def _go_train(self, nodes_to_go, verbose=0):
self.min_depth = maxint
self.num_of_leaves = 0
sum_of_depth = 0.
solver = SoftDecisionSolver()
solver.set_up(self.gd_param, self.sgd_param, self.sd_param)
for node in nodes_to_go:
if verbose > 0:
print 'Node: {id}, {num_nodes} more nodes to go.' \
'\n\tdepth = {node.depth}, ' \
'num_of_labels = {data.num_of_labels}, ' \
'num_of_samples = {data.num_of_samples}, ' \
'entropy = {data.entropy}' \
.format(id=id(node),
num_nodes=len(nodes_to_go),
node=node, data=node.data)
if self._should_be_leaf(node):
if verbose > 0:
print '\tThis is a leaf.'
if self.min_depth > node.depth: self.min_depth = node.depth
if self.max_depth < node.depth: self.max_depth = node.depth
sum_of_depth += node.depth
self.num_of_leaves += 1
if node.data.entropy > 0 and self.tree_param.ovr_in_leaves:
if verbose > 0:
print '\tTraining One-vs-Rest.'
self._train_ovr_leaf(node)
else:
if verbose > 0:
print '\tTraining SoftDecisionModel.'
node.model = SoftDecisionModel(node.data, self.model_param)
solver.train(node.data, node.model)
if node.model.indices_of_pos_samples.shape[0] == 0 or \
node.model.indices_of_neg_samples.shape[0] == 0:
if verbose > 0:
print '\t*** Trivial solutions. This is a leaf.'
if self.tree_param.ovr_in_leaves:
if verbose > 0:
print '\tTraining One-vs-Rest.'
self._train_ovr_leaf(node)
else:
node.model = None
else:
lchild = TreeNode()
rchild = TreeNode()
lchild.data = Data(node.data,
node.model.indices_of_pos_samples)
rchild.data = Data(node.data,
node.model.indices_of_neg_samples)
node.attach_lchild(lchild)
node.attach_rchild(rchild)
nodes_to_go.append(lchild)
nodes_to_go.append(rchild)
if self.num_of_leaves == 0: self.num_of_leaves = 1
self.avg_depth = sum_of_depth / self.num_of_leaves
if verbose > 0:
print 'Done.'
return self
def _copy(self, some):
self.root = None
self.solver = SoftDecisionSolver()
self.gd_param = SoftDecisionSolver.GDParam(some.gd_param)
self.sgd_param = SoftDecisionSolver.SGDParam(some.sgd_param)
self.sd_param = SoftDecisionSolver.SDParam(some.sd_param)
self.model_param = SoftDecisionModel.Param(some.model_param)
self.ovr_param = some.ovr_param.copy()
self.tree_param = copy(some.tree_param)
self.solver.set_up(self.gd_param, self.sgd_param, self.sd_param)
self.min_depth = some.min_depth
self.max_depth = some.max_depth
self.avg_depth = some.avg_depth
self.num_of_leaves = some.num_of_leaves
if some.root is not None:
self.root = some.root.__copy__()
nodes_to_go = deque()
nodes_to_go.append((some.root, self.root))
while len(nodes_to_go) > 0:
some_node, self_node = nodes_to_go.popleft()
if some_node.lchild is not None:
self_node.attach_lchild(some_node.lchild.__copy__())
nodes_to_go.append((some_node.lchild, self_node.lchild))
if some_node.rchild is not None:
self_node.attach_rchild(some_node.rchild.__copy__())
nodes_to_go.append((some_node.rchild, self_node.rchild))
return self
def __init__(self, copy_from=None):
if copy_from is not None:
self._copy(copy_from)
else:
self.root = None
self.solver = SoftDecisionSolver()
self.gd_param = SoftDecisionSolver.GDParam()
self.sgd_param = SoftDecisionSolver.SGDParam()
self.sd_param = SoftDecisionSolver.SDParam()
self.model_param = SoftDecisionModel.Param()
self.ovr_param = {'alpha' : self.model_param.regularizor,
