def applyAlgo(algo, p, Xtrain, Ytrain, Xtest, Ytest):
if a.startswith("o"):
nbMinority = len(Xtrain[Ytrain == minClass])
if nbMinority <= 5:
sm = SMOTE(random_state=42, k_neighbors=nbMinority-1)
else:
sm = SMOTE(random_state=42)
Xtrain2, Ytrain2 = sm.fit_sample(Xtrain, Ytrain)
elif a.startswith("u"):
rus = RandomUnderSampler(random_state=42)
Xtrain2, Ytrain2 = rus.fit_sample(Xtrain, Ytrain)
else:
Xtrain2, Ytrain2 = Xtrain, Ytrain
scaler = StandardScaler()
scaler.fit(Xtrain2)
Xtrain2 = scaler.transform(Xtrain2)
Xtest = scaler.transform(Xtest)
if algo.endswith("IML"):
ml = IML(pClass=minClass, k=K, m=p["m"], Lambda=p["Lambda"], a=p["a"])
elif algo.endswith("LMNN"):
ml = LMNN(k=K, mu=p["mu"], randomState=np.random.RandomState(1))
elif algo.endswith("GMML"):
ml = GMML(t=p["t"], randomState=np.random.RandomState(1))
elif algo.endswith("ITML"):
ml = ITML(gamma=p["gamma"], randomState=np.random.RandomState(1))
if not algo.endswith("Euclidean"):
ml.fit(Xtrain2, Ytrain2)
Xtrain2 = ml.transform(Xtrain2)
Xtest = ml.transform(Xtest)
# Apply kNN to predict classes of test examples
Ytrain_pred = knnSame(K, Xtrain2, Ytrain2)
Ytest_pred = knn(K, Xtrain2, Ytrain2, Xtest)
perf = {}
for true, pred, name in [(Ytrain2, Ytrain_pred, "train"),
(Ytest, Ytest_pred, "test")]:
# Compute performance measures by comparing prediction with true labels
tn, fp, fn, tp = confusion_matrix(true, pred,
labels=[majClass, minClass]).ravel()
perf[name] = ((int(tn), int(fp), int(fn), int(tp)))
return perf
def applyAlgo(algo, p, Xtrain, Ytrain, Xtest, Ytest):
Xtrain2, Ytrain2 = Xtrain, Ytrain
scaler = StandardScaler()
scaler.fit(Xtrain2)
Xtrain2 = scaler.transform(Xtrain2)
Xtest = scaler.transform(Xtest)
if algo == "IML":
ml = IML(pClass=minClass, k=K, m=p["m"], Lambda=p["Lambda"], a=p["a"])
elif algo == "ML2":
ml = ML2(pClass=minClass, k=K, m=p["m"], Lambda=p["Lambda"], a=p["a"])
elif algo == "ML1":
ml = ML1(num_const=2*K*len(Xtrain),
m=p["m"], Lambda=p["Lambda"], a=p["a"])
elif algo == "LMNN":
ml = LMNN(k=K, mu=p["mu"], randomState=np.random.RandomState(1))
elif algo == "GMML":
ml = GMML(t=p["t"], randomState=np.random.RandomState(1))
elif algo == "ITML":
ml = ITML(gamma=p["gamma"], randomState=np.random.RandomState(1))
if not algo.endswith("Euclidean"):
ml.fit(Xtrain2, Ytrain2)
Xtrain2 = ml.transform(Xtrain2)
Xtest = ml.transform(Xtest)
# Apply kNN to predict classes of test examples
Ytrain_pred = knnSame(K, Xtrain2, Ytrain2)
Ytest_pred = knn(K, Xtrain2, Ytrain2, Xtest)
perf = {}
for true, pred, name in [(Ytrain2, Ytrain_pred, "train"),
(Ytest, Ytest_pred, "test")]:
# Compute performance measures by comparing prediction with true labels
tn, fp, fn, tp = confusion_matrix(true, pred,
labels=[majClass, minClass]).ravel()
perf[name] = ((int(tn), int(fp), int(fn), int(tp)))
return perf
