feat(Evaluation): Implemented fitting curve plotting

aucmedi/evaluation/fitting.py

@@ -0,0 +1,99 @@
+#==============================================================================#
+#  Author:       Dominik Müller                                                #
+#  Copyright:    2022 IT-Infrastructure for Translational Medical Research,    #
+#                University of Augsburg                                        #
+#                                                                              #
+#  This program is free software: you can redistribute it and/or modify        #
+#  it under the terms of the GNU General Public License as published by        #
+#  the Free Software Foundation, either version 3 of the License, or           #
+#  (at your option) any later version.                                         #
+#                                                                              #
+#  This program is distributed in the hope that it will be useful,             #
+#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #
+#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #
+#  GNU General Public License for more details.                                #
+#                                                                              #
+#  You should have received a copy of the GNU General Public License           #
+#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #
+#==============================================================================#
+#-----------------------------------------------------#
+#                   Library imports                   #
+#-----------------------------------------------------#
+# External Libraries
+import numpy as np
+import pandas as pd
+import os
+from plotnine import *
+
+#-----------------------------------------------------#
+#              Evaluation - Plot Fitting              #
+#-----------------------------------------------------#
+def evaluate_fitting(keras_history, out_path, monitor=["loss"],
+                     prefix_split=".", suffix=None):
+    """ blabla
+
+    Args:
+        monitor (list of str):      List of metrics which should be visualized in the fitting plot.
+        suffix (str):               Special suffix to add in the created figure filename.
+
+    """
+    # Convert to pandas dataframe
+    dt = pd.DataFrame.from_dict(keras_history, orient="columns")
+
+    # Identify all selected colums
+    selected_cols = []
+    for key in keras_history:
+        for m in monitor:
+            if m in key:
+                selected_cols.append(key)
+                break
+
+    # Add epoch column
+    dt["epoch"] = dt.index + 1
+    # Melt dataframe
+    dt_melted = dt.melt(id_vars=["epoch"],
+                        value_vars=selected_cols,
+                        var_name="metric",
+                        value_name="score")
+
+    # Handle special prefix tags (if split-able by '.')
+    if prefix_split is not None:
+        for c in selected_cols:
+            valid_split = True
+            if prefix_split not in c:
+                valid_split = False
+                break
+
+        if valid_split:
+            dt_melted[["prefix", "metric"]] = dt_melted["metric"].str.split(".",
+                                                        expand=True)
+
+    # Preprocess dataframe
+    dt_melted["subset"] = np.where(dt_melted["metric"].str.startswith("val_"),
+                                   "validation", "training")
+    dt_melted["metric"] = dt_melted["metric"].apply(remove_val_tag)
+
+    # Plot results via plotnine
+    fig = (ggplot(dt_melted, aes("epoch", "score", color="subset"))
+               + geom_line(size=1)
+               + ggtitle("Fitting Curve during Training Process")
+               + xlab("Epoch")
+               + ylab("Score")
+               + scale_colour_discrete(name="Subset")
+               + theme_bw()
+               + theme(subplots_adjust={'wspace':0.15}))
+
+    if prefix_split is not None and valid_split:
+        fig += facet_grid("prefix ~ metric")
+    else : fig += facet_wrap("metric", scales="free_y")
+
+    # Store figure to disk
+    fig.save(filename="plot.fitting_course." + str(suffix) + ".png",
+             path=out_path, dpi=200, limitsize=False)
+
+#-----------------------------------------------------#
+#                     Subroutines                     #
+#-----------------------------------------------------#
+def remove_val_tag(x):
+    if x.startswith("val_") : return x[4:]
+    else : return x