def predict(self, X, ids, weight, feature_names=None):
self.feature_names = feature_names
query_indptr, query_ids = self._build_query_indptr(ids)
# We wont be using this, but Queries wont instantiate without it
y = np.zeros(X.shape[0])
q = Queries(X, y, query_indptr, query_ids=query_ids)
y_pred = self.model.predict(q, n_jobs=self.params['n_jobs'])
return y_pred
def _build_queries(self, X, y, ids, w):
query_indptr, query_ids = self._build_query_indptr(ids)
q = Queries(X, y, query_indptr, query_ids=query_ids)
# weights as per query instead of per-row ... just guess
wn = [
np.mean(w[query_indptr[i]:query_indptr[i + 1]])
for i in range(len(query_indptr) - 1)
]
wn = [w[i] for i in query_indptr[:-1]]
return q, np.ascontiguousarray(wn, dtype='float64')
