Source code for pygenstability.io
"""I/O functions."""
+
import pickle
diff --git a/_modules/pygenstability/optimal_scales.html b/_modules/pygenstability/optimal_scales.html
index 2c4c149..853b0cb 100644
--- a/_modules/pygenstability/optimal_scales.html
+++ b/_modules/pygenstability/optimal_scales.html
@@ -158,6 +158,7 @@
Source code for pygenstability.optimal_scales
"""Detect optimal scales from a scale scan."""
+
import numpy as np
import pandas as pd
from numpy.lib.stride_tricks import as_strided
@@ -256,8 +258,8 @@ Source code for pygenstability.optimal_scales
result dictionary with two new keys: 'selected_partitions' and 'block_detection_curve'
References:
- .. [1] D. J. Schindler, J. Clarke, and M. Barahona, ‘Multiscale Mobility Patterns and
- the Restriction of Human Movement’, *arXiv:2201.06323*, 2023
+ .. [1] D. J. Schindler, J. Clarke, and M. Barahona, 'Multiscale Mobility Patterns and
+ the Restriction of Human Movement', *arXiv:2201.06323*, 2023
"""
# get NVI(t) and NVI(t,t')
nvi_t = np.asarray(results["NVI"])
diff --git a/_modules/pygenstability/plotting.html b/_modules/pygenstability/plotting.html
index 3641fcf..a8f6046 100644
--- a/_modules/pygenstability/plotting.html
+++ b/_modules/pygenstability/plotting.html
@@ -158,6 +158,7 @@
Constructors module
The pygenstability cli
Plotting module
+DataClustering module
Optimal scales module
I/O module
Example: Markov Stability with PyGenStability
@@ -196,12 +197,18 @@
Source code for pygenstability.plotting
"""Plotting functions."""
+
import logging
import os
import matplotlib
import matplotlib.pyplot as plt
-import networkx as nx
+
+try:
+ import networkx as nx
+except ImportError: # pragma: no cover
+ print('Please install networkx via pip install "pygenstability[networkx]" for full plotting.')
+
import numpy as np
from matplotlib import gridspec
from matplotlib import patches
diff --git a/_modules/pygenstability/pygenstability.html b/_modules/pygenstability/pygenstability.html
index 4c202fb..b6902bf 100644
--- a/_modules/pygenstability/pygenstability.html
+++ b/_modules/pygenstability/pygenstability.html
@@ -158,6 +158,7 @@
Constructors module
The pygenstability cli
Plotting module
+DataClustering module
Optimal scales module
I/O module
Example: Markov Stability with PyGenStability
@@ -208,6 +209,7 @@ Source code for pygenstability.pygenstability
Markov Stability setting, and can be parametrised via built-in constructors, or specified by
the user via the constructor module.
"""
+
import itertools
import logging
import multiprocessing
@@ -216,15 +218,27 @@ Source code for pygenstability.pygenstability
from functools import wraps
from time import time
-import igraph as ig
-import leidenalg
+try:
+ import igraph as ig
+ import leidenalg
+
+ _NO_LEIDEN = False
+except ImportError: # pragma: no cover
+ _NO_LEIDEN = True
+
import numpy as np
import scipy.sparse as sp
from sklearn.metrics import mutual_info_score
from sklearn.metrics.cluster import entropy
from tqdm import tqdm
-from pygenstability import generalized_louvain
+try:
+ from pygenstability import generalized_louvain
+
+ _NO_LOUVAIN = False
+except ImportError: # pragma: no cover
+ _NO_LOUVAIN = True
+
from pygenstability.constructors import load_constructor
from pygenstability.io import save_results
from pygenstability.optimal_scales import identify_optimal_scales
@@ -294,10 +308,29 @@ Source code for pygenstability.pygenstability
@_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,
+ )
)
+def _check_method(method): # pragma: no cover
+ if _NO_LEIDEN and _NO_LOUVAIN:
+ raise Exception("Without Louvain or Leiden solver, we cannot run PyGenStability")
+
+ if method == "louvain" and _NO_LOUVAIN:
+ print("Louvain is not available, we fallback to leiden.")
+ return "leiden"
+
+ if method == "leiden" and _NO_LEIDEN:
+ print("Leiden is not available, we fallback to louvain.")
+ return "louvain"
+
+ return method
+
+
[docs]@_timing
def run(
graph=None,
@@ -364,10 +397,14 @@ Source code for pygenstability.pygenstability
- '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())
graph = _graph_checks(graph)
scales = _get_scales(
@@ -414,7 +451,7 @@ Source code for pygenstability.pygenstability
if with_postprocessing:
L.info("Apply postprocessing...")
- _apply_postprocessing(all_results, pool, constructor_data, tqdm_disable)
+ _apply_postprocessing(all_results, pool, constructor_data, tqdm_disable, method=method)
if with_ttprime or with_optimal_scales:
L.info("Compute ttprimes...")
