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: