def internal_cons(self, splithalfno, test_inds):
rng = np.random.RandomState(splithalfno)
split1, split2 = utils.splithalf(self.neural_feats_reps[:, test_inds],
rng=rng)
r = utils.pearsonr_matrix(split1, split2)
rc = utils.spearman_brown_correct(r, n=2)
return rc
def splithalf_corr(data, niter=10, seed=None):
rng = np.random.RandomState(seed)
df = []
for i in range(niter):
split1, split2 = utils.splithalf(data, rng=rng)
r = utils.pearsonr_matrix(split1, split2)
rc = utils.spearman_brown_correct(r, n=2)
df.extend([(i, site, rci) for site, rci in enumerate(rc)])
df = pandas.DataFrame(df, columns=['splithalf', 'site', 'internal_cons'])
return df
def mapping_cons(self, splithalfno, train_inds, test_inds):
"""
Split data in half over reps, run PLS on each half on the train set,
get predictions for the test set, correlate the two, Spearman-Brown
"""
rng = np.random.RandomState(splithalfno)
split1, split2 = utils.splithalf(self.neural_feats_reps[:, train_inds],
rng=rng)
self.reg.fit(self.model_feats[train_inds], split1)
pred1 = self.reg.predict(self.model_feats[test_inds])
self.reg.fit(self.model_feats[train_inds], split2)
pred2 = self.reg.predict(self.model_feats[test_inds])
rs = utils.pearsonr_matrix(pred1, pred2)
return np.squeeze(utils.spearman_brown_correct(rs, n=2))
def mapping_cons(self, splithalfno, train_inds, test_inds):
"""
Split data in half over reps, run PLS on each half on the train set,
get predictions for the test set, correlate the two, Spearman-Brown
"""
rs = []
rng = np.random.RandomState(splithalfno)
for site in range(self.neural_feats_reps.shape[-1]):
split1, split2 = utils.splithalf(self.neural_feats_reps[:,
train_inds,
site],
rng=rng)
self.reg.fit(self.model_feats[train_inds], split1)
pred1 = self.reg.predict(self.model_feats[test_inds])
self.reg.fit(self.model_feats[train_inds], split2)
pred2 = self.reg.predict(self.model_feats[test_inds])
r = scipy.stats.pearsonr(pred1, pred2)[0]
rs.append(r)
return np.squeeze(utils.spearman_brown_correct(rs, n=2))