wehub-resource-sync
483 lines
18 KiB
Python
483 lines
18 KiB
Python
# Copyright (c) 2022 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 unittest
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import numpy as np
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from op_test import is_custom_device
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import paddle
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from paddle import base
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class TestGraphSampleNeighbors(unittest.TestCase):
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def setUp(self):
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num_nodes = 20
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edges = np.random.randint(num_nodes, size=(100, 2))
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edges = np.unique(edges, axis=0)
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self.edges_id = np.arange(0, len(edges)).astype("int64")
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sorted_edges = edges[np.argsort(edges[:, 1])]
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# Calculate dst index cumsum counts, also means colptr
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dst_count = np.zeros(num_nodes)
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dst_src_dict = {}
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for dst in range(0, num_nodes):
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true_index = sorted_edges[:, 1] == dst
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dst_count[dst] = np.sum(true_index)
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dst_src_dict[dst] = sorted_edges[:, 0][true_index]
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dst_count = dst_count.astype("int64")
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colptr = np.cumsum(dst_count)
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colptr = np.insert(colptr, 0, 0)
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self.row = sorted_edges[:, 0].astype("int64")
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self.colptr = colptr.astype("int64")
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self.nodes = np.unique(np.random.randint(num_nodes, size=5)).astype(
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"int64"
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)
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self.sample_size = 5
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self.dst_src_dict = dst_src_dict
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def test_sample_result(self):
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paddle.disable_static()
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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out_neighbors, out_count = paddle.incubate.graph_sample_neighbors(
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row, colptr, nodes, sample_size=self.sample_size
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)
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out_count_cumsum = paddle.cumsum(out_count)
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for i in range(len(out_count)):
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if i == 0:
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neighbors = out_neighbors[0 : out_count_cumsum[i]]
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else:
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neighbors = out_neighbors[
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out_count_cumsum[i - 1] : out_count_cumsum[i]
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]
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# Ensure the correct sample size.
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self.assertTrue(
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out_count[i] == self.sample_size
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or out_count[i] == len(self.dst_src_dict[self.nodes[i]])
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)
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# Ensure no repetitive sample neighbors.
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self.assertTrue(neighbors.shape[0] == np.unique(neighbors).shape[0])
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# Ensure the correct sample neighbors.
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in_neighbors = np.isin(
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neighbors.numpy(), self.dst_src_dict[self.nodes[i]]
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)
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_sample_result_fisher_yates_sampling(self):
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paddle.disable_static()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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perm_buffer = paddle.to_tensor(self.edges_id)
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out_neighbors, out_count = paddle.incubate.graph_sample_neighbors(
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row,
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colptr,
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nodes,
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perm_buffer=perm_buffer,
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sample_size=self.sample_size,
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flag_perm_buffer=True,
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)
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out_count_cumsum = paddle.cumsum(out_count)
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for i in range(len(out_count)):
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if i == 0:
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neighbors = out_neighbors[0 : out_count_cumsum[i]]
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else:
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neighbors = out_neighbors[
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out_count_cumsum[i - 1] : out_count_cumsum[i]
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]
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# Ensure the correct sample size.
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self.assertTrue(
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out_count[i] == self.sample_size
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or out_count[i] == len(self.dst_src_dict[self.nodes[i]])
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)
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# Ensure no repetitive sample neighbors.
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self.assertTrue(
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neighbors.shape[0] == np.unique(neighbors).shape[0]
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)
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# Ensure the correct sample neighbors.
