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IngoScholtesIngoScholtes
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254 potential problems with graphweighted outdegrees, fixes #254 (#290)
* new semantics of degree calculation * updated tutorial, fixing str type * updated degree statistics
1 parent 5767c53 commit 61c6f6e

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‎docs/tutorial/basic_concepts.ipynb‎

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@@ -171,9 +171,18 @@
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},
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"EdgeIndex([[0, 0, 1],\n",
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" [2, 1, 2]], sparse_size=(4, 4), nnz=3, sort_order=row)\n"
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]
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}
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],
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"source": [
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"print(g.data.edge_index)"
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"name": "stdout",
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"text": [
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"a\n",
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"b\n",
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"c\n",
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"d\n",
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"('a', 'c')\n",
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"('a', 'b')\n",
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"('b', 'c')\n"
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"0\n",
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"1\n",
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"2\n",
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"3\n",
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"(0, 2)\n",
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"(0, 1)\n",
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"(1, 2)\n"
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}
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"tensor([2, 1])"
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]
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"cell_type": "code",
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@@ -627,6 +636,136 @@
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" print(f\"{v} -> {g.in_degrees[v]}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `in_degree` and `out_degree` properties are shortcuts to a general `degree` function that can be used to calculate (weighted) in- and outdegrees. "
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]
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},
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{
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"{'a': 0, 'c': 2, 'b': 1}"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"g.degrees(mode='in')"
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]
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},
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"data": {
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"{'a': 2, 'c': 0, 'b': 1}"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"g.degrees(mode='out')"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Degrees can be alternatively returned as torch.tensors."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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"data": {
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"text/plain": [
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"tensor([0, 2, 1], dtype=torch.int32)"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"g.degrees(mode='in', return_tensor=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can also use arbitrary numerical edge attributes that will be used for a weighted (in- or out) degree calculation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"g.data.edge_weight=torch.tensor([1.0, 2.0, 3.0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"outputs": [
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"tensor([0., 5., 1.])"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"g.degrees(mode='in', edge_attr='edge_weight', return_tensor=True)"
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]
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"tensor([3., 0., 3.])"
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]
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"g.degrees(mode='out', edge_attr='edge_weight', return_tensor=True)"
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]
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},
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"Data(edge_index=[2, 3], num_nodes=3, node_sequence=[3, 1])"
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"Data(edge_index=[2, 3], num_nodes=3, node_sequence=[3, 1], edge_weight=[3])"
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{
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m g \u001b[38;5;241m=\u001b[39m \u001b[43mpp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_edge_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m g\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mis_cuda\n",
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"File \u001b[0;32m/workspaces/pathpyG/src/pathpyG/core/graph.py:180\u001b[0m, in \u001b[0;36mGraph.from_edge_list\u001b[0;34m(edge_list, is_undirected, mapping, num_nodes, device)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_nodes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 177\u001b[0m num_nodes \u001b[38;5;241m=\u001b[39m mapping\u001b[38;5;241m.\u001b[39mnum_ids()\n\u001b[1;32m 179\u001b[0m edge_index \u001b[38;5;241m=\u001b[39m EdgeIndex(\n\u001b[0;32m--> 180\u001b[0m \u001b[43mmapping\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_idxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mcontiguous(),\n\u001b[1;32m 181\u001b[0m sparse_size\u001b[38;5;241m=\u001b[39m(num_nodes, num_nodes),\n\u001b[1;32m 182\u001b[0m is_undirected\u001b[38;5;241m=\u001b[39mis_undirected,\n\u001b[1;32m 183\u001b[0m )\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Graph(Data(edge_index\u001b[38;5;241m=\u001b[39medge_index, num_nodes\u001b[38;5;241m=\u001b[39mnum_nodes), mapping\u001b[38;5;241m=\u001b[39mmapping)\n",
