f8175e0221bbf31ffc8c10ee809b7ee2e118362d,oddt/scoring/functions/NNScore.py,nnscore,train,#nnscore#,42

Before Change

        self.model = ensemble_model(trained_nets[-20:])

        error = rmse(self.model.predict(self.test_descs), self.test_target)
        r2 = self.model.score(self.test_descs, self.test_target)
        r = np.sqrt(r2)
        print("Test set:",
              "R**2: %.4f" % r2,
              "R: %.4f" % r,
              "RMSE: %.4f" % error,
              sep="\t", file=sys.stderr)

After Change

                                                self.test_target.flatten()))
        self.model = ensemble_model(trained_nets[-20:])

        sets = [
            ("Test", self.model.predict(self.test_descs), self.test_target),
            ("Train", self.model.predict(self.train_descs), self.train_target)]

        for name, pred, target in sets:
            print("%s set:" % name,
                  "R2_score: %.4f" % r2_score(target, pred),
                  "Rp: %.4f" % pearsonr(target, pred)[0],
                  "RMSE: %.4f" % rmse(target, pred),
                  sep="\t", file=sys.stderr)

        if sf_pickle is None:
            return self.save("NNScore_pdbbind%i.pickle" % (pdbbind_version))
        else:
            return self.save(sf_pickle)

Instances