def score(self,
actual: np.array,
predicted: np.array,
sample_weight: typing.Optional[np.array] = None,
labels: typing.Optional[np.array] = None) -> float:
if sample_weight is not None:
sample_weight = sample_weight.ravel()
enc_actual, enc_predicted, labels = prep_actual_predicted(
actual, predicted, labels)
cm_weights = sample_weight if sample_weight is not None else None
# multiclass
if enc_predicted.shape[1] > 1:
enc_predicted = enc_predicted.ravel()
enc_actual = label_binarize(enc_actual, labels).ravel()
cm_weights = np.repeat(
cm_weights,
predicted.shape[1]).ravel() if cm_weights is not None else None
assert enc_predicted.shape == enc_actual.shape
assert cm_weights is None or enc_predicted.shape == cm_weights.shape
cms = daicx.confusion_matrices(enc_actual.ravel(),
enc_predicted.ravel(),
sample_weight=cm_weights)
cms = cms.loc[cms[[
self.__class__._threshold_optimizer
]].idxmax()] # get row(s) for optimal metric defined above
cms['metric'] = cms[['tp', 'fp', 'tn',
'fn']].apply(lambda x: self.protected_metric(*x),
axis=1,
raw=True)
return cms['metric'].mean() # in case of ties
def score(self,
actual: np.array,
predicted: np.array,
sample_weight: typing.Optional[np.array] = None,
labels: typing.Optional[np.array] = None,
X: typing.Optional[dt.Frame] = None,
**kwargs) -> float:
# can compute arbitrary cost from all original features
if X is not None:
assert X.nrows == len(actual)
assert X.ncols >= 1
X_pd = X.to_pandas()
if sample_weight is not None:
sample_weight = sample_weight.ravel()
enc_actual, enc_predicted, labels = prep_actual_predicted(
actual, predicted, labels)
cm_weights = sample_weight if sample_weight is not None else None
# multiclass
if enc_predicted.shape[1] > 1:
enc_predicted = enc_predicted.ravel()
enc_actual = label_binarize(enc_actual, labels).ravel()
cm_weights = np.repeat(
cm_weights,
predicted.shape[1]).ravel() if cm_weights is not None else None
assert enc_predicted.shape == enc_actual.shape
assert cm_weights is None or enc_predicted.shape == cm_weights.shape
cms = daicx.confusion_matrices(enc_actual.ravel(),
enc_predicted.ravel(),
sample_weight=cm_weights)
cms = cms.loc[cms[[
self.__class__._threshold_optimizer
]].idxmax()] # get row(s) for optimal metric defined above
cms['metric'] = cms[['tp', 'fp', 'tn', 'fn']].apply(
lambda x: self.protected_metric(*x, X_pd), axis=1, raw=True)
return cms['metric'].mean() # in case of ties