def fit(self, data, args): self.model = NuSVC(probability=True) with Timer() as t: self.model.fit(data.X_train, data.y_train) return t.interval
def fit(self, X, y=None): self._sklearn_model = SKLModel(**self._hyperparams) if (y is not None): self._sklearn_model.fit(X, y) else: self._sklearn_model.fit(X) return self
def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight='balanced', verbose=False, max_iter=(-1), decision_function_shape='ovr', random_state=None): self._hyperparams = { 'nu': nu, 'kernel': kernel, 'degree': degree, 'gamma': gamma, 'coef0': coef0, 'shrinking': shrinking, 'probability': probability, 'tol': tol, 'cache_size': cache_size, 'class_weight': class_weight, 'verbose': verbose, 'max_iter': max_iter, 'decision_function_shape': decision_function_shape, 'random_state': random_state } self._wrapped_model = Op(**self._hyperparams)
class NuSVCImpl(): def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight='balanced', verbose=False, max_iter=(- 1), decision_function_shape='ovr', random_state=None): self._hyperparams = { 'nu': nu, 'kernel': kernel, 'degree': degree, 'gamma': gamma, 'coef0': coef0, 'shrinking': shrinking, 'probability': probability, 'tol': tol, 'cache_size': cache_size, 'class_weight': class_weight, 'verbose': verbose, 'max_iter': max_iter, 'decision_function_shape': decision_function_shape, 'random_state': random_state} def fit(self, X, y=None): self._sklearn_model = SKLModel(**self._hyperparams) if (y is not None): self._sklearn_model.fit(X, y) else: self._sklearn_model.fit(X) return self def predict(self, X): return self._sklearn_model.predict(X) def predict_proba(self, X): return self._sklearn_model.predict_proba(X)
def test_kernel_sigmoid(self): clf = NuSVC(kernel='sigmoid', gamma=0.001, random_state=0) self._port_model(clf) php_preds, py_preds = [], [] min_vals = np.amin(self.X, axis=0) max_vals = np.amax(self.X, axis=0) for n in range(self.n_random_tests): x = [ random.uniform(min_vals[f], max_vals[f]) for f in range(self.n_features) ] php_preds.append(self.make_pred_in_php(x)) py_preds.append(self.make_pred_in_py(x)) self.assertListEqual(py_preds, php_preds)
def test_kernel_poly(self): clf = NuSVC(kernel='poly', gamma=0.001, random_state=0) self._port_model(clf) Y, Y_py = [], [] min_vals = np.amin(self.X, axis=0) max_vals = np.amax(self.X, axis=0) for n in range(self.n_random_tests): x = [ random.uniform(min_vals[f], max_vals[f]) for f in range(self.n_features) ] Y.append(self.make_pred_in_custom(x)) Y_py.append(self.make_pred_in_py(x)) self.assertListEqual(Y, Y_py)
def setUp(self): super(NuSVCPHPTest, self).setUp() self.estimator = NuSVC(kernel='rbf', gamma=0.001, random_state=0)
def setUp(self): super(SVCTest, self).setUp() self.porter = Porter(language='php') clf = NuSVC(kernel='rbf', gamma=0.001, random_state=0) self._port_model(clf)
def setUp(self): super(NuSVCJavaTest, self).setUp() self.mdl = NuSVC(kernel='rbf', gamma=0.001, random_state=0)
BernoulliNB(), CalibratedClassifierCV(), DecisionTreeClassifier(), ExtraTreeClassifier(), ExtraTreesClassifier(), GaussianNB(), GaussianProcessClassifier(), GradientBoostingClassifier(), KNeighborsClassifier(), LabelPropagation(), LabelSpreading(), LinearDiscriminantAnalysis(), LogisticRegression(), LogisticRegressionCV(), MLPClassifier(), NuSVC(probability=True), QuadraticDiscriminantAnalysis(), RandomForestClassifier(), SGDClassifier(loss='log'), SVC(probability=True), XGBClassifier() ] names = [ 'AdaBoostClassifier', 'BaggingClassifier', 'BernoulliNB', 'CalibratedClassifierCV', 'DecisionTreeClassifier', 'ExtraTreeClassifier', 'ExtraTreesClassifier', 'GaussianNB', 'GaussianProcessClassifier', 'GradientBoostingClassifier', 'KNeighborsClassifier', 'LabelPropagation', 'LabelSpreading', 'LinearDiscriminantAnalysis', 'LogisticRegression', 'LogisticRegressionCV', 'MLPClassifier', 'NuSVC', 'QuadraticDiscriminantAnalysis', 'RandomForestClassifier', 'SGDClassifier',
'MeanShift':MeanShift(), 'MinCovDet':MinCovDet(), 'MinMaxScaler':MinMaxScaler(), 'MiniBatchDictionaryLearning':MiniBatchDictionaryLearning(), 'MiniBatchKMeans':MiniBatchKMeans(), 'MiniBatchSparsePCA':MiniBatchSparsePCA(), 'MultiTaskElasticNet':MultiTaskElasticNet(), 'MultiTaskElasticNetCV':MultiTaskElasticNetCV(), 'MultiTaskLasso':MultiTaskLasso(), 'MultiTaskLassoCV':MultiTaskLassoCV(), 'MultinomialNB':MultinomialNB(), 'NMF':NMF(), 'NearestCentroid':NearestCentroid(), 'NearestNeighbors':NearestNeighbors(), 'Normalizer':Normalizer(), 'NuSVC':NuSVC(), 'NuSVR':NuSVR(), 'Nystroem':Nystroem(), 'OAS':OAS(), 'OneClassSVM':OneClassSVM(), 'OrthogonalMatchingPursuit':OrthogonalMatchingPursuit(), 'OrthogonalMatchingPursuitCV':OrthogonalMatchingPursuitCV(), 'PCA':PCA(), 'PLSCanonical':PLSCanonical(), 'PLSRegression':PLSRegression(), 'PLSSVD':PLSSVD(), 'PassiveAggressiveClassifier':PassiveAggressiveClassifier(), 'PassiveAggressiveRegressor':PassiveAggressiveRegressor(), 'Perceptron':Perceptron(), 'ProjectedGradientNMF':ProjectedGradientNMF(), 'QuadraticDiscriminantAnalysis':QuadraticDiscriminantAnalysis(),
def setUp(self): super(SVCTest, self).setUp() mdl = NuSVC(kernel='rbf', gamma=0.001, random_state=0) self._port_model(mdl)