def __new__(self, y, clf='lda', kern='rbf', n_knn=10, n_tree=100,
priors=False, **kwargs):
# Use a pre-defined classifier :
if isinstance(clf, (str, int)):
# Default value for priors :
priors = np.array([1/len(np.unique(y))]*len(np.unique(y)))
if isinstance(clf, str):
clf = clf.lower()
# LDA :
if clf == 'lda' or clf == 0:
clfObj = LinearDiscriminantAnalysis(
priors=priors, **kwargs)
clfObj.lgStr = 'Linear Discriminant Analysis'
clfObj.shStr = 'LDA'
# SVM : ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable
elif clf == 'svm' or clf == 1:
clfObj = SVC(kernel=kern, probability=True, **kwargs)
clfObj.lgStr = 'Support Vector Machine (kernel=' + kern + ')'
clfObj.shStr = 'SVM-' + kern
# Linear SVM:
elif clf == 'linearsvm' or clf == 2:
clfObj = LinearSVC(**kwargs)
clfObj.lgStr = 'Linear Support Vector Machine'
clfObj.shStr = 'LSVM'
# Nu SVM :
elif clf == 'nusvm' or clf == 3:
clfObj = NuSVC(**kwargs)
clfObj.lgStr = 'Nu Support Vector Machine'
clfObj.shStr = 'NuSVM'
# Naive Bayesian :
elif clf == 'nb' or clf == 4:
clfObj = GaussianNB(**kwargs)
clfObj.lgStr = 'Naive Baysian'
clfObj.shStr = 'NB'
# KNN :
elif clf == 'knn' or clf == 5:
clfObj = KNeighborsClassifier(n_neighbors=n_knn, **kwargs)
clfObj.lgStr = 'k-Nearest Neighbor (neighbor=' + str(n_knn) + ')'
clfObj.shStr = 'KNN-' + str(n_knn)
# Random forest :
elif clf == 'rf' or clf == 6:
clfObj = RandomForestClassifier(n_estimators=n_tree, **kwargs)
clfObj.lgStr = 'Random Forest (tree=' + str(n_tree) + ')'
clfObj.shStr = 'RF-' + str(n_tree)
# Logistic regression :
elif clf == 'lr' or clf == 7:
clfObj = LogisticRegression(**kwargs)
clfObj.lgStr = 'Logistic Regression'
clfObj.shStr = 'LogReg'
# QDA :
elif clf == 'qda' or clf == 8:
clfObj = QuadraticDiscriminantAnalysis(**kwargs)
clfObj.lgStr = 'Quadratic Discriminant Analysis'
clfObj.shStr = 'QDA'
else:
raise ValueError('No classifier "'+str(clf)+'"" found')
# Use a custom classifier :
else:
clfObj = clf
clfObj.shStr = 'custom'
clfObj.lgStr = 'Custom classifier'
return clfObj
def __new__(self,
y,
clf='lda',
kern='rbf',
n_knn=10,
n_tree=100,
priors=False,
**kwargs):
# Use a pre-defined classifier :
if isinstance(clf, (str, int)):
# Default value for priors :
priors = np.array([1 / len(np.unique(y))] * len(np.unique(y)))
if isinstance(clf, str):
clf = clf.lower()
# LDA :
if clf == 'lda' or clf == 0:
clfObj = LinearDiscriminantAnalysis(priors=priors, **kwargs)
clfObj.lgStr = 'Linear Discriminant Analysis'
clfObj.shStr = 'LDA'
# SVM : ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable
elif clf == 'svm' or clf == 1:
clfObj = SVC(kernel=kern, probability=True, **kwargs)
clfObj.lgStr = 'Support Vector Machine (kernel=' + kern + ')'
clfObj.shStr = 'SVM-' + kern
# Linear SVM:
elif clf == 'linearsvm' or clf == 2:
clfObj = LinearSVC(**kwargs)
clfObj.lgStr = 'Linear Support Vector Machine'
clfObj.shStr = 'LSVM'
# Nu SVM :
elif clf == 'nusvm' or clf == 3:
clfObj = NuSVC(**kwargs)
clfObj.lgStr = 'Nu Support Vector Machine'
clfObj.shStr = 'NuSVM'
# Naive Bayesian :
elif clf == 'nb' or clf == 4:
clfObj = GaussianNB(**kwargs)
clfObj.lgStr = 'Naive Baysian'
clfObj.shStr = 'NB'
# KNN :
elif clf == 'knn' or clf == 5:
clfObj = KNeighborsClassifier(n_neighbors=n_knn, **kwargs)
clfObj.lgStr = 'k-Nearest Neighbor (neighbor=' + str(
n_knn) + ')'
clfObj.shStr = 'KNN-' + str(n_knn)
# Random forest :
elif clf == 'rf' or clf == 6:
clfObj = RandomForestClassifier(n_estimators=n_tree, **kwargs)
clfObj.lgStr = 'Random Forest (tree=' + str(n_tree) + ')'
clfObj.shStr = 'RF-' + str(n_tree)
# Logistic regression :
elif clf == 'lr' or clf == 7:
clfObj = LogisticRegression(**kwargs)
clfObj.lgStr = 'Logistic Regression'
clfObj.shStr = 'LogReg'
# QDA :
elif clf == 'qda' or clf == 8:
clfObj = QuadraticDiscriminantAnalysis(**kwargs)
clfObj.lgStr = 'Quadratic Discriminant Analysis'
clfObj.shStr = 'QDA'
else:
raise ValueError('No classifier "' + str(clf) + '"" found')
# Use a custom classifier :
else:
clfObj = clf
clfObj.shStr = 'custom'
clfObj.lgStr = 'Custom classifier'
return clfObj
