def or_cut(self):
""" WRITEME """
# print(" > trying to cut ... ")
sys.stdout.flush()
found = False
bestlik = self.orig_ll
best_clt_l = None
best_clt_r = None
best_feature_cut = None
best_left_weight = 0.0
best_right_weight = 0.0
best_right_data = None
best_left_data = None
best_v_ll = 0.0
best_gain = -np.inf
best_left_sample_weight = None
best_right_sample_weight = None
best_left_vdata = None
best_right_vdata = None
if self.sum_nodes:
# check for clustering
n_clusters = 2
cov_type = 'tied'
rand_gen = None
n_iters = 1000
n_restarts = 1
gmm_c = sklearn.mixture.GMM(n_components=n_clusters,
covariance_type=cov_type,
random_state=rand_gen,
n_iter=n_iters,
n_init=n_restarts)
gmm_c.fit(self.data)
clustering = gmm_c.predict(self.data)
# preventing to have a cluster with zero instances
cardinality = np.sum(clustering)
# print(" - Clustering instances:",self.data.shape[0], "-", cardinality,self.data.shape[0] - cardinality, end=" ")
if cardinality > 0 and (self.data.shape[0] - cardinality) > 0:
cluster_0 = (clustering == 0)
cluster_0_data = self.data[cluster_0]
cluster_1_data = self.data[~cluster_0]
cluster_0_tree = Cltree()
cluster_1_tree = Cltree()
cluster_0_weight = cluster_0_data.shape[0] / self.data.shape[0]
cluster_1_weight = cluster_1_data.shape[0] / self.data.shape[0]
cluster_0_tree.fit(cluster_0_data,
vdata=self.vdata,
m_priors=self.m_priors,
j_priors=self.j_priors,
scope=self.scope,
alpha=self.alpha * cluster_0_weight,
and_leaves=self.and_leaves,
sample_weight=None)
cluster_1_tree.fit(cluster_1_data,
m_priors=self.m_priors,
j_priors=self.j_priors,
scope=self.scope,
alpha=self.alpha * cluster_1_weight,
and_leaves=self.and_leaves,
sample_weight=None)
cluster_0_ll = cluster_0_tree.score_samples_log_proba(
cluster_0_data, sample_weight=None)
cluster_1_ll = cluster_1_tree.score_samples_log_proba(
cluster_1_data, sample_weight=None)
# log sum exp
clustering_ll = 0.0
for d in self.data:
clustering_ll = clustering_ll + logr(
cluster_0_weight *
np.exp(cluster_0_tree.score_sample_log_proba(d)) +
cluster_1_weight *
np.exp(cluster_1_tree.score_sample_log_proba(d)))
clustering_ll = clustering_ll / self.data.shape[0]
# print("ll:", clustering_ll)
else:
clustering_ll = -np.inf
else:
clustering_ll = -np.inf
if self.random_forest:
if self.d > self.node.cltree.n_features:
selected = range(self.node.cltree.n_features)
else:
selected = sorted(
random.sample(range(self.node.cltree.n_features), self.d))
else:
selected = range(self.node.cltree.n_features)
for feature in selected:
condition = self.data[:, feature] == 0
new_features = np.ones(self.data.shape[1], dtype=bool)
new_features[feature] = False
left_data = self.data[condition, :][:, new_features]
right_data = self.data[~condition, :][:, new_features]
vdata_condition = self.vdata[:, feature] == 0
left_vdata = self.vdata[vdata_condition, :][:, new_features]
right_vdata = self.vdata[~vdata_condition, :][:, new_features]
if self.sample_weight is not None:
left_sample_weight = self.sample_weight[condition]
right_sample_weight = self.sample_weight[~condition]
left_weight = np.sum(left_sample_weight) / np.sum(
self.sample_weight)
right_weight = np.sum(right_sample_weight) / np.sum(
self.sample_weight)
else:
left_sample_weight = None
right_sample_weight = None
left_weight = (left_data.shape[0]) / (self.data.shape[0])
right_weight = (right_data.shape[0]) / (self.data.shape[0])
if left_data.shape[0] > 0 and right_data.shape[
0] > 0 and left_vdata.shape[0] > 0 and right_vdata.shape[
0] > 0:
left_scope = np.concatenate(
(self.node.cltree.scope[0:feature],
self.node.cltree.scope[feature + 1:]))
right_scope = np.concatenate(
(self.node.cltree.scope[0:feature],
self.node.cltree.scope[feature + 1:]))
CL_l = Cltree()
CL_r = Cltree()
CL_l.fit(left_data,
self.m_priors,
self.j_priors,
scope=left_scope,
alpha=self.alpha * left_weight,
and_leaves=self.and_leaves,
sample_weight=left_sample_weight,
noise=self.noise)
CL_r.fit(right_data,
self.m_priors,
self.j_priors,
scope=right_scope,
alpha=self.alpha * right_weight,
and_leaves=self.and_leaves,
sample_weight=right_sample_weight,
noise=self.noise)
l_ll = CL_l.score_samples_log_proba(
left_data, sample_weight=left_sample_weight)
r_ll = CL_r.score_samples_log_proba(
