I'm using the scikit-learn library for my daily MVA business. One of the functions I use is the roc_curve() method to get my ROC curves. The function returns the false-positive and true-positive rates. In my field (HEP), we prefer working with efficiencies, i.e. I want to normalise the number of true positives to the total number of positives.
To get what I need, I want to re-use the _binary_clf_curve() function in sklearn.metric.rankings, i.e.
def roc_curve_eff(y_true, y_score, pos_label=None, sample_weight=None) :
fps, tps, _ = _binary_clf_curve(
y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)
teff = tps / (tps[-1] - tps)
feff = fps / (fps[-1] - fps)
return feff, teff, _
and to load it into the local namespace I call
from sklearn.metrics.rankings import _binary_clf_curve .
Doing so his gives me the following error:
ImportError
Traceback (most recent call last)
<ipython-input-10-d1d6b7292e7f> in <module>()
11 from root_numpy import root2array, rec2array
12 from sklearn.metrics import roc_curve, auc
---> 13 from sklearn.metrics.rankings import _binary_clf_curve
14
15 #import sklearn as sklearn_
/Users/pigard/ROOT/install/lib/ROOT.pyc in _importhook(name, *args, **kwds)
299 except Exception:
300 pass
--> 301 return _orig_ihook( name, *args, **kwds )
302
303 __builtin__.__import__ = _importhook
ImportError: No module named rankings
I think the issue here is the distinction between module and file: the _binary_clf_curve helper function is not added to the module (because it need not be exposed to the user) so I need to load it not from the module, but from the file?