'class_weight' : None,
'epsilon' : 0.1,
'eta0' : 0.01,
'fit_intercept': True,
'l1_ratio' : 0.15,
'learning_rate': 'optimal',
'loss' : 'log',
'n_iter' : self.gd_param.num_of_iter,
'n_jobs' : 1,
'penalty' : 'l2',
'power_t' : 0.5,
'random_state' : None,
'rho' : None,
'shuffle' : True,
'verbose' : 0,
'warm_start' : False}
self.tree_param = SoftLabelTree.Param()
self.solver.set_up(self.gd_param, self.sgd_param, self.sd_param)
self.min_depth = 0
self.max_depth = 0
self.avg_depth = 0
self.num_of_leaves = 1
class SoftLabelTree(object):
class Param(object):
def __init__(self):
self.min_entropy = 1
self.max_depth = 0
self.min_num_of_samples_per_node = 1
self.ovr_in_leaves = True
def __copy__(self):
param = SoftLabelTree.Param()
param.__dict__.update(self.__dict__)
return param
def __init__(self, copy_from=None):
if copy_from is not None:
self._copy(copy_from)
else:
self.root = None
self.solver = SoftDecisionSolver()
self.gd_param = SoftDecisionSolver.GDParam()
self.sgd_param = SoftDecisionSolver.SGDParam()
self.sd_param = SoftDecisionSolver.SDParam()
self.model_param = SoftDecisionModel.Param()
self.ovr_param = {'alpha' : self.model_param.regularizor,
'class_weight' : None,
'epsilon' : 0.1,
'eta0' : 0.01,
'fit_intercept': True,
'l1_ratio' : 0.15,
'learning_rate': 'optimal',
'loss' : 'log',
'n_iter' : self.gd_param.num_of_iter,
'n_jobs' : 1,
'penalty' : 'l2',
'power_t' : 0.5,
'random_state' : None,
'rho' : None,
'shuffle' : True,
'verbose' : 0,
'warm_start' : False}
self.tree_param = SoftLabelTree.Param()
self.solver.set_up(self.gd_param, self.sgd_param, self.sd_param)
self.min_depth = 0
self.max_depth = 0
self.avg_depth = 0
self.num_of_leaves = 1
def set_up(self, *args, **kwargs):
args = unpack_tuple(args)
self.gd_param,
self.sgd_param,
self.sd_param,
self.model_param,
self.ovr_param,
self.tree_param = parse_args(args,
(SoftDecisionSolver.GDParam,
SoftDecisionSolver.SGDParam,
SoftDecisionSolver.SDParam,
SoftDecisionModel.Param,
dict,
SoftLabelTree.Param),
(self.gd_param,
self.sgd_param,
self.sd_param,
self.model_param,
self.ovr_param,
self.tree_param))
self.__dict__.update(kwargs)
if self.gd_param is not None and \
self.sgd_param is not None and \
self.sd_param is not None:
self.solver.set_up(self.gd_param, self.sgd_param, self.sd_param)
def params(self):
return (self.gd_param, self.sgd_param, self.sd_param,
self.model_param, self.ovr_param, self.tree_param)
def is_set_up(self):
return self.gd_param is not None and \
self.sgd_param is not None and \
self.sd_param is not None and \
self.model_param is not None and \
self.ovr_param is not None and \
self.tree_param is not None
def train(self, X, Y, verbose=0):
self.root = SoftLabelTree.Node()
self.root.data = Data(X, Y)
nodes_to_go = deque()
nodes_to_go.append(self.root)
return self._go_train(nodes_to_go, verbose)
def continue_train(self, X, Y, verbose=0):
if self.root is None:
return self.train(X, Y)
nodes_to_go = deque()
nodes_to_search = deque()
nodes_to_search.append(self.root)
while len(nodes_to_search) > 0:
node = nodes_to_search.popleft()
if not (node.is_leaf() or self._should_be_leaf(node)):
nodes_to_search.append(node.lchild)
nodes_to_search.append(node.rchild)
else:
nodes_to_go.append(node)