right_data, sample_weight=right_sample_weight)
if self.sample_weight is not None:
ll = ((l_ll + logr(left_weight)) *
np.sum(left_sample_weight) +
(r_ll + logr(right_weight)) *
np.sum(right_sample_weight)) / np.sum(
self.sample_weight)
else:
ll = ((l_ll + logr(left_weight)) * left_data.shape[0] +
(r_ll + logr(right_weight)) *
right_data.shape[0]) / self.data.shape[0]
else:
ll = -np.inf
if ll > bestlik:
bestlik = ll
best_clt_l = CL_l
best_clt_r = CL_r
best_feature_cut = feature
best_left_weight = left_weight
best_right_weight = right_weight
best_right_data = right_data
best_left_data = left_data
best_right_vdata = right_vdata
best_left_vdata = left_vdata
best_l_ll = l_ll
best_r_ll = r_ll
best_left_sample_weight = left_sample_weight
best_right_sample_weight = right_sample_weight
found = True
"""
if (self.depth+1) % 2 == 0:
bestlik = self.orig_ll
else:
clustering_ll = self.orig_ll
"""
gain = (bestlik - self.orig_ll)
# print (" - gain cut:", gain, end = "")
gain_c = (clustering_ll - self.orig_ll)
# print (" gain clustering:", gain_c)
if (found == True
and gain > self.min_gain) or (gain_c > gain
and gain_c > self.min_gain):
if (gain > gain_c):
self.node = OrNode()
Csn._or_nodes = Csn._or_nodes + 1
Csn._or_edges = Csn._or_edges + 2
self.node.or_feature = best_feature_cut
# print(" - cutting on feature ", self.node.or_feature, "[#l:",best_left_data.shape[0],", #r:",best_right_data.shape[0],"], gain:", bestlik - self.orig_ll)
instances = self.data.shape[0]
self.node.left_weight = best_left_weight
self.node.right_weight = best_right_weight
# free memory before to recurse
self.free_memory()
self.node.left_child = Csn(
data=best_left_data,
vdata=best_left_vdata,
clt=best_clt_l,
ll=best_l_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha * best_left_weight,
d=self.d,
random_forest=self.random_forest,
m_priors=self.m_priors,
j_priors=self.j_priors,
n_original_samples=self.n_original_samples,
and_leaves=self.and_leaves,
and_inners=self.and_inners,
min_gain=self.min_gain,
depth=self.depth + 1,
sample_weight=best_left_sample_weight,
forest_approach=self.forest_approach,
noise=self.noise)
self.node.right_child = Csn(
data=best_right_data,
vdata=best_right_vdata,
clt=best_clt_r,
ll=best_r_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha * best_right_weight,
d=self.d,
random_forest=self.random_forest,
m_priors=self.m_priors,
j_priors=self.j_priors,
n_original_samples=self.n_original_samples,
and_leaves=self.and_leaves,
and_inners=self.and_inners,
min_gain=self.min_gain,
depth=self.depth + 1,
sample_weight=best_right_sample_weight,
forest_approach=self.forest_approach,
noise=self.noise)
else:
self.node = SumNode()
# print(" - Adding a sum node")
Csn._sum_nodes = Csn._sum_nodes + 1
instances = self.data.shape[0]
self.node.weights.append(cluster_0_weight)
self.node.weights.append(cluster_1_weight)
# free memory before to recurse
self.free_memory()
self.node.children.append(
Csn(data=cluster_0_data,
vdata=None,
clt=cluster_0_tree,
ll=cluster_0_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha * cluster_0_weight,
d=self.d,
random_forest=self.random_forest,
m_priors=self.m_priors,
j_priors=self.j_priors,
n_original_samples=self.n_original_samples,
and_leaves=self.and_leaves,
and_inners=self.and_inners,
min_gain=self.min_gain,
depth=self.depth + 1,
sample_weight=None,
forest_approach=self.forest_approach,
noise=self.noise))
self.node.children.append(
Csn(data=cluster_1_data,
vdata=None,
clt=cluster_1_tree,
ll=cluster_1_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha * cluster_1_weight,
d=self.d,
random_forest=self.random_forest,
m_priors=self.m_priors,
j_priors=self.j_priors,
n_original_samples=self.n_original_samples,
and_leaves=self.and_leaves,
and_inners=self.and_inners,
min_gain=self.min_gain,
depth=self.depth + 1,
sample_weight=None,
forest_approach=self.forest_approach,
noise=self.noise))
else:
# Make a forest
if self.and_leaves:
self.node.cltree.makeForest(
vdata=self.vdata, forest_approach=self.forest_approach)
# print(" no cutting")
"""if self.node.cltree.is_forest():
def __init__(self,
data,
vdata,
clt=None,
ll=0.0,
min_instances=5,
min_features=3,
alpha=1.0,
d=None,
n_original_samples=None,
random_forest=False,
