Prettify visualize_preferences.py

Merge branch 'main' of https://github.com/monikajot/llm-ethics-2

calculate_scores.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-from mock_results import single_preference_var, pair_preference_var, pair_preference_gpt_4o
+from mock_results import single_preference_var, pair_preference_var, pair_preference_gpt_4o, pair_preference_gpt_35, triple_preference_gpt_35
 import matplotlib.pyplot as plt
 from constants import MORAL_VALUES
 import numpy as np
@@ -23,17 +23,17 @@ def get_single_prefs_means_var():
 
 
 def normalise_pair_prefs(pair_preference_var):
-    first_comb = pair_preference_var[0]
+    first_comb = pair_preference_var
     num = sum(first_comb[list(first_comb.keys())[0]].values())
     # print(num)
     normalised_pair_prefs = []
-    for i, preference_list in enumerate(pair_preference_var):
-        new_dict = {}
-        for comb, results in preference_list.items():
-            new_dict[comb] = {k: int(v * 100 / num) for k, v in results.items()}
-        normalised_pair_prefs.append(new_dict)
+    # for i, preference_list in enumerate(pair_preference_var):
+    new_dict = {}
+    for comb, results in pair_preference_var.items():
+        new_dict[comb] = {k: int(v * 100 / num) for k, v in results.items()}
+    # normalised_pair_prefs.append(new_dict)
     # print(normalised_pair_prefs)
-    return normalised_pair_prefs
+    return new_dict# normalised_pair_prefs[0]
 
 
 def pair_preference_matrix(preference_dict):
@@ -85,18 +85,26 @@ def get_pair_pref_ranking(pair_preferences=pair_preference_var[0]):
     matrix, preference_matrix = pair_preference_matrix(pair_preferences)
     # best_ranking, min_distance = kemeny_young_method(MORAL_VALUES, pair_preference_var[0])
     # print("Kemeny-Young ranking:", best_ranking, min_distance)
-    # matrix["row_sums"] = matrix.apply(sum, axis=1)
-    # matrix["row_sums"] = matrix["row_sums"].apply(int)
+    for col in matrix.columns:
+        matrix[col] = matrix[col].astype(int)
+    new_matrix = matrix
+    new_matrix["row_sums"] = new_matrix.apply(sum, axis=1)
+    new_matrix["row_sums"] = new_matrix["row_sums"].apply(int)
+    new_matrix = new_matrix.sort_values(by="row_sums", ascending=False)
+
+    matrix = new_matrix[MORAL_VALUES]
+
 
     rankings = kemeny_young(preference_matrix)
 
     # total_scores = sorted(zip(MORAL_VALUES, rankings, matrix["row_sums"].values), key=lambda x:x[1])
-    print(matrix)
+    # print(new_matrix)
     print("Kemeny-Young Ranking Scores:", rankings)
     # print(matrix["row_sums"].values)
     sns.heatmap(matrix, cmap="Blues",annot=True, fmt=".3g")
     plt.show()
     return matrix, rankings
 
+
 if __name__ == "__main__":
-    get_pair_pref_ranking(pair_preference_gpt_4o)
+    get_pair_pref_ranking(normalise_pair_prefs(pair_preference_gpt_35))

check_inconsistencies.py

@@ -26,15 +26,20 @@
 
 
 def plot(what):
-    nodes = [x for x, _ in what]
+    nodes = [x for x in what]
 
     edges = [(nodes[i], nodes[i + 1]) for i in range(len(nodes) - 1)]
     node_coords = {node: (0, 10 * i) for i, node in enumerate(nodes)}
     print(edges)
     plot_single_graph(edges=edges, node_coords=node_coords)
 
+<<<<<<< HEAD
 
 plot(ordered_pair_prefs_list)
+=======
+plot(['liberty', 'sanctity', 'loyalty', 'care', 'authority', 'fairness'])
+# plot(ordered_total_prefs_list)
+>>>>>>> c22fd9004dad46f4af85f6d77864a8ea121fc53e
 # check  monotonic ordering 1 vs 2
 