return self._go_train(nodes_to_go, verbose)
def test_proba(self, *args, **kwargs):
''' Usage:
Y_proba[, complexity][, depths]
= test_proba(X, param=None,
return_complexity=False,
return_depth=False,
verbose=0)
'''
X, param, return_complexity, return_depth, verbose \
= parse_args(args, (np.ndarray,
SoftLabelTree.Param,
bool,
bool,
int),
(None,
self.tree_param,
False,
False,
0))
X = kwargs.get('X', X)
param = kwargs.get('param', param)
return_complexity = kwargs.get('return_complexity', return_complexity)
return_depth = kwargs.get('return_depth', return_depth)
verbose = kwargs.get('verbose', verbose)
Y_proba = np.empty((X.shape[0], self.root.data.num_of_labels),
dtype=np.double)
complexity = None
depths = None
Y_proba[...] = -np.inf
if return_depth: depths = np.zeros(X.shape[0], dtype=np.int)
if return_complexity: complexity = np.zeros(X.shape[0], dtype=np.int)
for i in xrange(X.shape[0]):
if verbose > 0:
print '\rTesting sample {}/{} ...'.format(i+1, X.shape[0]),
x = X[i]
node = self.root
while not (node.is_leaf() or self._should_be_leaf(node, param)):
if return_complexity:
complexity[i] += node.model.n_nonzeros
if node.model.test_one(x) > 0:
node = node.lchild
else:
node = node.rchild
if node.model is None or not param.ovr_in_leaves:
num_distrib = node.data.num_of_samples_of_each_label. \
astype(np.double)
proba = num_distrib / np.sum(num_distrib)
Y_proba[i, node.data.labels-1] = proba
else:
proba = node.model.decision_function(x).ravel()
num_model_classes = node.model.classes_.shape[0]
if num_model_classes <= 2:
proba = np.r_[-proba, proba]
num_model_classes -= 1
Y_proba[i, node.model.classes_-1] = proba
if return_complexity:
complexity[i] += np.count_nonzero(node.model.coef_)
if return_depth:
depths[i] = node.depth
if verbose > 0:
print '\rDone.'
return pack_tuple(Y_proba, complexity, depths)
def test(self, *args, **kwargs):
''' Usage:
Y_label[, ap][, complexity][, depths]
= test(X, Y=None, param=None,
return_complexity=False, return_depth=False,
verbose=0)
'''
X, Y, param, return_complexity, return_depth, verbose \
= parse_args(args,
(np.ndarray, np.ndarray, SoftLabelTree.Param,
bool, bool, int),
(None, None, None,
False, False, 0))
X = kwargs.get('X', X)
Y = kwargs.get('Y', Y)
param = kwargs.get('param', param)
return_complexity = kwargs.get('return_complexity', return_complexity)
return_depth = kwargs.get('return_depth', return_depth)
verbose = kwargs.get('verbose', verbose)
Y_predict = None
ap = None
complexity = None
depths = None
if X is not None and X.shape[-1] == self.root.data.dimension:
result = self.test_proba(X, param, return_complexity, return_depth)
if return_complexity and return_depth:
Y_proba, complexity, depths = result
elif not return_complexity and return_depth:
Y_proba, depths = result
elif return_complexity and not return_depth:
Y_proba, complexity = result
else:
Y_proba = result
Y_predict = np.argmax(Y_proba, axis=-1) + 1
if Y is not None and Y.shape[0] == X.shape[0]:
Y_truth = np.zeros((X.shape[0], self.root.data.num_of_labels),
dtype=np.int8)
Y_truth[xrange(Y_truth.shape[0]), Y - 1] = 1
posinfs = np.where(np.isposinf(Y_proba))
Y_proba[posinfs] = 0
Y_proba[posinfs] = np.max(Y_proba) + 1
neginfs = np.where(np.isneginf(Y_proba))
Y_proba[neginfs] = 0