m_priors=None,
j_priors=None,
and_leaves=False,
and_inners=False,
min_gain=None,
depth=1,
sample_weight=None,
sum_nodes=False,
forest_approach=None,
noise=None):
self.min_instances = min_instances
self.min_features = min_features
self.and_leaves = and_leaves
self.and_inners = and_inners
self.alpha = alpha
self.depth = depth
self.data = data
self.node = TreeNode()
self.sample_weight = sample_weight
self.sum_nodes = sum_nodes
self.vdata = vdata
self.noise = noise
self.forest_approach = forest_approach
if n_original_samples is None:
self.n_original_samples = self.data.shape[0]
else:
self.n_original_samples = n_original_samples
if min_gain is None:
self.min_gain = np.log(
self.data.shape[0]) / (2 * self.data.shape[0])
else:
self.min_gain = min_gain
self.random_forest = random_forest
self.lprior = 1 # laplace prior
if m_priors is None:
self.m_priors = np.zeros((self.data.shape[1], 2))
for i in range(self.data.shape[1]):
self.m_priors[i, 1] = (self.data[:, i].sum() + self.lprior /
2) / (self.data.shape[0] + self.lprior)
self.m_priors[i, 0] = 1 - self.m_priors[i][1]
self.j_priors = np.zeros(
(self.data.shape[1], self.data.shape[1], 2, 2))
sparse_cond = sparse.csr_matrix(self.data)
cond0 = sparse_cond.T.dot(sparse_cond)
cond = np.array(cond0.todense())
""" FIXME to consider the weights """
for i in range(self.data.shape[1]):
for j in range(self.data.shape[1]):
if i != j:
self.j_priors[i, j, 1, 1] = cond[i, j]
self.j_priors[i, j, 0, 1] = cond[j, j] - cond[i, j]
self.j_priors[i, j, 1, 0] = cond[i, i] - cond[i, j]
self.j_priors[i, j, 0,
0] = self.data.shape[0] - self.j_priors[
i, j, 1, 1] - self.j_priors[
i, j, 0, 1] - self.j_priors[i, j,
1, 0]
# Laplace
self.j_priors[i, j, 1, 1] = (
self.j_priors[i, j, 1, 1] + self.lprior / 4) / (
self.data.shape[0] + self.lprior)
self.j_priors[i, j, 0, 1] = (
self.j_priors[i, j, 0, 1] + self.lprior / 4) / (
self.data.shape[0] + self.lprior)
self.j_priors[i, j, 1, 0] = (
self.j_priors[i, j, 1, 0] + self.lprior / 4) / (
self.data.shape[0] + self.lprior)
self.j_priors[i, j, 0, 0] = (
self.j_priors[i, j, 0, 0] + self.lprior / 4) / (
self.data.shape[0] + self.lprior)
sparse_cond = None
cond0 = None
cond = None
else:
self.m_priors = m_priors
self.j_priors = j_priors
if clt is None:
self.node.cltree = Cltree()
self.node.cltree.fit(data,
self.m_priors,
self.j_priors,
alpha=self.alpha,
and_leaves=self.and_leaves,
sample_weight=self.sample_weight,
noise=self.noise)
self.orig_ll = self.node.cltree.score_samples_log_proba(
self.data, sample_weight=self.sample_weight)
self.d = int(math.sqrt(self.data.shape[1]))
sparsity = 0.0
sparsity = len(self.data.nonzero()[0])
sparsity /= (self.data.shape[1] * self.data.shape[0])
# logger.info("Dataset sparsity: %f", sparsity)
else:
self.node.cltree = clt
self.orig_ll = ll
self.d = d
self.scope = self.node.cltree.scope
self.id = Csn._id_node_counter
Csn._id_node_counter = Csn._id_node_counter + 1
# print("Block", self.id, "on", len(self.scope), "features and", self.data.shape[0], "instances, local ll:", self.orig_ll)
if self.data.shape[0] > self.min_instances:
if self.data.shape[1] >= self.min_features:
if not self.node.cltree.is_forest():
self.or_cut()
else:
if self.and_inners == True:
self.and_cut()
else:
self.or_cut()
"""else:
print( " > no cutting due to few features")
else:
print(" > no cutting due to few instances")"""
if is_tree_node(self.node):
if self.depth > Csn._depth:
Csn._depth = self.depth
Csn._mean_depth = Csn._mean_depth + self.depth
Csn._leaf_nodes = Csn._leaf_nodes + 1
if self.node.cltree.is_forest():
Csn._clforests = Csn._clforests + 1
else:
Csn._cltrees = Csn._cltrees + 1
Csn._clt_edges = Csn._clt_edges + self.node.cltree.num_edges
def and_cut(self):
""" WRITEME """
n_features = self.data.shape[1]
self.forest = np.zeros(n_features, dtype=np.int)
self.roots = []
# naive approach to build the tree_forest
for i in range(n_features):
if self.node.cltree.tree[i] == -1:
self.roots.append(i)
for i in range(n_features):
if self.node.cltree.tree[i] != -1:
parent = self.node.cltree.tree[i]
while self.node.cltree.tree[parent] != -1:
parent = self.node.cltree.tree[parent]
self.forest[i] = parent
else:
self.forest[i] = i
self.tree_forest = []
for r in self.roots:
