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Graph_Transformer
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cleaning up 2

master
Yassaman Ommi 3 years ago
parent
ef5bbb5380
commit
cb406e4c89
2 changed files with 250 additions and 284 deletions
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  1. 242
    283
      .ipynb_checkpoints/tensorboarded-checkpoint.ipynb
  2. 8
    1
      tensorboarded.ipynb

+ 242
- 283
.ipynb_checkpoints/tensorboarded-checkpoint.ipynb View File

@@ -118,10 +118,6 @@
"outputs": [],
"source": [
"def Graph_load_batch(min_num_nodes=20, max_num_nodes=1000, name='ENZYMES', node_attributes=True, graph_labels=True):\n",
" '''\n",
" load many graphs, e.g. enzymes\n",
" :return: a list of graphs\n",
" '''\n",
" print('Loading graph dataset: ' + str(name))\n",
" G = nx.Graph()\n",
" # load data\n",
@@ -161,12 +157,10 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"Constructing"
"Constructing feature matrix of a graph"
]
},
{
@@ -180,12 +174,80 @@
"outputs": [],
"source": [
"def feature_matrix(g, max_nodes=40):\n",
" esm = nx.get_node_attributes(g, 'label')\n",
" piazche = np.zeros((max_nodes, 3))\n",
" for i, (k, v) in enumerate(esm.items()):\n",
" # print(i, k , v)\n",
" piazche[i][v-1] = 1\n",
" return piazche"
" esm = nx.get_node_attributes(g, 'label')\n",
" piazche = np.zeros((max_nodes, 3))\n",
" for i, (k, v) in enumerate(esm.items()):\n",
" # print(i, k , v)\n",
" piazche[i][v-1] = 1\n",
" return piazche"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Removing a random node from a graph\n",
"\n",
"Returns remaining graph with removed node links"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "PkeAYLDQ2W5D"
},
"outputs": [],
"source": [
"def remove_random_node(graph, max_size=40, min_size=10):\n",
" if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size:\n",
" return None\n",
" relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph)\n",
" choice = np.random.choice(list(relabeled_graph.nodes()))\n",
" remaining_graph = nx.to_numpy_matrix(relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes()))))\n",
" removed_node = nx.to_numpy_matrix(relabeled_graph)[choice]\n",
" graph_length = len(remaining_graph)\n",
" source_graph = np.pad(remaining_graph, [(0, max_size - graph_length), (0, max_size - graph_length)])\n",
" # target_graph = np.copy(source_graph)\n",
" removed_node_row = np.asarray(removed_node)[0]\n",
" # target_graph[graph_length] = np.pad(removed_node_row, [(0, max_size - len(removed_node_row))])\n",
" return remaining_graph, removed_node_row"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prepare graphs for the model\n",
"\n",
"returns a graph with a randomly removed node adj matrix [0], its feature matrix [1], the removed node true links [2] "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "FXFQJIvE2Ync"
},
"outputs": [],
"source": [
"def prepare_graph_data(graph, max_size=40, min_size=10):\n",
" if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size:\n",
" return None\n",
" relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph)\n",
" choice = np.random.choice(list(relabeled_graph.nodes()))\n",
" remaining_graph = relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes())))\n",
" remaining_graph_adj = nx.to_numpy_matrix(remaining_graph)\n",
" graph_length = len(remaining_graph)\n",
" remaining_graph_adj = np.pad(remaining_graph_adj, [(0, max_size - graph_length), (0, max_size - graph_length)])\n",
" removed_node = nx.to_numpy_matrix(relabeled_graph)[choice]\n",
" removed_node_row = np.asarray(removed_node)[0]\n",
" removed_node_row = np.pad(removed_node_row, [(0, max_size - len(removed_node_row))])\n",
" return remaining_graph_adj, feature_matrix(remaining_graph), removed_node_row"
]
},