 # check  monotonic ordering 1 vs all

mock_results.py

@@ -282,6 +282,8 @@
 
 #graph
 pair_preference_gpt_35 = {('authority', 'care'): {'authority': 335, 'care': 741, 'neither': 3}, ('authority', 'fairness'): {'authority': 433, 'fairness': 642, 'neither': 4}, ('authority', 'liberty'): {'authority': 545, 'liberty': 525, 'neither': 9}, ('authority', 'loyalty'): {'authority': 733, 'loyalty': 331, 'neither': 15}, ('authority', 'sanctity'): {'authority': 592, 'sanctity': 482, 'neither': 5}, ('care', 'fairness'): {'care': 600, 'fairness': 478, 'neither': 1}, ('care', 'liberty'): {'care': 687, 'liberty': 386, 'neither': 6}, ('care', 'loyalty'): {'care': 900, 'loyalty': 170, 'neither': 9}, ('care', 'sanctity'): {'care': 797, 'sanctity': 276, 'neither': 6}, ('fairness', 'liberty'): {'fairness': 673, 'liberty': 401, 'neither': 5}, ('fairness', 'loyalty'): {'fairness': 790, 'loyalty': 281, 'neither': 8}, ('fairness', 'sanctity'): {'fairness': 674, 'sanctity': 403, 'neither': 2}, ('liberty', 'loyalty'): {'liberty': 717, 'loyalty': 343, 'neither': 19}, ('liberty', 'sanctity'): {'liberty': 606, 'sanctity': 460, 'neither': 13}, ('loyalty', 'sanctity'): {'loyalty': 383, 'sanctity': 678, 'neither': 18}}
+triple_preference_gpt_35 = {('authority', 'care', 'fairness'): {'authority': 305, 'care': 427, 'fairness': 345, 'neither': 2}, ('authority', 'care', 'liberty'): {'authority': 332, 'care': 450, 'liberty': 287, 'neither': 10}, ('authority', 'care', 'loyalty'): {'authority': 317, 'care': 468, 'loyalty': 285, 'neither': 9}, ('authority', 'care', 'sanctity'): {'authority': 293, 'care': 474, 'sanctity': 300, 'neither': 12}, ('authority', 'fairness', 'liberty'): {'authority': 344, 'fairness': 389, 'liberty': 341, 'neither': 5}, ('authority', 'fairness', 'loyalty'): {'authority': 331, 'fairness': 427, 'loyalty': 314, 'neither': 7}, ('authority', 'fairness', 'sanctity'): {'authority': 339, 'fairness': 385, 'sanctity': 352, 'neither': 3}, ('authority', 'liberty', 'loyalty'): {'authority': 387, 'liberty': 364, 'loyalty': 312, 'neither': 16}, ('authority', 'liberty', 'sanctity'): {'authority': 407, 'liberty': 333, 'sanctity': 332, 'neither': 7}, ('authority', 'loyalty', 'sanctity'): {'authority': 384, 'loyalty': 300, 'sanctity': 382, 'neither': 13}, ('care', 'fairness', 'liberty'): {'care': 417, 'fairness': 357, 'liberty': 301, 'neither': 4}, ('care', 'fairness', 'loyalty'): {'care': 416, 'fairness': 370, 'loyalty': 285, 'neither': 8}, ('care', 'fairness', 'sanctity'): {'care': 434, 'fairness': 347, 'sanctity': 289, 'neither': 9}, ('care', 'liberty', 'loyalty'): {'care': 463, 'liberty': 278, 'loyalty': 320, 'neither': 18}, ('care', 'liberty', 'sanctity'): {'care': 452, 'liberty': 304, 'sanctity': 314, 'neither': 9}, ('care', 'loyalty', 'sanctity'): {'care': 456, 'loyalty': 277, 'sanctity': 325, 'neither': 21}, ('fairness', 'liberty', 'loyalty'): {'fairness': 416, 'liberty': 344, 'loyalty': 306, 'neither': 13}, ('fairness', 'liberty', 'sanctity'): {'fairness': 418, 'liberty': 312, 'sanctity': 344, 'neither': 5}, ('fairness', 'loyalty', 'sanctity'): {'fairness': 415, 'loyalty': 309, 'sanctity': 339, 'neither': 16}, ('liberty', 'loyalty', 'sanctity'): {'liberty': 360, 'loyalty': 330, 'sanctity': 369, 'neither': 20}}
+results_trp = [3., 5. ,4., 1., 0., 2.]
 