t_forest = []
for i in range(n_features):
if self.forest[i] == r:
t_forest.append(i)
self.tree_forest.append(t_forest)
"""print ("AND node")
print (self.tree_forest)"""
for i in range(self.node.cltree.num_trees):
# print(" tree", self.tree_forest[i])
sys.stdout.flush()
tree_n_features = len(self.tree_forest[i])
if self.data.shape[0] > self.min_instances:
if tree_n_features >= self.min_features:
tree_data = self.data[:, self.tree_forest[i]]
found = False
orig_ll = self.node.cltree.score_samples_scope_log_proba(
self.data, self.tree_forest[i])
bestlik = orig_ll
best_clt_l = None
best_clt_r = None
best_feature_cut = None
best_left_weight = 0.0
best_right_weight = 0.0
best_right_data = None
best_left_data = None
best_v_ll = 0.0
best_gain = -np.inf
if self.random_forest:
if self.d > tree_n_features:
selected = range(tree_n_features)
else:
selected = sorted(
random.sample(range(tree_n_features), self.d))
else:
selected = range(tree_n_features)
for feature in selected:
condition = tree_data[:, feature] == 0
new_features = np.ones(tree_data.shape[1], dtype=bool)
new_features[feature] = False
left_data = tree_data[condition, :][:, new_features]
right_data = tree_data[~condition, :][:, new_features]
left_weight = (left_data.shape[0]) / (
tree_data.shape[0])
right_weight = (right_data.shape[0]) / (
tree_data.shape[0])
if self.sample_weight is not None:
left_sample_weight = self.sample_weight[condition]
right_sample_weight = self.sample_weight[
~condition]
else:
left_sample_weight = None
right_sample_weight = None
if left_data.shape[0] > 1 and right_data.shape[0] > 1:
# compute the tree features id
tree_scope = np.zeros(tree_n_features,
dtype=np.int)
for f in range(tree_n_features):
tree_scope[f] = self.node.cltree.scope[
self.tree_forest[i][f]]
left_scope = np.concatenate(
(tree_scope[0:feature],
tree_scope[feature + 1:]))
right_scope = np.concatenate(
(tree_scope[0:feature],
tree_scope[feature + 1:]))
CL_l = Cltree()
CL_r = Cltree()
CL_l.fit(left_data,
vdata=self.vdata,
m_priors=self.m_priors,
j_priors=self.j_priors,
scope=left_scope,
alpha=self.alpha * left_weight,
and_leaves=self.and_leaves,
sample_weight=left_sample_weight)
CL_r.fit(right_data,
vdata=self.vdata,
m_priors=self.m_priors,
j_priors=self.j_priors,
scope=right_scope,
alpha=self.alpha * right_weight,
and_leaves=self.and_leaves,
sample_weight=right_sample_weight)
l_ll = CL_l.score_samples_log_proba(left_data)
r_ll = CL_r.score_samples_log_proba(right_data)
ll = ((l_ll + logr(left_weight)) *
left_data.shape[0] +
(r_ll + logr(right_weight)) *
right_data.shape[0]) / self.data.shape[0]
else:
ll = -np.inf
if ll > bestlik:
bestlik = ll
best_clt_l = CL_l
best_clt_r = CL_r
best_feature_cut = feature
best_left_weight = left_weight
best_right_weight = right_weight
best_right_data = right_data
best_left_data = left_data
best_l_ll = l_ll
best_r_ll = r_ll
best_left_sample_weight = left_sample_weight
best_right_sample_weight = right_sample_weight
found = True
gain = (bestlik - orig_ll)
# print (" gain:", gain, end = " ")
"""if gain <= self.min_gain:
print("no improvement")"""
if found == True and gain > self.min_gain:
if not is_and_node(self.node):
clt = self.node.cltree
self.node = AndNode()
self.node.cltree = clt
self.node.children_left = [
None
] * self.node.cltree.num_trees
self.node.children_right = [
None
] * self.node.cltree.num_trees
self.node.or_features = [
None
] * self.node.cltree.num_trees
self.node.left_weights = [
None
] * self.node.cltree.num_trees
self.node.right_weights = [
None
] * self.node.cltree.num_trees
self.node.tree_forest = self.tree_forest
Csn._or_nodes = Csn._or_nodes + 1
Csn._or_edges = Csn._or_edges + 2
self.node.or_features[i] = best_feature_cut
# print(" cutting on feature ", self.node.or_features[i])
instances = self.data.shape[0]
self.node.left_weights[i] = best_left_weight
self.node.right_weights[i] = best_right_weight
self.node.children_left[i] = Csn(
data=best_left_data,
vdata=self.vdata,
clt=best_clt_l,
ll=best_l_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha * best_left_weight,
d=self.d,
random_forest=self.random_forest,
m_priors=self.m_priors,
j_priors=self.j_priors,
n_original_samples=self.n_original_samples,
and_leaves=self.and_leaves,
