Non convergence of sparse SVC

test_sparse_SVC_convergence.py

from pprint import pprint
import numpy as np
from scipy import sparse
from scikits.learn.grid_search import GridSearchCV
from scikits.learn.cross_val import StratifiedKFold
from scikits.learn.metrics import f1_score, classification_report
from scikits.learn import svm
from scikits.learn.linear_model import LogisticRegression
from scikits.learn.linear_model.sparse import LogisticRegression as SparseLogisticRegression

data = np.loadtxt('featset1.csv', delimiter=',')

Y = data[:,-1]

# Dense classifiers
X = data[:,:-1]
# grid_clf = svm.LinearSVC(tol=1e-10)
# grid_clf = LogisticRegression(tol=1e-10)

# Sparse classifiers
X = sparse.csr_matrix(data[:,:-1])

# grid_clf = SparseLogisticRegression(tol=1e-10)
# grid_clf = svm.sparse.LinearSVC(tol=1e-10)
grid_clf = svm.sparse.SVC(kernel = 'linear' , tol = 0.0001)

print grid_clf

C_start, C_end, C_step = -5, 15, 3

train, test = iter(StratifiedKFold(Y, 2, indices = True)).next()

# Generate grid search values for C, gamma
C_val = 2. ** np.arange(C_start, C_end + C_step, C_step)


#linear_SVC_params = {'C': C_val}
sparse_SVC_params = {'C': C_val}
# n_jobs = 100
n_jobs = 2
grid_search = GridSearchCV(grid_clf, sparse_SVC_params, n_jobs=n_jobs,
                           score_func=f1_score)
grid_search.fit(X[train], Y[train], cv=StratifiedKFold(Y[train], 10, indices=True))
y_true, y_pred = Y[test], grid_search.predict(X[test])

print "Classification report for the best estimator: "
print grid_search.best_estimator

print "Tuned for  with optimal value: %0.3f" % f1_score(y_true, y_pred)
print classification_report(y_true, y_pred)

print "Grid scores:"
pprint(grid_search.grid_scores_)

print "Best score: %0.3f" % grid_search.best_score

best_parameters = grid_search.best_estimator._get_params()
print "Best C: %0.3f " % best_parameters['C']

Small C values : Tried with C_start, C_end, C_step = -3, 4, 2 non-convergence still holds. Is this to do with non-scaling of data ?

if your data are not scale C around 1 do not make sense. So first scale your data (without centering) : something like max of each column is 1 or l-2 norm of each column is 1 (eventually after taking the log as Olivier suggested) and then play with C. If it does not converge decrease C or increase tol.

I get the drift but to be honest I am terribly confused as to "how to scale my data". I tried to do this :

from scikits.learn.preprocessing import scale
X = scale(sparse_feats)

but it throws me an error.

Can you please point me out to an example ?

from scikits.learn.preprocessing.sparse import Normalizer
X_scaled = Normalizer().transform(X, copy=True) # copy = False means inplace scaling

Alexandre : Thanks. It's now converging. However, it gives rise to a different issue. I will post it on the scikits-learn mailings list along with a gist. :-)

…

--Regards, Denzil

On Mon, May 23, 2011 at 4:19 AM, agramfort ***@***.*** wrote: Normalizer().transform(X, copy=True)