def challenge():
## use dev openml to run
# Download task, run learner, publish results
task = tasks.get_task(14951)
## clf = BaggingClassifier(SVC(), n_estimators = 128)
'''
clf = RandomForestClassifier(n_estimators = 128, class_weight = 'balanced_subsample')
'''
'''
clf = BaggingClassifier(ExtraTreeClassifier(), n_estimators = 20)
'''
'''
param_grid = {'max_depth': np.linspace(1, 15, num = 15, dtype = np.int64),
'class_weight': ['balanced', 'balanced_subsample', None],
'min_samples_split': np.linspace(1, 15, num = 15, dtype = np.int64),
'criterion': ['gini', 'entropy']
}
base_clf = RandomForestClassifier(n_estimators = 20)
clf = GridSearchCV(base_clf, param_grid = param_grid, scoring = 'roc_auc',
cv = 10, pre_dispatch = '2*n_jobs', n_jobs = 4)
'''
'''
## grid search - gamma and C, grid_den = 20, time needed = 13.36s
grid_den = 1
param_grid = {#'C': np.logspace(-5, 5, num = grid_den, base = 2.0),
'gamma': np.logspace(-5, 5, num = grid_den, base = 2.0)
}
clf = GridSearchCV(SVC(probability = True), param_grid = param_grid, scoring = 'roc_auc',
cv = 10, pre_dispatch = '2*n_jobs', n_jobs = 4)
'''
clf = KNeighborsClassifier(n_neighbors = 5, algorithm = 'brute', metric = 'cosine')
run = runs.run_task(task, clf)
return_code, response = run.publish()
# get the run id for reference
if(return_code == 200):
response_dict = xmltodict.parse(response)
run_id = response_dict['oml:upload_run']['oml:run_id']
print("Uploaded run with id %s. Check it at www.openml.org/r/%s" % (run_id,run_id))
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