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)