 pair_preference_gpt_4o = {('authority', 'care'): {'authority': 168, 'care': 368, 'neither': 4}, ('authority', 'fairness'): {'authority': 229, 'fairness': 310, 'neither': 1}, ('authority', 'liberty'): {'authority': 223, 'liberty': 316, 'neither': 1}, ('authority', 'loyalty'): {'authority': 376, 'loyalty': 157, 'neither': 7}, ('authority', 'sanctity'): {'authority': 287, 'sanctity': 250, 'neither': 3}, ('care', 'fairness'): {'care': 322, 'fairness': 217, 'neither': 1}, ('care', 'liberty'): {'care': 365, 'liberty': 175, 'neither': 0}, ('care', 'loyalty'): {'care': 468, 'loyalty': 68, 'neither': 4}, ('care', 'sanctity'): {'care': 413, 'sanctity': 127, 'neither': 0}, ('fairness', 'liberty'): {'fairness': 299, 'liberty': 241, 'neither': 0}, ('fairness', 'loyalty'): {'fairness': 417, 'loyalty': 121, 'neither': 2}, ('fairness', 'sanctity'): {'fairness': 332, 'sanctity': 207, 'neither': 1}, ('liberty', 'loyalty'): {'liberty': 406, 'loyalty': 128, 'neither': 6}, ('liberty', 'sanctity'): {'liberty': 320, 'sanctity': 218, 'neither': 2}, ('loyalty', 'sanctity'): {'loyalty': 160, 'sanctity': 376, 'neither': 4}}
 triple_preference_gpt_4o = {('authority', 'care', 'fairness'): {'authority': 143, 'care': 222, 'fairness': 175, 'neither': 0}, ('authority', 'care', 'liberty'): {'authority': 157, 'care': 231, 'liberty': 152, 'neither': 0}, ('authority', 'care', 'loyalty'): {'authority': 152, 'care': 256, 'loyalty': 132, 'neither': 0}, ('authority', 'care', 'sanctity'): {'authority': 142, 'care': 254, 'sanctity': 143, 'neither': 1}, ('authority', 'fairness', 'liberty'): {'authority': 157, 'fairness': 218, 'liberty': 165, 'neither': 0}, ('authority', 'fairness', 'loyalty'): {'authority': 182, 'fairness': 229, 'loyalty': 128, 'neither': 1}, ('authority', 'fairness', 'sanctity'): {'authority': 163, 'fairness': 220, 'sanctity': 156, 'neither': 1}, ('authority', 'liberty', 'loyalty'): {'authority': 203, 'liberty': 205, 'loyalty': 131, 'neither': 1}, ('authority', 'liberty', 'sanctity'): {'authority': 156, 'liberty': 205, 'sanctity': 178, 'neither': 1}, ('authority', 'loyalty', 'sanctity'): {'authority': 190, 'loyalty': 152, 'sanctity': 196, 'neither': 2}, ('care', 'fairness', 'liberty'): {'care': 201, 'fairness': 193, 'liberty': 146, 'neither': 0}, ('care', 'fairness', 'loyalty'): {'care': 224, 'fairness': 190, 'loyalty': 124, 'neither': 2}, ('care', 'fairness', 'sanctity'): {'care': 204, 'fairness': 196, 'sanctity': 140, 'neither': 0}, ('care', 'liberty', 'loyalty'): {'care': 249, 'liberty': 141, 'loyalty': 146, 'neither': 4}, ('care', 'liberty', 'sanctity'): {'care': 246, 'liberty': 149, 'sanctity': 142, 'neither': 3}, ('care', 'loyalty', 'sanctity'): {'care': 259, 'loyalty': 137, 'sanctity': 143, 'neither': 1}, ('fairness', 'liberty', 'loyalty'): {'fairness': 226, 'liberty': 180, 'loyalty': 133, 'neither': 1}, ('fairness', 'liberty', 'sanctity'): {'fairness': 224, 'liberty': 172, 'sanctity': 143, 'neither': 1}, ('fairness', 'loyalty', 'sanctity'): {'fairness': 238, 'loyalty': 136, 'sanctity': 165, 'neither': 1}, ('liberty', 'loyalty', 'sanctity'): {'liberty': 202, 'loyalty': 150, 'sanctity': 184, 'neither': 4}}

triple_preprocess.py

@@ -3,9 +3,9 @@
 import seaborn as sns
 import matplotlib.pyplot as plt
 
-from mock_results import triple_preference
+from mock_results import triple_preference, triple_preference_gpt_35
 from constants import MORAL_VALUES
-
+from calculate_scores import kemeny_young
 
 def tuple_to_pairwise_matrix(triples):
     # Get all unique items and map them to indices
@@ -37,4 +37,32 @@ def triples_to_preference_matrix(triple_preference):
     df = pd.DataFrame(pairwise_matrix, columns=MORAL_VALUES, index=MORAL_VALUES)
     return df
 
+def get_triple_pref_ranking(preference_matrix):
+    # best_ranking, min_distance = kemeny_young_method(MORAL_VALUES, pair_preference_var[0])
+    # print("Kemeny-Young ranking:", best_ranking, min_distance)
+
+    matrix = pd.DataFrame(data=preference_matrix, columns=MORAL_VALUES, index=MORAL_VALUES)
+    matrix["row_sums"] = matrix.apply(sum, axis=1)
+    matrix["row_sums"] = matrix["row_sums"].apply(int)
+    matrix = matrix.sort_values(by="row_sums", ascending=False)
+
+    matrix = matrix[MORAL_VALUES]
+    for col in matrix.columns:
+        matrix[col] = matrix[col].astype(int)
+        matrix[col] = matrix[col].apply(lambda val: val*100/1079/5)
+    rankings = kemeny_young(preference_matrix)
+
+    # total_scores = sorted(zip(MORAL_VALUES, rankings, matrix["row_sums"].values), key=lambda x:x[1])
+    print(matrix)
+    print("Kemeny-Young Ranking Scores:", rankings)
+    # print(matrix["row_sums"].values)
+    sns.heatmap(matrix, cmap="Blues",annot=True, fmt=".0f")
+    plt.show()
+    return rankings
+
+if __name__ == "__main__":
+    matrix = triples_to_preference_matrix(triple_preference_gpt_35)
+    print(matrix)
+    x= get_triple_pref_ranking(matrix.to_numpy())
+    # print([MORAL_VALUES[int(i)] for i in x ])
 

visualize_preference_rankings.py

@@ -144,7 +144,7 @@ def plot_single_graph(edges, node_coords, output_filename="single_graph.png"):
     ax.margins(0.20)
     plt.axis("off")
     plt.show()
-    plt.close()
+    # plt.close()
     # ax.figure.savefig(output_filename)
 
 def get_counts(new_dict):