{
@@ -229,86 +291,43 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab": {},
"colab_type": "code",
"id": "KdO6zZruosHe",
"outputId": "e226f7eb-b285-4e02-b576-2b2998c9240f"
"id": "tQdkjvY22_Kf"
},
"outputs": [
{
"data": {
"text/plain": [
"469"
]
},
"execution_count": 10,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"# len(train)"
"# coop = sum([list(filter(lambda x: x is not None, [prepare_graph_data(g) for g in graphs])) for i in range(10)], [])\n",
"coop = list(filter(lambda x: x is not None, [prepare_graph_data(g) for g in train]))\n",
"dale = list(filter(lambda x: x is not None, [prepare_graph_data(g) for g in test]))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 47,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "PkeAYLDQ2W5D"
"id": "qChhpZuCpHWv"
},
"outputs": [],
"source": [
"def remove_random_node(graph, max_size=40, min_size=10):\n",
" if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size:\n",
" return None\n",
" relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph)\n",
" choice = np.random.choice(list(relabeled_graph.nodes()))\n",
" remaining_graph = nx.to_numpy_matrix(relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes()))))\n",
" removed_node = nx.to_numpy_matrix(relabeled_graph)[choice]\n",
" graph_length = len(remaining_graph)\n",
" source_graph = np.pad(remaining_graph, [(0, max_size - graph_length), (0, max_size - graph_length)])\n",
" # target_graph = np.copy(source_graph)\n",
" removed_node_row = np.asarray(removed_node)[0]\n",
" # target_graph[graph_length] = np.pad(removed_node_row, [(0, max_size - len(removed_node_row))])\n",
" return remaining_graph, removed_node_row"
"trainloader = torch.utils.data.DataLoader(coop, collate_fn=lambda x: x[0], batch_size=1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 32,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "FXFQJIvE2Ync"
"id": "8FHvrVFlqNh2"
},
"outputs": [],
"source": [
"def prepare_graph_data(graph, max_size=40, min_size=10):\n",
" '''\n",
" gets a graph as an input\n",
" returns a graph with a randomly removed node adj matrix [0], its feature matrix [1], the removed node true links [2] \n",
" '''\n",
" if len(graph.nodes()) >= max_size or len(graph.nodes()) < min_size:\n",
" return None\n",
" relabeled_graph = nx.relabel.convert_node_labels_to_integers(graph)\n",
" choice = np.random.choice(list(relabeled_graph.nodes()))\n",
" remaining_graph = relabeled_graph.subgraph(filter(lambda x: x != choice, list(relabeled_graph.nodes())))\n",
" remaining_graph_adj = nx.to_numpy_matrix(remaining_graph)\n",
" graph_length = len(remaining_graph)\n",
" remaining_graph_adj = np.pad(remaining_graph_adj, [(0, max_size - graph_length), (0, max_size - graph_length)])\n",
" removed_node = nx.to_numpy_matrix(relabeled_graph)[choice]\n",
" removed_node_row = np.asarray(removed_node)[0]\n",
" removed_node_row = np.pad(removed_node_row, [(0, max_size - len(removed_node_row))])\n",
" return remaining_graph_adj, feature_matrix(remaining_graph), removed_node_row"
"testloader = torch.utils.data.DataLoader(dale, collate_fn=lambda x: x[0], batch_size=1)"
]
},
{
@@ -318,7 +337,14 @@
"id": "JEvec2nosVn7"
},
"source": [
"# Model"
"# Building Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Graph convolutional layer for extracting initial features"
]
},
{
@@ -351,6 +377,13 @@
" return y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Attention calculator using given key, query and value"
]
},
{
"cell_type": "code",
"execution_count": 15,
@@ -362,13 +395,20 @@
"outputs": [],
"source": [
"def attention(query, key, value, key_dim):\n",
" # print('key:', key.transpose(-2,-1))\n",
" scores = torch.matmul(query, key.transpose(-2,-1)) / math.sqrt(key_dim)\n",
" scores = torch.matmul(scores, value)\n",
