scores = [
('precision', ),
('recall', recall_score),
]
# for score_name, score_func in scores:
# print "# Tuning hyper-parameters for %s" % score_name
print
clf = IPythonGridSearchCV(SVC(C=1), tuned_parameters, score_func=precision_score, view=v)
clf.fit(X_train, y_train, cv=5)
print "fit submitted"
while v.outstanding:
v.wait(timeout=0.1)
grid_scores = clf.collect_results()
# import IPython
# IPython.embed()
#
# 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, mean_duration, durations in grid_scores[:3]:
print "%0.3f (+/-%0.03f) [%i] for %r (took %0.3f s)" % (
mean_score, scores.std() / 2, len(scores), params, mean_duration)
print
search = IPythonGridSearchCV(SVC(C=1), tuned_parameters,
score_func=precision_score, view=v, cv=5,
local_store='/tmp')
search.fit_async(X_train, y_train)
print "Launched asynchronous fit on a cluster."
def print_scores(scores):
for params, mean_score, scores, mean_duration, durations in scores:
print "%0.3f (+/-%0.03f) [%i] for %r (%0.3fs)" % (
mean_score, scores.std() / 2, len(scores), params, mean_duration)
while v.outstanding:
v.wait(timeout=0.5)
completed_scores, n_remaining = search.collect_results()
top_scores = completed_scores[:3]
print "Current top %d parameters on development set:" % len(top_scores)
print
print_scores(top_scores)
print "%d tasks remaining" % n_remaining
print
print "Final scores:"
print
all_scores, _ = search.collect_results()
print_scores(all_scores)
print
print "Fitting best parameters on the full development set"