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in_neighbors = np.isin(
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neighbors.numpy(), self.dst_src_dict[self.nodes[i]]
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)
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_sample_result_static(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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row = paddle.static.data(
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name="row", shape=self.row.shape, dtype=self.row.dtype
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)
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colptr = paddle.static.data(
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name="colptr", shape=self.colptr.shape, dtype=self.colptr.dtype
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)
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nodes = paddle.static.data(
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name="nodes", shape=self.nodes.shape, dtype=self.nodes.dtype
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)
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out_neighbors, out_count = paddle.incubate.graph_sample_neighbors(
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row, colptr, nodes, sample_size=self.sample_size
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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ret = exe.run(
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feed={
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'row': self.row,
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'colptr': self.colptr,
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'nodes': self.nodes,
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},
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fetch_list=[out_neighbors, out_count],
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)
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out_neighbors, out_count = ret
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out_count_cumsum = np.cumsum(out_count)
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out_neighbors = np.split(out_neighbors, out_count_cumsum)[:-1]
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for neighbors, node, count in zip(
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out_neighbors, self.nodes, out_count
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):
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self.assertTrue(
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count == self.sample_size
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or count == len(self.dst_src_dict[node])
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)
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self.assertTrue(
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neighbors.shape[0] == np.unique(neighbors).shape[0]
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)
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in_neighbors = np.isin(neighbors, self.dst_src_dict[node])
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_raise_errors(self):
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paddle.disable_static()
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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def check_eid_error():
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paddle.incubate.graph_sample_neighbors(
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row,
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colptr,
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nodes,
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sample_size=self.sample_size,
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return_eids=True,
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)
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def check_perm_buffer_error():
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paddle.incubate.graph_sample_neighbors(
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row,
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colptr,
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nodes,
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sample_size=self.sample_size,
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flag_perm_buffer=True,
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)
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self.assertRaises(ValueError, check_eid_error)
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self.assertRaises(ValueError, check_perm_buffer_error)
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def test_sample_result_with_eids(self):
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paddle.disable_static()
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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eids = paddle.to_tensor(self.edges_id)
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perm_buffer = paddle.to_tensor(self.edges_id)
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(
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out_neighbors,
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out_count,
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out_eids,
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) = paddle.incubate.graph_sample_neighbors(
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row,
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colptr,
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nodes,
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eids=eids,
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sample_size=self.sample_size,
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return_eids=True,
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)
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(
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out_neighbors,
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out_count,
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out_eids,
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) = paddle.incubate.graph_sample_neighbors(
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row,
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colptr,
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nodes,
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eids=eids,
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perm_buffer=perm_buffer,
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sample_size=self.sample_size,
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return_eids=True,
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flag_perm_buffer=True,
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)
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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row = paddle.static.data(
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name="row", shape=self.row.shape, dtype=self.row.dtype
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)
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colptr = paddle.static.data(
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name="colptr", shape=self.colptr.shape, dtype=self.colptr.dtype
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)
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nodes = paddle.static.data(
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name="nodes", shape=self.nodes.shape, dtype=self.nodes.dtype
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)
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eids = paddle.static.data(
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name="eids", shape=self.edges_id.shape, dtype=self.nodes.dtype
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)
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(
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out_neighbors,
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out_count,
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out_eids,
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) = paddle.incubate.graph_sample_neighbors(
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row,
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colptr,
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nodes,
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eids,
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sample_size=self.sample_size,
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return_eids=True,
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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ret = exe.run(
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feed={
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'row': self.row,
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'colptr': self.colptr,
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'nodes': self.nodes,
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'eids': self.edges_id,
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},
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fetch_list=[out_neighbors, out_count, out_eids],
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)
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class TestGeometricGraphSampleNeighbors(unittest.TestCase):
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def setUp(self):
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num_nodes = 20
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edges = np.random.randint(num_nodes, size=(100, 2))
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edges = np.unique(edges, axis=0)
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self.edges_id = np.arange(0, len(edges)).astype("int64")
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sorted_edges = edges[np.argsort(edges[:, 1])]
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# Calculate dst index cumsum counts, also means colptr
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dst_count = np.zeros(num_nodes)
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dst_src_dict = {}
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for dst in range(0, num_nodes):
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true_index = sorted_edges[:, 1] == dst
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dst_count[dst] = np.sum(true_index)
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dst_src_dict[dst] = sorted_edges[:, 0][true_index]
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dst_count = dst_count.astype("int64")
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colptr = np.cumsum(dst_count)
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colptr = np.insert(colptr, 0, 0)
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self.row = sorted_edges[:, 0].astype("int64")
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self.colptr = colptr.astype("int64")
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self.nodes = np.unique(np.random.randint(num_nodes, size=5)).astype(
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"int64"
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)
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self.sample_size = 5
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self.dst_src_dict = dst_src_dict
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def test_sample_result(self):
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paddle.disable_static()
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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out_neighbors, out_count = paddle.geometric.sample_neighbors(
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row, colptr, nodes, sample_size=self.sample_size
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)
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out_count_cumsum = paddle.cumsum(out_count)
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for i in range(len(out_count)):
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if i == 0:
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neighbors = out_neighbors[0 : out_count_cumsum[i]]
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else:
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neighbors = out_neighbors[
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out_count_cumsum[i - 1] : out_count_cumsum[i]
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]
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# Ensure the correct sample size.
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self.assertTrue(
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out_count[i] == self.sample_size
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or out_count[i] == len(self.dst_src_dict[self.nodes[i]])
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)
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# Ensure no repetitive sample neighbors.
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self.assertTrue(neighbors.shape[0] == np.unique(neighbors).shape[0])
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# Ensure the correct sample neighbors.
