# Python SpeakEasy2 package
Provides the SpeakEasy2 community detection algorithm to cluster graph's stored as igraph's data type. The algorithm is described in the [Genome Biology article](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-03062-0).
Example:
```python
import igraph as ig
import speakeasy2 as se2
g = ig.Graph.Famous("Zachary")
memb = se2.cluster(g)
```
From the results, a node ordering can be computed to group nodes in a community together. This can be used as an index and works to display the community structure using a heatmap to view the adjacency matrix.
```python
ordering = se2.order_nodes(g, memb)
```
SpeakEasy 2 can work with weighted graphs by either passing weights as a list with length equal to the number of edges or by using the igraph attribute table.
```python
g.es["weight"] = [1 for _ in range(g.ecount())]
memb = se2.cluster(g)
```
By default, SpeakEasy 2 will check if there is an edge attribute associated with the graph named `weight` and use those as weights. If you want to use a different edge attribute, pass the name of the attribute.
```python
memb = se2.cluster(g, weights="tie_strength")
```
Or if a graph has a weight edge attribute but you don't want to use them, explicitly pass `None` to the `weights` keyword argument.
Subclustering can be used to detect hierarchical community structure.
```python
memb = se2.cluster(g, subcluster=2)
```
The number determines how many levels to perform community detection at. The default 1 means only to perform community detection at the top level (i.e. no subclustering). When subclustering, membership will be a list of `igraph.VertexClustering` objects, the top level membership will be the object at index 0.
A few other useful keywords arguments are `max_threads`, `verbose`, and `seed`. The `max_thread` keyword determines how many processors SpeakEasy 2 is allowed to use. By default the value returned by OpenMP is used. To prevent parallel processing, explicitly pass `max_threads = 1` to the method.
The `verbose` option will cause the algorithm to print out some information about the process.
For reproducible results, the `seed` option sets the seed of the random number generator. Note: this is a random number generator managed by the underlying C library and is independent of other random number generators that might have been set in python.
## Installation
speakeasy2 is available from pypi so it can be installed with `pip` or other package managers.
```bash
pip install --user speakeasy2
```
## Building from source
This package can be built using `CMake`. It requires the `flex`, `bison`, and `libxml2` dependencies for building `igraph` and `poetry` for managing python dependencies.
Since the `igraph` package is supplied by the vendored SE2 C library, after cloning the source directory, submodules most be recursively initialized.
```bash
git clone "https://github.com/SpeakEasy-2/python-speakeasy2"
cd python-speakeasy2
git submodule update --init --recursive
```
Then build the package using `cmake`:
```bash
cmake -B build
camke --build build
```
Since the compiled python shared libraries are built in the `build` directory, to use the local python package, link the shared libs to the python package:
```bash
cd speakeasy2
ln -s ../build/_speakeasy2.so
cd ..
```
Use `poetry` to install dependencies in a virtual env:
```bash
poetry install
```
It should now be possible to run scripts through `poetry`:
```bash
poetry run ipython path/to/script.py
```
Or enter a python repository with the private environment activate in the same way.
```bash
poetry run ipython
```
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