diff --git a/.github/workflows/run-tox.yml b/.github/workflows/run-tox.yml index 2827728..86adbca 100644 --- a/.github/workflows/run-tox.yml +++ b/.github/workflows/run-tox.yml @@ -30,11 +30,3 @@ jobs: - name: Run tox run: | tox - - name: Upload to codecov - if: ${{matrix.python-version == '3.10'}} - uses: codecov/codecov-action@v1 - with: - fail_ci_if_error: false - files: ./coverage.xml - flags: pytest - name: "PyGenStability-py310" diff --git a/README.md b/README.md index 892b97e..53cc885 100755 --- a/README.md +++ b/README.md @@ -18,53 +18,53 @@ We further provide specific analysis tools to process and analyse the results fr ## Documentation -A documentation of all features of the *PyGenStability* is available here: https://barahona-research-group.github.io/PyGenStability/, or in pdf [here](pygenstability_doc.pdf). +A documentation of all features of the *PyGenStability* package is available here: https://barahona-research-group.github.io/PyGenStability/, or in pdf [here](pygenstability_doc.pdf). ## Installation You can install the package using [pypi](https://pypi.org/project/PyGenStability/): -``` +```bash pip install pygenstability ``` Using a fresh python3 virtual environment, e.g. conda, may be recommended to avoid conflicts with other python packages. By default, the package uses the Louvain algorithm [4] for optimizing generalized Markov Stability. To use the Leiden algorithm [5], install this package with: -``` +```bash pip install pygenstability[leiden] ``` To plot network partitions using `networkx`, install this package with: -``` +```bash pip install pygenstability[networkx] ``` To use `plotly` for interactive plots in the browser, install this package with: -``` +```bash pip install pygenstability[plotly] ``` To install all dependencies, run: -``` +```bash pip install pygenstability[all] ``` ### Installation from GitHub You can also install the source code of this package from GitHub directly by first cloning this repo with: -``` +```bash git clone --recurse-submodules https://github.com/ImperialCollegeLondon/PyGenStability.git ``` (if the `--recurse-submodules` has not been used, just do `git submodule update --init --recursive` to fetch the submodule with M. Schaub's code). The wrapper for the submodule uses Pybind11 https://github.com/pybind/pybind11 and, to install the package, simply run (within the `PyGenStability` directory): -``` +```bash pip install . ``` using a fresh python3 virtual environment to avoid conflicts. Similar to above, you can also specify additional dependencies, e.g. to install the package with `networkx` run: -``` +```bash pip install .[networkx] ``` @@ -72,14 +72,14 @@ pip install .[networkx] The code is simple to run with the default settings. We can input our graph (of type scipy.csgraph), run a scan in scales with a chosen Markov Stability constructor and plot the results in a summary figure presenting different partition quality measures across scales (values of MS cost function, number of communities, etc.) with an indication of optimal scales. -``` +```python import pygenstability as pgs results = pgs.run(graph) pgs.plot_scan(results) ``` Although it is enforced in the code, it is advised to set environment variables -``` +```bash export OPENBLAS_NUM_THREADS=1 export OMP_NUM_THREADS=1 export NUMEXPR_MAX_THREADS=1 @@ -92,7 +92,7 @@ There are a variety of further choices that users can make that will impact the While Louvain is defined as the default due to its familiarity within the research community, Leiden is known to produce better partitions and can be used by specifying the run function. -``` +```python results = pgs.run(graph, method="leiden") ``` @@ -102,7 +102,7 @@ There are also additional post-processing and analysis functions, including: Optimal scale selection [6] is performed by default with the run function but can be repeated with different parameters if needed, see `pygenstability/optimal_scales.py`. To reduce noise, e.g., one can increase the parameter values for `block_size` and `window_size`. The optimal network partitions can then be plotted given a NetworkX nx_graph. -``` +```python results = pgs.identify_optimal_scales(results, block_size=10, window_size=5) pgs.plot_optimal_partitions(nx_graph, results) ``` @@ -126,6 +126,33 @@ For those of you that wish to implement their own constructor, you will need to - take a scipy.csgraph `graph` and a float `time` as argument - return a `quality_matrix` (sparse scipy matrix) and a `null_model` (multiples of two, in a numpy array) +## Graph-based data clustering + +PyGenStability can also be used to perform multiscale graph-based data clustering on data that comes in the form of a sample-by-feature matrix. This approach was shown to achieve better performance than other popular clustering methods without the need of setting the number of clusters externally [9]. + +We provide an easy-to-use interface in our `pygenstability.data_clustering.py` module. Given a sample-by-feature matrix `X`, one can apply graph-based data clustering as follows: + +```python +clustering = pgs.DataClustering( + graph_method="cknn", + k=5, + constructor="continuous_normalized") + +# apply graph-based data clustering to X +results = clustering.fit(X) + +# identify optimal scales and plot scan +clustering.scale_selection(kernel_size=0.2) +clustering.plot_scan() +``` + +We