# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
'C': [1, 10, 100, 1000]},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
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
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target
# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_fraction=0.5, random_state=0)
# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
'C': [1, 10, 100, 1000]},
{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
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]
