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Created May 24, 2011
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LinearSVC bug with n_jobs
import sys
import numpy as np
from pprint import pprint
from scikits.learn.cross_val import StratifiedKFold
from scikits.learn.grid_search import GridSearchCV
from scikits.learn import svm
from scikits.learn.metrics import zero_one_score, f1_score, classification_report
from scikits.learn.preprocessing import Scaler
n_jobs = int(sys.argv[1])
A = np.loadtxt("featset1.csv", delimiter=",")
X = A[:, :-1]
Y = A[:, -1]
C_start, C_end, C_step = -3, 15, 2
train, test = iter(StratifiedKFold(Y, 2, indices=True)).next()
mean, std = X[train].mean(axis=0), X[train].std(axis=0)
std[std == 0.0] = 1.0
X[train] = (X[train] - mean) / std
X[test] = (X[test] - mean) / std
#print np.unique(Y)
#print X[train].mean(axis=0), X[train].std(axis=0)
# Generate grid search values for C, gamma
C_val = 2. ** np.arange(C_start, C_end + C_step, C_step)
grid_clf = svm.sparse.LinearSVC()
print grid_clf
linear_SVC_params = {'C': C_val}
grid_search = GridSearchCV(grid_clf, linear_SVC_params, n_jobs=n_jobs,
score_func=f1_score)[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:"
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']
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