" # print('scores:', scores)\n",
" scores = F.softmax(scores)\n",
" # scores = torch.sigmoid(scores)\n",
" return scores"
" # print('key:', key.transpose(-2,-1))\n",
" scores = torch.matmul(query, key.transpose(-2,-1)) / math.sqrt(key_dim)\n",
" scores = torch.matmul(scores, value)\n",
" # print('scores:', scores)\n",
" scores = F.softmax(scores)\n",
" # scores = torch.sigmoid(scores)\n",
" return scores"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Graph attention layer for more features"
]
},
{
@@ -382,37 +422,44 @@
"outputs": [],
"source": [
"class GraphAttn(nn.Module):\n",
" def __init__(self, heads, model_dim, dropout=0.1):\n",
" super().__init__()\n",
" self.model_dim = model_dim\n",
" self.key_dim = model_dim // heads\n",
" self.heads = heads\n",
" def __init__(self, heads, model_dim, dropout=0.1):\n",
" super().__init__()\n",
" self.model_dim = model_dim\n",
" self.key_dim = model_dim // heads\n",
" self.heads = heads\n",
"\n",
" self.q_linear = nn.Linear(model_dim, model_dim).cuda()\n",
" self.v_linear = nn.Linear(model_dim, model_dim).cuda()\n",
" self.k_linear = nn.Linear(model_dim, model_dim).cuda()\n",
" self.q_linear = nn.Linear(model_dim, model_dim).cuda()\n",
" self.v_linear = nn.Linear(model_dim, model_dim).cuda()\n",
" self.k_linear = nn.Linear(model_dim, model_dim).cuda()\n",
"\n",
" self.dropout = nn.Dropout(dropout)\n",
" self.out = nn.Linear(model_dim, model_dim).cuda()\n",
" self.dropout = nn.Dropout(dropout)\n",
" self.out = nn.Linear(model_dim, model_dim).cuda()\n",
"\n",
" def forward(self, query, key, value):\n",
" # print(q, k, v)\n",
" bs = query.size(0)\n",
" def forward(self, query, key, value):\n",
" # print(q, k, v)\n",
" bs = query.size(0)\n",
"\n",
" key = self.k_linear(key.float()).view(bs, -1, self.heads, self.key_dim)\n",
" query = self.q_linear(query.float()).view(bs, -1, self.heads, self.key_dim)\n",
" value = self.v_linear(value.float()).view(bs, -1, self.heads, self.key_dim)\n",
" \n",
" key = key.transpose(1,2)\n",
" query = query.transpose(1,2)\n",
" value = value.transpose(1,2)\n",
" key = self.k_linear(key.float()).view(bs, -1, self.heads, self.key_dim)\n",
" query = self.q_linear(query.float()).view(bs, -1, self.heads, self.key_dim)\n",
" value = self.v_linear(value.float()).view(bs, -1, self.heads, self.key_dim)\n",
"\n",
" scores = attention(query, key, value, self.key_dim)\n",
" concat = scores.transpose(1,2).contiguous().view(bs, -1, self.model_dim)\n",
" output = self.out(concat)\n",
" output = output.view(bs, self.model_dim)\n",
" key = key.transpose(1,2)\n",
" query = query.transpose(1,2)\n",
" value = value.transpose(1,2)\n",
"\n",
" return output"
" scores = attention(query, key, value, self.key_dim)\n",
" concat = scores.transpose(1,2).contiguous().view(bs, -1, self.model_dim)\n",
" output = self.out(concat)\n",
" output = output.view(bs, self.model_dim)\n",
"\n",
" return output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MLP layer to map features to links"
]
},
{
@@ -443,6 +490,13 @@
" return output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assembeled model"
]
},
{
"cell_type": "code",
"execution_count": 18,
@@ -454,19 +508,26 @@
"outputs": [],
"source": [
"class Hydra(nn.Module):\n",
" def __init__(self, gcn_input, model_dim, head):\n",
" super().__init__()\n",
" def __init__(self, gcn_input, model_dim, head):\n",
" super().__init__()\n",
"\n",
" self.GCN = GraphConv(input_dim=gcn_input, output_dim=model_dim).cuda()\n",
" self.GAT = GraphAttn(heads=head, model_dim=model_dim).cuda()\n",
" self.MLP = FeedForward(input_size=model_dim, hidden_size=gcn_input).cuda()\n",
" self.GCN = GraphConv(input_dim=gcn_input, output_dim=model_dim).cuda()\n",
" self.GAT = GraphAttn(heads=head, model_dim=model_dim).cuda()\n",
