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)