scripts related to MVGC

gunfolds/conversions.py

@@ -334,6 +334,28 @@ def vec2g(v, n):
     A, B = vec2adj(v, n)
     return adjs2graph(A, B)
 
+def Glag2CG(results):
+    """Converts lag graph format to gunfolds graph format,
+   and A and B matrices representing directed and bidirected edges weights.
+
+   Args:
+       results (dict): A dictionary containing:
+           - 'graph': A 3D NumPy array of shape [N, N, 2] representing the graph structure.
+           - 'val_matrix': A NumPy array of shape [N, N, 2] storing edge weights.
+
+   Returns:
+       tuple: (graph_dict, A_matrix, B_matrix)
+   """
+
+    graph_array = results['graph']
+    bidirected_edges = np.where(graph_array == 'o-o', 1, 0).astype(int)
+    directed_edges = np.where(graph_array == '-->', 1, 0).astype(int)
+
+    graph_dict = adjs2graph(np.transpose(directed_edges[:, :, 1]), np.transpose((bidirected_edges[:, :, 0])))
+    A_matrix = results['val_matrix'][:, :, 1]
+    B_matrix = results['val_matrix'][:, :, 0]
+
+    return graph_dict, A_matrix, B_matrix
 
 def rate(u):
     """

gunfolds/scripts/MVGC_F1_improvment_undersampling.py

@@ -0,0 +1,181 @@
+import os
+from gunfolds.viz import gtool as gt
+from gunfolds.utils import bfutils
+import numpy as np
+import pandas as pd
+from gunfolds import conversions as cv
+import matplotlib.pyplot as plt
+from datetime import datetime
+from scipy.io import loadmat
+from scipy.io import savemat
+import matplotlib.patches as mpatches
+from gunfolds.scripts.datasets.simple_networks import simp_nets
+from gunfolds.scripts import my_functions as mf
+
+PreFix = 'MVGC'
+concat = True
+POSTFIX = 'Ruben_data' + 'concat' if concat else 'individual'
+
+save_results = []
+Precision_O = []
+Recall_O = []
+Precision_O2 = []
+Recall_O2 = []
+Precision_A = []
+Recall_A = []
+Precision_A2 = []
+Recall_A2 = []
+Precision_C = []
+Recall_C = []
+Precision_C2 = []
+Recall_C2 = []
+F1_O = []
+F1_A = []
+F1_C = []
+F1_O2 = []
+F1_A2 = []
+F1_C2 = []
+
+for nn in [5]:
+
+    for fl in range(1, 61):
+        num = str(fl) if fl > 9 else '0' + str(fl)
+        print('reading file:' + num)
+        if not concat:
+            data = pd.read_csv(
+                './DataSets_Feedbacks/1. Simple_Networks/Network' + str(
+                    nn) + '_amp/data_fslfilter/BOLDfslfilter_{0}.txt'.format(
+                    num), delimiter='\t')
+        else:
+            data = pd.read_csv(
+                './DataSets_Feedbacks/1. Simple_Networks/Network' + str(
+                    nn) + '_amp/data_fslfilter_concat/concat_BOLDfslfilter_{0}.txt'.format(
+                    num), delimiter='\t')
+
+        network_GT = simp_nets(nn, True)
+
+        dd = np.transpose(data.values)
+        folder = 'expo_to_mat/expo_to_mat_n' + str(nn) + '_' + ('concat' if concat else 'individual')
+        if not os.path.exists(folder):
+            os.makedirs(folder)
+        savemat(folder + '/expo_to_mat_' + str(fl) + '.mat', {'dd': dd})
+
+    for fl in range(1, 61):
+        #######presision and recall (orintattion)
+        folder_read = 'expo_to_mat/expo_to_py_n' + str(nn) + '_' + ('concat' if concat else 'individual')
+        mat_data = loadmat(folder_read + '/mat_file_' + str(fl) + '.mat')
+        mat = mat_data['sig']
+        for i in range(len(network_GT)):
+            mat[i, i] = 1
+        MVGC = cv.adjs2graph(mat, np.zeros((len(network_GT), len(network_GT))))
+        res_graph = MVGC
+        gt.plotg(MVGC, output='./figs/Gopt_GC_N' + str(nn) + '_file_' + str(fl) + '.pdf')
+
+        normal_GT = mf.precision_recall(res_graph, network_GT)
+        Precision_O.append(normal_GT['orientation']['precision'])
+        Recall_O.append(normal_GT['orientation']['recall'])
+        F1_O.append(normal_GT['orientation']['F1'])
+
+        Precision_A.append(normal_GT['adjacency']['precision'])
+        Recall_A.append(normal_GT['adjacency']['recall'])
+        F1_A.append(normal_GT['adjacency']['F1'])
+
+        Precision_C.append(normal_GT['cycle']['precision'])
+        Recall_C.append(normal_GT['cycle']['recall'])
+        F1_C.append(normal_GT['cycle']['F1'])