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in_neighbors = np.isin(
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neighbors.numpy(), self.dst_src_dict[self.nodes[i]]
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)
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_sample_result_fisher_yates_sampling(self):
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paddle.disable_static()
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if base.core.is_compiled_with_cuda() or is_custom_device():
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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perm_buffer = paddle.to_tensor(self.edges_id)
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out_neighbors, out_count = paddle.geometric.sample_neighbors(
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row,
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colptr,
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nodes,
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perm_buffer=perm_buffer,
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sample_size=self.sample_size,
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)
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out_count_cumsum = paddle.cumsum(out_count)
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for i in range(len(out_count)):
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if i == 0:
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neighbors = out_neighbors[0 : out_count_cumsum[i]]
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else:
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neighbors = out_neighbors[
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out_count_cumsum[i - 1] : out_count_cumsum[i]
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]
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# Ensure the correct sample size.
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self.assertTrue(
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out_count[i] == self.sample_size
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or out_count[i] == len(self.dst_src_dict[self.nodes[i]])
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)
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# Ensure no repetitive sample neighbors.
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self.assertTrue(
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neighbors.shape[0] == np.unique(neighbors).shape[0]
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)
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# Ensure the correct sample neighbors.
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in_neighbors = np.isin(
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neighbors.numpy(), self.dst_src_dict[self.nodes[i]]
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)
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_sample_result_static(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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row = paddle.static.data(
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name="row", shape=self.row.shape, dtype=self.row.dtype
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)
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colptr = paddle.static.data(
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name="colptr", shape=self.colptr.shape, dtype=self.colptr.dtype
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)
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nodes = paddle.static.data(
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name="nodes", shape=self.nodes.shape, dtype=self.nodes.dtype
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)
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out_neighbors, out_count = paddle.geometric.sample_neighbors(
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row, colptr, nodes, sample_size=self.sample_size
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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ret = exe.run(
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feed={
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'row': self.row,
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'colptr': self.colptr,
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'nodes': self.nodes,
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},
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fetch_list=[out_neighbors, out_count],
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)
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out_neighbors, out_count = ret
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out_count_cumsum = np.cumsum(out_count)
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out_neighbors = np.split(out_neighbors, out_count_cumsum)[:-1]
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for neighbors, node, count in zip(
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out_neighbors, self.nodes, out_count
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):
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self.assertTrue(
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count == self.sample_size
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or count == len(self.dst_src_dict[node])
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)
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self.assertTrue(
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neighbors.shape[0] == np.unique(neighbors).shape[0]
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)
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in_neighbors = np.isin(neighbors, self.dst_src_dict[node])
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self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
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def test_raise_errors(self):
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paddle.disable_static()
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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def check_eid_error():
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paddle.geometric.sample_neighbors(
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row,
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colptr,
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nodes,
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sample_size=self.sample_size,
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return_eids=True,
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)
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self.assertRaises(ValueError, check_eid_error)
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def test_sample_result_with_eids(self):
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paddle.disable_static()
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row = paddle.to_tensor(self.row)
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colptr = paddle.to_tensor(self.colptr)
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nodes = paddle.to_tensor(self.nodes)
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eids = paddle.to_tensor(self.edges_id)
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perm_buffer = paddle.to_tensor(self.edges_id)
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out_neighbors, out_count, out_eids = paddle.geometric.sample_neighbors(
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row,
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colptr,
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nodes,
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eids=eids,
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sample_size=self.sample_size,
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return_eids=True,
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)
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out_neighbors, out_count, out_eids = paddle.geometric.sample_neighbors(
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row,
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colptr,
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nodes,
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eids=eids,
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perm_buffer=perm_buffer,
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sample_size=self.sample_size,
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return_eids=True,
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)
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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row = paddle.static.data(
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name="row", shape=self.row.shape, dtype=self.row.dtype
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)
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colptr = paddle.static.data(
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name="colptr", shape=self.colptr.shape, dtype=self.colptr.dtype
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)
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nodes = paddle.static.data(
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name="nodes", shape=self.nodes.shape, dtype=self.nodes.dtype
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)
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eids = paddle.static.data(
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name="eids", shape=self.edges_id.shape, dtype=self.nodes.dtype
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)
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(
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out_neighbors,
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out_count,
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out_eids,
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) = paddle.geometric.sample_neighbors(
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row,
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colptr,
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nodes,
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sample_size=self.sample_size,
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eids=eids,
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return_eids=True,
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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ret = exe.run(
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feed={
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'row': self.row,
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'colptr': self.colptr,
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'nodes': self.nodes,
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'eids': self.edges_id,
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},
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fetch_list=[out_neighbors, out_count, out_eids],
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)
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if __name__ == "__main__":
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unittest.main()
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