wehub-resource-sync
116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import numpy as np
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import paddle
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import pgl
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from paddle.io import Dataset
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from pgl.sampling import graphsage_sample
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__all__ = [
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"TrainData",
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"PredictData",
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"batch_fn",
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]
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class TrainData(Dataset):
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def __init__(self, graph_work_path):
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trainer_id = paddle.distributed.get_rank()
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trainer_count = paddle.distributed.get_world_size()
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print("trainer_id: %s, trainer_count: %s." % (trainer_id, trainer_count))
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edges = np.load(os.path.join(graph_work_path, "train_data.npy"), allow_pickle=True)
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# edges is bidirectional.
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train_src = edges[trainer_id::trainer_count, 0]
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train_dst = edges[trainer_id::trainer_count, 1]
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returns = {"train_data": [train_src, train_dst]}
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if os.path.exists(os.path.join(graph_work_path, "neg_samples.npy")):
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neg_samples = np.load(os.path.join(graph_work_path, "neg_samples.npy"), allow_pickle=True)
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if neg_samples.size != 0:
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train_negs = neg_samples[trainer_id::trainer_count]
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returns["train_data"].append(train_negs)
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print("Load train_data done.")
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self.data = returns
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def __getitem__(self, index):
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return [data[index] for data in self.data["train_data"]]
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def __len__(self):
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return len(self.data["train_data"][0])
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class PredictData(Dataset):
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def __init__(self, num_nodes):
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trainer_id = paddle.distributed.get_rank()
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trainer_count = paddle.distributed.get_world_size()
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self.data = np.arange(trainer_id, num_nodes, trainer_count)
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def __getitem__(self, index):
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return [self.data[index], self.data[index]]
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def __len__(self):
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return len(self.data)
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def batch_fn(batch_ex, samples, base_graph, term_ids):
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batch_src = []
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batch_dst = []
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batch_neg = []
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for batch in batch_ex:
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batch_src.append(batch[0])
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batch_dst.append(batch[1])
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if len(batch) == 3: # default neg samples
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batch_neg.append(batch[2])
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batch_src = np.array(batch_src, dtype="int64")
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batch_dst = np.array(batch_dst, dtype="int64")
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if len(batch_neg) > 0:
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batch_neg = np.unique(np.concatenate(batch_neg))
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else:
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batch_neg = batch_dst
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nodes = np.unique(np.concatenate([batch_src, batch_dst, batch_neg], 0))
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subgraphs = graphsage_sample(base_graph, nodes, samples)
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subgraph, sample_index, node_index = subgraphs[0]
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from_reindex = {int(x): i for i, x in enumerate(sample_index)}
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term_ids = term_ids[sample_index].astype(np.int64)
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sub_src_idx = pgl.graph_kernel.map_nodes(batch_src, from_reindex)
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sub_dst_idx = pgl.graph_kernel.map_nodes(batch_dst, from_reindex)
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sub_neg_idx = pgl.graph_kernel.map_nodes(batch_neg, from_reindex)
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user_index = np.array(sub_src_idx, dtype="int64")
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pos_item_index = np.array(sub_dst_idx, dtype="int64")
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neg_item_index = np.array(sub_neg_idx, dtype="int64")
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user_real_index = np.array(batch_src, dtype="int64")
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pos_item_real_index = np.array(batch_dst, dtype="int64")
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return (
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np.array([subgraph.num_nodes], dtype="int32"),
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subgraph.edges.astype("int32"),
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term_ids,
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user_index,
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pos_item_index,
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neg_item_index,
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user_real_index,
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pos_item_real_index,
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)
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