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)