From 39bac2e30cd44120f5c37c6854e44c59f33d568b Mon Sep 17 00:00:00 2001
From: Liyao Zhu <l.zhu34@student.unimelb.edu.au>
Date: Tue, 7 May 2019 20:51:32 +1000
Subject: [PATCH] 2 NEW 3D graphs for alpha/threshold
---
game.py | 79 ++++++++++++++++++++++++++++++++++++++++++++++++---------
1 file changed, 67 insertions(+), 12 deletions(-)
diff --git a/game.py b/game.py
index 7f74446..d2242ff 100644
--- a/game.py
+++ b/game.py
@@ -142,7 +142,7 @@ def stackPlot(data, r, Actions, Iterations, titleComment=""):
fig, ax = plt.subplots()
# grays = np.arange(0, 1, (max(Actions) - min(Actions))/A)
ax.stackplot(x, y, labels=Actions, colors=[str(0.9 - 0.9 * x) for x in Actions])
- ax.legend(loc='lower right')
+ ax.legend(loc='best')
plt.ylabel('Number of Actions')
plt.xlabel('Time(iterations)')
@@ -152,7 +152,7 @@ def stackPlot(data, r, Actions, Iterations, titleComment=""):
plt.title(title)
- plt.savefig(titleComment + " in round " + str(r+1) + ".jpg")
+ plt.savefig(titleComment + " in round " + str(r+1) + ".png")
plt.show()
@@ -171,7 +171,7 @@ def rep(repeat=30, R=1, Actions=[0, 0.2, 0.4, 0.6, 0.8], I=1000, **kwargs):
return data
-def averageOfLast(data, Actions, r=0, lastIterations=100):
+def averageOfLast(data, Actions, N=100, r=0, lastIterations=100):
sum = 0
action_counter = {action: 0 for action in Actions}
@@ -179,10 +179,10 @@ def averageOfLast(data, Actions, r=0, lastIterations=100):
sum += np.sum(data[i, r] * Actions)
for a in range(len(Actions)):
action_counter[Actions[a]] += data[i, r, a] / lastIterations
- return (sum / 100, action_counter)
+ return (sum / (lastIterations * N), action_counter)
-def graph_kp3d(Actions, Klist=[99], Plist=[0, 0.3, 0.6, 0.9], repeat=30):
+def graph_kp3d(Actions, Klist=[2, 4, 8, 10], Plist=[0, 0.3, 0.6, 0.9], repeat=30, N=100):
K = Klist
P = Plist
@@ -191,7 +191,7 @@ def graph_kp3d(Actions, Klist=[99], Plist=[0, 0.3, 0.6, 0.9], repeat=30):
for k in range(len(K)):
for p in range(len(P)):
data = rep(repeat, K=K[k], P=P[p], Actions=Actions) # Specify other params by adding here
- meanA[k][p] = averageOfLast(data, Actions, lastIterations=100)[0] # Doing the first round only -- for now
+ meanA[k][p] = averageOfLast(data, Actions, lastIterations=100, N=N)[0] # Doing the first round only -- for now
print("k, p, mean", k, p, meanA[k][p])
P, K = np.meshgrid(P, K)
@@ -205,6 +205,51 @@ def graph_kp3d(Actions, Klist=[99], Plist=[0, 0.3, 0.6, 0.9], repeat=30):
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
+def graph3d_alpha_threshold(Actions, repeat=30, AlphaList=np.arange(0, 1.01, 0.05), ThreshList=np.arange(0.1, 1.1, 0.1), N=100, **kwargs):
+
+ mean = np.zeros((len(ThreshList), len(AlphaList)))
+ ratio_by_threshold = np.zeros((len(ThreshList), len(AlphaList)))
+
+ for t in range(len(ThreshList)):
+ for a in range(len(AlphaList)):
+ print("Calculating... t, alpha = ", t, a)
+ data = rep(repeat=repeat, Actions=Actions, alpha=AlphaList[a], threshold=ThreshList[t], **kwargs)
+ mean[t][a] = averageOfLast(data, Actions, lastIterations=100, N=N)[0]
