def __init__(self, pmml):
PMMLBaseClassifier.__init__(self, pmml)
OneHotEncodingMixin.__init__(self)
LinearSVC.__init__(self)
# Import coefficients and intercepts
model = self.root.find('RegressionModel')
if model is None:
raise Exception('PMML model does not contain RegressionModel.')
tables = [
table for table in model.findall('RegressionTable')
if table.find('NumericPredictor') is not None
]
self.coef_ = [
_linear_get_coefficients(self, table) for table in tables
]
self.intercept_ = [float(table.get('intercept')) for table in tables]
if len(self.coef_) == 1:
self.coef_ = [self.coef_[0]]
if len(self.intercept_) == 1:
self.intercept_ = [self.intercept_[0]]
self.coef_ = np.array(self.coef_)
self.intercept_ = np.array(self.intercept_)
class RandomSVM(ActiveLearning):
def __init__(self, X_train, y_train, X_test, y_test):
super().__init__(X_train, y_train, X_test, y_test)
self.model = LinearSVC()
def init_model(self):
y_labeled = self.y_train[self.unlabeled_msk==False]
self.model.__init__(class_weight='balanced')
def get_next(self):
idx = np.random.choice(self.X_train.index[self.unlabeled_msk], 1)[0]
return idx
def __init__(self,
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):
_skLinearSVC.__init__(self, penalty, loss, dual, tol, C, multi_class,
fit_intercept, intercept_scaling, class_weight,
verbose, random_state, max_iter)
BaseWrapperClf.__init__(self)
def __init__(self,
threshold=0.01,
dual=False,
tol=1e-4,
C=0.01,
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
random_state=0,
max_iter=100,
multi_class='ovr',
verbose=0):
self.threshold = threshold
LinearSVC.__init__(self,
penalty='l1',
dual=dual,
tol=tol,
C=C,
fit_intercept=fit_intercept,
intercept_scaling=intercept_scaling,
class_weight=class_weight,
random_state=random_state,
max_iter=max_iter,
multi_class=multi_class,
verbose=verbose)
# 使用同样的参数创建L2逻辑回归
self.l2 = LinearSVC(penalty='l2',
dual=dual,
tol=tol,
C=C,
fit_intercept=fit_intercept,
intercept_scaling=intercept_scaling,
class_weight=class_weight,
random_state=random_state,
max_iter=max_iter,
multi_class=multi_class,
verbose=verbose)
def __init__(self, penalty='l2', loss='squared_hinge', dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000):
LinearSVC.__init__(self, penalty, loss, dual, tol, C, multi_class, fit_intercept, intercept_scaling, class_weight, verbose, random_state, max_iter)