and_inners=self.and_inners,
min_gain=self.min_gain,
depth=self.depth + 1,
sample_weight=best_left_sample_weight)
self.node.children_right[i] = Csn(
data=best_right_data,
vdata=self.vdata,
clt=best_clt_r,
ll=best_r_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha * best_right_weight,
d=self.d,
random_forest=self.random_forest,
m_priors=self.m_priors,
j_priors=self.j_priors,
n_original_samples=self.n_original_samples,
and_leaves=self.and_leaves,
and_inners=self.and_inners,
min_gain=self.min_gain,
depth=self.depth + 1,
sample_weight=best_right_sample_weight)
"""else:
print( " > no cutting due to few features")
else:
print(" > no cutting due to few instances")"""
if is_and_node(self.node):
Csn._and_nodes += 1
# free memory before to recurse
self.free_memory()
def or_cut(self):
""" WRITEME """
print(" > trying to cut ... ")
sys.stdout.flush()
found = False
bestlik = self.orig_ll
best_clt_l = None
best_clt_r = None
best_feature_cut = None
best_left_weight = 0.0
best_right_weight = 0.0
best_right_data = None
best_left_data = None
best_v_ll = 0.0
best_gain = -np.inf
best_left_sample_weight = None
best_right_sample_weight = None
if self.sum_nodes:
# check for clustering
n_clusters = 2
cov_type = 'tied'
rand_gen = None
n_iters = 1000
n_restarts=1
gmm_c = sklearn.mixture.GMM(n_components=n_clusters, covariance_type=cov_type,
random_state=rand_gen, n_iter=n_iters, n_init=n_restarts)
gmm_c.fit(self.data)
clustering = gmm_c.predict(self.data)
# preventing to have a cluster with zero instances
cardinality = np.sum(clustering)
print(" - Clustering instances:",self.data.shape[0], "-", cardinality,self.data.shape[0] - cardinality, end=" ")
if cardinality > 0 and (self.data.shape[0] - cardinality) > 0:
cluster_0 = (clustering == 0)
cluster_0_data = self.data[cluster_0]
cluster_1_data = self.data[~cluster_0]
cluster_0_tree = Cltree()
cluster_1_tree = Cltree()
cluster_0_weight = cluster_0_data.shape[0] / self.data.shape[0]
cluster_1_weight = cluster_1_data.shape[0] / self.data.shape[0]
cluster_0_tree.fit(cluster_0_data,self.m_priors,self.j_priors,scope=self.scope,alpha=self.alpha*cluster_0_weight,
and_leaves=self.and_leaves, sample_weight = None)
cluster_1_tree.fit(cluster_1_data,self.m_priors,self.j_priors,scope=self.scope,alpha=self.alpha*cluster_1_weight,
and_leaves=self.and_leaves, sample_weight = None)
cluster_0_ll = cluster_0_tree.score_samples_log_proba(cluster_0_data, sample_weight = None)
cluster_1_ll = cluster_1_tree.score_samples_log_proba(cluster_1_data, sample_weight = None)
# log sum exp
clustering_ll = 0.0
for d in self.data:
clustering_ll = clustering_ll + logr( cluster_0_weight * np.exp(cluster_0_tree.score_sample_log_proba(d)) + cluster_1_weight * np.exp(cluster_1_tree.score_sample_log_proba(d)))
clustering_ll = clustering_ll / self.data.shape[0]
print("ll:", clustering_ll)
else:
clustering_ll = -np.inf
else:
clustering_ll = -np.inf
cutting_features = []
for f in range(self.node.cltree.n_features):
if self.scope[f] not in self.leaf_vars:
cutting_features.append(f)
if self.random_forest:
if self.d > len(cutting_feaures):
selected = cutting_features
else:
selected = sorted(random.sample(cutting_features, self.d))
else:
selected = cutting_features
PQ = []
ll = 0.0
CL_l = None
CL_r = None
feature = None
left_weight = 0.0
right_weight = 0.0
left_data = None
right_data = None
l_ll = 0.0
r_ll = 0.0
left_sample_weight = 0.0
right_sample_weight = 0.0
for feature in selected:
condition = self.data[:,feature]==0
new_features = np.ones(self.data.shape[1], dtype=bool)
new_features[feature] = False
left_data = self.data[condition,:][:, new_features]
right_data = self.data[~condition,:][:, new_features]
if self.sample_weight is not None:
left_sample_weight = self.sample_weight[condition]
right_sample_weight = self.sample_weight[~condition]
left_weight = np.sum(left_sample_weight ) / np.sum(self.sample_weight )
right_weight = np.sum(right_sample_weight ) / np.sum(self.sample_weight )
else:
left_sample_weight = None
right_sample_weight = None
left_weight = (left_data.shape[0] ) / (self.data.shape[0] )
right_weight = (right_data.shape[0] ) / (self.data.shape[0] )
if left_data.shape[0] > 0 and right_data.shape[0] > 0:
