def print_tree(event, neighborhood, dataset, wave, c_0, balance, alpha):
treefile = 'results/' + '_'.join(
[event, neighborhood, dataset, wave, 'c_0=' + str(c_0), 'balance=' + balance, 'alpha=' + alpha]) + '.tree'
tree = TreeNode('dummy')
with open(treefile) as f:
tree.load(f)
print str(tree)
def stuff():
# events = ['FL','SG','AR','CH']
# events = ['SG']
events = ["AR"]
# events = ['FL','SG','FI','CH','AR','SS']
# neighborhoods = ['rook','queen','rooktemp']
# neighborhoods = ['rook','rooktemp','rooktemplong']
neighborhoods = ["rook"]
datasets = ["1DAY"]
thetas = [0.7] # theta is the classification parameter
grid = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
alphas = [x for x in itertools.product(grid, grid) if sum(x) <= 1]
# alphas = [(0.3,0.3),(0.6,0.3),(0.3,0.6)]
# alphas = [(0.3,0.3)]
# waves = [['0193'],['0171'],['0094']]
waves = [["0193"]]
# balances = ['Mirror','Duplication','Random']
balances = ["Mirror"]
c_0Dict = dict([(("AR", "1DAY"), 40), (("SG", "3DAYDEMO"), 9), (("AR", "3DAYDEMO"), 111)])
X = [x for x in itertools.product(events, neighborhoods, datasets, waves, balances, alphas)]
for event, neighborhood, dataset, wave, balance, alpha in X:
alphastr = str(alpha).replace(",", "-").replace("(", "[").replace(")", "]").replace(" ", "")
wavestr = wave[0]
c_0 = c_0Dict[(event, dataset)]
treefile = (
"results/"
+ "_".join(
[event, neighborhood, dataset, wavestr, "c_0=" + str(c_0), "balance=" + balance, "alpha=" + alphastr]
)
+ ".tree"
)
Tree = TreeNode("dummy")
with open(treefile) as f:
Tree.load(f)
# print str(Tree)
print event, neighborhood, dataset, wave, balance, alpha
S = Tree.size()
print "size:", S
print "balance:", Tree.balance()
print "splits:", Tree.total_splits_evaluated()
TBSR = Tree.total_best_split_runtime()
print "total time:", TBSR
print "avg time:", TBSR / ((S - 1) / 2)
print
def stuff():
treefileA = 'results/AR_rook_1DAY_0193_c_0=40_balance=Mirror_alpha=[0.0-0.0].tree'
treefileB = 'hopefullyslowerresults/AR_rook_1DAY_0193_c_0=40_balance=Mirror_alpha=[0.0-0.0].tree'
for treefile in [treefileA,treefileB]:
Tree = TreeNode('dummy')
with open(treefile) as f:
Tree.load(f)
print treefile
S = Tree.size()
print 'size:', S
print 'balance:', Tree.balance()
print 'splits:', Tree.total_splits_evaluated()
TBSR = Tree.total_best_split_runtime()
print 'total time:', TBSR
print 'avg time:', TBSR/((S-1)/2)
print
theta = 0.7
alphaSpatial = '[0.3-0.0]'
alphaSpatiotemp = '0.3-0.4]'
alphaNonSpatial = '[0.0-0.0]'
c_0 = cref[e]
eventClass = e
dataset = d
print eventClass,dataset
headers,matches = SOLARGenImageList.image_event_matches(dataset=d,waves = w)
print 'matches calculated'
treefileSpatial = "results/"+"_".join([str(e),str(n),str(d),"-".join(w),'c_0='+str(c_0),'balance='+str(b),'alpha='+str(alphaSpatial)])+".tree"
treefileNonSpatial = "results/"+"_".join([str(e),str(n),str(d),"-".join(w),'c_0='+str(c_0),'balance='+str(b),'alpha='+str(alphaNonSpatial)])+".tree"
treeSpatial = TreeNode('dummy')
treeNonSpatial = TreeNode('dummy')
with open(treefileSpatial) as f:
treeSpatial.load(f)
with open(treefileNonSpatial) as f:
treeNonSpatial.load(f)
S_train,S_test, = read_data(e,n,d,w,b) # read the data set
cells_train, adj = S_train
cells_test, adj_test = S_test
counter = 0
for x in sorted(matches.keys()): # for each image of the data set
paramsFilename = x[0]
imageFilename = paramsFilename[:-4]+'_th.png'
ISpatial = m.imread(imageFilename) # read the image
INonSpatial = ISpatial.copy()
outputname = os.path.join(outputFolder,e,os.path.basename(imageFilename)[:-4]+'_'+e+'_'+d+'.png')
cells_testWeWant = [x for x in cells_test if x['id'][3][0] == paramsFilename] # get the test cells tied to this image
for x in range(2):
if x == 0:
