(self.eval_file(state)))
+ torch.save(output, self.eval_file(state))
- # save model predictions to list
- inference[state.stem] = output
+ # save model predictions to list
+ inference[state.stem] = output
# check whether to aggregate the results of the different model runs
if self.aggregate:
@@ -2722,11 +2742,16 @@ class NetworkInference(BaseConfig):
report_name = self.report_path.joinpath(self.report_name(kfold))
LOGGER.info('Calculating classification report: {}'
.format(report_name))
+ report = classification_report(y_true, y_pred,
+ target_names=self.class_names,
+ output_dict=True)
+
+ # save aggregated classification report
+ inference['report'] = report
- # export aggregated report to Latex table
- report = classification_report(
- y_true, y_pred, target_names=cr_labels, output_dict=True)
- report2latex(report, filename=report_name)
+ # plot classification report
+ fig = plot_classification_report(report, self.class_names)
+ fig.savefig(report_name, dpi=300, bbox_inches='tight')
# chech whether to compute the aggregated confusion matrix
if self.cm:
@@ -2739,11 +2764,11 @@ class NetworkInference(BaseConfig):
cm_agg += output['cm']
# save aggregated confusion matrix to dictionary
- inference['cm_agg'] = cm_agg
+ inference['cm'] = cm_agg
# plot aggregated confusion matrix and save to file
plot_confusion_matrix(
- cm_agg, self.source_labels, state_file=kfold,
+ cm_agg, self.class_names, state_file=kfold,
outpath=self.perfmc_path)
return inference