currently support $k$-Nearest Neighbor (kNN) and Continuous $k$-Nearest Neighbor (CkNN) [10] graph constructions (specified by `graph_method`) and `k` refers to the number of neighbours considered in the construction. See documentation for a list of all parameters. All functionalities of PyGenStability including plotting and scale selection are also available for data clustering. For example, given two-dimensional coordinates of the data points one can plot the optimal partitions directly: + +```python +# plot robust partitions +clustering.plot_robust_partitions(x_coord=x_coord,y_coord=y_coord) +``` + ## Contributors - Alexis Arnaudon, GitHub: `arnaudon ` @@ -170,11 +197,11 @@ The original paper for Markov Stability can also be cited as: In the `example` folder, a demo script with a stochastic block model can be tried with -``` +```bash python simple_example.py ``` or using the click app: -``` +```bash ./run_simple_example.sh ``` @@ -188,6 +215,7 @@ Other examples can be found as jupyter-notebooks in the `examples/` directory, i - Example 4: Custom constructors - Example 5: Hypergraphs - Example 6: Signed networks +- Example 7: Graph-based data clustering Finally, we provide applications to real-world networks in the `examples/real_examples/` directory, including: @@ -224,6 +252,10 @@ If you are interested in trying our other packages, see the below list: [8] S. Gomez, P. Jensen, and A. Arenas, 'Analysis of community structure in networks of correlated data'. *Physical Review E*, vol. 80, no. 1, p. 016114, Jul. 2009, doi: 10.1103/PhysRevE.80.016114. +[9] Z. Liu and M. Barahona, 'Graph-based data clustering via multiscale community detection', *Applied Network Science*, vol. 5, no. 1, p. 3, Dec. 2020, doi: 10.1007/s41109-019-0248-7. + +[10] T. Berry and T. Sauer, 'Consistent manifold representation for topological data analysis', *Foundations of Data Science*, vol. 1, no. 1, p. 1-38, Feb. 2019, doi: 10.3934/fods.2019001. + ## Licence This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. @@ -231,3 +263,4 @@ This program is free software: you can redistribute it and/or modify it under th This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/. + diff --git a/docs/index_readme.md b/docs/index_readme.md index 1570b71..b327c1c 100644 --- a/docs/index_readme.md +++ b/docs/index_readme.md @@ -9,47 +9,47 @@ We further provide specific analysis tools to process and analyse the results fr You can install the package using [pypi](https://pypi.org/project/PyGenStability/): -``` +```bash pip install pygenstability ``` Using a fresh python3 virtual environment, e.g. conda, may be recommended to avoid conflicts with other python packages. By default, the package uses the Louvain algorithm [4] for optimizing generalized Markov Stability. To use the Leiden algorithm [5], install this package with: -``` +```bash pip install pygenstability[leiden] ``` To plot network partitions using `networkx`, install this package with: -``` +```bash pip install pygenstability[networkx] ``` To use `plotly` for interactive plots in the browser, install this package with: -``` +```bash pip install pygenstability[plotly] ``` To install all dependencies, run: -``` +```bash pip install pygenstability[all] ``` ### Installation from GitHub You can also install the source code of this package from GitHub directly by first cloning this repo with: -``` +```bash git clone --recurse-submodules https://github.com/ImperialCollegeLondon/PyGenStability.git ``` (if the `--recurse-submodules` has not been used, just do `git submodule update --init --recursive` to fetch the submodule with M. Schaub's code). The wrapper for the submodule uses Pybind11 https://github.com/pybind/pybind11 and, to install the package, simply run (within the `PyGenStability` directory): -``` +```bash pip install . ``` using a fresh python3 virtual environment to avoid conflicts. Similar to above, you can also specify additional dependencies, e.g. to install the package with `networkx` run: -``` +```bash pip install .[networkx] ``` @@ -57,14 +57,14 @@ pip install .[networkx] The code is simple to run with the default settings. We can input our graph (of type scipy.csgraph), run a scan in scales with a chosen Markov Stability constructor and plot the results in a summary figure presenting different partition quality measures across scales (values of MS cost function, number of communities, etc.) with an indication of optimal scales. -``` +```python import pygenstability as pgs results = pgs.run(graph) pgs.plot_scan(results) ``` Although it is enforced in the code, it is advised to set environment variables -``` +```bash export OPENBLAS_NUM_THREADS=1 export OMP_NUM_THREADS=1 export NUMEXPR_MAX_THREADS=1 @@ -77,7 +77,7 @@ There are a variety of further choices that users can make that will impact the While Louvain is defined as the default due to its familiarity within the research community, Leiden is known to produce better partitions and can be used by specifying the run function. -``` +```python results = pgs.run(graph, method="leiden") ``` @@ -87,7 +87,7 @@ There are also additional post-processing and analysis functions, including: Optimal scale selection [6] is performed by default with the run function but can be repeated with different parameters if