def run(self, X_train, X_test, y_train, classes):
wsd = wp.Wisard(self.addr_size)
for i, y in enumerate(y_train):
start_time = time.time()
y.sort()
y_ps = "-".join(y)
ds = wp.DataSet()
ds.add(wp.BinInput(X_train[i]), y_ps)
wsd.train(ds)
self.training_time.append(time.time() - start_time)
y_pred_ps = []
for x in X_test:
start_time = time.time()
ds_test = wp.DataSet()
ds_test.add(wp.BinInput(x))
y_pred_ps.append(wsd.classify(ds_test)[0])
self.classification_time.append(time.time() - start_time)
y_pred = []
for y in y_pred_ps:
y_pred.append(y.split("-"))
return y_pred
def run(self, X_train, X_test, y_train, classes):
wsds = {}
for label in classes:
wsds[label] = wp.ClusWisard(self.addr_size, self.minScore,
self.threshold,
self.discriminatorLimit)
start_time = time.time()
for i in range(len(X_train)):
ds = wp.DataSet()
if label in y_train[i]:
ds.add(wp.BinInput(X_train[i]), "true")
wsds[label].train(ds)
else:
ds.add(wp.BinInput(X_train[i]), "false")
wsds[label].train(ds)
self.training_time.append(time.time() - start_time)
y_pred = [[]] * len(X_test)
ds_test = wp.DataSet()
for i in range(len(X_test)):
ds_test.add(X_test[i])
for label in classes:
start_time = time.time()
outputs = wsds[label].classify(ds_test)
self.classification_time.append(
(time.time() - start_time) / len(X_test))
for i in range(len(outputs)):
if outputs[i] == "true":
y_pred[i].append(label)
return y_pred
def run(self, X_train, X_test, y_train, classes):
clus = wp.ClusWisard(self.addr_size, self.minScore, self.threshold,
self.discriminatorLimit)
for i, y in enumerate(y_train):
start_time = time.time()
y.sort()
y_ps = "-".join(y)
ds = wp.DataSet()
ds.add(wp.BinInput(X_train[i]), y_ps)
clus.train(ds)
self.training_time.append(time.time() - start_time)
y_pred_ps = []
for x in X_test:
start_time = time.time()
ds_test = wp.DataSet()
ds_test.add(x)
y_pred_ps.append(clus.classify(ds_test)[0])
self.classification_time.append(time.time() - start_time)
y_pred = []
for y in y_pred_ps:
y_pred.append(y.split("-"))
return y_pred
def classify(self, test_dataset):
results = []
for i in range(0, len(test_dataset)):
result = {}
for j in range(0, len(self.nets)):
test = wp.DataSet()
bi = wp.BinInput(test_dataset.get(i))
test.add(bi, test_dataset.getLabel(i))
r = self.nets[j].classify(test)
if (r[0] in result):
result[r[0]] += self.ensemble_weights[j]
else:
result[r[0]] = 0
results.append(max(result, key=result.get))
return results
def classify(self, test_dataset):
results = []
for i in range(0, len(test_dataset)):
scores = []
test = wp.DataSet()
bi = wp.BinInput(test_dataset.get(i))
test.add(bi, test_dataset.getLabel(i))
for j in range(0, len(self.nets)):
scores.append(self.nets[j].classify(test)[0])
out = self.nets[0].getAllScores(test)
labels = self.get_labels(out)
result = 0
if (self.voting == "borda0"):
result = self.borda_count_0(scores, labels)
else:
if (self.voting == "borda1"):
result = self.borda_count_1(scores, labels)
else:
result = self.dowdall(scores, labels)
results.append(result)
return results
def classify(self, test_dataset):
results = []
for i in range(0, len(test_dataset)):
votes = []
scores = []
test = wp.DataSet()
bi = wp.BinInput(test_dataset.get(i))
test.add(bi, test_dataset.getLabel(i))
for j in range(0, len(self.nets)):
votes.append(self.get_scores(self.nets[j].getAllScores(test)))
labels = votes[0]
for i in range(len(labels)):
score = []
for j in range(len(votes)):
if(labels[i] in votes[j]):
score.append(votes[j].index(labels[i]))
else:
score.append(votes[j].index(max(votes[j])))
scores.append(score)
result = 0
if(self.voting == "plurality1"):
result = self.plurality1(scores, labels)
else:
if(self.voting == "plurality2"):
result = self.plurality2(scores, labels)
else:
if(self.voting == "plurality3"):
result = self.plurality3(scores, labels)
else:
result = self.plurality4(scores, labels, 0.3)
results.append(result)
return results