def cross_validate(args):
# import some functions
encode = Data_Loader().encode
find_error = NFL_Predictor().compareVector
try:
nn = Neural_Network.createFromFile(args.file)
print "Loaded Neural Network with %i hidden nodes" % len(nn.hidden_nodes)
totalCorrect = 0.0
total_tested = 0.0
for y in range(args.start,args.end+1):
classRight = [0, 0, 0, 0, 0, 0]
correct = incorrect = 0
if(args.db == 'u'):
dl = Data_Loader()
teams = dl.getAllTeams(y)
elif(args.db == 'b'):
dl = Data_Loader()
teams = dl.getAllTeams(y)
elif(args.db == 'p'):
dl = Data_Loader('playoffTeams.csv')
teams = dl.getAllTeams(y)
elif(args.db == 'o'):
dl = Data_Loader('balancedData.csv')
teams = dl.getAllTeams(y)
total_tested += len(teams)
total_error = 0.0
for t in teams:
t.result = nn.feed_forward(t.stats)
error = (find_error(t.result, encode(t.classification)))
total_error += error**2
if error < .08:
correct += 1
classRight
if args.v:
print "team %s, results %s, class %s, error %s" % (t.name, t.result, encode(t.classification), error)
if not args.q:
print "%d \t within threshold: %d/%d \t error: %s" % (y, correct, len(teams), str(total_error))
totalCorrect += correct
print "totalCorrect: %i/%i, %.2f%%" % (totalCorrect, total_tested, (totalCorrect/total_tested)*100)
except Exception as e:
print "invalid formatting, consult neural_main.py c --help \nError: %s" % e
def train(args):
try:
inputs = []
targets = []
y = range(args.start,args.end+1)
if(args.db == 'u'):
dl = Data_Loader()
i,t = dl.getTargets(y)
elif(args.db == 'b'):
dl = Data_Loader()
i,t = dl.getBalancedTargets(y)
elif(args.db == 'p'):
dl = Data_Loader('playoffTeams.csv')
i,t = dl.getTargets(y)
elif(args.db == 'o'):
dl = Data_Loader('balancedData.csv')
i,t = dl.getTargets(y)
elif(args.db == 's'):
dl = Data_Loader()
i,t = dl.getBLSmoteTargets(y,.25)
#i,t = dl.getSmoteTargets(y)
inputs += i
targets += t
#create NN
# if file already exists, build on that training
if (os.path.exists(args.file)):
print "file exists"
nn = Neural_Network.createFromFile(args.file)
pass
else:
print "file does not exist"
nn = Neural_Network.createWithRandomWeights(len(inputs[0]),args.nodes,len(targets[0]))
#train NN with the given data
print 'Beginning Training...'
nn = nn.train(args.epochs,inputs,targets,args.learn_rate)
nn.saveToFile(args.file)
print "Neural Network saved to %s" % (args.file)
except Exception as e:
print "invalid formatting, consult neural_main.py t --help \n Error: %s" % e
def predict(args):
try:
nn = Neural_Network.createFromFile(args.file)
dl = Data_Loader()
#team = dl.getTeam(args.team, args.year)
team = dl.getTeam(args.team, args.year)
result = nn.feed_forward(team.stats)
print "\n\nPredicting the %i %s..." % (args.year, team_dict.teams[args.team])
print "RESULTS: %.3f\n" % result[0]
if args.show_expected:
print "EXPECTED: %s" % (dl.encode(team.classification))[0]
results_graph = "\t|" + "".join(repeat("-",int(result[0]/.8*50))) + "|" + "".join(repeat("-",(int((1-result[0]/.8)*50)))) + "|"
print " Not in playoffs" + "".join(repeat(" ",35)) + "Super Bowl Champs"
print results_graph
post_processor = NFL_Predictor(nn)
similar_teams = post_processor.compareWithPastTeams(dl.getEveryTeam(), team, 16)
print"\nThe 15 most similar teams throughout history:"
del similar_teams[0]
for t in similar_teams:
print "%s \tScore: %f" % t
except Exception as e:
print "invalid formatting, consult neural_main.py t --help \n Error: %s" % e
DL = Data_Loader()
'''
nn = Neural_Network.createWithRandomWeights(66,40,6)
# train! with learning rate proportional to # of teams in the situations
inputs = []
targets = []
for y in range(2005,2007):
i,t = DL.getTargets(y)
inputs += i
targets += t
#print targets
nn = nn.train(10000,inputs,targets,1.5)
nn.saveToFile("predictortest.txt")
'''
nn = Neural_Network.createFromFile("predictortest.txt")
teams_2011 = DL.getAllTeams(2011)
pats_2011 = filter(lambda t: t.name == "nwe", teams_2011)[0]
all_other_teams = filter(lambda t: t.name != "nwe", teams_2011)
predictor = NFL_Predictor(nn)
similar = predictor.compareWithPastTeams(all_other_teams, pats_2011, 3)
for t,d in similar:
print t.name + " " + str(d) + "\n"
