[DOC] Add graph alignment tutorial

README.md

@@ -120,7 +120,7 @@ python -m pip install -e .
 
 ## Quickstart
 
-### Input data preprocessing
+### Input data preprocessing (MSA pairing)
 
 First, parse your multiple sequence alignments (MSAs) in FASTA format
 into a list of tuples `(header, sequence)` using
@@ -205,21 +205,28 @@ msa_B_oh = one_hot_encode_msa(msa_B_for_pairing, device=device)
 
 ### Pairing optimization
 
-Finally, we can instantiate an
+Finally, we can instantiate a class from `diffpass.train` to find an
+optimal pairing between `x` and `y`. Here, `x` and `y` are MSAs, so we
+can look for a pairing that optimizes the mutual information between `x`
+and `y`. For this, we use
 [`InformationPairing`](https://Bitbol-Lab.github.io/DiffPaSS/train.html#informationpairing)
-object and optimize the mutual information between the paired MSAs using
-the DiffPaSS bootstrapped optimization algorithm. The results are stored
-in a
-[`DiffPaSSResults`](https://Bitbol-Lab.github.io/DiffPaSS/base.html#diffpassresults)
-container. The lists of (hard) losses and permutations found during the
-optimization can be accessed as attributes of the container.
+and the DiffPaSS bootstrapped optimization algorithm. See the tutorials
+below for other examples, including for graph alignment when `x` and `y`
+are weighted adjacency matrices.
 
 ``` python
 from diffpass.train import InformationPairing
 
 information_pairing = InformationPairing(group_sizes=species_sizes).to(device)
 bootstrap_results = information_pairing.fit_bootstrap(x, y)
+```
 
+The results are stored in a
+[`DiffPaSSResults`](https://Bitbol-Lab.github.io/DiffPaSS/base.html#diffpassresults)
+container. The lists of (hard) losses and permutations found during the
+optimization can be accessed as attributes of the container:
+
+``` python
 print(f"Final hard loss: {bootstrap_results.hard_losses[-1].item()}")
 print(f"Final hard permutations (one permutation per species): {bootstrap_results.hard_perms[-1][-1].item()}")
 ```
@@ -229,11 +236,14 @@ the tutorials.
 
 ## Tutorials
 
-See the
-[`mutual_information_msa_pairing.ipynb`](https://github.com/Bitbol-Lab/DiffPaSS/blob/main/nbs/tutorials/mutual_information_msa_pairing.ipynb)
-notebook for an example of paired MSA optimization in the case of
-well-known prokaryotic datasets, for which ground truth pairings are
-given by genome proximity.
+- [`mutual_information_msa_pairing.ipynb`](https://github.com/Bitbol-Lab/DiffPaSS/blob/main/nbs/tutorials/mutual_information_msa_pairing.ipynb):
+  paired MSA optimization using mutual information in the case of
+  well-known prokaryotic datasets, for which ground truth pairings are
+  given by genome proximity.
+- [`graph_alignment.ipynb`](https://github.com/Bitbol-Lab/DiffPaSS/blob/main/nbs/tutorials/graph_alignment.ipynb):
+  general graph alignment using
+  [`diffpass.train.GraphAlignment`](https://Bitbol-Lab.github.io/DiffPaSS/train.html#graphalignment),
+  with an example of aligning two weighted adjacency matrices.
 
 ## Documentation
 

nbs/index.ipynb

@@ -91,7 +91,7 @@
    "source": [
     "## Quickstart\n",
     "\n",
-    "### Input data preprocessing\n",
+    "### Input data preprocessing (MSA pairing)\n",
     "\n",
     "First, parse your multiple sequence alignments (MSAs) in FASTA format into a list of tuples ``(header, sequence)`` using `read_msa`.\n",
     "\n",
@@ -164,14 +164,17 @@
     "\n",
     "### Pairing optimization\n",
     "\n",
-    "Finally, we can instantiate an `InformationPairing` object and optimize the mutual information between the paired MSAs using the DiffPaSS bootstrapped optimization algorithm. The results are stored in a `DiffPaSSResults` container. The lists of (hard) losses and permutations found during the optimization can be accessed as attributes of the container.\n",
+    "Finally, we can instantiate a class from `diffpass.train` to find an optimal pairing between `x` and `y`. Here, `x` and `y` are MSAs, so we can look for a pairing that optimizes the mutual information between `x` and `y`. For this, we use `InformationPairing` and the DiffPaSS bootstrapped optimization algorithm. See the tutorials below for other examples, including for graph alignment when `x` and `y` are weighted adjacency matrices.\n",
     "\n",
     "```python\n",
     "from diffpass.train import InformationPairing\n",
     "\n",
     "information_pairing = InformationPairing(group_sizes=species_sizes).to(device)\n",
     "bootstrap_results = information_pairing.fit_bootstrap(x, y)\n",
+    "```\n",
     "\n",
+    "The results are stored in a `DiffPaSSResults` container. The lists of (hard) losses and permutations found during the optimization can be accessed as attributes of the container:\n",
+    "```python\n",
     "print(f\"Final hard loss: {bootstrap_results.hard_losses[-1].item()}\")\n",
     "print(f\"Final hard permutations (one permutation per species): {bootstrap_results.hard_perms[-1][-1].item()}\")\n",
     "```\n",
@@ -185,7 +188,8 @@
    "source": [
     "## Tutorials\n",
     "\n",
-    "See the [`mutual_information_msa_pairing.ipynb`](https://github.com/Bitbol-Lab/DiffPaSS/blob/main/nbs/tutorials/mutual_information_msa_pairing.ipynb) notebook for an example of paired MSA optimization in the case of well-known prokaryotic datasets, for which ground truth pairings are given by genome proximity."
+    "- [`mutual_information_msa_pairing.ipynb`](https://github.com/Bitbol-Lab/DiffPaSS/blob/main/nbs/tutorials/mutual_information_msa_pairing.ipynb): paired MSA optimization using mutual information in the case of well-known prokaryotic datasets, for which ground truth pairings are given by genome proximity.\n",
+    "- [`graph_alignment.ipynb`](https://github.com/Bitbol-Lab/DiffPaSS/blob/main/nbs/tutorials/graph_alignment.ipynb): general graph alignment using `diffpass.train.GraphAlignment`, with an example of aligning two weighted adjacency matrices."
    ]
   },
   {

nbs/sidebar.yml

@@ -14,4 +14,5 @@ website:
       - train.ipynb
       - section: tutorials
         contents:
+          - tutorials/graph_alignment.ipynb
           - tutorials/mutual_information_msa_pairing.ipynb

nbs/tutorials/mutual_information_msa_pairing.ipynb

@@ -180,9 +180,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "## 3. Optimize pairings by maximising mutual information between chains: ``InformationAlignment``"
-   ]
+   "source": "## 3. Optimize pairings by maximising mutual information between chains: `InformationAlignment`"
   },
   {
    "cell_type": "code",