added DOC and ATC

ClassifierAccuracy/util/plotting.py

@@ -0,0 +1,46 @@
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+from os import makedirs
+from os.path import join
+
+from ClassifierAccuracy.util.commons import get_method_names, open_results
+
+
+def plot_diagonal(basedir, cls_name, measure_name, dataset_name='*'):
+    methods = get_method_names()
+    results = open_results(basedir, cls_name, measure_name, dataset_name=dataset_name, method_name=methods)
+    methods, xs, ys = [], [], []
+    for method_name in results.keys():
+        methods.append(method_name)
+        xs.append(results[method_name]['true_acc'])
+        ys.append(results[method_name]['estim_acc'])
+    plotsubdir = 'all' if dataset_name=='*' else dataset_name
+    save_path = join('plots', basedir, plotsubdir, 'diagonal.png')
+    _plot_diagonal(methods, xs, ys, save_path, measure_name)
+
+
+def _plot_diagonal(methods_names, true_xs, estim_ys, save_path, measure_name, title=None):
+
+    makedirs(Path(save_path).parent, exist_ok=True)
+
+    # Create scatter plot
+    plt.figure(figsize=(10, 10))
+    plt.xlim(0, 1)
+    plt.ylim(0, 1)
+    plt.plot([0, 1], [0, 1], color='black', linestyle='--')
+
+    for (method_name, xs, ys) in zip(methods_names, true_xs, estim_ys):
+        plt.scatter(xs, ys, label=f'{method_name}', alpha=0.6)
+
+    plt.legend()
+
+    # Add labels and title
+    if title is not None:
+        plt.title(title)
+    plt.xlabel(f'True {measure_name}')
+    plt.ylabel(f'Estimated {measure_name}')
+
+    # Display the plot
+    plt.savefig(save_path)
+    plt.cla()