def accuracy_auc(y, ypred, weights=None):
"""Compute the accuracy, AUC, precision, recall and f1"""
from mozsci.evaluation import classification_error, auc_wmw_fast, precision_recall_f1
prf1 = precision_recall_f1(y, ypred, weights=weights)
return {'accuracy': 1.0 - classification_error(y, ypred, weights=weights),
'auc': auc_wmw_fast(y, ypred, weights=weights),
'precision': prf1[0],
'recall': prf1[1],
'f1': prf1[2]}
def test_auc_wmw_fast(self):
t = [-1, -1, -1, 1, 1, 1, -1, 1, -1, 1, 1, -1, 1]
p = [0.01, 0.05, 0.2, 0.25, 0.1, 0.9, 0.6, 0.01, 0.90, 1.0, 0.33, 0.55, 0.555]
auc_act = 0.54761904761904767
auc = evaluation.auc_wmw_fast(t, p)
self.assertTrue(abs(auc_act - auc) < 1.0e-8)
def accuracy_auc(y, ypred, weights=None):
"""Compute the accuracy, AUC, precision, recall and f1"""
from mozsci.evaluation import classification_error, auc_wmw_fast, precision_recall_f1
prf1 = precision_recall_f1(y, ypred, weights=weights)
return {'accuracy': 1.0 - classification_error(y, ypred, weights=weights),
'auc': auc_wmw_fast(y, ypred, weights=weights),
'precision': prf1[0],
'recall': prf1[1],
'f1': prf1[2]}
def test_auc_wmw_fast(self):
t = [-1, -1, -1, 1, 1, 1, -1, 1, -1, 1, 1, -1, 1]
p = [
0.01, 0.05, 0.2, 0.25, 0.1, 0.9, 0.6, 0.01, 0.90, 1.0, 0.33, 0.55,
0.555
]
auc_act = 0.54761904761904767
auc = evaluation.auc_wmw_fast(t, p)
self.assertTrue(abs(auc_act - auc) < 1.0e-8)
def test_auc_degenerate(self):
y = np.array([0])
ypred = np.array([[ 1.0]])
weights = np.array([1])
auc = evaluation.auc_wmw_fast(y, ypred, weights=weights)
self.assertTrue(auc == 0)
def agg_err(yactual, ypred):
ret = {}
ret['accuracy'] = classification_error(yactual, ypred)
ret['auc'] = auc_wmw_fast(yactual, ypred)
return ret
def test_auc_degenerate(self):
y = np.array([0])
ypred = np.array([[1.0]])
weights = np.array([1])
auc = evaluation.auc_wmw_fast(y, ypred, weights=weights)
self.assertTrue(auc == 0)
def agg_err(yactual, ypred):
ret = {}
ret['accuracy'] = classification_error(yactual, ypred)
ret['auc'] = auc_wmw_fast(yactual, ypred)
return ret