Y_proba[neginfs] = np.min(Y_proba) - 1
ap = np.empty(self.root.data.num_of_labels)
for i_label in xrange(self.root.data.num_of_labels):
ap[i_label] = average_precision_score(Y_truth[:, i_label],
Y_proba[:, i_label])
return pack_tuple(Y_predict, ap, complexity, depths)
def __copy__(self):
return SoftLabelTree(self)
def __repr__(self):
return 'gd_param.verbosity = {gd_repr}\n' \
'gd_param.show_obj_each_iter = {self.gd_param.show_obj_each_iter}\n' \
'gd_param.show_learning_rate_each_iter = {self.gd_param.show_learning_rate_each_iter}\n' \
'gd_param.num_of_iter = {self.gd_param.num_of_iter}\n' \
'gd_param.optimal_error = {self.gd_param.optimal_error}\n' \
'gd_param.init_learning_rate = {self.gd_param.init_learning_rate}\n' \
'gd_param.init_learning_rate_try_1st = {self.gd_param.init_learning_rate_try_1st}\n' \
'gd_param.init_learning_rate_try_factor = {self.gd_param.init_learning_rate_try_factor}\n' \
'gd_param.init_learning_rate_try_subsample_rate = {self.gd_param.init_learning_rate_try_subsample_rate}\n' \
'gd_param.init_learning_rate_try_min_sample = {self.gd_param.init_learning_rate_try_min_sample}\n\n' \
'sgd_param.size_of_batch = {self.sgd_param.size_of_batch}\n\n' \
'sd_param.verbosity = {sd_repr}\n' \
'sd_param.show_p_each_iter = {self.sd_param.show_p_each_iter}\n' \
'sd_param.num_of_trials = {self.sd_param.num_of_trials}\n' \
'sd_param.num_of_iter_update_p_per_train = {self.sd_param.num_of_iter_update_p_per_train}\n' \
'sd_param.num_of_iter_update_p_per_epoch = {self.sd_param.num_of_iter_update_p_per_epoch}\n' \
'sd_param.num_of_iter_update_p_per_batch = {self.sd_param.num_of_iter_update_p_per_batch}\n' \
'sd_param.num_of_iter_confirm_converge = {self.sd_param.num_of_iter_confirm_converge}\n\n' \
'model_param.regularizor = {self.model_param.regularizor}\n' \
'model_param.reg_l1_ratio = {self.model_param.reg_l1_ratio}\n' \
'model_param.bias_learning_rate_factor = {self.model_param.bias_learning_rate_factor}\n' \
'model_param.init_var_subsample_rate = {self.model_param.init_var_subsample_rate}\n' \
'model_param.init_var_subsample_min = {self.model_param.init_var_subsample_min}\n\n' \
'tree_param.min_entropy = {self.tree_param.min_entropy}\n' \
'tree_param.max_depth = {self.tree_param.max_depth}\n' \
'tree_param.min_num_of_samples_per_node = {self.tree_param.min_num_of_samples_per_node}\n' \
'tree_param.ovr_in_leaves = {self.tree_param.ovr_in_leaves}\n\n' \
'ovr_param.update( {self.ovr_param} )\n\n' \
'self.min_depth = {self.min_depth}\n' \
'self.max_depth = {self.max_depth}\n' \
'self.avg_depth = {self.avg_depth}\n' \
'self.num_of_leaves = {self.num_of_leaves}' \
.format(self=self, gd_repr=self.gd_param.verbosity.__repr__(),
sd_repr=self.sd_param.verbosity.__repr__())
def _copy(self, some):
self.root = None
self.solver = SoftDecisionSolver()
self.gd_param = SoftDecisionSolver.GDParam(some.gd_param)
self.sgd_param = SoftDecisionSolver.SGDParam(some.sgd_param)
self.sd_param = SoftDecisionSolver.SDParam(some.sd_param)
self.model_param = SoftDecisionModel.Param(some.model_param)
self.ovr_param = some.ovr_param.copy()
self.tree_param = copy(some.tree_param)
self.solver.set_up(self.gd_param, self.sgd_param, self.sd_param)
self.min_depth = some.min_depth
self.max_depth = some.max_depth
