def predict_on_batch(self, inputs):
# inputs shape (,10), corresponding to 5 module predictions of mut and wt
X = inputs[:, :5] - inputs[:, -5:]
X = transform(X, True)
X = np.concatenate([inputs, X[:, -3:]], axis=-1)
pred = self.model.predict_proba(X)
return pred
def predict_on_batch(self, inputs):
# inputs shape (,10), corresponding to 5 module predictions of mut and wt
X = inputs[:, :5] - inputs[:, -5:]
X = transform(X, True)
X = X[:, [1, 2, 3, 5]]
pred = self.model.predict(X)
return pred
def predict_on_batch(self, inputs):
return LINEAR_MODEL.predict(transform(inputs, False))
def predict_on_batch(self, inputs):
X = transform(inputs, False)
pred = self.model.predict(X)
return pred
def predict_on_batch(self, inputs):
'''inputs shape (,10), corresponding to 5 module predictions of mut and wt'''
X_alt, X_ref = inputs[:, 5:], inputs[:, :-5]
X = transform(X_alt - X_ref, True)
X = np.concatenate([X_ref, X_alt, X[:, -3:]], axis=-1)
return LOGISTIC_MODEL.predict_proba(X)
def predict_on_batch(self, inputs):
'''inputs shape (,10), corresponding to 5 module predictions of mut and wt'''
X = transform(inputs[:, :5] - inputs[:, -5:], True)[:, [1, 2, 3, 5]]
return EFFICIENCY_MODEL.predict(X)