" self.MLP = FeedForward(input_size=model_dim, hidden_size=gcn_input).cuda()\n",
"\n",
" def forward(self, x, adj):\n",
" gcn_outputs = self.GCN(x, adj)\n",
" gat_output = self.GAT(gcn_outputs, gcn_outputs, gcn_outputs)\n",
" mlp_output = self.MLP(gat_output).reshape(1,-1)\n",
" def forward(self, x, adj):\n",
" gcn_outputs = self.GCN(x, adj)\n",
" gat_output = self.GAT(gcn_outputs, gcn_outputs, gcn_outputs)\n",
" mlp_output = self.MLP(gat_output).reshape(1,-1)\n",
"\n",
" return mlp_output"
" return mlp_output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Building model with given inputs"
]
},
{
@@ -485,85 +546,10 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "hKeg1-8P2kqK"
},
"outputs": [],
"source": [
"# adj, features, true_links = prepare_graph_data(graphs[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "8QgGRjyt2sFN"
},
"outputs": [],
"source": [
"# adj, features, true_links = torch.tensor(adj).cuda(), torch.tensor(features).cuda(), torch.tensor(true_links).cuda()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "tcSaiDch3CUG"
},
"outputs": [],
"source": [
"# kyle = build_model(3, 243, 9)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173
},
"colab_type": "code",
"id": "tjioYsvs2tUR",
"outputId": "06033c55-7e06-4d6c-8b98-a4bbff7bf9f2"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
" \n"
]
},
{
"data": {
"text/plain": [
"tensor([[0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425,\n",
" 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425,\n",
" 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425, 0.5425,\n",
" 0.5425, 0.5425, 0.5425, 0.5548, 0.5548, 0.5425, 0.5548, 0.5425, 0.5548,\n",
" 0.5548, 0.5548, 0.5548, 0.5548]], device='cuda:0',\n",
" grad_fn=<ViewBackward>)"
]
},
"execution_count": 20,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"cell_type": "markdown",
"metadata": {},
"source": [
"# kyle(features, adj)"
"# Evaluating Functions"
]
},
{
@@ -573,49 +559,56 @@
"id": "FxNdr2zR3Zgo"
},
"source": [
"# Hala Train"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "t_k4B_3ko8ps"
},
"outputs": [],
"source": [
"def fn(batch):\n",
" return batch[0]"
"# Training Model"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "tQdkjvY22_Kf"
},
"outputs": [],
"cell_type": "markdown",
"metadata": {},
"source": [
"# coop = sum([list(filter(lambda x: x is not None, [prepare_graph_data(g) for g in graphs])) for i in range(10)], [])\n",
"coop = list(filter(lambda x: x is not None, [prepare_graph_data(g) for g in train]))\n",
"dale = list(filter(lambda x: x is not None, [prepare_graph_data(g) for g in test]))"
"Training the model with given data and number of epochs"
]
},
{
"cell_type": "code",
"execution_count": 47,
"execution_count": 57,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "qChhpZuCpHWv"
"id": "PEXlurWtpII2"
},
"outputs": [],
"source": [
"trainloader_train = torch.utils.data.DataLoader(coop, collate_fn=fn, batch_size=1)"
"def train_model(model, trainloader, epoch, print_every=100):\n",
" optim = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.98), eps=1e-9)\n",
"\n",
" model.train()\n",
" start = time.time()\n",
" temp = start\n",
" total_loss = 0\n",
"\n",
" for i in range(epoch):\n",
" for batch, data in enumerate(trainloader, 0):\n",
" adj, features, true_links = data\n",
" adj, features, true_links = torch.tensor(adj).cuda(), torch.tensor(features).cuda(), torch.tensor(true_links).cuda()\n",
" # print(adj.shape)\n",
" # print(features.shape)\n",
" # print(true_links.shape)\n",
" preds = model(features, adj)\n",
" optim.zero_grad()\n",
" loss = F.binary_cross_entropy(preds.double(), true_links.double())\n",
" writer.add_scalar('Loss/train', float(loss), i)\n",
" loss.backward()\n",
" optim.step()\n",
" total_loss += loss.item()\n",
