feat(Evaluation): Added dataset analysis evaluation function

aucmedi/evaluation/__init__.py

@@ -30,3 +30,4 @@
 from aucmedi.evaluation.fitting import evaluate_fitting
 from aucmedi.evaluation.performance import evaluate_performance
 from aucmedi.evaluation.comparison import evaluate_comparison
+from aucmedi.evaluation.dataset import evaluate_dataset

aucmedi/evaluation/dataset.py

@@ -0,0 +1,120 @@
+#==============================================================================#
+#  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 *
+# Internal libraries/scripts
+
+#-----------------------------------------------------#
+#            Evaluation - Dataset Analysis            #
+#-----------------------------------------------------#
+def evaluate_dataset(samples,
+                     labels,
+                     out_path,
+                     class_names=None,
+                     suffix=None):
+    """ Function for dataset evaluation (descriptive statistics).
+    """
+
+    # Generate barplot
+    df_cf = evalby_barplot(labels, out_path, class_names, suffix)
+
+    # Generate heatmap
+    evalby_heatmap(samples, labels, out_path, class_names, suffix)
+
+    # Return table with class distribution
+    return df_cf
+
+#-----------------------------------------------------#
+#             Dataset Analysis - Barplot              #
+#-----------------------------------------------------#
+def evalby_barplot(labels, out_path, class_names, suffix=None):
+    # compute class frequency
+    cf_list = []
+    for c in range(0, labels.shape[1]):
+        n_samples = labels.shape[0]
+        class_freq = np.sum(labels[:, c])
+        if class_names is None : curr_class = str(c)
+        else : curr_class = class_names[c]
+        class_percentage = np.round(class_freq / n_samples, 2) * 100
+        cf_list.append([curr_class, class_freq, class_percentage])
+
+    # Convert class frequency results to dataframe
+    df_cf = pd.DataFrame(np.array(cf_list),
+                         columns=["class", "class_freq", "class_perc"])
+    df_cf["class_perc"] = pd.to_numeric(df_cf["class_perc"])
+
+    # Plot class frequency results
+    fig = (ggplot(df_cf, aes("class", "class_perc", fill="class",
+                             label=class_freq))
+               + geom_bar(stat="identity", color="black")
+               + geom_text(nudge_y=3)
+               + coord_flip()
+               + ggtitle("Dataset Analysis: Class Distribution")
+               + xlab("Classes")
+               + ylab("Class Frequency (in %)")
+               + scale_y_continuous(limits=[0, 100],
+                                    breaks=np.arange(0,110,10))
+               + theme_bw()
+               + theme(legend_position="none"))
+
+    # Store figure to disk
+    filename = "plot.dataset.barplot"
+    if suffix is not None : filename += "." + str(suffix)
+    filename += ".png"
+    fig.save(filename=filename, path=out_path, width=10, height=9, dpi=200)
+
+    # Return class table
+    return df_cf
+
+
+#-----------------------------------------------------#
+#             Dataset Analysis - Heatmap              #
+#-----------------------------------------------------#
+def evalby_heatmap(samples, labels, out_path, class_names, suffix=None):
+    # Create dataframe
+    if class_names is None : df = pd.DataFrame(labels, index=samples)
+    else : df = pd.DataFrame(labels, index=samples, columns=class_names)
+
+    # Preprocess dataframe
+    df = df.reset_index()
+    df_melted = pd.melt(df, id_vars="index", var_name="class",
+                        value_name="presence")
+
+    # Plot heatmap
+    fig = (ggplot(df_melted, aes("index", "class", fill="presence"))
+               + geom_tile()
+               + coord_flip()
+               + ggtitle("Dataset Analysis: Overview")
+               + xlab("Samples")
+               + ylab("Classes")
+               + scale_fill_gradient(low="white", high="#3399FF")
+               + theme_classic()
+               + theme(legend_position="none"))
+
+    # Store figure to disk
+    filename = "plot.dataset.heatmap"
+    if suffix is not None : filename += "." + str(suffix)
+    filename += ".png"
+    fig.save(filename=filename, path=out_path, width=10, height=9, dpi=200)