update: interaction and actions compute method!

tc_core_analyzer_lib/utils/compute_interaction_per_acc.py

@@ -1,9 +1,10 @@
+import copy
+
 import numpy as np
 import numpy.typing as npt
 from networkx import DiGraph
 
 from .generate_graph import make_graph
-from .activity import DiscordActivity
 
 
 def thr_int(
@@ -58,18 +59,8 @@ def thr_int(
     graph : networkx.DiGraph
         the network graph of active members
     """
-
-    ignore_axis_0_activities: list[str]
-    ignore_axis_1_activities: list[str]
-
-    if "ignore_axis_0_activities" not in kwargs:
-        ignore_axis_0_activities = []
-    else:
-        ignore_axis_0_activities = kwargs["ignore_axis_0_activities"]
-    if "ignore_axis_1_activities" not in kwargs:
-        ignore_axis_1_activities = []
-    else:
-        ignore_axis_1_activities = kwargs["ignore_axis_1_activities"]
+    ignore_axis_0_activities: list[str] = kwargs.get("ignore_axis_0_activities", [])
+    ignore_axis_1_activities: list[str] = kwargs.get("ignore_axis_1_activities", [])
 
     # int_analysis is for all actions and interactions
     int_analysis = get_analysis_vector(
@@ -83,16 +74,11 @@ def thr_int(
     # all the activities has the same interaction matrix
     # with the same shape
     matrix = np.zeros_like(int_mat[activities[0]])
-    interaction_matrix = np.zeros_like(int_mat[activities[0]])
 
     for activity in activities:
-        if activity in [DiscordActivity.Reaction, DiscordActivity.Mention, DiscordActivity.Reaction]:
-            interaction_matrix += int_mat[activity]
-
         # matrix for action and interactions
         matrix += int_mat[activity]
 
-    interaction_graph = make_graph(interaction_matrix)
     graph = make_graph(matrix)
 
     # # # TOTAL INTERACTIONS # # #
@@ -110,17 +96,26 @@ def thr_int(
 
     # # # THRESHOLDED CONNECTIONS # # #
 
+    # make copy of graph for thresholding
+    thresh_graph = copy.deepcopy(graph)
+
     # remove edges below threshold from copy
-    interaction_graph.remove_edges_from(
+    thresh_graph.remove_edges_from(
         [
             (n1, n2)
-            for n1, n2, w in interaction_graph.edges(data="weight")
+            for n1, n2, w in thresh_graph.edges(data="weight")
             if w < EDGE_STR_THR
         ]
     )
 
-    # get unweighted node degree value for each node from thresholded network
-    all_degrees_thresh = np.array([val for (_, val) in interaction_graph.degree()])
+    # preparing matrix with no `action` and just interactions
+    # actions were self-intereaction and are on diagonal
+    matrix_interaction = copy.deepcopy(matrix)
+    matrix_interaction[np.diag_indices_from[matrix_interaction]] = 0
+    graph_interaction = make_graph(matrix_interaction)
+
+    # get unweighted node degree value for each node from interaction network
+    all_degrees_thresh = np.array([val for (_, val) in graph_interaction.degree()])
 
     # compare total unweighted node degree after thresholding to threshold
     thr_uw_thr_deg = np.where(all_degrees_thresh > UW_THR_DEG_THR)[0]