def ts_exp(common, binary, out_path):
exp = Exp()
lines = exp(None, binary, "A")
lines += exp(common, binary, "F")
exp = Exp(select_ens)
lines += exp(common, binary, "H")
files.save_txt(lines, out_path)
def full_exp(common, binary, out_path):
if (type(binary) != list):
binary = [binary]
simple_exp = SimpleExp()
lines = ["common,binary,n_clf,n_feats,accuracy,precision,recall,f1-score"]
for binary_i in binary:
lines += simple_exp(common, binary_i)
files.save_txt(lines, out_path)
def exp_desc(paths,out_path=None,clf="LR",info="info",transform=None):
all_systems=[systems.borda_count,
systems.bucklin,
systems.coombs]
lines=[get_soft_voting(paths,clf,info,transform)]
for system_i in all_systems:
name_i=system_i.__name__
result_i=pref.ensemble(paths,system=system_i,
clf=clf,transform=transform)
lines.append(get_line(name_i,result_i,info))
if(out_path):
files.save_txt(lines,out_path)
else:
print(lines)
def exp_template(datasets, clf, gens, loss, out_path):
train = [data_i.split()[0] for data_i in datasets]
names = list(train[0].keys())
lines = []
for info_i, gen_i in gens.items():
votes_i = get_votes(train, clf, names, gen_i)
loss_i = {name_j: loss_j(votes_i) for name_j, loss_j in loss.items()}
for info_j, loss_j in loss_i.items():
weights = optimize(loss_j, len(votes_i[0]))
result_j = test_weights(datasets, clf, weights)
metrics = exp.get_metrics(result_j)
line_ij = "%s,%s,%s" % (info_i, info_j, metrics)
print(line_ij)
lines.append(line_ij)
if (out_path):
files.save_txt(lines, out_path)
return lines
def save_dataset(common, binary, out_path):
datasets = ens.read_dataset(common, binary)
files.make_dir(out_path)
subdirs = {
desc_i: "%s/%s" % (out_path, desc_i)
for desc_i in ["features", "votes"]
}
for path_i in subdirs.values():
files.make_dir(path_i)
for i, data_i in enumerate(datasets):
feat_i = "%s/%d" % (subdirs["features"], i)
data_i.save(feat_i)
results = learn.train_ens(datasets, clf="LR")
for i, result_i in enumerate(results):
votes_i = "%s/%d" % (subdirs["votes"], i)
raise Exception(result_i.y_pred.dtype)
text_i = list(result_i.y_pred.astype(str))
lines = []
for line_j, name_j in zip(text_i, result_i.names):
line_j = str(line_j).replace("\n", "")
lines.append("%s#%s" % (line_j, name_j))
files.save_txt(lines, votes_i)
def result_exp(prefix, result_dict):
lines = []
for info_i, result_i in result_dict.items():
line_i = "%s,%s,%s" % (prefix, info_i, get_metrics(result_i))
lines.append(line_i)
files.save_txt(lines, out_path)