" if (i + 1) % print_every == 0:\n",
" loss_avg = total_loss / print_every\n",
" print(\"time = %dm, epoch %d, iter = %d, loss = %.3f,\\\n",
" %ds per %d iters\" % ((time.time() - start) // 60,\\\n",
" epoch + 1, i + 1, loss_avg, time.time() - temp,\\\n",
" print_every))\n",
" total_loss = 0\n",
" temp = time.time()"
]
},
{
@@ -631,48 +624,6 @@
"kyle = build_model(3, 243, 9)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "PEXlurWtpII2"
},
"outputs": [],
"source": [
"def train_model(model, trainloader, epoch, print_every=100):\n",
" optim = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.98), eps=1e-9)\n",
"\n",
" model.train()\n",
" start = time.time()\n",
" temp = start\n",
" total_loss = 0\n",
"\n",
" for i in range(epoch):\n",
" for batch, data in enumerate(trainloader, 0):\n",
" adj, features, true_links = data\n",
" adj, features, true_links = torch.tensor(adj).cuda(), torch.tensor(features).cuda(), torch.tensor(true_links).cuda()\n",
" # print(adj.shape)\n",
" # print(features.shape)\n",
" # print(true_links.shape)\n",
" preds = model(features, adj)\n",
" optim.zero_grad()\n",
" loss = F.binary_cross_entropy(preds.double(), true_links.double())\n",
" writer.add_scalar('Loss/train', float(loss), i)\n",
" loss.backward()\n",
" optim.step()\n",
" total_loss += loss.item()\n",
" if (i + 1) % print_every == 0:\n",
" loss_avg = total_loss / print_every\n",
" print(\"time = %dm, epoch %d, iter = %d, loss = %.3f,\\\n",
" %ds per %d iters\" % ((time.time() - start) // 60,\\\n",
" epoch + 1, i + 1, loss_avg, time.time() - temp,\\\n",
" print_every))\n",
" total_loss = 0\n",
" temp = time.time()"
]
},
{
"cell_type": "code",
"execution_count": 58,
@@ -717,16 +668,17 @@
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "8FHvrVFlqNh2"
},
"outputs": [],
"cell_type": "markdown",
"metadata": {},
"source": [
"trainloader_test = torch.utils.data.DataLoader(dale, collate_fn=fn, batch_size=1)"
"# Testing Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing model and printing the loss"
]
},
{
@@ -745,18 +697,18 @@
" # total_loss = 0\n",
"\n",
" for batch, data in enumerate(trainloader, 0):\n",
" adj, features, true_links = data\n",
" adj, features, true_links = torch.tensor(adj).cuda(), torch.tensor(features).cuda(), torch.tensor(true_links).cuda()\n",
" # print(adj.shape)\n",
" # print(features.shape)\n",
" # print(true_links.shape)\n",
" preds = model(features, adj)\n",
" loss = F.binary_cross_entropy(preds.double(), true_links.double())\n",
" # total_loss += loss.item()\n",
" # loss_avg = total_loss / print_every\n",
" if (batch + 1) % print_every == 0:\n",
" print(\"loss = \", float(loss))\n",
" temp = time.time()"
" adj, features, true_links = data\n",
" adj, features, true_links = torch.tensor(adj).cuda(), torch.tensor(features).cuda(), torch.tensor(true_links).cuda()\n",
" # print(adj.shape)\n",
" # print(features.shape)\n",
" # print(true_links.shape)\n",
" preds = model(features, adj)\n",
" loss = F.binary_cross_entropy(preds.double(), true_links.double())\n",
" # total_loss += loss.item()\n",
" # loss_avg = total_loss / print_every\n",
" if (batch + 1) % print_every == 0:\n",
" print(\"loss = \", float(loss))\n",
" temp = time.time()"
]
},
{
@@ -800,6 +752,13 @@
"test_model(kyle, trainloader_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluating Model"
]
},
{
"cell_type": "markdown",
"metadata": {

+ 8
- 1
tensorboarded.ipynb View File

@@ -549,7 +549,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluating Model"
"# Evaluating Functions"
]
},
{
@@ -752,6 +752,13 @@
"test_model(kyle, trainloader_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluating Model"
]
},
{
"cell_type": "markdown",
"metadata": {

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