from plots import EloPlot, Data
import numpy as np
for discount in [0.5, 0.75, 0.95]:
l = [(f'SL-discount-{discount}-NoTrain-alpha-0.000003', 'Simple Linear',
'#9C27B0'),
(f'LA-discount-{discount}-NoTrain-alpha-0.000001',
'Linear Approximation', '#2196F3'),
(f'NN-discount-{discount}-NoTrain-lr-0.0002', 'Neural Network',
'#4CAF50')]
with EloPlot(title=f'Agents, discount={discount} - Elo Rating3',
saves=[f'GoodPlots/Agents-{discount}.png']) as plt:
for EloOverTime, name, agent, color in Data(
[f".\\outputs\\{a[0]}\\csv" for a in l],
colors=[a[2] for a in l],
elo=False):
name = [a[1] for a in l if a[0] == name][0]
plt.varPlot(plt, EloOverTime, name, color)
from plots import EloPlot, allDirs, Data
maxi, maxiplot = 0, None
for Elo, EloOverTime, name, agent, color in Data(allDirs()):
with EloPlot(xlabel='Players',
title=f'{name} - Elo Rating',
saves=[
f'plots/EloRating/' + f'{name}' + '.png',
f'outputs/{name}/' + f'{name} - Elo Rating' + '.png'
]) as plt:
plt.varPlot(plt, Elo, agent, color)
with EloPlot(
title=f'{name} - Elo Rating over Time',
saves=[
f'plots/EloRatingOverTime/' + f'{name}' + '.png',
f'outputs/{name}/' + f'{name} - Elo Rating over Time' + '.png'
]) as plt:
plt.varPlot(plt, EloOverTime, agent, color)
# plt.maxPlot(plt, EloOverTime, agent, color)
try:
temp = EloOverTime.max()
if temp > maxi:
maxi, maxiplot = temp, name
except:
pass
print(maxi, maxiplot)
from plots import EloPlot, Data
with EloPlot(title=f'Probability - Elo Rating',
saves=[f'GoodPlots/Probability.png']) as plt:
for EloOverTime, name, agent, color in Data([
".\\outputs\\4000-calcprob-True\\csv",
".\\outputs\\4000-calcprob-false\\csv"
],
colors=['#4CAF50', '#2196F3'],
elo=False):
name = 'NNAgent+probs' if name == '4000-calcprob-True' else 'NNAgent'
plt.varPlot(plt, EloOverTime, name, color)
l, d = 'LAMBDA-', '_DISCOUNT-'
files, vs, lamds, diss, mini, maxi = [], {}, set(), set(), float(
'inf'), -float('inf')
for x in xs:
vs[x] = {}
for filename in allDirs():
if l in filename and d in filename:
files.append(filename)
def meanAround(data, x, width=10):
return data[0, x - width:x + width, :].mean()
for EloOverTime, name, agent, color in Data(files, elo=False):
lamb = find_number(name, l)
dis = find_number(name, d)
lamds.add(lamb)
diss.add(dis)
for x in xs:
val = meanAround(EloOverTime, x)
vs[x][(lamb, dis)] = meanAround(EloOverTime, x)
mini = val if val < mini else mini
maxi = val if val > maxi else maxi
labdas = np.array(list(sorted(lamds, reverse=True)))
discounts = np.array(list(sorted(diss)))
for x, values in vs.items():
heat = np.zeros((len(labdas), len(discounts)))