self.avg_depth = some.avg_depth
self.num_of_leaves = some.num_of_leaves
if some.root is not None:
self.root = some.root.__copy__()
nodes_to_go = deque()
nodes_to_go.append((some.root, self.root))
while len(nodes_to_go) > 0:
some_node, self_node = nodes_to_go.popleft()
if some_node.lchild is not None:
self_node.attach_lchild(some_node.lchild.__copy__())
nodes_to_go.append((some_node.lchild, self_node.lchild))
if some_node.rchild is not None:
self_node.attach_rchild(some_node.rchild.__copy__())
nodes_to_go.append((some_node.rchild, self_node.rchild))
return self
def _should_be_leaf(self, node, param=None):
if param is None:
param = self.tree_param
return (param.max_depth >= 0 and
node.depth >= param.max_depth) or \
(node.data.num_of_samples <=
param.min_num_of_samples_per_node) or \
(node.data.entropy <= param.min_entropy)
def _go_train(self, nodes_to_go, verbose=0):
self.min_depth = maxint
self.num_of_leaves = 0
sum_of_depth = 0.
while len(nodes_to_go) > 0:
node = nodes_to_go.popleft()
if verbose > 0:
print 'Node: {id}, {num_nodes} more nodes to go.' \
'\n\tdepth = {node.depth}, ' \
'num_of_labels = {data.num_of_labels}, ' \
'num_of_samples = {data.num_of_samples}, ' \
'entropy = {data.entropy}' \
.format(id=id(node),
num_nodes=len(nodes_to_go),
node=node, data=node.data)
if self._should_be_leaf(node):
if verbose > 0:
print '\tThis is a leaf.'
if self.min_depth > node.depth: self.min_depth = node.depth
if self.max_depth < node.depth: self.max_depth = node.depth
sum_of_depth += node.depth
self.num_of_leaves += 1
if node.data.entropy > 0 and self.tree_param.ovr_in_leaves:
print '\tTraining One-vs-Rest.'
self._train_ovr_leaf(node)
else:
if verbose > 0:
print '\tTraining SoftDecisionModel.'
node.model = SoftDecisionModel(node.data, self.model_param)
self.solver.train(node.data, node.model)
if node.model.indices_of_pos_samples.shape[0] == 0 or \
node.model.indices_of_neg_samples.shape[0] == 0:
if verbose > 0:
print '\t*** Trivial solutions. This is a leaf.'
if self.tree_param.ovr_in_leaves:
if verbose > 0:
print '\tTraining One-vs-Rest.'
self._train_ovr_leaf(node)
else:
node.model = None
else:
lchild = SoftLabelTree.Node()
rchild = SoftLabelTree.Node()
lchild.data = Data(node.data,
node.model.indices_of_pos_samples)
rchild.data = Data(node.data,
node.model.indices_of_neg_samples)
node.attach_lchild(lchild)
node.attach_rchild(rchild)
nodes_to_go.append(lchild)
nodes_to_go.append(rchild)
self.avg_depth = sum_of_depth / self.num_of_leaves
if self.num_of_leaves == 0: self.num_of_leaves = 1
if verbose > 0:
print 'Done.'
return self
def _train_ovr_leaf(self, node):
node.model = SGDClassifier(**self.ovr_param)
X, Y = node.data.data
indices = node.data.indices
node.model.fit(X[indices, :], Y[indices])
return self
class Node(object):
def __init__(self):
self.depth = 0
self.model = None
self.data = None
self.lchild = None
self.rchild = None
def is_leaf(self):
return self.lchild is None and self.rchild is None
def attach_lchild(self, node):
self.lchild = node
node.depth = self.depth + 1
def attach_rchild(self, node):
self.rchild = node
node.depth = self.depth + 1
def __copy__(self):
node = SoftLabelTree.Node()
node.depth = self.depth
node.model = self.model
node.data = self.data
return node