@@ -525,7 +562,10 @@ Source code for pygenstability.pygenstability
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()
@@ -539,18 +579,34 @@ Source code for pygenstability.pygenstability
return stability + global_shift, community_id
-def _evaluate_quality(partition_id, qualities_index, null_model, global_shift):
+def _evaluate_quality(partition_id, qualities_index, null_model, global_shift, method="louvain"):
"""Worker for generalized Markov Stability optimisation runs."""
- quality = generalized_louvain.evaluate_quality(
- qualities_index[0][0],
- qualities_index[0][1],
- qualities_index[1],
- len(qualities_index[1]),
- null_model,
- np.shape(null_model)[0],
- 1.0,
- partition_id,
- )
+ if method == "louvain":
+ quality = generalized_louvain.evaluate_quality(
+ qualities_index[0][0],
+ qualities_index[0][1],
+ qualities_index[1],
+ len(qualities_index[1]),
+ null_model,
+ np.shape(null_model)[0],
+ 1.0,
+ partition_id,
+ )
+
+ if method == "leiden":
+ quality = np.mean(
+ [
+ leidenalg.CPMVertexPartition(
+ ig.Graph(edges=zip(*qualities_index[0]), directed=True),
+ initial_membership=partition_id,
+ weights=qualities_index[1],
+ node_sizes=null.tolist(),
+ correct_self_loops=True,
+ ).quality()
+ for null in null_model[::2]
+ ]
+ )
+
return quality + global_shift
@@ -587,7 +643,7 @@ Source code for pygenstability.pygenstability
@_timing
-def _apply_postprocessing(all_results, pool, constructors, tqdm_disable=False):
+def _apply_postprocessing(all_results, pool, constructors, tqdm_disable=False, method="louvain"):
"""Apply postprocessing."""
all_results_raw = all_results.copy()
@@ -599,6 +655,7 @@ Source code for pygenstability.pygenstability
qualities_index=_to_indices(constructor["quality"]),
null_model=constructor["null_model"],
global_shift=constructor.get("shift", 0.0),
+ method=method,
)
best_quality_id = np.argmax(
pool.map(
diff --git a/_sources/dataclustering.rst.txt b/_sources/dataclustering.rst.txt
new file mode 100644
index 0000000..de88c6e
--- /dev/null
+++ b/_sources/dataclustering.rst.txt
@@ -0,0 +1,4 @@
+DataClustering module
+=====================
+.. automodule:: pygenstability.DataClustering
+ :members:
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
index e71b6b4..52fde51 100644
--- a/_sources/index.rst.txt
+++ b/_sources/index.rst.txt
@@ -13,6 +13,7 @@ Documentation of the API of *PyGenStability*.
constructors
app
plotting
+ dataclustering
optimal_scales
io
examples/example
diff --git a/app.html b/app.html
index d9dd37a..fb2757b 100644
--- a/app.html
+++ b/app.html
@@ -159,6 +159,7 @@
Constructors module
The pygenstability cli
Plotting module
+DataClustering module
Optimal scales module
I/O module
Example: Markov Stability with PyGenStability
diff --git a/constructors.html b/constructors.html
index 0958a6d..b84bdca 100644
--- a/constructors.html
+++ b/constructors.html
@@ -159,6 +159,7 @@
Constructors module
The pygenstability cli
Plotting module
+DataClustering module
Optimal scales module
I/O module
Example: Markov Stability with PyGenStability
@@ -348,7 +349,7 @@
diff --git a/dataclustering.html b/dataclustering.html
new file mode 100644
index 0000000..a346c01
--- /dev/null
+++ b/dataclustering.html
@@ -0,0 +1,402 @@
+
+
+
+
+
+
+
+
+ DataClustering module - PyGenStability documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Back to top
+
+
+
+
+DataClustering module#
+Class for multiscale graph-based data clustering.
+This class provides an interface for multiscale graph-based data clustering [1]
+with PyGenStability.
+
+- param metric:
+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’.
+- type metric:
+str or function, default=’euclidean’
+- param graph_method:
+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.
+
+
+- type graph_method:
+{‘knn-mst’, ‘cknn-mst’, ‘precomputed’}, default=’cknn-mst’
+- param k:
+Number of neighbors considered in graph construction. This parameter is expected
+to be positive.
+- type k:
+int, default=5
+- param delta:
+Density parameter for Continunous k-Nearest Neighbor graph. This parameter is expected
+to be positive.
+- type delta:
+float, default=1.0
+- param distance_threshold:
+Optional thresholding of distance matrix.
+- type distance_threshold:
+float, optional
+- param pgs_kwargs:
+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
+- type pgs_kwargs:
+dict, optional
+
+
+-
+pygenstability.DataClustering.adjacency_#
+Sparse adjacency matrix of constructed graph.
+
+- Type:
+sparse matrix of shape (n_samples, n_samples)
+
+
+
+
+-
+pygenstability.DataClustering.results_#
+PyGenStability results dictionary, see documentation for all arguments.
+
+- Type:
+dict
+
+
+
+
+-
+pygenstability.DataClustering.labels_#
+List of robust partitions identified with optimal scale selection.
