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add gfql colab links to readme #547

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add gfql colab links to readme #547

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0aef036
add gfql colab links to readme
dcolinmorgan Feb 23, 2024
62e5d80
update dcat version
dcolinmorgan Feb 23, 2024
f909e10
update sklearn version
dcolinmorgan Feb 23, 2024
1b426ee
update dcat version
dcolinmorgan Feb 23, 2024
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4 changes: 3 additions & 1 deletion README.md
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Expand Up @@ -147,7 +147,7 @@ It is easy to turn arbitrary data into insightful graphs. PyGraphistry comes wit
g2.plot()
```

* GFQL: Cypher-style graph pattern mining queries on dataframes with optional GPU acceleration ([ipynb demo](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb), [benchmark](demos/gfql/benchmark_hops_cpu_gpu.ipynb))
* GFQL: Cypher-style graph pattern mining queries on dataframes with optional GPU acceleration ([ipynb demo](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb), [benchmark](demos/gfql/benchmark_hops_cpu_gpu.ipynb)). One run this [full benchmark on colab](https://github.com/dcolinmorgan/grph/blob/main/clean_gfql_cpu_gpu_benchmark.ipynb), or run a [smaller demo on colab](https://github.com/dcolinmorgan/grph/blob/main/simple_GFQL.ipynb))

Run Cypher-style graph queries natively on dataframes without going to a database or Java with GFQL:

Expand Down Expand Up @@ -1250,6 +1250,8 @@ PyGraphistry supports GFQL, its PyData-native variant of the popular Cypher grap

See also [graph pattern matching tutorial](demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb) and the CPU/GPU [benchmark](demos/gfql/benchmark_hops_cpu_gpu.ipynb)

Run a [small demo on google colab](https://github.com/dcolinmorgan/grph/blob/main/simple_GFQL.ipynb) T4 GPU for free, or run the [entire benchmark on colab](https://github.com/dcolinmorgan/grph/blob/main/clean_gfql_cpu_gpu_benchmark.ipynb)

Traverse within a graph, or expand one graph against another

Simple node and edge filtering via `filter_edges_by_dict()` and `filter_nodes_by_dict()`:
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -42,7 +42,7 @@ def unique_flatten_dict(d):
}

base_extras_heavy = {
'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn<=1.3.2'],
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maybe we keep >=1.0 here, and make just ci require <=1.3.2? I don't want to mess up people's installs as we're just one lib of many

also, do we know what's going on here?

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ok i moved this guidance to test deps in #548, so you can rebase as that lands

}
# https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
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