Updating q5

q5/q5.py

@@ -2,7 +2,32 @@ import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import csv
-i1=[]
+
+
+df = pd.read_csv("data.csv")
+
+count=1
+for instance_id, grp1 in df.groupby(['instance']):
+    if instance_id == 'instance-1' or True:
+        plt.figure(count)
+        count = count + 1
+        for algorithm, grp2 in grp1.groupby(['algorithm', 'epsilon']):
+            rlist = []
+            hlist = []
+            algo_label = algorithm[0]
+            if algorithm[1]:
+                algo_label = algo_label + " " + str(algorithm[1])
+            for horizon, grp3 in grp2.groupby(['horizon']):
+                hlist.append(horizon)
+                maxr = grp3['regret'].max()
+                rlist.append(grp3['regret'].apply(lambda x: maxr - x).sum())
+            plt.plot(hlist, rlist, label=algo_label)
+        plt.xscale("log")
+        plt.yscale("log")
+        plt.legend()
+        plt.savefig(f"instance{instance_id.split('-')[1]}.png")
+
+""" i1=[]
 i2=[]
 i3=[]
 with open('data.csv', newline='') as csvfile:
@@ -21,14 +46,29 @@ instance_3=pd.DataFrame(i3)
 
 #instance_1.set_index('horizon', inplace=True)
 #instance_1.groupby(['algorithm'])['regret'].plot(legend=True)
-fig, ax = plt.subplots(figsize=(12,5))
+
+print(instance_1)
+
+for key, grp in instance_1.groupby(['algorithm']):
+    plt.plot(grp['horizon'], grp['regret'], label=key, marker='o')
+    pass
+
 for key, grp in instance_1.groupby(['algorithm']):
-    ax.plot(grp['horizon'], grp['regret'], label=key, marker='o')
+    hori_list = []
+    regreti_list = []
+    for hori, grp2 in grp.groupby(['horizon']):
+        regrmax = ((grp2['regret'].explode().astype(int).max()))
+        regreti = ((grp2['regret'].apply(lambda x: regrmax-int(x))).sum())
+        hori_list.append(hori)
+        regreti_list.append(regreti_list)
+    plt.plot(hori_list, regreti_list)
+
 
-ax.legend()
 plt.xscale("log")
 plt.yscale("log")
 plt.show()
+ """
+#plt.show()
 
 #grouped_df=instance_1.groupby(['algorithm'])