needed, see `pygenstability/optimal_scales.py`. To reduce noise, e.g., one can increase the parameter values for `block_size` and `window_size`. The optimal network partitions can then be plotted given a NetworkX nx_graph. -``` +```python results = pgs.identify_optimal_scales(results, block_size=10, window_size=5) pgs.plot_optimal_partitions(nx_graph, results) ``` @@ -111,6 +111,33 @@ For those of you that wish to implement their own constructor, you will need to - take a scipy.csgraph `graph` and a float `time` as argument - return a `quality_matrix` (sparse scipy matrix) and a `null_model` (multiples of two, in a numpy array) +## Graph-based data clustering + +PyGenStability can also be used to perform multiscale graph-based data clustering on data that comes in the form of a sample-by-feature matrix. This approach was shown to achieve better performance than other popular clustering methods without the need of setting the number of clusters externally [9]. + +We provide an easy-to-use interface in our `pygenstability.data_clustering.py` module. Given a sample-by-feature matrix `X`, one can apply graph-based data clustering as follows: + +```python +clustering = pgs.DataClustering( + graph_method="cknn", + k=5, + constructor="continuous_normalized") + +# apply graph-based data clustering to X +results = clustering.fit(X) + +# identify optimal scales and plot scan +clustering.scale_selection(kernel_size=0.2) +clustering.plot_scan() +``` + +We currently support $k$-Nearest Neighbor (kNN) and Continuous $k$-Nearest Neighbor (CkNN) [10] graph constructions (specified by `graph_method`) and `k` refers to the number of neighbours considered in the construction. See documentation for a list of all parameters. All functionalities of PyGenStability including plotting and scale selection are also available for data clustering. For example, given two-dimensional coordinates of the data points one can plot the optimal partitions directly: + +```python +# plot robust partitions +clustering.plot_robust_partitions(x_coord=x_coord,y_coord=y_coord) +``` + ## Contributors - Alexis Arnaudon, GitHub: `arnaudon ` @@ -155,11 +182,11 @@ The original paper for Markov Stability can also be cited as: In the `example` folder, a demo script with a stochastic block model can be tried with -``` +```bash python simple_example.py ``` or using the click app: -``` +```bash ./run_simple_example.sh ``` @@ -173,6 +200,7 @@ Other examples can be found as jupyter-notebooks in the `examples/` directory, i - Example 4: Custom constructors - Example 5: Hypergraphs - Example 6: Signed networks +- Example 7: Graph-based data clustering Finally, we provide applications to real-world networks in the `examples/real_examples/` directory, including: @@ -193,21 +221,25 @@ If you are interested in trying our other packages, see the below list: ## References -[1] J.-C. Delvenne, S. N. Yaliraki, and M. Barahona, ‘Stability of graph communities across time scales’, *Proceedings of the National Academy of Sciences*, vol. 107, no. 29, pp. 12755–12760, Jul. 2010, doi: 10.1073/pnas.0903215107. +[1] J.-C. Delvenne, S. N. Yaliraki, and M. Barahona, 'Stability of graph communities across time scales', *Proceedings of the National Academy of Sciences*, vol. 107, no. 29, pp. 12755–12760, Jul. 2010, doi: 10.1073/pnas.0903215107. + +[2] R. Lambiotte, J.-C. Delvenne, and M. Barahona, 'Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks', *IEEE Trans. Netw. Sci. Eng.*, vol. 1, no. 2, pp. 76–90, Jul. 2014, doi: 10.1109/TNSE.2015.2391998. + +[3] M. T. Schaub, J.-C. Delvenne, R. Lambiotte, and M. Barahona, 'Multiscale dynamical embeddings of complex networks', *Phys. Rev. E*, vol. 99, no. 6, Jun. 2019, doi: 10.1103/PhysRevE.99.062308. -[2] R. Lambiotte, J.-C. Delvenne, and M. Barahona, ‘Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks’, *IEEE Trans. Netw. Sci. Eng.*, vol. 1, no. 2, pp. 76–90, Jul. 2014, doi: 10.1109/TNSE.2015.2391998. +[4] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, 'Fast unfolding of communities in large networks', *J. Stat. Mech.*, vol. 2008, no. 10, Oct. 2008, doi: 10.1088/1742-5468/2008/10/p10008. -[3] M. T. Schaub, J.-C. Delvenne, R. Lambiotte, and M. Barahona, ‘Multiscale dynamical embeddings of complex networks’, *Phys. Rev. E*, vol. 99, no. 6, Jun. 2019, doi: 10.1103/PhysRevE.99.062308. +[5] V. A. Traag, L. Waltman, and N. J. van Eck, 'From Louvain to Leiden: guaranteeing well-connected communities', *Sci Rep*, vol. 9, no. 1, p. 5233, Mar. 2019, doi: 10.1038/s41598-019-41695-z. -[4] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, ‘Fast unfolding of communities in large networks’, *J. Stat. Mech.