left_scope = np.concatenate((self.node.cltree.scope[0:feature],self.node.cltree.scope[feature+1:]))
right_scope = np.concatenate((self.node.cltree.scope[0:feature],self.node.cltree.scope[feature+1:]))
CL_l = Cltree()
CL_r = Cltree()
CL_l.fit(left_data,self.m_priors,self.j_priors,scope=left_scope,alpha=self.alpha*left_weight,
and_leaves=self.and_leaves, sample_weight = left_sample_weight,
multilabel = self.multilabel, n_labels=self.n_labels, ml_tree_structure=self.ml_tree_structure)
CL_r.fit(right_data,self.m_priors,self.j_priors,scope=right_scope,alpha=self.alpha*right_weight,
and_leaves=self.and_leaves, sample_weight = right_sample_weight,
multilabel = self.multilabel, n_labels=self.n_labels, ml_tree_structure=self.ml_tree_structure)
l_ll = CL_l.score_samples_log_proba(left_data, sample_weight = left_sample_weight)
r_ll = CL_r.score_samples_log_proba(right_data, sample_weight = right_sample_weight)
if self.sample_weight is not None:
ll = ((l_ll+logr(left_weight))*np.sum(left_sample_weight) + (r_ll+logr(right_weight))*np.sum(right_sample_weight))/np.sum(self.sample_weight)
else:
ll = ((l_ll+logr(left_weight))*left_data.shape[0] + (r_ll+logr(right_weight))*right_data.shape[0])/self.data.shape[0]
else:
ll = -np.inf
if len(PQ) == 0:
PQ.append((ll, CL_l, CL_r, feature, left_weight, right_weight,
left_data, right_data, l_ll, r_ll,
left_sample_weight, right_sample_weight))
else:
for e in range(len(PQ)):
if PQ[e][0] < ll:
PQ.insert(e, (ll, CL_l, CL_r, feature, left_weight, right_weight,
left_data, right_data, l_ll, r_ll,
left_sample_weight, right_sample_weight))
break
if len(PQ)>3:
PQ.pop()
if ll>bestlik:
bestlik = ll
best_clt_l = CL_l
best_clt_r = CL_r
best_feature_cut = feature
best_left_weight = left_weight
best_right_weight = right_weight
best_right_data = right_data
best_left_data = left_data
best_l_ll = l_ll
best_r_ll = r_ll
best_left_sample_weight = left_sample_weight
best_right_sample_weight = right_sample_weight
found = True
gain = (bestlik - self.orig_ll)
print (" - gain cut:", gain, end = "")
gain_c = (clustering_ll - self.orig_ll)
print (" gain clustering:", gain_c)
if (found==True and gain > self.min_gain) or (gain_c > gain and gain_c > self.min_gain):
PQ = []
if self.depth < 4 and len(PQ)>1:
# Csn._sum_nodes = Csn._sum_nodes + 1
instances = self.data.shape[0]
sum_w = 0.0
for i in range(len(PQ)):
sum_w += np.exp(PQ[i][0])
# sum_w += PQ[i][0]
for i in range(len(PQ)):
self.node.weights.append(np.exp(PQ[i][0])/sum_w)
# self.node.weights.append(PQ[i][0]/sum_w)
# self.node.weights.append(1/len(PQ))
self.node.children.append(OrNode())
(pq_ll, pq_CL_l, pq_CL_r, pq_feature, pq_left_weight, pq_right_weight,
pq_left_data, pq_right_data, pq_l_ll, pq_r_ll,
pq_left_sample_weight, pq_right_sample_weight) = PQ[i]
self.node.children[i].or_feature_scope = self.scope[pq_feature]
self.node.children[i].or_feature = pq_feature
instances = self.data.shape[0]
self.node.children[i].left_weight = pq_left_weight
self.node.children[i].right_weight = pq_right_weight
self.node.children[i].left_child = Csn(data=pq_left_data,
clt=pq_CL_l, ll=pq_l_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha*pq_left_weight,
d=self.d, random_forest=self.random_forest,
leaf_vars = self.leaf_vars,
m_priors = self.m_priors, j_priors = self.j_priors,
n_original_samples = self.n_original_samples,
and_leaves=self.and_leaves,
and_inners=self.and_inners,
min_gain = self.min_gain,
depth=self.depth+1,
sample_weight = pq_left_sample_weight,
multilabel = self.multilabel,
n_labels=self.n_labels,
ml_tree_structure=self.ml_tree_structure)
self.node.children[i].right_child = Csn(data=pq_right_data,
clt=pq_CL_r, ll=pq_r_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha*pq_right_weight, d=self.d,
random_forest=self.random_forest,
leaf_vars = self.leaf_vars,
m_priors = self.m_priors, j_priors = self.j_priors,
n_original_samples = self.n_original_samples,
and_leaves=self.and_leaves,
and_inners=self.and_inners,
min_gain = self.min_gain,
depth=self.depth+1,
sample_weight = pq_right_sample_weight,
multilabel = self.multilabel,
n_labels=self.n_labels,
ml_tree_structure=self.ml_tree_structure)
elif (gain > gain_c):
self.node = OrNode()
self.node.or_feature_scope = self.scope[best_feature_cut]
