def train(self, iterations=10, maxmiss=10):
"""
Train using Recommended Greedy Algorithm
"""
scores = {
'Iteration': [],
#'Network':[],
'Score': []
}
score_check = maxmiss
niter = iterations
nodes = [i for i in self.data.columns]
best = score_pom(export_pom(self.net, by='label'), self.data)
#print("START LOOP")
while score_check > 0 and niter > 0:
n = dc(self.net)
upd = set()
ops = [n.add_edge, n.del_edge, n.rev_edge]
for f in ops:
edge = np.random.choice(nodes, size=2, replace=False)
f(edge[0], edge[1])
upd.add(edge[0])
upd.add(edge[1])
if n.acyclic():
n.calc_cpt(self.data, alpha=self.alpha, change=upd)
score = score_pom(export_pom(n, by='label'), self.data)
scores['Iteration'].append(iterations - niter)
#scores['Network'].append(n)
scores['Score'].append(score)
#print(best, score, niter, score_check)
if score > best:
self.net = n
best = score
niter = niter - 1
score_check = maxmiss
continue
else:
score_check = score_check - 1
niter = niter - 1
continue
else:
niter = niter - 1
continue
self.scores = scores
def train(self, iterations=10, maxmiss=10):
"""
Train using Recommended Greedy Algorithm
"""
scores = {'Iteration': [], 'Network': [], 'Score': []}
score_check = maxmiss
niter = iterations
nodes = [i for i in self.data.columns]
best = score_pom(export_pom(self.net, by='label'), self.data)
#print("START LOOP")
while score_check > 0 and niter > 0:
n = net(data=self.data)
n.import_dag(self.net.export_dag())
ops = [n.add_edge, n.del_edge, n.rev_edge]
for f in ops:
# Choose the first node in a uniform, random way
v1 = np.random.choice(nodes)
# Choose the second with probabilities weighted by mi
v2 = np.random.choice(self.mi_weights[v1].index,
p=self.mi_weights[v1])
f(v1, v2)
if n.acyclic():
n.calc_cpt(self.data, alpha=self.alpha)
score = score_pom(export_pom(n, by='label'), self.data)
scores['Iteration'].append(iterations - niter)
scores['Network'].append(n)
scores['Score'].append(score)
#print(best, score, niter, score_check)
if score > best:
self.net = n
best = score
niter = niter - 1
score_check = maxmiss
continue
else:
score_check = score_check - 1
niter = niter - 1
continue
else:
niter = niter - 1
continue
self.scores = scores