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"Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m g \u001b[38;5;241m=\u001b[39m \u001b[43mpp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_edge_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m g\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mis_cuda\n",
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"File \u001b[0;32m/workspaces/pathpyG/src/pathpyG/core/graph.py:179\u001b[0m, in \u001b[0;36mGraph.from_edge_list\u001b[0;34m(edge_list, is_undirected, mapping, device)\u001b[0m\n\u001b[1;32m 174\u001b[0m mapping \u001b[38;5;241m=\u001b[39m IndexMap(node_ids)\n\u001b[1;32m 176\u001b[0m num_nodes \u001b[38;5;241m=\u001b[39m mapping\u001b[38;5;241m.\u001b[39mnum_ids()\n\u001b[1;32m 178\u001b[0m edge_index \u001b[38;5;241m=\u001b[39m EdgeIndex(\n\u001b[0;32m--> 179\u001b[0m \u001b[43mmapping\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_idxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mT\u001b[38;5;241m.\u001b[39mcontiguous(),\n\u001b[1;32m 180\u001b[0m sparse_size\u001b[38;5;241m=\u001b[39m(num_nodes, num_nodes),\n\u001b[1;32m 181\u001b[0m is_undirected\u001b[38;5;241m=\u001b[39mis_undirected,\n\u001b[1;32m 182\u001b[0m )\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Graph(Data(edge_index\u001b[38;5;241m=\u001b[39medge_index, num_nodes\u001b[38;5;241m=\u001b[39mnum_nodes), mapping\u001b[38;5;241m=\u001b[39mmapping)\n",
705844
"File \u001b[0;32m/workspaces/pathpyG/src/pathpyG/core/index_map.py:361\u001b[0m, in \u001b[0;36mIndexMap.to_idxs\u001b[0;34m(self, nodes, device)\u001b[0m\n\u001b[1;32m 359\u001b[0m shape \u001b[38;5;241m=\u001b[39m nodes\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid_shape \u001b[38;5;241m==\u001b[39m (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,):\n\u001b[0;32m--> 361\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid_to_idx\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnodes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflatten\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreshape(shape)\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 363\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid_to_idx[\u001b[38;5;28mtuple\u001b[39m(node)] \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m nodes\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid_shape)], device\u001b[38;5;241m=\u001b[39mdevice)\u001b[38;5;241m.\u001b[39mreshape(\n\u001b[1;32m 364\u001b[0m shape[: \u001b[38;5;241m-\u001b[39m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid_shape) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 365\u001b[0m )\n",
706845
"File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/torch/cuda/__init__.py:314\u001b[0m, in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCUDA_MODULE_LOADING\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39menviron:\n\u001b[1;32m 313\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCUDA_MODULE_LOADING\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLAZY\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 314\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cuda_init\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;66;03m# Some of the queued calls may reentrantly call _lazy_init();\u001b[39;00m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;66;03m# we need to just return without initializing in that case.\u001b[39;00m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;66;03m# However, we must not let any *other* threads in!\u001b[39;00m\n\u001b[1;32m 318\u001b[0m _tls\u001b[38;5;241m.\u001b[39mis_initializing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
707846
"\u001b[0;31mRuntimeError\u001b[0m: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 500: named symbol not found"
@@ -1024,7 +1163,7 @@
10241163
},
10251164
{
10261165
"cell_type": "code",
1027-
"execution_count": 50,
1166+
"execution_count": 17,
10281167
"metadata": {},
10291168
"outputs": [
10301169
{
@@ -1052,7 +1191,7 @@
10521191
},
10531192
{
10541193
"cell_type": "code",
1055-
"execution_count": 51,
1194+
"execution_count": 18,
10561195
"metadata": {},
10571196
"outputs": [
10581197
{
@@ -1084,7 +1223,7 @@
10841223
},
10851224
{
10861225
"cell_type": "code",
1087-
"execution_count": 52,
1226+
"execution_count": 19,
10881227
"metadata": {},
10891228
"outputs": [
10901229
{
@@ -1118,7 +1257,7 @@
11181257
"\n",
11191258
"</style>\n",
11201259
"\n",
1121-
"<div id = \"x7a28caf1b6044e7c89e451a75c0ef22c\"> </div>\n",
1260+
"<div id = \"x34cf302f998c48d2a9a314fd81856883\"> </div>\n",
11221261
"<script charset=\"utf-8\" src=\"https://d3js.org/d3.v5.min.js\"></script>\n",
11231262
"<script charset=\"utf-8\">\n",
11241263
"// Load via requireJS if available (jupyter notebook environment)\n",
@@ -1150,8 +1289,8 @@
11501289
" }\n",
11511290
"};\n",
11521291
"require(['d3'], function(d3){ //START\n",
1153-
"const data = {\"edges\": [{\"uid\": \"a-b\", \"source\": \"a\", \"target\": \"b\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"b-c\", \"source\": \"b\", \"target\": \"c\", \"weight\": 1, \"color\": \"gray\"}, {\"uid\": \"c-a\", \"source\": \"c\", \"target\": \"a\", \"weight\": 1, \"color\": \"gray\"}], \"nodes\": [{\"uid\": \"a\", \"label\": \"a\"}, {\"uid\": \"b\", \"label\": \"b\"}, {\"uid\": \"c\", \"label\": \"c\"}]}\n",
1154-
"const config = {\"edge_color\": \"gray\", \"node_label\": [\"a\", \"b\", \"c\"], \"directed\": true, \"curved\": true, \"selector\": \"#x7a28caf1b6044e7c89e451a75c0ef22c\"}\n",
1292+
"const data = {\"edges\": [{\"uid\": \"a-b\", \"source\": \"a\", \"target\": \"b\", \"color\": \"gray\", \"weight\": 1}, {\"uid\": \"b-c\", \"source\": \"b\", \"target\": \"c\", \"color\": \"gray\", \"weight\": 1}, {\"uid\": \"c-a\", \"source\": \"c\", \"target\": \"a\", \"color\": \"gray\", \"weight\": 1}], \"nodes\": [{\"uid\": \"a\", \"label\": \"a\"}, {\"uid\": \"b\", \"label\": \"b\"}, {\"uid\": \"c\", \"label\": \"c\"}]}\n",
1293+
"const config = {\"edge_color\": \"gray\", \"node_label\": [\"a\", \"b\", \"c\"], \"directed\": true, \"curved\": true, \"selector\": \"#x34cf302f998c48d2a9a314fd81856883\"}\n",
11551294
"console.log(\"Static Network Template\");\n",
11561295
"/* Resources\n",
11571296
" https://bl.ocks.org/mapio/53fed7d84cd1812d6a6639ed7aa83868\n",
@@ -1732,7 +1871,7 @@
17321871
},
17331872
{
17341873
"cell_type": "code",
1735-
"execution_count": 77,
1874+
"execution_count": 20,
17361875
"metadata": {},
17371876
"outputs": [
17381877
{
@@ -1759,7 +1898,7 @@
17591898
},
17601899
{
17611900
"cell_type": "code",
1762-
"execution_count": 78,
1901+
"execution_count": 21,
17631902
"metadata": {},
17641903
"outputs": [
17651904
{

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