def port_svm(clf, **kwargs):
"""Port a SVC / OneClassSVC classifier"""
assert isinstance(clf.gamma, float), 'You probably didn\'t set an explicit value for gamma: 0.001 is a good default'
support_v = clf.support_vectors_
n_classes = len(clf.n_support_)
return jinja('svm/svm.jinja', {
'kernel': {
'type': clf.kernel,
'gamma': clf.gamma,
'coef0': clf.coef0,
'degree': clf.degree
},
'sizes': {
'features': len(support_v[0]),
'vectors': len(support_v),
'classes': n_classes,
'decisions': n_classes * (n_classes - 1) // 2,
'supports': clf.n_support_
},
'arrays': {
'supports': support_v,
'intercepts': clf.intercept_,
'coefs': clf.dual_coef_
}
}, {
'classname': 'OneClassSVM' if check_type(clf, 'OneClassSVM') else 'SVM'
}, **kwargs)
def is_logisticregression(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'LogisticRegression')
def is_svm(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'SVC', 'OneClassSVM')
def is_principalfft(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'PrincipalFFT') or check_type(clf, 'KFFT')
def is_gaussiannb(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'GaussianNB')
def is_randomforest(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'RandomForestClassifier')
def is_pca(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'PCA')
def is_linear_regression(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'LinearRegression')
def is_xgboost(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'XGBClassifier')
def is_rvm(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'RVC')
def is_sefr(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'SEFR')
def is_decisiontree(clf):
"""Test if classifier can be ported"""
return check_type(clf, 'DecisionTreeClassifier')