+ ratio_by_threshold[t] = mean[t] / ThreshList[t]
+
+
+ A, T = np.meshgrid(AlphaList, ThreshList)
+
+ fig = plt.figure()
+ ax = fig.gca(projection='3d')
+
+ surf = ax.plot_surface(A, T, mean, cmap=cm.Greys,
+ linewidth=0, antialiased=False)
+ ax.set_xlabel('Alpha')
+ # ax.invert_xaxis()
+ ax.set_ylabel('Threshold')
+ ax.set_zlabel('Average contribution')
+ fig.colorbar(surf, shrink=0.5, aspect=5)
+ # plt.show()
+
+
+ fig2 = plt.figure()
+ ax2 = fig2.gca(projection='3d')
+ surf2 = ax2.plot_surface(A, T, ratio_by_threshold, cmap=cm.Greys,
+ linewidth=0, antialiased=False)
+ ax2.set_xlabel('Alpha')
+ # ax.invert_xaxis()
+ ax2.set_ylabel('Threshold')
+ # ax.invert_yaxis()
+ ax2.set_zlabel('Average contribution by threshold')
+ fig2.colorbar(surf2, shrink=0.5, aspect=5)
+ plt.show()
+
+
+# def hist2d_alpha_threshold(Actions, repeat=30, AlphaList=np.arange(0, 1.1, 0.1), ThreshList=np.arange(0, 1.1, 0.2), **kwargs):
+
+
+
def stackBar(r, Actions, repeat=30, multiArm='greedy', **kwargs): # Plotting the data for round r
@@ -222,7 +267,7 @@ def stackBar(r, Actions, repeat=30, multiArm='greedy', **kwargs): # Plotting th
else:
print("ERROR, Stack Bar Graph Expects Only 1 List to Compare")
exit(4)
- del kwargs[k]
+ del kwargs[key]
print("Comparing:", key)
print("On:", alist)
@@ -286,9 +331,14 @@ def main():
# for r in range(R):
# stackPlot(data, r, Actions, I, titleComment="N="+ str(N) + ", R=" + str(R) + ", alpha=" +str(alpha) + ", Well-Mixed graph")
- for k in [2, 4, 10, 40, 90, 99]:
- data = rep(repeat=30, N=100, K=k, Actions=Actions, R=1, I=I, P=P)
- stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment=("K=" + str(k) + ", P=" + str(P)))
+
+ # for k in [2, 4, 10, 40, 90, 99]:
+ # data = rep(repeat=30, N=100, K=k, Actions=Actions, R=1, I=I, P=P)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment=("K=" + str(k) + ", P=" + str(P)))
+
+
+ # data = rep(repeat=30, Actions=Actions, R=1, I=I, RF=2, threshold=0.3)
+ # stackPlot(data, r=0, Iterations=I, Actions=Actions, titleComment="threshold = 0.3")
"""
@@ -298,10 +348,10 @@ def main():
# graph_kp3d(Actions)
"""
- Graph3: Actions by different alpha value
+ Graph3: Comparing a parameter (put in a list)
"""
# stackBar(0, Actions, repeat=1, alpha=[0, 0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1])
- # stackBar(0, Actions, repeat=1, N=[5, 10, 20, 50, 100])
+ # stackBar(0, Actions, repeat=30, N=[5, 10, 20, 50, 100], threshold=0.6, RF=2)
# stackBar(0, Actions, repeat=1, RF=2, threshold=[0.2, 0.4, 0.6, 0.8, 1])
"""
@@ -310,6 +360,11 @@ def main():
# stackBar(0, Actions, repeat=1, multiArm='greedy', epsilon=[0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
# stackBar(0, Actions, repeat=30, multiArm='decrease', epsilon=[0.8, 0.9, 0.95, 0.98, 0.99, 0.999, 0.9999])
+ """
+ Graph4: Average contribution by Alpha and Threshold
+ """
+
+ graph3d_alpha_threshold(Actions, repeat=30, RF=2)
if __name__ == '__main__':
main()
\ No newline at end of file
--
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