*, vol. 2008, no. 10, Oct. 2008, doi: 10.1088/1742-5468/2008/10/p10008. +[6] D. J. Schindler, J. Clarke, and M. Barahona, 'Multiscale Mobility Patterns and the Restriction of Human Movement', *Royal Society Open Science*, vol. 10, no. 10, p. 230405, Oct. 2023, doi: 10.1098/rsos.230405. -[5] V. A. Traag, L. Waltman, and N. J. van Eck, ‘From Louvain to Leiden: guaranteeing well-connected communities’, *Sci Rep*, vol. 9, no. 1, p. 5233, Mar. 2019, doi: 10.1038/s41598-019-41695-z. +[7] A. Arnaudon, D. J. Schindler, R. L. Peach, A. Gosztolai, M. Hodges, M. T. Schaub, and M. Barahona, 'PyGenStability: Multiscale community detection with generalized Markov Stability', *arXiv pre-print*, Mar. 2023, doi: 10.48550/arXiv.2303.05385. -[6] D. J. Schindler, J. Clarke, and M. Barahona, ‘Multiscale Mobility Patterns and the Restriction of Human Movement’, *Royal Society Open Science*, vol. 10, no. 10, p. 230405, Oct. 2023, doi: 10.1098/rsos.230405. +[8] S. Gomez, P. Jensen, and A. Arenas, 'Analysis of community structure in networks of correlated data'. *Physical Review E*, vol. 80, no. 1, p. 016114, Jul. 2009, doi: 10.1103/PhysRevE.80.016114. -[7] A. Arnaudon, D. J. Schindler, R. L. Peach, A. Gosztolai, M. Hodges, M. T. Schaub, and M. Barahona, ‘PyGenStability: Multiscale community detection with generalized Markov Stability‘, *arXiv pre-print*, Mar. 2023, doi: 10.48550/arXiv.2303.05385. +[9] Z. Liu and M. Barahona, 'Graph-based data clustering via multiscale community detection', *Applied Network Science*, vol. 5, no. 1, p. 3, Dec. 2020, doi: 10.1007/s41109-019-0248-7. -[8] S. Gómez, P. Jensen, and A. Arenas, ‘Analysis of community structure in networks of correlated data‘. *Physical Review E*, vol. 80, no. 1, p. 016114, Jul. 2009, doi: 10.1103/PhysRevE.80.016114. +[10] T. Berry and T. Sauer, 'Consistent manifold representation for topological data analysis', *Foundations of Data Science*, vol. 1, no. 1, p. 1-38, Feb. 2019, doi: 10.3934/fods.2019001. ## Licence diff --git a/docs/source/dataclustering.rst b/docs/source/dataclustering.rst new file mode 100644 index 0000000..de88c6e --- /dev/null +++ b/docs/source/dataclustering.rst @@ -0,0 +1,4 @@ +DataClustering module +===================== +.. automodule:: pygenstability.DataClustering + :members: diff --git a/docs/source/index.rst b/docs/source/index.rst index e71b6b4..52fde51 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -13,6 +13,7 @@ Documentation of the API of *PyGenStability*. constructors app plotting + dataclustering optimal_scales io examples/example diff --git a/examples/Example_7_data_clustering.ipynb b/examples/Example_7_data_clustering.ipynb new file mode 100644 index 0000000..420f978 --- /dev/null +++ b/examples/Example_7_data_clustering.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 7: Multiscale data clustering with PyGenStability" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pygenstability as pgs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We generate two circles with multiscale structure." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.utils import shuffle\n", + "\n", + "def generate_circles(n_samples_out=300,n_groups_out=3,gap_out=np.pi/15,n_samples_in=300,n_groups_in=3,gap_in=np.pi/8,offset_in=np.pi/7,factor=0.5, noise=0.03,with_shuffle=True,seed=0):\n", + " \"\"\"Generate two circles with multiscale structure.\n", + " \n", + " Code adapted from: https://github.com/scikit-learn/scikit-learn/blob/f07e0138bfee41cd2c0a5d0251dc3fe03e6e1084/sklearn/datasets/_samples_generator.py#L738\n", + "\n", + " \"\"\"\n", + "\n", + " rng = np.random.default_rng(seed)\n", + "\n", + " # generate outer circle that is split into groups\n", + " linspace_out = []\n", + " for i in range(n_groups_out):\n", + " linspace_out += list(np.linspace(i* 2/n_groups_out * np.pi + gap_out/2, (i+1) * 2/n_groups_out * np.pi - gap_out/2, int(n_samples_out/n_groups_out), endpoint=False))\n", + " outer_circ_x = np.cos(linspace_out)\n", + " outer_circ_y = np.sin(linspace_out)\n", + "\n", + " # generate inner circle that is split into groups\n", + " linspace_in = []\n", + " for j in range(n_groups_out):\n", + " linspace_in += list(np.linspace(j* 2/n_groups_in * np.pi + gap_in/2, (j+1) * 2/n_groups_in * np.pi - gap_in/2, int(n_samples_in/n_groups_in), endpoint=False)+offset_in)\n", + " inner_circ_x = np.cos(linspace_in) * factor\n", + " inner_circ_y = np.sin(linspace_in) * factor\n", + "\n", + " # combine circles\n", + " X = np.vstack(\n", + " [np.append(outer_circ_x, inner_circ_x), np.append(outer_circ_y, inner_circ_y)]\n", + " ).T\n", + "\n", + " # create group labels\n", + " y = np.zeros(n_samples_out+n_samples_in)\n", + " for i in range(n_groups_in):\n", + " y[int(i*n_samples_in/n_groups_in):int((i+1)*n_samples_in/n_groups_in)] = i\n", + " for i in range(n_groups_out):\n", + " y[int(n_samples_in+i*n_samples_out/n_groups_out):int(n_samples_in+(i+1)*n_samples_out/n_groups_out)] = i+n_groups_in\n", + "\n", + " # add noise to data\n", + " if noise is not None:\n", + " X += rng.normal(scale=noise, size=X.shape)\n", + "\n", + " # shuffle data\n", + " if with_shuffle:\n", + " X, y = shuffle(X,y,random_state=seed)\n", + " \n", + " return X, y\n", + "\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = generate_circles()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# define coordinates for plotting\n", + "x_coord = X[:,0]\n", + "y_coord = X[:,1]\n", + "\n", + "# plot\n", + "fig, ax = plt.subplots(1,figsize=(10,10))\n", + "scatter = ax.scatter(x_coord,y_coord,s=20, c=y, zorder=10, cmap=\"tab20\")\n", + "ax.set(xlabel=\"X1\", ylabel=\"X2\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can run multiscale data clustering with PyGenStability, where first a geometric graph is constructed from the data that gets than analysed with Markov Stability." