I think that I have to re-phrase my post, since I discovered an awkward
behavior using SVC and the linear kernel - exactly THIS kernel takes ages
on my dataset.
E.g. the "RBF" kernel runs perfect, and so does the GridSearch! :)
Exclusion of the "linear" kernel from GridSearch gives now the following:
"
# Tuning hyper-parameters for precision
Best parameters set found on development set:
SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel=rbf, probability=False, shrinking=True, tol=0.001, verbose=False)
Grid scores on development set:
0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 1}
0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 10}
0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 100}
0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 1000}
0.000 (+/-0.000) for {'kernel': 'sigmoid', 'C': 1}
0.000 (+/-0.000) for {'kernel': 'sigmoid', 'C': 10}
0.000 (+/-0.000) for {'kernel': 'sigmoid', 'C': 100}
0.000 (+/-0.000) for {'kernel': 'sigmoid', 'C': 1000}
# Tuning hyper-parameters for recall
Best parameters set found on development set:
SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel=rbf, probability=False, shrinking=True, tol=0.001, verbose=False)
Grid scores on development set:
0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 1}
0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 10}
0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 100}
0.000 (+/-0.000) for {'kernel': 'rbf', 'C': 1000}
0.000 (+/-0.000) for {'kernel': 'sigmoid', 'C': 1}
0.000 (+/-0.000) for {'kernel': 'sigmoid', 'C': 10}
0.000 (+/-0.000) for {'kernel': 'sigmoid', 'C': 100}
0.000 (+/-0.000) for {'kernel': 'sigmoid', 'C': 1000}
"
by using this code
"
tuned_parameters = [{'kernel': ['rbf'],'C': [1,10,100,1000]},
{'kernel': ['sigmoid'],'C': [1,10,100,1000]}]
scores = [('precision', precision_score),('recall', recall_score)]
for score_name, score_func in scores:
print "# Tuning hyper-parameters for %s" % score_name
print
clf = GridSearchCV(SVC(C=1), tuned_parameters,
score_func=score_func,n_jobs=1)
clf.fit(trainDescrs, trainActs, cv=5,n_jobs=1)
print "Best parameters set found on development set:"
print
print clf.best_estimator_
print
print "Grid scores on development set:"
print
for params, mean_score, scores in clf.grid_scores_:
print "%0.3f (+/-%0.03f) for %r" % (
mean_score, scores.std() / 2, params)
print
"
Question:
Bad statistics - am I outputting anything wrong?
Apologies:
Sorry guys to have mis-lead with my first posting. Hopefully I have not
discourage anyone from further answering...
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