+
+        ###trying undersampled GT by 2
+
+        new_GT = bfutils.all_undersamples(network_GT)[1]
+        new_GT = mf.remove_bidir_edges(new_GT)
+        undersampled_GT = mf.precision_recall(res_graph, new_GT)
+        Precision_O2.append(undersampled_GT['orientation']['precision'])
+        Recall_O2.append(undersampled_GT['orientation']['recall'])
+        F1_O2.append(undersampled_GT['orientation']['F1'])
+
+        Precision_A2.append(undersampled_GT['adjacency']['precision'])
+        Recall_A2.append(undersampled_GT['adjacency']['recall'])
+        F1_A2.append(undersampled_GT['adjacency']['F1'])
+
+        Precision_C2.append(undersampled_GT['cycle']['precision'])
+        Recall_C2.append(undersampled_GT['cycle']['recall'])
+        F1_C2.append(undersampled_GT['cycle']['F1'])
+
+        # ###trying undersampled GT by 3
+        #
+        # new_GT3 = bfutils.all_undersamples(network_GT)[2]
+        # new_GT3 = mf.remove_bidir_edges(new_GT3)
+        # undersampled_GT3 = mf.precision_recall(res_graph, new_GT3)
+        # Precision_O3.append(undersampled_GT3['orientation']['precision'])
+        # Recall_O3.append(undersampled_GT3['orientation']['recall'])
+        # F1_O3.append(undersampled_GT3['orientation']['F1'])
+        #
+        # Precision_A3.append(undersampled_GT3['adjacency']['precision'])
+        # Recall_A3.append(undersampled_GT3['adjacency']['recall'])
+        # F1_A3.append(undersampled_GT3['adjacency']['F1'])
+        #
+        # Precision_C3.append(undersampled_GT3['cycle']['precision'])
+        # Recall_C3.append(undersampled_GT3['cycle']['recall'])
+        # F1_C3.append(undersampled_GT3['cycle']['F1'])
+
+now = str(datetime.now())
+now = now[:-7].replace(' ', '_')
+
+###saving files
+filename = PreFix + '_with_selfloop_nets_456_amp_' + now + '_' + ('concat' if concat else 'individual')
+
+# Data for group 1
+data_group1 = [
+    [Precision_O, Recall_O, F1_O],
+    [Precision_A, Recall_A, F1_A],
+    [Precision_C, Recall_C, F1_C]
+]
+
+# Data for group 2
+data_group2 = [
+    [Precision_O2, Recall_O2, F1_O2],
+    [Precision_A2, Recall_A2, F1_A2],
+    [Precision_C2, Recall_C2, F1_C2]
+]
+
+# data_group3 = [
+#     [Precision_O3, Recall_O3, F1_O3],
+#     [Precision_A3, Recall_A3, F1_A3],
+#     [Precision_C3, Recall_C3, F1_C3]
+# ]
+
+# Labels and titles for subplots
+titles = ['Orientation', 'Adjacency', '2 cycles']
+colors = ['blue', 'orange']
+
+fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 5))
+
+for i, (data1, data2, title) in enumerate(zip(data_group1, data_group2, titles)):
+    ax1 = axes[i]
+    ax2 = ax1.twinx()
+    ax1.boxplot(data1, positions=np.array(range(len(data1))) * 2.0 - 0.4, patch_artist=True, showmeans=True,
+                boxprops=dict(facecolor=colors[0]), widths=0.6)
+    ax2.boxplot(data2, positions=np.array(range(len(data2))) * 2.0 + 0.4, patch_artist=True, showmeans=True,
+                boxprops=dict(facecolor=colors[1]), widths=0.6)
+
+
+    ax1.set_xticks(range(0, len(data1) * 2, 2))
+    ax1.set_xticklabels(['Precision', 'Recall', 'F1-score'])
+    ax1.set_xlabel('Metrics')
+    ax1.set_ylabel('Value')
+    ax1.set_title(f'({title})')
+    ax1.grid(True)
+    ax1.set_ylim(0, 1)
+    ax2.set_ylim(0, 1)
+# Add super title
+plt.suptitle('Networks 4,5,6 ' + ('concat' if concat else 'individual' ) + ' data')
+# Legend
+blue_patch = mpatches.Patch(color='blue', label='ORG. GT')
+orange_patch = mpatches.Patch(color='orange', label='GT^2')
+plt.legend(handles=[blue_patch, orange_patch], loc='upper right')
+
+plt.tight_layout()
+
+# Save the figure
+plt.savefig(filename + '_grouped_boxplot.png')
+plt.close()

gunfolds/scripts/MVGC_expo_impo.py

@@ -336,7 +336,7 @@ def precision_recall_2cycles(graph1, graph2):
                     else:
                         TP += 1
                 else:
-                    if (edge[1], edge[0]) in GT_nx.edges():
+                    if (edge[1], edge[0]) in GT_nx.edges() and (edge[1] != edge[0]):
                         FN += 0.5
                     else:
                         FN += 1