def fit(self, X, y):
sss = StratifiedShuffleSplit(n_splits=self.hsic_splits,
random_state=42)
idxs = []
hsics = []
for train_index, test_index in list(sss.split(X, y)):
hsic_lasso2 = HSICLasso()
hsic_lasso2.input(X[train_index], y[train_index])
hsic_lasso2.classification(
self.n_features, B=self.B,
M=self.M) #(self.n_features, B=self.B, M=self.M)
hsics.append(hsic_lasso2)
# not just best features - get their neighbors (similar features) too
all_ft_idx = np.array(hsic_lasso2.get_index(), dtype=int).ravel()
for i in range(len(all_ft_idx)):
idx = np.array(hsic_lasso2.get_index_neighbors(
feat_index=i, num_neighbors=10),
dtype=int)
score = np.array(hsic_lasso2.get_index_neighbors_score(
feat_index=i, num_neighbors=10),
dtype=int)
idx = idx[np.where(score > self.neighbor_threshold)[0]]
all_ft_idx = np.concatenate((all_ft_idx, idx))
all_ft_idx = np.unique(all_ft_idx)
idxs.append(all_ft_idx)
if len(idxs) == 1:
self.hsic_idx_ = idxs[0]
else:
self.hsic_idx_ = np.intersect1d(idxs[-1], self.hsic_idx_)
print("HSIC done.", len(self.hsic_idx_))
print("Upsampling with ADASYN... (features: " +
str(len(self.hsic_idx_)) + ")")
sm = ADASYN(sampling_strategy="minority",
n_neighbors=self.adasyn_neighbors,
n_jobs=-1)
sX, sy = X[:, self.hsic_idx_], y
if self.adasyn_neighbors > 0:
try:
sX, sy = sm.fit_resample(X[:, self.hsic_idx_], y)
for i in range(len(np.unique(y) - 1)):
sX, sy = sm.fit_resample(sX, sy)
except:
pass
print("ADASYN done. Starting clf")
self.clf_ = LGBMClassifier(n_estimators=1000).fit(sX, sy)
print("done")
return self
def fit(self, X, y):
if X.shape[1] > 10000:
#clf = RandomForestClassifier(n_estimators=1000,n_jobs=-1).fit(X,y)
clf = LGBMClassifier(n_estimators=1000, n_jobs=-1).fit(X, y)
ftimp = clf.feature_importances_
relevant = np.where(ftimp > 0)[0]
print("relevant ft:", len(relevant), "/", X.shape[1])
else:
relevant = np.arange(X.shape[1])
sss = StratifiedShuffleSplit(n_splits=self.hsic_splits,
random_state=42)
idxs = []
hsics = []
for train_index, test_index in list(sss.split(X, y)):
hsic_lasso2 = HSICLasso()
hsic_lasso2.input(X[:, relevant][train_index], y[train_index])
hsic_lasso2.classification(
self.n_features, B=self.B,
M=self.M) #(self.n_features, B=self.B, M=self.M)
hsics.append(hsic_lasso2)
# not just best features - get their neighbors (similar features) too
all_ft_idx = np.array(hsic_lasso2.get_index(), dtype=int).ravel()
for i in range(len(all_ft_idx)):
idx = np.array(hsic_lasso2.get_index_neighbors(
feat_index=i, num_neighbors=10),
dtype=int)
score = np.array(hsic_lasso2.get_index_neighbors_score(
feat_index=i, num_neighbors=10),
dtype=int)
idx = idx[np.where(score > self.neighbor_threshold)[0]]
all_ft_idx = np.concatenate((all_ft_idx, idx))
all_ft_idx = np.unique(all_ft_idx)
idxs.append(relevant[all_ft_idx])
#if len(idxs) == 1:
# self.hsic_idx_ = idxs[0]
#else:
# self.hsic_idx_ = np.intersect1d(idxs[-1], self.hsic_idx_)
self.hsic_idx_ = []
stability_concession = 0
while len(self.hsic_idx_) == 0:
featurecandidates = np.unique(np.concatenate(idxs))
for candidate in featurecandidates:
occurrences = np.sum(
[1 if candidate in idx else 0 for idx in idxs])
if occurrences > self.stability_minimum_across_splits - stability_concession:
self.hsic_idx_.append(candidate)
if len(self.hsic_idx_) > 1:
break
else:
# failed to find commonly occurring features - reduce threshold
stability_concession += 1
print("HSIC done.", len(self.hsic_idx_), "(out of ",
len(featurecandidates), " candidates)")
print("Upsampling with ADASYN... (features: " +
str(len(self.hsic_idx_)) + ")")
sm = ADASYN(sampling_strategy="minority",
n_neighbors=self.adasyn_neighbors,
n_jobs=-1)
sX, sy = X[:, self.hsic_idx_], y
if self.adasyn_neighbors > 0:
try:
sX, sy = sm.fit_resample(X[:, self.hsic_idx_], y)
for i in range(len(np.unique(y) - 1)):
sX, sy = sm.fit_resample(sX, sy)
except:
pass
print("ADASYN done. Starting clf")
self.clf_ = LGBMClassifier(n_estimators=1000).fit(sX, sy)
print("done")
return self
