def svc_rbf(name, C=None, gamma=None, shrinking=None, tol=None, max_iter=None, verbose=False, random_state=None, cache_size=_svc_default_cache_size): """ Return a pyll graph with hyperparamters that will construct a sklearn.svm.SVC model with an RBF kernel. """ def _name(msg): return '%s.%s_%s' % (name, 'rbf', msg) rval = scope.sklearn_SVC( kernel='rbf', C=_svc_C(name + '.rbf') if C is None else C, gamma=_svc_gamma(name) if gamma is None else gamma, shrinking=hp_bool( _name('shrinking')) if shrinking is None else shrinking, tol=_svc_tol(name + '.rbf') if tol is None else tol, max_iter=(_svc_max_iter(name + '.rbf') if max_iter is None else max_iter), verbose=verbose, cache_size=cache_size, random_state=_random_state(_name('rstate'), random_state), ) return rval
def svc_rbf(name, C=None, gamma=None, shrinking=None, tol=None, max_iter=None, verbose=False, random_state=None, cache_size=_svc_default_cache_size): """ Return a pyll graph with hyperparamters that will construct a sklearn.svm.SVC model with an RBF kernel. """ def _name(msg): return '%s.%s_%s' % (name, 'rbf', msg) rval = scope.sklearn_SVC( kernel='rbf', C=_svc_C(name + '.rbf') if C is None else C, gamma=_svc_gamma(name) if gamma is None else gamma, shrinking=hp_bool(_name('shrinking')) if shrinking is None else shrinking, tol=_svc_tol(name + '.rbf') if tol is None else tol, max_iter=(_svc_max_iter(name + '.rbf') if max_iter is None else max_iter), verbose=verbose, cache_size=cache_size, random_state=_random_state(_name('rstate'), random_state), ) return rval
def svc_kernel(name, kernel, random_state=None, **kwargs): """ Return a pyll graph with hyperparamters that will construct a sklearn.svm.SVC model with a user specified kernel. See help(hpsklearn.components._svm_hp_space) for info on additional SVM arguments. """ def _name(msg): return '%s.%s_%s' % (name, kernel, msg) hp_space = _svm_hp_space(_name, kernel=kernel, **kwargs) hp_space.update(_svc_hp_space(_name, random_state)) return scope.sklearn_SVC(**hp_space)
def svc_poly(name, C=None, gamma=None, coef0=None, degree=None, shrinking=None, tol=None, max_iter=None, verbose=False, random_state=None, cache_size=_svc_default_cache_size): """ Return a pyll graph with hyperparamters that will construct a sklearn.svm.SVC model with an RBF kernel. """ def _name(msg): return '%s.%s_%s' % (name, 'poly', msg) # -- (K(x, y) + coef0)^d coef0nz = hp.choice(_name('coef0nz'), [0, 1]) coef0 = hp.uniform(_name('coef0'), 0.0, 1.0) poly_coef0 = coef0nz * coef0 rval = scope.sklearn_SVC( kernel='poly', C=_svc_C(name + '.poly') if C is None else C, gamma=_svc_gamma(name + '.poly') if gamma is None else gamma, coef0=poly_coef0 if coef0 is None else coef0, degree=hp.quniform( _name('degree'), low=1.5, high=8.5, q=1) if degree is None else degree, shrinking=hp_bool( _name('shrinking')) if shrinking is None else shrinking, tol=_svc_tol(name + '.poly') if tol is None else tol, max_iter=(_svc_max_iter(name + '.poly') if max_iter is None else max_iter), verbose=verbose, random_state=_random_state(_name('.rstate'), random_state), cache_size=cache_size, ) return rval
def svc_poly(name, C=None, gamma=None, coef0=None, degree=None, shrinking=None, tol=None, max_iter=None, verbose=False, random_state=None, cache_size=_svc_default_cache_size): """ Return a pyll graph with hyperparamters that will construct a sklearn.svm.SVC model with an RBF kernel. """ def _name(msg): return '%s.%s_%s' % (name, 'poly', msg) # -- (K(x, y) + coef0)^d coef0nz = hp.choice(_name('coef0nz'), [0, 1]) coef0 = hp.uniform(_name('coef0'), 0.0, 1.0) poly_coef0 = coef0nz * coef0 rval = scope.sklearn_SVC( kernel='poly', C=_svc_C(name + '.poly') if C is None else C, gamma=_svc_gamma(name + '.poly') if gamma is None else gamma, coef0=poly_coef0 if coef0 is None else coef0, degree=hp.quniform(_name('degree'), low=1.5, high=8.5, q=1) if degree is None else degree, shrinking=hp_bool(_name('shrinking')) if shrinking is None else shrinking, tol=_svc_tol(name + '.poly') if tol is None else tol, max_iter=(_svc_max_iter(name + '.poly') if max_iter is None else max_iter), verbose=verbose, random_state=_random_state(_name('.rstate'), random_state), cache_size=cache_size, ) return rval
def svc_sigmoid(name, C=None, gamma=None, coef0=None, shrinking=None, tol=None, max_iter=None, verbose=False, random_state=None, cache_size=_svc_default_cache_size): """ Return a pyll graph with hyperparamters that will construct a sklearn.svm.SVC model with an RBF kernel. """ def _name(msg): return '%s.%s_%s' % (name, 'sigmoid', msg) # -- tanh(K(x, y) + coef0) coef0nz = hp.choice(_name('coef0nz'), [0, 1]) coef0 = hp.normal(_name('coef0'), 0.0, 1.0) sigm_coef0 = coef0nz * coef0 rval = scope.sklearn_SVC( kernel='sigmoid', C=_svc_C(name + '.sigmoid') if C is None else C, gamma=_svc_gamma(name + '.sigmoid') if gamma is None else gamma, coef0=sigm_coef0 if coef0 is None else coef0, shrinking=hp_bool( _name('shrinking')) if shrinking is None else shrinking, tol=_svc_tol(name + '.sigmoid') if tol is None else tol, max_iter=(_svc_max_iter(name + '.sigmoid') if max_iter is None else max_iter), verbose=verbose, random_state=_random_state(_name('rstate'), random_state), cache_size=cache_size) return rval
def svc_sigmoid(name, C=None, gamma=None, coef0=None, shrinking=None, tol=None, max_iter=None, verbose=False, random_state=None, cache_size=_svc_default_cache_size): """ Return a pyll graph with hyperparamters that will construct a sklearn.svm.SVC model with an RBF kernel. """ def _name(msg): return '%s.%s_%s' % (name, 'sigmoid', msg) # -- tanh(K(x, y) + coef0) coef0nz = hp.choice(_name('coef0nz'), [0, 1]) coef0 = hp.normal(_name('coef0'), 0.0, 1.0) sigm_coef0 = coef0nz * coef0 rval = scope.sklearn_SVC( kernel='sigmoid', C=_svc_C(name + '.sigmoid') if C is None else C, gamma=_svc_gamma(name + '.sigmoid') if gamma is None else gamma, coef0=sigm_coef0 if coef0 is None else coef0, shrinking=hp_bool(_name('shrinking')) if shrinking is None else shrinking, tol=_svc_tol(name + '.sigmoid') if tol is None else tol, max_iter=(_svc_max_iter(name + '.sigmoid') if max_iter is None else max_iter), verbose=verbose, random_state=_random_state(_name('rstate'), random_state), cache_size=cache_size) return rval