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
def best_svm_split(self, node, X, y, alfa, complexity):
T = self.classification_tree
#Creo un nuovo sottoalbero fittizio con root nel nodo
new_node = TreeNode.copy_node(node)
#if new_node.is_leaf:
#complexity += 1
rho = 0
data = X[new_node.data_idxs]
label = y[new_node.data_idxs]
if not all(i == label[0] for i in label) and len(data) > 0:
#Questo fa SVM multiclasse 1 vs rest
clf = LinearSVC(tol=1e-6,
C=10,
max_iter=10000,
loss='squared_hinge',
penalty='l1',
dual=False)
clf.fit(data, label)
#for n_class in range(n_classes):
#n_misclassified = np.count_nonzero(label-np.sign(np.dot(data, clf.coef_[n_class].T)+clf.intercept_[n_class]))
#Devo capire come ottenere i coefficienti migliori tra il numero di iperpiani addestrati
weights = clf.coef_.reshape((len(X[0])), )
intercept = clf.intercept_
if new_node.is_leaf:
ClassificationTree.create_new_children(node,
X,
y,
self.max_id,
None,
None,
oblique=T.oblique,
weights=weights,
intercept=intercept)
rho = 1
else:
new_node.weights = weights
new_node.intercept = intercept
return new_node, ClassificationTree.misclassification_loss(
new_node, X, y, new_node.data_idxs,
T.oblique) + alfa * (complexity + rho)
return new_node, np.inf
thresh = 1.5 * X[sorted_indexes[i], j]
node.threshold = -thresh
actual_loss = zero_one_loss(node.threshold, node, X, labels)
if actual_loss < error_best:
error_best = actual_loss
best_tresh = -thresh
best_feature = j
i += 1
return best_tresh, best_feature
best = np.inf
best_f = 0
best_t = 0
node = TreeNode(0, 0, None, None, None, None, 0, 0, 0, None)
for j in range(len(X[0])):
node.feature = j
node.threshold = 1
#opt_t = gradient_descend(1, node, X, labels)
opt_t = minimize(quadratically_loss,
0,
args=(node, X, labels),
method='BFGS',
tol=1e-04).x
print("ottimizzo feature: ", j)
err = quadratically_loss(opt_t, node, X, labels)
if err < best:
best = err
best_f = j
def best_parallel_split(self, node, X, y, alfa, complexity):
T = self.classification_tree
#Creo un nuovo sottoalbero fittizio con root nel nodo
new_node = TreeNode.copy_node(node)
error_best = np.inf
#if new_node.is_leaf:
#complexity += 1
was_leaf = False
improve = False
rho = 0
if new_node.is_leaf:
was_leaf = True
rho = 1
if new_node.data_idxs:
for j in range(len(X[0])):
#Prendo tutte le j-esime componenti del dataset e le ordino
#in modo crescente
vals = {}
for point_idx in new_node.data_idxs:
vals[point_idx] = X[point_idx, j]
values = sorted(vals.items(), key=lambda x: x[1])
sorted_indexes = [tuple[0] for tuple in values]
#plt.scatter(X[sorted_indexes, j], range(len(values)), s=0.4, c=list(correct_classification_tuples.values()))
#plt.show()
new_node.feature = j
#if j==2:
#base = actual_loss
#actual_loss = self.binary_loss(node_id, X, y, sorted_indexes[i], correct_classification_tuples[sorted_indexes[i]], actual_loss, thresh)
#print ("loss: ", actual_loss, "n punti: ", len(care_points_idxs))
#print("vecchia: ", self.vecchia_loss(node_id, X, y, care_points_idxs, correct_classification_tuples, thresh))
#Ciclo su ogni valore di quella componente e provo tutti gli split
#possibili
'''
new_node.threshold = 0.5*X[sorted_indexes[0], j]
'''
i = -1
actual_loss = ClassificationTree.misclassification_loss(
new_node, X, y, sorted_indexes, self.classification_tree.
oblique) + alfa * (complexity + rho)
while i < len(sorted_indexes):
pre_thresh = new_node.threshold
#print("Ottimizzo best parallel: ", i*100/len(sorted_indexes))
if i < 0:
thresh = 0.5 * X[sorted_indexes[0], j]
if i < len(sorted_indexes) - 1:
thresh = 0.5 * (X[sorted_indexes[i], j] +
X[sorted_indexes[i + 1], j])
else:
thresh = 1.5 * X[sorted_indexes[i], j]
new_node.threshold = thresh
'''
#Se il nodo da ottimizzare era una foglia allora dobbiamo creare le foglie figlie
#queste vengono ottimizzate subito per maggioranza
if was_leaf:
self.create_new_children(new_node, j, thresh)
inc, k = self.binary_loss(X, new_node, sorted_indexes, i, y, pre_thresh)
actual_loss += inc
else:
inc, k = self.binary_loss(X, new_node, sorted_indexes, i, y, pre_thresh)
actual_loss += inc
'''
#Se il nodo da ottimizzare era una foglia allora dobbiamo creare le foglie figlie
#queste vengono ottimizzate subito per maggioranza
if was_leaf:
ClassificationTree.create_new_children(
new_node,
X,
y,
self.max_id,
j,
thresh,
oblique=T.oblique)
actual_loss = ClassificationTree.misclassification_loss(
new_node, X, y, sorted_indexes,
self.classification_tree.oblique) + alfa * (
complexity + rho)
if actual_loss < error_best:
improve = True
error_best = actual_loss
best_t = thresh
best_feature = j
i += 1
#print ("error best: ", error_best)
new_node.threshold = best_t
new_node.feature = best_feature
if was_leaf and improve:
self.max_id += 2
return new_node, error_best
n = 'rooktemp'
w = ['0193']
b = 'Mirror'
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')
for i in range(1):
idx = np.random.permutation(len(data))
data = data[idx]
y = y[idx]
val_split = 0.2
#data = dataset.data[idx]
#label = dataset.target[idx]
valid_id = int(len(data) * (1 - val_split))
X = data[0:valid_id]
labels = y[0:valid_id]
X_valid = data[valid_id:]
y_valid = y[valid_id:]
depth = 3
to_optimize = []
node = TreeNode(0, 0, None, None, None, None, 0, 0, 0, None)
node.data_idxs = range(len(X))
to_optimize.append(node)
while to_optimize:
actual_node = to_optimize.pop()
if actual_node.depth <= depth - 1 and len(actual_node.data_idxs) > 0:
actual_node.is_leaf = False
try:
best_feature = np.nanargmax([
np.abs(
spearmanr(X[actual_node.data_idxs, j],
labels[actual_node.data_idxs])[0])
for j in range(len(X[0]))
])
except:
best_feature = np.random.randint(0, len(X[0]))
