def apply_SA(trainX, trainY, testX, testY, window, source_pos, target_pos):
# Decision Tree
print("\n Subspace Alignment (Fernando et al., 2013) ")
classifier = SubspaceAlignedClassifier(loss="dtree")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_DT_SA, acc_DT_SA_INFO = check_accuracy(testY, pred_naive)
# Logistic Regression
print("\n Subspace Alignment (Fernando et al., 2013) ")
classifier = SubspaceAlignedClassifier(loss="logistic")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_LR_SA, acc_LR_SA_INFO = check_accuracy(testY, pred_naive)
# Naive Bayes Bernoulli
print("\n Subspace Alignment (Fernando et al., 2013) ")
classifier = SubspaceAlignedClassifier(loss="berno")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_NB_SA, acc_NB_SA_INFO = check_accuracy(testY, pred_naive)
#
return pd.DataFrame(
[{
'window': window,
'source_position': source_pos,
'target_position': target_pos,
'acc_LR_SA': acc_LR_SA,
'acc_LR_SA_INFO': str(acc_LR_SA_INFO),
'acc_DT_SA': acc_DT_SA,
'acc_DT_SA_INFO': str(acc_DT_SA_INFO),
'acc_NB_SA': acc_NB_SA,
'acc_NB_SA_INFO': str(acc_NB_SA_INFO),
}]
)
def test_predict():
"""Test for making predictions."""
X = rnd.randn(10, 2)
y = np.hstack((-np.ones((5, )), np.ones((5, ))))
Z = rnd.randn(10, 2) + 1
clf = SubspaceAlignedClassifier()
clf.fit(X, y, Z)
u_pred = clf.predict(Z)
labels = np.unique(y)
assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0
if v == testY[i]:
p += 1
acc = p * 100 / len(result)
# print(result)
#print("ACC:{0}%, Total:{1}/{2} with positive {3}".format(acc, len(result), len(testY), p))
return acc #, check_accuracy(result, testY)
########################
#### WITHOUT TL ########
########################
# Decision Tree
#print("\n Subspace Alignment (Fernando et al., 2013) ")
classifier_SA = SubspaceAlignedClassifier(loss="dtree")
classifier_SA.fit(trainX, trainY, testX)
pred_naive = classifier_SA.predict(testX)
acc_DT_SA = checkAccuracy(testY, pred_naive)
prob = classifier_SA.predict_proba(testX)
#print(prob)
print("acc_DT_SA:", acc_DT_SA)
# Logistic Regression
#print("\n Subspace Alignment (Fernando et al., 2013) ")
classifier = SubspaceAlignedClassifier(loss="logistic")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
prob = classifier.predict_proba(testX)
#print(prob)
acc_LR_SA = checkAccuracy(testY, pred_naive)
print("acc_LR_SA:", acc_LR_SA)
#i = 0
X_s_t = np.vstack([X_s, X_t_init]) y_s_t = np.concatenate([y_s, y_t_init]) clf_sup = RandomForestClassifier().fit(X_s_t, y_s_t) pred = clf_sup.predict(X_test) print(accuracy_score(y_test, pred)) clf_sup = MLPClassifier().fit(X_s_t, y_s_t) pred = clf_sup.predict(X_test) print(accuracy_score(y_test, pred)) clf_sup = DecisionTreeClassifier().fit(X_s_t, y_s_t) pred = clf_sup.predict(X_test) print(accuracy_score(y_test, pred)) clf_sup = SVC().fit(X_s_t, y_s_t) pred = clf_sup.predict(X_test) print(accuracy_score(y_test, pred)) # ENCO learning clf_sup = encolearning.EnCoLearning(iteration=20).fit(X_s_t, y_s_t, X_t) pred = clf_sup.predict(X_test) print(accuracy_score(y_test, pred)) #%% TCA clf_tca = SubspaceAlignedClassifier(num_components=10) clf_tca.fit(X_s, y_s.reshape(len(y_s), ), X_t) pred = clf_tca.predict(X_test) acc = accuracy_score(y_test, pred) print(acc)