def build_tree(self, input_file, output_file, model_path = './cur_set/', reset_contextual_features = False):
TreeBuilder.build_tree(self, input_file, output_file, model_path, reset_contextual_features)
# print len(self.stumps)
while len(self.stumps) > 1:
best_one = None
max_bin_score = -2000
for (index, bin_score) in enumerate(self.stumps_bin_scores):
if bin_score > max_bin_score:
best_one = index
max_bin_score = bin_score
if best_one is None:
best_one = utils.utils.argsmax(self.stumps_bin_scores, 1)[0]
best_score = self.stumps_bin_scores[best_one]
#print self.stumps
if self.verbose:
print 'best_one'
print self.stumps[best_one]
print self.stumps[best_one + 1]
print 'best_score', best_score
print 'bin scores'
for i in range(len(self.stumps_bin_scores)):
print i, self.stumps_bin_scores[i]
print
instance_clone = (self.stumps, self.stumps_mc_scores, self.stumps_bin_scores, self.offsets, self.tree_score)
self.connect_stumps(best_one, instance_clone)
return (self.stumps[0], best_score)
def __init__(self, _model_path = [paths.MODEL_PATH], _bin_model_file = None, _bin_scale_model_file = None,
_mc_model_file = None, _mc_scale_model_file = None,
_name = "FengHirst", verbose = False, use_contextual_features = False):
TreeBuilder.__init__(self, _model_path, _bin_model_file, _bin_scale_model_file, _mc_model_file, _mc_scale_model_file,
_name, verbose, use_contextual_features)