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Multiclass SVMs
Multiclass SVMs (Crammer-Singer formulation).
A pure Python re-implementation of:
Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex.
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
ICPR 2014.
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils import check_random_state
from sklearn.preprocessing import LabelEncoder
def projection_simplex(v, z=1):
Projection onto the simplex:
w^* = argmin_w 0.5 ||w-v||^2 s.t. \sum_i w_i = z, w_i >= 0
# For other algorithms computing the same projection, see
n_features = v.shape[0]
u = np.sort(v)[::-1]
cssv = np.cumsum(u) - z
ind = np.arange(n_features) + 1
cond = u - cssv / ind > 0
rho = ind[cond][-1]
theta = cssv[cond][-1] / float(rho)
w = np.maximum(v - theta, 0)
return w
class MulticlassSVM(BaseEstimator, ClassifierMixin):
def __init__(self, C=1, max_iter=50, tol=0.05,
random_state=None, verbose=0):
self.C = C
self.max_iter = max_iter
self.tol = tol,
self.random_state = random_state
self.verbose = verbose
def _partial_gradient(self, X, y, i):
# Partial gradient for the ith sample.
g =[i], self.coef_.T) + 1
g[y[i]] -= 1
return g
def _violation(self, g, y, i):
# Optimality violation for the ith sample.
smallest = np.inf
for k in range(g.shape[0]):
if k == y[i] and self.dual_coef_[k, i] >= self.C:
elif k != y[i] and self.dual_coef_[k, i] >= 0:
smallest = min(smallest, g[k])
return g.max() - smallest
def _solve_subproblem(self, g, y, norms, i):
# Prepare inputs to the projection.
Ci = np.zeros(g.shape[0])
Ci[y[i]] = self.C
beta_hat = norms[i] * (Ci - self.dual_coef_[:, i]) + g / norms[i]
z = self.C * norms[i]
# Compute projection onto the simplex.
beta = projection_simplex(beta_hat, z)
return Ci - self.dual_coef_[:, i] - beta / norms[i]
def fit(self, X, y):
n_samples, n_features = X.shape
# Normalize labels.
self._label_encoder = LabelEncoder()
y = self._label_encoder.fit_transform(y)
# Initialize primal and dual coefficients.
n_classes = len(self._label_encoder.classes_)
self.dual_coef_ = np.zeros((n_classes, n_samples), dtype=np.float64)
self.coef_ = np.zeros((n_classes, n_features))
# Pre-compute norms.
norms = np.sqrt(np.sum(X ** 2, axis=1))
# Shuffle sample indices.
rs = check_random_state(self.random_state)
ind = np.arange(n_samples)
violation_init = None
for it in range(self.max_iter):
violation_sum = 0
for ii in range(n_samples):
i = ind[ii]
# All-zero samples can be safely ignored.
if norms[i] == 0:
g = self._partial_gradient(X, y, i)
v = self._violation(g, y, i)
violation_sum += v
if v < 1e-12:
# Solve subproblem for the ith sample.
delta = self._solve_subproblem(g, y, norms, i)
# Update primal and dual coefficients.
self.coef_ += (delta * X[i][:, np.newaxis]).T
self.dual_coef_[:, i] += delta
if it == 0:
violation_init = violation_sum
vratio = violation_sum / violation_init
if self.verbose >= 1:
print("iter", it + 1, "violation", vratio)
if vratio < self.tol:
if self.verbose >= 1:
return self
def predict(self, X):
decision =, self.coef_.T)
pred = decision.argmax(axis=1)
return self._label_encoder.inverse_transform(pred)
if __name__ == '__main__':
from sklearn.datasets import load_iris
iris = load_iris()
X, y =,
clf = MulticlassSVM(C=0.1, tol=0.01, max_iter=100, random_state=0, verbose=1), y)
print(clf.score(X, y))
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scienceML commented Feb 15, 2017

small comment: For Python 3.x use instead of "xrange" range

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mab85 commented Mar 30, 2018

How can run for own dataset?

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oottoohh commented Apr 6, 2018

im new with SVM, can you tell me what kind of Multiclass SVM is this?

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Matt-arch commented Dec 26, 2019

hello mathew, first of all i would like to thank you for your source code to the multi class svm it is help full for some like me who is new to machine learning, my question is that i don't clear idea on the 'violation'

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mblondel commented Dec 26, 2019

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leme-lab commented Feb 5, 2020

Hello Mathieu.
First of all I would like to thank you for sharing your code.
I have a question concerning a biais. In classical SVM usually the separator of type wx+b is used but in the multiclass SVM version there is no b. According to Crammer and Singer 2001 it leads to some complexity in dual problem so they omitted it but they leave the opportunity to add it if needed. If there is no b, it's mean that your hyperplan is linear function and no affine and it's crossing a zero point. Do you need to do something with data to assure the linear separation because obviously it can change dramatically ?

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mblondel commented Feb 5, 2020

@leme-lab: Indeed, fitting b leads to more complicated dual. The usual trick is to add a feature x_0 to all inputs, so that a weight w_0 is going to be learned. This is not exactly the same as as fitting b though (due to regularization).

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