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naep_fusion_experiments.py
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51 lines (45 loc) · 42.2 KB
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import numpy as np
from scipy import stats
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v1 = np.array(v1.split(',')).astype(float) # Higher kappa
v2 = np.array(v2.split(',')).astype(float) # Higher AUC
print(v1)
print(v2)
print('v1 > .5', sum(v1 > .5))
print('v2 > .5', sum(v2 > .5))
print('v1 >= .5', sum(v1 >= .5))
print('v2 >= .5', sum(v2 >= .5))
print('unique v1', len(np.unique(v1)))
print('unique v2', len(np.unique(v2)))
print('v1 <=> v2 r', np.corrcoef(v1, v2)[0, 1]) # Only .81, probably lots of room for ensembling
print('binary v1 <=> v2 r', np.corrcoef(v1 > .5, v2 > .5)[0, 1]) # Only .62!
fusion = np.mean([v1, v2], axis=0)
print('fusion > .5', sum(fusion > .5))
print('fusion >= .5', sum(fusion >= .5))
print('unique fusion', len(np.unique(fusion)))
print('fusion <=> v1 r', np.corrcoef(fusion, v1)[0, 1])
print('fusion <=> v2 r', np.corrcoef(fusion, v2)[0, 1])
print('binary fusion <=> v1 r', np.corrcoef(fusion > .5, v1 > .5)[0, 1])
print('binary fusion <=> v2 r', np.corrcoef(fusion > .5, v2 > .5)[0, 1])
def kappa_preserving_auc_adjustment(kappa_preds, auc_preds, gt_threshold=.5):
# For a good kappa model and a good AUC model, come up with a set of predictions that yields
# the same kappa but matches the good AUC as closely as possible by rearranging positive and
# negative predictions (positive defined by > gt_threshold)
kappa_preds, auc_preds = np.array(kappa_preds), np.array(auc_preds)
pos_auc = auc_preds[kappa_preds > gt_threshold]
neg_auc = auc_preds[kappa_preds <= gt_threshold]
pos_auc = (pos_auc - pos_auc.min()) / pos_auc.max() / 3 + .51
neg_auc = (neg_auc - neg_auc.min()) / neg_auc.max() / 3
auc_preds[kappa_preds > gt_threshold] = pos_auc
auc_preds[kappa_preds <= gt_threshold] = neg_auc
return auc_preds
adjusted_v2 = kappa_preserving_auc_adjustment(v1, v2)
print('binary v1 <=> adjusted v2 r', np.corrcoef(v1 > .5, adjusted_v2 > .5)[0, 1]) # 1.0
print('v2 <=> adjusted v2 r', np.corrcoef(v2, adjusted_v2)[0, 1])
print('v2 <=> adjusted v2 rho', stats.spearmanr(v2, adjusted_v2)[0]) # .97, order is well preserved
print('v1 <=> v2 rho', stats.spearmanr(v1, v2)[0]) # .74, order is much worse
print('v1 <=> adjusted v2 rho', stats.spearmanr(v1, adjusted_v2)[0])
print('fusion <=> v2 rho', stats.spearmanr(fusion, v2)[0])
print('fusion <=> adjusted v2 rho', stats.spearmanr(fusion, adjusted_v2)[0]) # .95, almost a match
print('fusion <=> v1 rho', stats.spearmanr(fusion, v1)[0])