def predict(self, X):
"""Hard decision."""
# print("PREDICT")
# Check is fit had been called
check_is_fitted(self, "classes_")
# Input validation
X = check_array(X)
if X.shape[1] != self.X_.shape[1]:
raise ValueError("number of features does not match")
X_dsel = self.previous_X
y_dsel = self.previous_y
if self.oversampled:
ros = RandomOverSampler(random_state=42)
X_dsel, y_dsel = ros.fit_resample(X_dsel, y_dsel)
if self.desMethod == "KNORAE":
des = KNORAE(self.ensemble_, random_state=42)
elif self.desMethod == "KNORAU":
des = KNORAU(self.ensemble_, random_state=42)
elif self.desMethod == "LCA":
des = LCA(self.ensemble_, random_state=42)
elif self.desMethod == "Rank":
des = Rank(self.ensemble_, random_state=42)
else:
des = KNORAE(self.ensemble_, random_state=42)
des.fit(X_dsel, y_dsel)
prediction = des.predict(X)
return prediction
def predict(self, X):
# Check is fit had been called
check_is_fitted(self, "classes_")
# Input validation
X = check_array(X)
if X.shape[1] != self.X_.shape[1]:
raise ValueError("number of features does not match")
X_dsel = self.previous_X
y_dsel = self.previous_y
unique, counts = np.unique(y_dsel, return_counts=True)
k_neighbors = 5
if counts[0] - 1 < 5:
k_neighbors = counts[0] - 1
if self.oversampler == "SMOTE" and k_neighbors > 0:
smote = SMOTE(random_state=42, k_neighbors=k_neighbors)
X_dsel, y_dsel = smote.fit_resample(X_dsel, y_dsel)
elif self.oversampler == "svmSMOTE" and k_neighbors > 0:
try:
svmSmote = SVMSMOTE(random_state=42, k_neighbors=k_neighbors)
X_dsel, y_dsel = svmSmote.fit_resample(X_dsel, y_dsel)
except ValueError:
pass
elif self.oversampler == "borderline1" and k_neighbors > 0:
borderlineSmote1 = BorderlineSMOTE(random_state=42,
k_neighbors=k_neighbors,
kind='borderline-1')
X_dsel, y_dsel = borderlineSmote1.fit_resample(X_dsel, y_dsel)
elif self.oversampler == "borderline2" and k_neighbors > 0:
borderlineSmote2 = BorderlineSMOTE(random_state=42,
k_neighbors=k_neighbors,
kind='borderline-2')
X_dsel, y_dsel = borderlineSmote2.fit_resample(X_dsel, y_dsel)
elif self.oversampler == "ADASYN" and k_neighbors > 0:
try:
adasyn = ADASYN(random_state=42, n_neighbors=k_neighbors)
X_dsel, y_dsel = adasyn.fit_resample(X_dsel, y_dsel)
except RuntimeError:
pass
except ValueError:
pass
elif self.oversampler == "SLS" and k_neighbors > 0:
sls = Safe_Level_SMOTE(n_neighbors=k_neighbors)
X_dsel, y_dsel = sls.sample(X_dsel, y_dsel)
if self.desMethod == "KNORAE":
des = KNORAE(self.ensemble_, random_state=42)
elif self.desMethod == "KNORAU":
des = KNORAU(self.ensemble_, random_state=42)
elif self.desMethod == "KNN":
des = DESKNN(self.ensemble_, random_state=42)
elif self.desMethod == "Clustering":
des = DESClustering(self.ensemble_, random_state=42)
else:
des = KNORAE(self.ensemble_, random_state=42)
if len(self.ensemble_) < 2:
prediction = self.ensemble_[0].predict(X)
else:
des.fit(X_dsel, y_dsel)
prediction = des.predict(X)
return prediction