def swap(tree):
internalNodes = [n for n in tree.all_nodes_itr() if n.var != None]
if len(internalNodes) == 1: return tree
internalNodes.remove(tree[0])
cNode = random.choice(internalNodes)
tagc = (cNode.identifier, cNode.var, cNode.split)
pid = cNode.bpointer
tree1 = Tree(tree, deep=True)
sub = tree1.remove_subtree(pid)
tags = recurTag(sub, pid)
tagp = tags[0]
tags[tags.index(tagc)] = (tagc[0], tagp[1], tagp[2])
tags[0] = (tagp[0], tagc[1], tagc[2])
string = f'{mi} swap {t}: {tags[0]}; '
try:
sub1 = genTree(tree[pid], tags)
except IndexError:
print(string + 'unswappable')
return tree
#rTransit = 1
rLike = get_like_ratio(tree.R, sub.leaves(), sub1.leaves())
rStruct = get_struct(sub.all_nodes_itr(), sub1.all_nodes_itr())
r = rLike * rStruct
print(string + f'{r.round(4)}')
if random.uniform(0, 1) < r:
if pid > 0:
gpid = tree[pid].bpointer
tree1.paste(gpid, sub1)
tree1[gpid].fpointer = sorted(tree1[gpid].fpointer)
else:
tree1 = sub1
tree1.w2 = tree.w2
tree1.R = tree.R
tree1.leaf = [n.identifier for n in tree1.leaves() if len(n.xvar) > 0]
tree1.show()
return tree1
return tree
def change(tree):
nidInternal = nidValid(tree)
choices = [getChoice(tree, n) for n in nidInternal]
n_choices = map(lambda L: sum([len(i) for i in L]), choices)
choiceDic = {
a: b
for (a, b, c) in zip(nidInternal, choices, n_choices) if c > 1
}
choices1 = list(choiceDic.keys())
nid = random.choice(choices1)
p = tree[nid].data.shape[1]
x0 = tree[nid].var
s0 = tree[nid].split
choices = choiceDic[nid] # choose nid to split
if s0 in choices[x0 - 1]:
choices[x0 - 1].remove(s0) # remove original split option
choices2 = [i for i in range(p - 1)
if len(choices[i]) > 0] # choose var to split
x = random.choice(choices2)
choices3 = choices[x] # choose value to split
x += 1
s = random.choice(choices3)
tree1 = Tree(tree, deep=True)
pid = tree1[nid].bpointer
sub = tree1.remove_subtree(nid)
tags = recurTag(sub, nid)
tags[0] = (nid, x, s)
try:
sub1 = genTree(sub[nid], tags)
except IndexError:
print(f'{mi} change {t}: {tags[0]}; unchangable')
return tree
if pid is not None:
tree1.paste(pid, sub1)
tree1[pid].fpointer = sorted(tree1[pid].fpointer)
else:
tree1 = sub1
nidInternal1 = set(nidValid(tree1))
choices1 = set(choices1)
choices11 = nidInternal1.intersection(choices1)
extra = nidInternal1 - choices1
n_choices = map(lambda L: sum([len(i) for i in L]),
[getChoice(tree1, n) for n in extra])
choices11 = list(choices11) + [
a for (a, b) in zip(extra, n_choices) if b > 1
]
choices31 = getChoice(tree1, nid, x0)[x0 - 1]
n31 = len(choices31)
if (sub1[nid].var == sub[nid].var) and (s0 in choices31):
n31 -= 1
rTransit = len(choices1) * len(choices3) / (len(choices11) * n31)
rLike = get_like_ratio(tree.R, sub.leaves(), sub1.leaves())
rStruct = get_struct(sub.all_nodes_itr(), sub1.all_nodes_itr())
r = rLike * rTransit * rStruct
print(f'{mi} change {t}: {tags[0]}; r={r.round(4)}')
if random.uniform(0, 1) < r:
tree1.w2 = tree.w2
tree1.R = tree.R
tree1.leaf = [n.identifier for n in tree1.leaves() if len(n.xvar) > 0]
tree1.show()
return tree1
return tree
gg = gamma.cdf(lamda, ig1, scale=1 / ig2)
g = stats.invgamma.cdf(var, ig1, scale=ig2)
mumu = (y.min() + y.max()) * 0.5 / m
sigma_mu = (y.max() - m * mumu) / (k * sqrt(m))
var_mu = sigma_mu**2
tau = 1 / var_mu
taumu = tau * mumu
DataTypes = df0.dtypes.map(lambda x: x.kind)
#%%
tree = Tree()
root = tree.create_node('0', 0, data=df0)
root.xvar = get_xvar(df0)
root.var = None
tree.w2 = []
tree.leaf = [0]
ProbDefault = array([2.5, 2.5, 4]).cumsum() / 9
## tree = trueTree
T = 1250
burn = 250
trees = [deepcopy(tree) for i in range(m)]
MM = pd.DataFrame(index=df0.index, columns=range(m), data=mumu)
Yhat = zeros((n0, T))
Depth_mu = zeros(T)
tdic = [None for i in range(m * T)]
#tdic = [tdic.copy() for i in range(T)]
def tree2dic(tree):
return {
i.identifier: i.M if i.var is None else (i.var, i.split)