Csn._or_nodes = Csn._or_nodes + 1
Csn._or_edges = Csn._or_edges + 2
self.node.or_feature = best_feature_cut
print(" - cutting on feature ", self.node.or_feature, "[#l:",best_left_data.shape[0],", #r:",best_right_data.shape[0],"], gain:", bestlik - self.orig_ll)
instances = self.data.shape[0]
self.node.left_weight = best_left_weight
self.node.right_weight = best_right_weight
# free memory before to recurse
self.free_memory()
self.node.left_child = Csn(data=best_left_data,
clt=best_clt_l, ll=best_l_ll,
min_instances=self.min_instances,
min_features=self.min_features, alpha=self.alpha*best_left_weight,
d=self.d, random_forest=self.random_forest,
leaf_vars = self.leaf_vars,
m_priors = self.m_priors, j_priors = self.j_priors,
n_original_samples = self.n_original_samples,
and_leaves=self.and_leaves, and_inners=self.and_inners,
min_gain = self.min_gain, depth=self.depth+1,
sample_weight = best_left_sample_weight,
multilabel = self.multilabel, n_labels=self.n_labels, ml_tree_structure=self.ml_tree_structure)
self.node.right_child = Csn(data=best_right_data,
clt=best_clt_r, ll=best_r_ll,
min_instances=self.min_instances,
min_features=self.min_features, alpha=self.alpha*best_right_weight, d=self.d,
random_forest=self.random_forest,
leaf_vars = self.leaf_vars,
m_priors = self.m_priors, j_priors = self.j_priors,
n_original_samples = self.n_original_samples,
and_leaves=self.and_leaves, and_inners=self.and_inners,
min_gain = self.min_gain, depth=self.depth+1,
sample_weight = best_right_sample_weight,
multilabel = self.multilabel, n_labels=self.n_labels, ml_tree_structure=self.ml_tree_structure)
else:
self.node = SumNode()
print(" - Adding a sum node")
Csn._sum_nodes = Csn._sum_nodes + 1
instances = self.data.shape[0]
self.node.weights.append(cluster_0_weight)
self.node.weights.append(cluster_1_weight)
# free memory before to recurse
self.free_memory()
self.node.children.append(Csn(data=cluster_0_data,
clt=cluster_0_tree, ll=cluster_0_ll,
min_instances=self.min_instances,
min_features=self.min_features, alpha=self.alpha*cluster_0_weight,
d=self.d, random_forest=self.random_forest,
m_priors = self.m_priors, j_priors = self.j_priors,
n_original_samples = self.n_original_samples,
and_leaves=self.and_leaves, and_inners=self.and_inners,
min_gain = self.min_gain, depth=self.depth+1,
sample_weight = None))
self.node.children.append(Csn(data=cluster_1_data,
clt=cluster_1_tree, ll=cluster_1_ll,
min_instances=self.min_instances,
min_features=self.min_features, alpha=self.alpha*cluster_1_weight, d=self.d,
random_forest=self.random_forest,
m_priors = self.m_priors, j_priors = self.j_priors,
n_original_samples = self.n_original_samples,
and_leaves=self.and_leaves, and_inners=self.and_inners,
min_gain = self.min_gain, depth=self.depth+1,
sample_weight = None))
else:
print(" no cutting")
if self.node.cltree.is_forest():
print(" -> Forest with",self.node.cltree.num_trees, "trees")
else:
print(" -> Tree")
def __init__(self,
data,
clt=None,
ll=0.0,
min_instances=5,
min_features=3,
alpha=1.0,
n_original_samples=None,
leaf_vars=[],
depth=1,
multilabel=False,
n_labels=0,
ml_tree_structure=0,
xcnet=False):
self.min_instances = min_instances
self.min_features = min_features
self.alpha = alpha
self.depth = depth
self.data = data
self.node = TreeNode()
self.multilabel = multilabel
self.n_labels = n_labels
self.ml_tree_structure = ml_tree_structure
self.xcnet = xcnet
self.leaf_vars = leaf_vars
self.n = data.shape[1]
if n_original_samples is None:
self.n_original_samples = self.data.shape[0]
else:
self.n_original_samples = n_original_samples
if clt is None:
COC = [[] for i in range(data.shape[0])]
for r in range(data.shape[0]):
for f in range(data.shape[1]):
if data[r, f] > 0:
COC[r].append(f)
self.node.cltree = Cltree()
self.node.cltree.fit(data,
alpha=self.alpha,
multilabel=self.multilabel,
n_labels=self.n_labels,
ml_tree_structure=self.ml_tree_structure)
self.orig_ll = self.node.cltree.score_samples_log_proba(self.data)
sparsity = 0.0
sparsity = len(self.data.nonzero()[0])
sparsity /= (self.data.shape[1] * self.data.shape[0])
logger.info("Dataset sparsity: %f", sparsity)
else:
self.node.cltree = clt
self.orig_ll = ll
self.scope = self.node.cltree.scope
