minor

examples/tuning/get_important_pattern.py

@@ -10,20 +10,21 @@
 import seaborn as sns
 from mlxtend.frequent_patterns import apriori
 from mlxtend.preprocessing import TransactionEncoder
+from mlxtend.frequent_patterns import association_rules
 from networkx import parse_adjlist
 from scipy import stats
 
 metric_name = "acc"
 ascending = False
 
 
-def get_important_pattern(test_accs, vis=True, alpha=0.8, title=""):
+def get_important_pattern(test_accs, vis=True, alpha=0.8, title="",test_acc_names=None):
     medians = [np.median(group) for group in test_accs]
     _, p_value = stats.kruskal(*test_accs)
     if vis:
         fig = plt.figure(figsize=(12, 4))
         sns.boxplot(data=test_accs)
-        plt.xticks(list(range(len(test_accs))), [f"{i}" for i in range(len(test_accs))])
+        plt.xticks(list(range(len(test_accs))), ([f"{i}" for i in range(len(test_accs))] if  test_acc_names is None else test_acc_names),rotation=45, fontsize=10)
         plt.title(title)
         plt.show()
     if p_value < alpha:
@@ -71,7 +72,7 @@ def get_com(step2_data, r=2, alpha=0.8, columns=None, vis=True):
             test_accs_arrays.append({"name": g[0], metric_name: list(g[1][metric_name])})
         test_accs = [i[metric_name] for i in test_accs_arrays]
         test_acc_names = [i["name"] for i in test_accs_arrays]
-        final_ranks = get_important_pattern(test_accs, alpha=alpha, title=" ".join(list(com)), vis=vis)
+        final_ranks = get_important_pattern(test_accs, alpha=alpha, title=" ".join(list(com)), vis=vis,test_acc_names=[" ".join(test_acc_name) for test_acc_name in test_acc_names])
         if len(final_ranks) > 0:
             max_rank = max(final_ranks)
             max_rank_count = final_ranks.count(max_rank)
@@ -82,9 +83,57 @@ def get_com(step2_data, r=2, alpha=0.8, columns=None, vis=True):
                             print(f"index={index},name={test_acc_name},rank={rank}")
                         ans.append(test_acc_name if isinstance(test_acc_name, tuple) else (test_acc_name, ))
     return ans
-
-
-def get_frequent_itemsets(step2_data, threshold_per=0.1):
+def draw_graph(rules, rules_to_show):
+  import networkx as nx  
+  G1 = nx.DiGraph()
+
+  color_map=[]
+  N = 50
+  colors = np.random.rand(N)    
+  strs=['R0', 'R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', 'R9', 'R10', 'R11']   
+
+
+  for i in range (rules_to_show):      
+    G1.add_nodes_from(["R"+str(i)])
+
+
+    for a in rules.iloc[i]['antecedents']:
+
+        G1.add_nodes_from([a])
+
+        G1.add_edge(a, "R"+str(i), color=colors[i] , weight = 2)
+
+    for c in rules.iloc[i]['consequents']:
+
+            G1.add_nodes_from([c])
+
+            G1.add_edge("R"+str(i), c, color=colors[i],  weight=2)
+
+  for node in G1:
+       found_a_string = False
+       for item in strs: 
+           if node==item:
+                found_a_string = True
+       if found_a_string:
+            color_map.append('yellow')
+       else:
+            color_map.append('green')       
+
+
+
+  edges = G1.edges()
+  colors = [G1[u][v]['color'] for u,v in edges]
+  weights = [G1[u][v]['weight'] for u,v in edges]
+
+  pos = nx.spring_layout(G1, k=16, scale=1)
+  nx.draw(G1, pos, node_color = color_map, edge_color=colors, width=weights, font_size=16, with_labels=False)            
+
+  for p in pos:  # raise text positions
+           pos[p][1] += 0.07
+  nx.draw_networkx_labels(G1, pos)
+  plt.show()
+
+def get_frequent_itemsets(step2_data, threshold_per=0.1,vis=False):
     threshold = int(len(step2_data) * threshold_per)
     df_sorted = step2_data.sort_values(metric_name, ascending=ascending)
     top_10_percent = df_sorted.head(threshold)
@@ -95,7 +144,16 @@ def get_frequent_itemsets(step2_data, threshold_per=0.1):
     df = pd.DataFrame(te_ary, columns=te.columns_)
     frequent_itemsets = apriori(df, min_support=0.3, use_colnames=True)
     # print(frequent_itemsets)
-    # rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.5)
+    rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.5)
+    if vis:
+        # print(frequent_itemsets)
+        # print(frequent_itemsets)
+        # draw_graph(rules=rules,rules_to_show=10)
+        frequent_itemsets_copy=frequent_itemsets.copy()
+        frequent_itemsets_copy=frequent_itemsets_copy.sort_values(by="support")
+        frequent_itemsets_copy.plot(x="itemsets",y="support",kind="bar")
+        plt.xticks(rotation=30, fontsize=7)
+        # print(type(rules))
     return [tuple(a) for a in frequent_itemsets["itemsets"]]
 
 
@@ -111,7 +169,7 @@ def summary_pattern(data_path, alpha=0.8, vis=False):
     step2_origin_data = pd.read_csv(data_path)
     step2_data = step2_origin_data.dropna()
     com_ans = get_com_all(step2_data, vis=vis, alpha=alpha)
-    apr_ans = get_frequent_itemsets(step2_data)
+    apr_ans = get_frequent_itemsets(step2_data,vis=vis)
     return list(set(com_ans) & set(apr_ans))
 
 
@@ -136,11 +194,11 @@ def list_files(directories, file_name="best_test_acc.csv", alpha=0.8, vis=False)
 
 
 if __name__ == "__main__":
-    directories = []
-    for path in Path('/home/zyxing/dance/examples/tuning').iterdir():
-        if path.is_dir():
-            if str(path.name).startswith("cluster"):
-                directories.append(path)
-    list_files(directories)
-
-    # print(summary_pattern("/home/zyxing/dance/examples/tuning/cta_scdeepsort/328_138/results/pipeline/best_test_acc.csv",alpha=0.3,vis=True))
+    # directories = []
+    # for path in Path('/home/zyxing/dance/examples/tuning').iterdir():
+    #     if path.is_dir():
+    #         if str(path.name).startswith("cluster"):
+    #             directories.append(path)
+    # list_files(directories)
+
+    print(summary_pattern("/home/zyxing/dance/examples/tuning/cluster_graphsc/mouse_ES_cell/results/pipeline/best_test_acc.csv",alpha=0.3,vis=False))