pytorch_geometric_temporal
osmnx
Our great sponsors
pytorch_geometric_temporal | osmnx | |
---|---|---|
18 | 14 | |
2,484 | 4,663 | |
- | - | |
1.8 | 9.6 | |
9 days ago | about 17 hours ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pytorch_geometric_temporal
-
Ask HN: ML Papers to Implement
I have done this a few times now. Alone (e.g. https://github.com/paulmorio/geo2dr) and in collaboration with others (e.g. https://github.com/benedekrozemberczki/pytorch_geometric_tem...) primarily as a way to learn about the methods I was interested in from a research perspective whilst improving my skills in software engineering. I am still learning.
Starting out I would recommend implementing fundamental building blocks within whatever 'subculture' of ML you are interested in whether that be DL, kernel methods, probabilistic models, etc.
Let's say you are interested in deep learning methods (as that's something I could at least speak more confidently about). In that case build yourself an MLP layer, then an RNN layer, then a GNN layer, then a CNN layer, and an attention layer along with some full models with those layers on some case studies exhibiting different data modalities (images, graphs, signals). This should give you a feel for the assumptions driving the inductive biases in each layer and what motivates their existence (vs. an MLP). It also gives you the all the building blocks you can then extend to build every other DL layer+model out there. Another reason is that these fundamental building blocks have been implemented many times so you have a reference to look to when you get stuck.
On that note: here are some fun GNN papers to implement in order of increasing difficulty (try building using vanilla PyTorch/Jax instead of PyG).
- GitHub - benedekrozemberczki/pytorch_geometric_temporal: PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
- PyTorch Geometric Temporal 0.37
- PyTorch Geometric Temporal - Spatiotemporal Signal Processing with Neural Machine Learning Models
- [P] PyTorch Geometric Temporal
- Show HN: Deep Learning for Windmill Output Forecasting with PyTorch
-
[R] PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Repo: https://github.com/benedekrozemberczki/pytorch_geometric_temporal
- PyTorch Geometric Temporal 0.27
- Show HN: Machine Learning on Spatiotemporal Data – PyTorch Geometric Temporal
- PyTorch Geometric Temporal
osmnx
- I played with a python module called OSMnx to create the roadmaps of some cities. These include major highways,motorways,roads and streets that carry most of the traffic.
- Planning a straight line across Norwich, the best route I could find just scraped a silver.
-
Anyone here familiar with GeoPandas in Python? Can GeoPandas calculate driving distances and drive time?
You want osmnx
-
[OC] Neighborhood walkability in Delhi
OSM road network data was analyzed using osmnx (https://github.com/gboeing/osmnx). Plotted using QGIS.
-
Rate Limit Causing Pause
I think you’re hitting this issue right now: https://github.com/gboeing/osmnx/issues/832
-
Video: Why Open Data Matters for Cycling: Visualizing a Cycling City
RDS-TMC transmits traffic data over FM radio. https://en.wikipedia.org/wiki/Traffic_message_channel Some is encrypted, some not.
It might be possible to model traffic with OSMnx to assign weights to roads by expected traffic levels https://github.com/gboeing/osmnx
Depending on where you live you might be able to get traffic data and maybe traffic models from public authorities using Freedom of Information requests.
- I just want things to work
-
Number of Public Transport Stations & Doctors in each city in Germany
Osmnx might also be a way to download data for the whole country. It breaks the area up in smaller parts automatically and also downloads the parts from Overpass. https://github.com/gboeing/osmnx
-
Tacoma!
You can make your own with a variety of tools, but I like the results from OSMnx offhand.
-
Need to get a national (US) file of cycleways.
I have no idea if this works for such large regions, but https://geoffboeing.com/publications/osmnx-complex-street-networks/ is an awesome tool to visualize street (and maybe bike) networks.
What are some alternatives?
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
BlenderGIS - Blender addons to make the bridge between Blender and geographic data
torchdrug - A powerful and flexible machine learning platform for drug discovery
pyosmium - Python bindings for libosmium
graphein - Protein Graph Library
overpass-wizard - :dizzy: Human friendly way to generate Overpass API queries
gnn - TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.
atlas - OSM in memory
pytorch_geometric - Graph Neural Network Library for PyTorch
qgis-outdoor-map - QGIS project for an outdoor map based on OpenStreetMap data.
awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations.
osmnx-examples - Gallery of OSMnx tutorials, usage examples, and feature demonstations.