self.id = Csn._id_node_counter
Csn._id_node_counter = Csn._id_node_counter + 1
print("Block", self.id, "on", len(self.scope), "features and",
self.data.shape[0], "instances, local ll:", self.orig_ll)
if self.data.shape[0] > self.min_instances:
if self.data.shape[1] >= self.min_features:
self.or_cut()
else:
print(" > no cutting due to few features")
else:
print(" > no cutting due to few instances")
if is_tree_node(self.node):
if self.depth > Csn._depth:
Csn._depth = self.depth
Csn._mean_depth = Csn._mean_depth + self.depth
Csn._leaf_nodes = Csn._leaf_nodes + 1
Csn._cltrees = Csn._cltrees + 1
Csn._clt_edges = Csn._clt_edges + self.node.cltree.num_edges
def or_cut(self):
print(" > trying to cut ... ")
sys.stdout.flush()
found = False
bestlik = self.orig_ll
best_clt_l = None
best_clt_r = None
best_feature_cut = None
best_left_weight = 0.0
best_right_weight = 0.0
best_right_data = None
best_left_data = None
best_v_ll = 0.0
best_gain = -np.inf
best_left_sample_weight = None
best_right_sample_weight = None
cutting_features = []
for f in range(self.node.cltree.n_features):
if self.scope[f] not in self.leaf_vars:
cutting_features.append(f)
selected = cutting_features
if self.xcnet:
selected = [random.choice(selected)]
bestlik = -np.inf
ll = 0.0
CL_l = None
CL_r = None
feature = None
left_weight = 0.0
right_weight = 0.0
left_data = None
right_data = None
l_ll = 0.0
r_ll = 0.0
for feature in selected:
condition = self.data[:, feature] == 0
new_features = np.ones(self.data.shape[1], dtype=bool)
new_features[feature] = False
left_data = self.data[condition, :][:, new_features]
right_data = self.data[~condition, :][:, new_features]
left_weight = (left_data.shape[0]) / (self.data.shape[0])
right_weight = (right_data.shape[0]) / (self.data.shape[0])
if left_data.shape[0] > 0 and right_data.shape[0] > 0:
left_scope = np.concatenate(
(self.node.cltree.scope[0:feature],
self.node.cltree.scope[feature + 1:]))
right_scope = np.concatenate(
(self.node.cltree.scope[0:feature],
self.node.cltree.scope[feature + 1:]))
CL_l = Cltree()
CL_r = Cltree()
CL_l.fit(left_data,
scope=left_scope,
alpha=self.alpha,
multilabel=self.multilabel,
n_labels=self.n_labels,
ml_tree_structure=self.ml_tree_structure)
CL_r.fit(right_data,
scope=right_scope,
alpha=self.alpha,
multilabel=self.multilabel,
n_labels=self.n_labels,
ml_tree_structure=self.ml_tree_structure)
l_ll = CL_l.score_samples_log_proba(left_data)
r_ll = CL_r.score_samples_log_proba(right_data)
ll = ((l_ll + np.log(left_weight)) * left_data.shape[0] +
(r_ll + np.log(right_weight)) *
right_data.shape[0]) / self.data.shape[0]
else:
ll = -np.inf
if ll > bestlik:
bestlik = ll
best_clt_l = CL_l
best_clt_r = CL_r
best_feature_cut = feature
best_left_weight = left_weight
best_right_weight = right_weight
best_right_data = right_data
best_left_data = left_data
best_l_ll = l_ll
best_r_ll = r_ll
found = True
gain = (bestlik - self.orig_ll)
print(" - gain cut:", gain, end="")
if (found == True):
self.node = OrNode()
self.node.or_feature_scope = self.scope[best_feature_cut]
Csn._or_nodes = Csn._or_nodes + 1
Csn._or_edges = Csn._or_edges + 2
self.node.or_feature = best_feature_cut
print(" - cutting on feature ", self.node.or_feature, "[#l:",
best_left_data.shape[0], ", #r:", best_right_data.shape[0],
"], gain:", bestlik - self.orig_ll)
instances = self.data.shape[0]
self.node.left_weight = best_left_weight
self.node.right_weight = best_right_weight
# free memory before to recurse
self.free_memory()
self.node.left_child = Csn(
data=best_left_data,
clt=best_clt_l,
ll=best_l_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha,
leaf_vars=self.leaf_vars,
n_original_samples=self.n_original_samples,
depth=self.depth + 1,
multilabel=self.multilabel,
n_labels=self.n_labels,
ml_tree_structure=self.ml_tree_structure,
xcnet=self.xcnet)
self.node.right_child = Csn(
data=best_right_data,
clt=best_clt_r,
ll=best_r_ll,
min_instances=self.min_instances,
min_features=self.min_features,
alpha=self.alpha,
leaf_vars=self.leaf_vars,
n_original_samples=self.n_original_samples,
depth=self.depth + 1,
multilabel=self.multilabel,
n_labels=self.n_labels,
ml_tree_structure=self.ml_tree_structure,
xcnet=self.xcnet)
else:
print(" no cutting")