remove input-output iters

cornac/data/dataset.py

@@ -1361,72 +1361,6 @@ def si_iter(self, batch_size=1, shuffle=False):
             batch_session_items = [[self.uir_tuple[1][i] for i in ids] for ids in batch_mapped_ids]
             yield batch_session_indices, batch_mapped_ids, batch_session_items
 
-    def io_iter(self, batch_size=1, shuffle=False):
-        """Paralellize mini-batch of input-output items. Create an iterator over data yielding batch of input item indices, batch of output item indices, 
-        batch of start masking, batch of end masking, and batch of valid ids (relative positions of current sequences in the last batch).
-
-        Parameters
-        ----------
-        batch_size: int, optional, default = 1
-
-        shuffle: bool, optional, default: False
-            If `True`, orders of triplets will be randomized. If `False`, default orders kept.
-
-        Returns
-        -------
-        iterator : batch of input item indices, batch of output item indices, batch of starting sequence mask, batch of ending sequence mask, batch of valid ids
-
-        """
-        start_mask = np.zeros(batch_size, dtype="int")
-        end_mask = np.ones(batch_size, dtype="int")
-        input_iids = None
-        output_iids = None
-        l_pool = []
-        c_pool = [None for _ in range(batch_size)]
-        sizes = np.zeros(batch_size, dtype="int")
-        for _, batch_mapped_ids in self.s_iter(batch_size, shuffle):
-            l_pool += batch_mapped_ids
-            while len(l_pool) > 0:
-                if end_mask.sum() == 0:
-                    input_iids = self.uir_tuple[1][[mapped_ids[-sizes[idx]] for idx, mapped_ids in enumerate(c_pool)]]
-                    output_iids = self.uir_tuple[1][[mapped_ids[-sizes[idx] + 1] for idx, mapped_ids in enumerate(c_pool)]]
-                    sizes -= 1
-                    for idx, size in enumerate(sizes):
-                        if size == 1:
-                            end_mask[idx] = 1
-                    yield input_iids, output_iids, start_mask, end_mask, np.arange(batch_size, dtype="int")
-                    start_mask.fill(0) # reset start masking
-                while end_mask.sum() > 0 and len(l_pool) > 0:
-                    next_seq = l_pool.pop()
-                    if len(next_seq) > 1:
-                        idx = np.nonzero(end_mask)[0][0]
-                        end_mask[idx] = 0
-                        start_mask[idx] = 1
-                        c_pool[idx] = next_seq
-                        sizes[idx] = len(c_pool[idx])
-
-        valid_id = np.ones(batch_size, dtype="int")
-        while True:
-            for idx, size in enumerate(sizes):
-                if size == 1:
-                    end_mask[idx] = 1
-                    valid_id[idx] = 0
-            input_iids = self.uir_tuple[1][[mapped_ids[-sizes[idx]] for idx, mapped_ids in enumerate(c_pool) if sizes[idx] > 1]]
-            output_iids = self.uir_tuple[1][[mapped_ids[-sizes[idx] + 1] for idx, mapped_ids in enumerate(c_pool) if sizes[idx] > 1]]
-            sizes -= 1
-            for idx, size in enumerate(sizes):
-                if size == 1:
-                    end_mask[idx] = 1
-            start_mask = start_mask[np.nonzero(valid_id)[0]]
-            end_mask = end_mask[np.nonzero(valid_id)[0]]
-            sizes = sizes[np.nonzero(valid_id)[0]]
-            c_pool = [_ for _, valid in zip(c_pool, valid_id) if valid > 0]
-            yield input_iids, output_iids, start_mask, end_mask, np.nonzero(valid_id)[0]
-            valid_id = np.ones(len(input_iids), dtype="int")
-            if end_mask.sum() == len(input_iids):
-                break
-            start_mask.fill(0) # reset start masking
-
     def usi_iter(self, batch_size=1, shuffle=False):
         """Create an iterator over data yielding batch of user indices, batch of session indices, batch of mapped ids, and batch of sessions' items