class SVM():
def __init__(self, task='cls', **kwargs):
if task == 'cls':
self.svm = SVC(**kwargs)
self._name = 'SVC'
elif task == 'prd':
self.svm = SVR(**kwargs)
self._name = 'SVR'
def decision_function(self, X):
'''
X (n_samples, n_features)
return: X (n_samples, n_classes * (n_classes-1) / 2)
'''
if self._name == 'SVC':
return self.svm.decision_function(X)
def fit(self, X, y, sample_weight=None):
'''
X (n_samples, n_features)
y (n_samples,)
sample_weight (n_samples,)
'''
return self.svm.fit(X, y, sample_weight)
def get_params(self, deep=True):
return self.svm.get_params(deep)
def predict(self, X):
return self.svm.predict(X)
def score(self, X, y, sample_weight=None):
'''
X (n_samples, n_features)
y (n_samples,) or (n_samples, n_outputs)
sample_weight (n_samples,), default=None
'''
return self.svm.score(X, y, sample_weight)
def set_params(self, **params):
'''
**params dict
'''
return self.svm.set_params(**params)
class SVM(object):
def __init__(self, task_type="linearsvc"):
self.task_type = task_type
assert self.task_type in {
"linearsvc", "linearsvr", "nusvc", "nusvr", "oneclasssvm", "svc",
"svr", "l1_min_c"
}
if self.task_type == "linearsvc": # 线性支持向量分类
self.model = LinearSVC(penalty='l2',
loss='squared_hinge',
dual=True,
tol=1e-4,
C=1.0,
multi_class='ovr',
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
verbose=0,
random_state=None,
max_iter=1000)
elif self.task_type == "linearsvr": # 线性支持向量回归
self.model = LinearSVR(epsilon=0.0,
tol=1e-4,
C=1.0,
loss='epsilon_insensitive',
fit_intercept=True,
intercept_scaling=1.,
dual=True,
verbose=0,
random_state=None,
max_iter=1000)
elif self.task_type == "nusvc": # Nu 支持向量分类
self.model = NuSVC(nu=0.5,
kernel='rbf',
degree=3,
gamma='scale',
coef0=0.0,
shrinking=True,
probability=False,
tol=1e-3,
cache_size=200,
class_weight=None,
verbose=False,
max_iter=-1,
decision_function_shape='ovr',
break_ties=False,
random_state=None)
elif self.task_type == "nusvr": # Nu支持向量回归
self.model = NuSVR(nu=0.5,
C=1.0,
kernel='rbf',
degree=3,
gamma='scale',
coef0=0.0,
shrinking=True,
tol=1e-3,
cache_size=200,
verbose=False,
max_iter=-1)
elif self.task_type == "oneclasssvm": # 无监督异常值检测
self.model = OneClassSVM(kernel='rbf',
degree=3,
gamma='scale',
coef0=0.0,
tol=1e-3,
nu=0.5,
shrinking=True,
cache_size=200,
verbose=False,
max_iter=-1)
elif self.task_type == "svc": # c支持向量分类
self.model = SVC(C=1.0,
kernel='rbf',
degree=3,
gamma='scale',
coef0=0.0,
shrinking=True,
probability=False,
tol=1e-3,
cache_size=200,
class_weight=None,
verbose=False,
max_iter=-1,
decision_function_shape='ovr',
break_ties=False,
random_state=None)
else: # Epsilion 支持向量回归
self.model = SVR(kernel='rbf',
degree=3,
gamma='scale',
coef0=0.0,
tol=1e-3,
C=1.0,
epsilon=0.1,
shrinking=True,
cache_size=200,
verbose=False,
max_iter=-1)
def decision_functions(self, x):
self.model.decision_function(X=x)