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50/50 [00:00<00:00, 952.91it/s]\n", + "100%|██████████| 50/50 [00:13<00:00, 3.68it/s]\n", + "100%|██████████| 50/50 [00:00<00:00, 277.15it/s]\n" + ] + } + ], + "source": [ + "# apply multiscale data clustering to synthetic data\n", + "clustering = pgs.DataClustering(\n", + " metric=\"euclidean\",\n", + " graph_method=\"cknn-mst\",\n", + " k=5,\n", + " delta=1.0,\n", + " constructor=\"linearized\",\n", + " min_scale=-3.0,\n", + " max_scale=0.0,\n", + " n_scale=50,\n", + " with_spectral_gap=True).fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# identify optimal scales and plot scan\n", + "labels = clustering.scale_selection(kernel_size=0.2)\n", + "clustering.plot_scan()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plot the (non-trivial) robust partitions found by PyGenStability and recover the ground truth partitions at the two different scales." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot robust partitions\n", + "clustering.plot_robust_partitions(x_coord=x_coord,y_coord=y_coord)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pygenstability", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/pygenstability/__init__.py b/src/pygenstability/__init__.py index 2e6ee8c..14d20ed 100644 --- a/src/pygenstability/__init__.py +++ b/src/pygenstability/__init__.py @@ -1,6 +1,7 @@ """Import main functions.""" from pygenstability.constructors import * +from pygenstability.data_clustering import DataClustering from pygenstability.io import load_results from pygenstability.io import save_results from pygenstability.optimal_scales import identify_optimal_scales diff --git a/src/pygenstability/data_clustering.py b/src/pygenstability/data_clustering.py new file mode 100644 index 0000000..ce2e053 --- /dev/null +++ b/src/pygenstability/data_clustering.py @@ -0,0 +1,373 @@ +"""Construct geometric graphs from data for multiscale clustering.""" + +import matplotlib.pyplot as plt +import numpy as np +from scipy.sparse import csr_matrix +from scipy.sparse.csgraph import minimum_spanning_tree +from scipy.spatial.distance import pdist +from scipy.spatial.distance import squareform +from sklearn.neighbors import kneighbors_graph + +from pygenstability.contrib.sankey import plot_sankey as pgs_plot_sankey +from pygenstability.optimal_scales import identify_optimal_scales +from pygenstability.plotting import plot_scan as pgs_plot_scan +from pygenstability.pygenstability import run as pgs_run + + +def _compute_CkNN(D, k=5, delta=1): + """Computes CkNN graph.""" + # obtain rescaled distance matrix, see CkNN paper + darray_n_nbrs = np.partition(D, k)[:, [k]] + ratio_matrix = D / np.sqrt(darray_n_nbrs.dot(darray_n_nbrs.T)) + # threshold rescaled distance matrix by delta + A = D * (ratio_matrix < delta) + return A + + +class _GraphConstruction: + """Graph construction.""" + + def __init__( + self, + metric="euclidean", + method="cknn-mst", + k=5, + delta=1.0, + distance_threshold=np.inf, + ): + # parameters + self.metric = metric + self.method = method + self.k = k + self.delta = delta + self.distance_threshold = distance_threshold + + # attributes + self.adjacency_ = None + + def get_graph(self, X): + """Construct graph from samples-by-features matrix.""" + # if precomputed take X as adjacency matrix + if self.method == "precomputed": + assert X.shape[0] == X.shape[1], "Precomputed matrix should be a square matrix." + self.adjacency_ = X + return self.adjacency_ + + # compute normalised distance matrix + D = squareform(pdist(X, metric=self.metric)) + D_norm = D / np.amax(D) + + # compute normalised similarity matrix + S = 1 - D_norm + + # sparsify distance matrix with CkNN or kNN method + if self.method == "cknn-mst": + sparse = _compute_CkNN(D_norm, self.k, self.delta) + + elif self.method == "knn-mst": + sparse = kneighbors_graph(D_norm, n_neighbors=self.k, metric="precomputed").toarray() + + # undirected distance backbone is given by sparse graph and MST + mst = minimum_spanning_tree(D_norm) + backbone = np.array((mst + mst.T + sparse + sparse.T) > 0, dtype=int) + + # apply distance threshold to backbone + backbone *= np.array(D <= self.distance_threshold, dtype=int) + + # adjacency matrix has weights of similarity matrix + self.adjacency_ = S * backbone + + return self.adjacency_ + + +class DataClustering(_GraphConstruction): + """Class for multiscale graph-based data clustering. + + This class provides an interface for multiscale graph-based data clustering [1]_ + with PyGenStability. + + Parameters + ---------- + metric : str or function, default='euclidean' + The distance metric to use. The distance function can be ‘braycurtis’, ‘canberra’, + ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, + ‘jaccard’, ‘jensenshannon’, ‘kulczynski1’, ‘mahalanobis’, ‘matching’, ‘minkowski’, + ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, + ‘sqeuclidean’, ‘yule’. + + graph_method : {'knn-mst', 'cknn-mst', 'precomputed'}, default='cknn-mst' + Method to construct graph from sample-by-feature matrix: + + - 'knn-mst' will use k-Nearest Neighbor graph combined with Miniumus Spanning Tree. + - 'cknn-mst' will use Continunous k-Nearest Neighbor graph [2]_ combined with + Miniumus Spanning Tree. + - 'precomputed' assumes that data is already provided as adjacency matrix of a + sparse graph. + + k : int, default=5 + Number of neighbors considered in graph construction. This parameter is expected + to be positive. + + delta : float, default=1.0 + Density