0.1.0 Release

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,35 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: bug
+assignees: markdouthwaite
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**System:**
+ - OS: [e.g. iOS]
+ - Version [e.g. 22]
+
+**Xanthus Version:**
+ - 0.1.0a1
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/workflows/python-publish.yml

@@ -22,6 +22,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install setuptools wheel twine
+        pip install -r requirements.txt
     - name: Build and publish
       env:
         TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}

README.md

@@ -7,12 +7,10 @@
 
 [**Quickstart**](#quickstart)
 | [**Install guide**](#installation)
-| [**Contributing**](docs/contributing.md)
-| [**Known issues**](#known-performance-issues)
 
 ## What is Xanthus?
 
-Xanthus is a package that was started as personal project to translate an academic ML paper into a 'production ready' software package. Itprovides the tools and model architectures necessary to utilise the techniques outlined in [He et al's work](https://dl.acm.org/doi/10.1145/3038912.3052569) on Neural Collaborative Filtering in your own projects. Over time, you'll find work from other research finding it's way in here. The aim of this package is to make state-of-the-art research into neural recommenders accessible and ultimately ready for deployment. You might find that it's not quite there yet on that latter point, but sit tight, it will be.
+Xanthus is a package that was started as personal project to translate an academic ML paper into a 'production ready' software package. It provides the tools and model architectures necessary to utilise the techniques outlined in [He et al's work](https://dl.acm.org/doi/10.1145/3038912.3052569) on Neural Collaborative Filtering in your own projects. Over time, you'll find work from other research finding it's way in here. The aim of this package is to make state-of-the-art research into neural recommenders accessible and ultimately ready for deployment. You might find that it's not quite there yet on that latter point, but sit tight, it will be.
 
 Sound good? Great, here goes.
 
@@ -23,7 +21,7 @@ Want to get straight into the code? Here's an [introductory notebook](docs/getti
 You can also find some examples of how to use Xanthus with sample datasets in this
 repo's docs. These include:
 
-* [A minimal example using the Movielens (100k) dataset.](examples/basics.py)
+* [A minimal example using the Movielens (100k) dataset.](docs/getting-started.ipynb)
 * [An example using the meta-data features of Xanthus on the Movielens (100k) dataset.](examples/metadata.py)
 
 If you're interested in seeing the results of benchmarking of Xanthus' models against 'classic' 
@@ -41,17 +39,7 @@ pip install xanthus
 
 That's it, you're good to go. Well, except for one thing...
 
-The package makes extensive use of [Tensorflow 2.0]() and the [Keras]() API. If
+The package makes extensive use of [Tensorflow 2.0](https://www.tensorflow.org/tutorials/quickstart/beginner) and the [Keras](https://keras.io/) API. If
 you want to make use of the GPU acceleration provided by Tensorflow, you'll need to 
-follow the [Tensorflow team's guide]() for setting that up. If you don't need GPUs
+follow the [Tensorflow team's guide](https://www.tensorflow.org/install/gpu) for setting that up. If you don't need GPUs
 right now, then great, you _really_ are all set.
-
-## Known performance issues
-
-This is a pre-release. There's likely to be a few performance issues. Here's a couple that are being worked on:
-
-* The negative sampling `xanthus.datasets.utils.negative_sampling` approach is expensive and can therefore be slow for large datasets.
-* The inference method (`NeuralRecommenderModel.predict`) is currently pretty slow. An alternative is on its way.
-* The training method (`NeuralRecommenderModel.fit`) is a little crude, again, improvements are inbound.
-
-You will likely find other performance issues on very large datasets. Hold tight for some optimizations, it'll get there.