parameter for Continunous k-Nearest Neighbor graph. This parameter is expected + to be positive. + + distance_threshold : float, optional + Optional thresholding of distance matrix. + + pgs_kwargs : dict, optional + Parameters for PyGenStability, see documentation. Some possible arguments: + + - constructor (str/function): name of the generalized Markov Stability constructor, + or custom constructor function. It must have two arguments, graph and scale. + - min_scale (float): minimum Markov scale + - max_scale (float): maximum Markov scale + - n_scale (int): number of scale steps + - with_spectral_gap (bool): normalise scale by spectral gap + + Attributes + ---------- + adjacency_ : sparse matrix of shape (n_samples, n_samples) + Sparse adjacency matrix of constructed graph. + + results_ : dict + PyGenStability results dictionary, see documentation for all arguments. + + labels_ : list of ndarray + List of robust partitions identified with optimal scale selection. + + References + ---------- + .. [1] Z. Liu and M. Barahona, 'Graph-based data clustering via multiscale + community detection', *Applied Network Science*, vol. 5, no. 1, p. 3, + Dec. 2020, doi: 10.1007/s41109-019-0248-7. + .. [2] T. Berry and T. Sauer, 'Consistent manifold representation for + topological data analysis', *Foundations of Data Science*, vol. 1, no. 1, + p. 1-38, Feb. 2019, doi: 10.3934/fods.2019001. + """ + + def __init__( + self, + metric="euclidean", + graph_method="cknn-mst", + k=5, + delta=1.0, + distance_threshold=np.inf, + **pgs_kwargs, + ): + + # initialise parameters for graph construction + super().__init__( + metric=metric, + method=graph_method, + k=k, + delta=delta, + distance_threshold=distance_threshold, + ) + + # store PyGenStability kwargs + self.pgs_kwargs = pgs_kwargs + + # attributes + self.results_ = {} + + @property + def labels_(self): + """Return labels for robust paritions.""" + labels = [] + + assert ( + "selected_partitions" in self.results_.keys() + ), "Run PyGenStability with optimal scale selection first." + + # store labels of robust partitions + for i in self.results_["selected_partitions"]: + + # only return non-trivial robust partitions + robust_partition = self.results_["community_id"][i] + if not np.allclose(robust_partition, np.zeros(self.adjacency_.shape[0])): + labels.append(robust_partition) + + return labels + + def fit(self, X): + """Fit multiscale graph-based data clustering with PyGenStability from data. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples,n_features) or \ + (n_samples,n_samples) if graph_method='precomputed' + Data to fit + + Returns + ------- + self : DataClustering + The fitted multiscale graph-based data clustering. + """ + # construct graph + self.adjacency_ = csr_matrix(self.get_graph(X)) + + # run PyGenStability + self.results_ = pgs_run(self.adjacency_, **self.pgs_kwargs) + + return self + + def scale_selection(self, kernel_size=0.1, window_size=0.1, max_nvi=1, basin_radius=0.01): + """Identify optimal scales [3]. + + Parameters + ---------- + kernel_size : int or float, default=0.1 + Size of kernel for average-pooling of the NVI(t,t') matrix. If float smaller + than one it's the relative number of scales. + + window_size : int or float, default=0.1 + Size of window for moving mean, to smooth the pooled diagonal. If float smaller + than one it's the relative number of scales. + + max_nvi: float, default=1 + Threshold for local minima of the pooled diagonal. + + basin_radius: int or float, default=0.01 + Radius of basin around local minima of the pooled diagonal. If float smaller + than one it's the relative number of scales. + + Returns + ------- + labels_ : list of ndarray + List of robust partitions identified with optimal scale selection. + + References + ---------- + .. [3] D. J. Schindler, J. Clarke, and M. Barahona, 'Multiscale Mobility Patterns and + the Restriction of Human Movement', *arXiv:2201.06323*, 2023 + """ + # transform relative values to absolute values + if kernel_size < 1: + kernel_size = int(kernel_size * self.results_["run_params"]["n_scale"]) + if window_size < 1: + window_size = int(window_size * self.results_["run_params"]["n_scale"]) + if basin_radius < 1: + basin_radius = max(1, int(basin_radius * self.results_["run_params"]["n_scale"])) + + # apply scale selection algorithm + self.results_ = identify_optimal_scales( + self.results_, + kernel_size=kernel_size, + window_size=window_size, + max_nvi=max_nvi, + basin_radius=basin_radius, + ) + + return self.labels_ + + def plot_scan(self, *args, **kwargs): + """Plot summary figure for PyGenStability scan.""" + if self.results_ is None: + return + + pgs_plot_scan(self.results_, *args, **kwargs) + + def plot_robust_partitions( + self, x_coord, y_coord, edge_width=1.0, node_size=20.0, cmap="tab20", show=True + ): + """Plot robust partitions with graph layout. + + Parameters + ---------- + x_coord : ndarray of shape (n_samples,) + X-coordinates provided for samples. + + y_coord : ndarray of shape (n_samples,) + Y-coordinates provided for samples. + + edge_width : float, default=1.0 + Edge width of graph. This parameter is expected to be positive. + + node_size : float, default=20.0 + Node size in graph. This parameter is expected to be positive. + + cmap : str, default='tab20' + Color map for cluster colors. + + show : book, default=True + Show the figures. + + Returns + ------- + figs : All matplotlib figures + + """ + figs = [] + for m, partition in enumerate(self.labels_): + fig, ax = plt.subplots(1, figsize=(10, 10)) + figs.append(fig) + + # plot edges + for i in range(self.adjacency_.shape[0]): + for j in range(i + 1, self.adjacency_.shape[0]): + if self.adjacency_[i, j] > 0: + ax.plot( + [x_coord[i], x_coord[j]], + [y_coord[i], y_coord[j]], + color="black", + alpha=0.5, + linewidth=edge_width, + ) + + # plot nodes + ax.scatter(x_coord, y_coord, s=node_size, c=partition, zorder=10, cmap=cmap) + + # set labels + ax.set( + xlabel="x", + ylabel="y", + title=f"Robust Partion {m+1} (with {len(np.unique(partition))} clusters)", + ) + if show: + plt.show() + + def plot_sankey( + self, + optimal_scales=True, + live=False, + filename="communities_sankey.html", + scale_index=None, + ): + """Plot Sankey diagram. + + Parameters + ---------- + optimal_scales : bool, default=True + Plot Sankey diagram of robust partitions only or not. + + live : bool, default=False + If True, interactive figure will appear in browser. + + filename : str, default="communities_sankey.html" + Filename to save the plot. + + scale_index : bool + Plot Sankey diagram for provided scale indices. + + Returns + ------- + fig : plotly figure + Sankey diagram figure. + """ + # plot non-trivial optimal scales only + if optimal_scales: + n_partitions = len(self.labels_) + # collect indices of non-trivial partitions + scale_index = self.results_["selected_partitions"][:n_partitions] + + # plot Sankey diagram + fig = pgs_plot_sankey( + self.results_, + optimal_scales=False, + live=live, + filename=filename, + scale_index=scale_index, + ) + + return fig diff --git a/src/pygenstability/pygenstability.py b/src/pygenstability/pygenstability.py index d526655..caf7c8f 100644 --- a/src/pygenstability/pygenstability.py +++ b/src/pygenstability/pygenstability.py @@ -110,7 +110,11 @@ def _get_params(all_locals): @_timing def _get_constructor_data(constructor, scales, pool, tqdm_disable=False): return list( - tqdm(pool.imap(constructor.get_data, scales), total=len(scales), disable=tqdm_disable) + tqdm( + pool.imap(constructor.get_data, scales), + total=len(scales), + disable=tqdm_disable, + ) ) @@ -195,9 +199,12 @@ def run( - 'run_params': dict with parameters used to run the code - 'scales': scales of the scan - 'number_of_communities': number of communities at each scale + - 'stability': value of stability cost function at each scale - 'community_id': community node labels at each scale - - 'NVI': NVI at each scale - - 'ttprime': ttprime matrix + - 'NVI': NVI(t) at each scale + - 'ttprime': NVI(t,tprime) matrix + - 'block_detection_curve': block detection curve (included if with_optimal_scales==True) + - 'selected_partitions': selected partitions (included if with_optimal_scales==True) """ method = _check_method(method) run_params = _get_params(locals()) @@ -357,7 +364,10 @@ def _optimise(_, quality_indices, quality_values, null_model, global_shift, meth for null in null_model[::2]: partitions.append( leidenalg.CPMVertexPartition( - G, weights=quality_values, node_sizes=null.tolist(), correct_self_loops=True + G, + weights=quality_values, + node_sizes=null.tolist(), + correct_self_loops=True, ) ) optimiser = leidenalg.Optimiser() diff --git a/tests/conftest.py b/tests/conftest.py index 2060e95..8e61ff6 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,6 @@ """Utils for tests.""" import networkx as nx +import numpy as np import pytest from pygenstability.constructors import load_constructor @@ -44,3 +45,76 @@ def graph_signed(): def results(graph): constructor = load_constructor("continuous_combinatorial", graph) return pgs.run(graph, constructor=constructor) + + +def generate_circles( + n_samples_out=300, + n_groups_out=3, + gap_out=np.pi / 15, + n_samples_in=300, + n_groups_in=3, + gap_in=np.pi / 8, + offset_in=np.pi / 7, + factor=0.5, + noise=0.03, + seed=42, +): + """Generate two circles with multiscale structure. + + Adapted from: sklearn.datasets.make_circles + """ + rng = np.random.default_rng(seed) + + # generate outer circle that is split into groups + linspace_out = [] + for i in range(n_groups_out): + linspace_out += list( + np.linspace( + i * 2 / n_groups_out * np.pi + gap_out / 2, + (i + 1) * 2 / n_groups_out * np.pi - gap_out / 2, + int(n_samples_out / n_groups_out), + endpoint=False, + ) + ) + outer_circ_x = np.cos(linspace_out) + outer_circ_y = np.sin(linspace_out) + + # generate inner circle that is split into groups + linspace_in = [] + for j in range(n_groups_out): + linspace_in += list( + np.linspace( + j * 2 / n_groups_in * np.pi + gap_in / 2, + (j + 1) * 2 / n_groups_in * np.pi - gap_in / 2, + int(n_samples_in / n_groups_in), + endpoint=False, + ) + + offset_in + ) + inner_circ_x = np.cos(linspace_in) * factor + inner_circ_y = np.sin(linspace_in) * factor + + # combine circles + X = np.vstack([np.append(outer_circ_x, inner_circ_x), np.append(outer_circ_y, inner_circ_y)]).T + + # create group labels + y = np.zeros(n_samples_out + n_samples_in) + for i in range(n_groups_in): + y[int(i * n_samples_in / n_groups_in) : int((i + 1) * n_samples_in / n_groups_in)] = i + for i in range(n_groups_out): + y[ + int(n_samples_in + i * n_samples_out / n_groups_out) : int( + n_samples_in + (i + 1) * n_samples_out / n_groups_out + ) + ] = (i + n_groups_in) + + # add noise to data + if noise is not None: + X += rng.normal(scale=noise, size=X.shape) + + return X, y + +@pytest.fixture() +def X(): + X, y = generate_circles() + return X diff --git a/tests/data/test_dataclustering_default.yaml b/tests/data/test_dataclustering_default.yaml new file mode 100644 index 0000000..7b9f0ae --- /dev/null +++ b/tests/data/test_dataclustering_default.yaml @@ -0,0 +1,14513 @@ +labels: +- - 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0.018329807108324356 + - 0.024816289228368264 + - 0.03359818286283781 + - 0.04548777947003778 + - 0.06158482110660264 + - 0.08337822234717891 + - 0.11288378916846889 + - 0.15283067326587688 + - 0.20691380811147891 + - 0.2801356761198867 + - 0.37926901907322497 + - 0.5134832907437552 + - 0.6951927961775606 + - 0.9412049672680666 + - 1.2742749857031335 + - 1.725210549942041 + - 2.3357214690901213 + - 3.1622776601683795 + selected_partitions: + - 7.0 + - 8.0 + - 15.0 + stability: + - 0.9887530863020778 + - 0.9865951504852386 + - 0.9845265857606754 + - 0.9823712294411688 + - 0.9800874011151254 + - 0.9776322579743608 + - 0.9750054209545764 + - 0.9720118546291812 + - 0.9686528726475023 + - 0.9646604398706127 + - 0.9602335228541667 + - 0.9550068040934091 + - 0.9492463208660485 + - 0.9354606649255252 + - 0.9354606649255252 + - 0.92659739900581 + - 0.916565471496394 + - 0.9060325012298833 + - 0.8937652008829704 + - 0.8796073624742182 diff --git a/tests/test_dataclustering.py b/tests/test_dataclustering.py new file mode 100644 index 0000000..7d927c0 --- /dev/null +++ b/tests/test_dataclustering.py @@ -0,0 +1,59 @@ +"""Test dataclustering module.""" +import pytest +from pathlib import Path +import yaml + +from pygenstability import DataClustering + +from dictdiffer import diff + +from .utils import _to_list + +DATA = Path(__file__).absolute().parent / "data" + + +def test_DataClustering(X): + """Test the DataClustering class""" + # default + clustering = DataClustering() + res = clustering.fit(X) + results = _to_list({"labels": res.labels_, "results": res.results_}) + yaml.dump(results, open(DATA / "test_dataclustering_default.yaml", "w")) + expected_results = yaml.safe_load(open(DATA / "test_dataclustering_default.yaml", "r")) + assert len(list(diff(expected_results, results, tolerance=1e-5))) == 0 + + # with knn + clustering = DataClustering(graph_method="knn-mst") + res = clustering.fit(X) + results = _to_list({"labels": res.labels_, "results": res.results_}) + yaml.dump(results, open(DATA / "test_dataclustering_knn.yaml", "w")) + expected_results = yaml.safe_load(open(DATA / "test_dataclustering_knn.yaml", "r")) + assert len(list(diff(expected_results, results, tolerance=1e-5))) == 0 + + # precomputed + clustering = DataClustering(graph_method="precomputed") + with pytest.raises(AssertionError): + res = clustering.fit(X) + + # precompute it as default + clustering.method = "cknn-mst" + X = clustering.get_graph(X) + clustering.method = "precomputed" + + res = clustering.fit(X) + results = _to_list({"labels": res.labels_, "results": res.results_}) + expected_results = yaml.safe_load(open(DATA / "test_dataclustering_default.yaml", "r")) + assert len(list(diff(expected_results, results, tolerance=1e-5))) == 0 + + # scales selection + scales = clustering.scale_selection() + assert len(scales) > 2 + + # test plots + clustering.plot_scan(live=False) + clustering.plot_robust_partitions(x_coord=X[:, 0], y_coord=X[:, 1], show=False) + clustering.plot_robust_partitions(x_coord=X[:, 0], y_coord=X[:, 1], show=True) + clustering.plot_sankey(live=False) + + clustering.results_ = None + assert clustering.plot_scan() == None diff --git a/tests/test_pygenstability.py b/tests/test_pygenstability.py index abdc00d..83821ff 100644 --- a/tests/test_pygenstability.py +++ b/tests/test_pygenstability.py @@ -10,19 +10,9 @@ from pygenstability.constructors import load_constructor from pygenstability import pygenstability as pgs -DATA = Path(__file__).absolute().parent / "data" - +from .utils import _to_list -def _to_list(data): - """Convert dict to list with floats for yaml encoding.""" - data.pop("NVI", None) # NVI computation is unstable, we don't test it - data.pop("ttprime", None) # ttprime computation is unstable, we don't test it - for key, val in data.items(): - if isinstance(val, dict): - data[key] = _to_list(data[key]) - if isinstance(val, (np.ndarray, list)): - data[key] = np.array(val, dtype=float).tolist() - return data +DATA = Path(__file__).absolute().parent / "data" def test_run(graph, graph_non_connected, graph_directed, graph_signed): diff --git a/tests/utils.py b/tests/utils.py new file mode 100644 index 0000000..16784db --- /dev/null +++ b/tests/utils.py @@ -0,0 +1,16 @@ +"""Utils for tests.""" +import numpy as np +from copy import deepcopy + + +def _to_list(_data): + """Convert dict to list with floats for yaml encoding.""" + data = deepcopy(_data) + data.pop("NVI", None) # NVI computation is unstable, we don't test it + data.pop("ttprime", None) # ttprime computation is unstable, we don't test it + for key, val in data.items(): + if isinstance(val, dict): + data[key] = _to_list(data[key]) + if isinstance(val, (np.ndarray, list)): + data[key] = np.array(val, dtype=float).tolist() + return data