+
+- Type:
+list of ndarray
+
+
+
+References
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/examples/example.html b/examples/example.html
index fd58a31..db7bfd5 100644
--- a/examples/example.html
+++ b/examples/example.html
@@ -159,6 +159,7 @@
Constructors module
The pygenstability cli
Plotting module
+DataClustering module
Optimal scales module
I/O module
Example: Markov Stability with PyGenStability
diff --git a/genindex.html b/genindex.html
index f3bb486..9ce9fa2 100644
--- a/genindex.html
+++ b/genindex.html
@@ -157,6 +157,7 @@
Constructors module
The pygenstability cli
Plotting module
+DataClustering module
Optimal scales module
I/O module
Example: Markov Stability with PyGenStability
@@ -196,7 +197,7 @@
Index
-
+
Symbols
@@ -362,6 +363,16 @@ Symbols
+
+ A
+
+
+
+
C
@@ -504,10 +515,12 @@ I
L
M
pygenstability.app
pygenstability.constructors
+
+ pygenstability.DataClustering
pygenstability.io
@@ -591,6 +606,13 @@ P
- module
+
+
+
+ pygenstability.DataClustering
+
+
+ - module
@@ -629,6 +651,8 @@ P
R
+ - results_ (in module pygenstability.DataClustering)
+
-
RESULTS_FILE
diff --git a/index.html b/index.html
index 302f7b1..0a954e3 100644
--- a/index.html
+++ b/index.html
@@ -159,6 +159,7 @@
- Constructors module
- The pygenstability cli
- Plotting module
+- DataClustering module
- Optimal scales module
- I/O module
- Example: Markov Stability with PyGenStability
@@ -203,41 +204,56 @@ PyGenStability
Installation#
-The wrapper uses Pybind11 https://github.com/pybind/pybind11 and the package can simply be installed by first cloning this repo with
-git clone --recurse-submodules https://github.com/ImperialCollegeLondon/PyGenStability.git
+You can install the package using pypi:
+pip install pygenstability
-(if the --recurse-submodules has not been used, just do git submodule update --init --recursive to fetch the submodule with M. Schaub’s code).
-Then, to install the package, simply run
-pip install .
+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:
+pip install pygenstability[leiden]
+
+
+To plot network partitions using networkx, install this package with:
+pip install pygenstability[networkx]
+
+
+To use plotly for interactive plots in the browser, install this package with:
+pip install pygenstability[plotly]
-using a fresh virtualenv in python3 may be recommended to avoid conflict of python packages.
-To use plotly for interactive plots in the browser, install this package with
-pip install .[plotly]
+To install all dependencies, run:
+pip install pygenstability[all]
-To use a contrib module, with additional tools, run
-pip install .[contrib]
+
+Installation from GitHub#
+You can also install the source code of this package from GitHub directly by first cloning this repo with:
+git clone --recurse-submodules https://github.com/ImperialCollegeLondon/PyGenStability.git
-To install all dependencies, run
-pip install .[all]
+(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):
+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:
+pip install .[networkx]
+
Using the code#
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.
-import pygenstability as pgs
+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
-export OPENBLAS_NUM_THREADS=1
-export OMP_NUM_THREADS=1
-export NUMEXPR_MAX_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export OMP_NUM_THREADS=1
+export NUMEXPR_MAX_THREADS=1
to ensure numpy does not use multi-threadings, which may clash with the parallelisation and slow down the computation.
@@ -247,7 +263,7 @@ Using the coderesults = pgs.run(graph, method="leiden")
+results = pgs.run(graph, method="leiden")
There are also additional post-processing and analysis functions, including:
@@ -256,7 +272,7 @@ Using the codepygenstability/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.
-results = pgs.identify_optimal_scales(results, block_size=10, window_size=5)
+results = pgs.identify_optimal_scales(results, block_size=10, window_size=5)
pgs.plot_optimal_partitions(nx_graph, results)
@@ -280,6 +296,29 @@ Constructorsquality_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:
+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:
+# plot robust partitions
+clustering.plot_robust_partitions(x_coord=x_coord,y_coord=y_coord)
+
+
+
Contributors#
@@ -319,11 +358,11 @@ Cite#
Run example#
In the example folder, a demo script with a stochastic block model can be tried with
-python simple_example.py
+python simple_example.py
or using the click app:
-./run_simple_example.sh
+./run_simple_example.sh
Other examples can be found as jupyter-notebooks in the examples/ directory, including:
@@ -334,6 +373,7 @@ Run exampleexamples/real_examples/ directory, including:
@@ -361,8 +401,10 @@ References
Licence#
@@ -408,6 +450,12 @@ API documentationplot_clustered_adjacency()
+- DataClustering module
+adjacency_results_labels_
+
- Optimal scales module
@@ -468,9 +516,13 @@
API documentation
- PyGenStability
-- Installation
+- Installation
+
- Using the code
- Constructors
+- Graph-based data clustering
- Contributors
- Cite
- Run example
diff --git a/io.html b/io.html
index 8e9e6fb..7baaa38 100644
--- a/io.html
+++ b/io.html
@@ -159,6 +159,7 @@
- Constructors module
- The pygenstability cli
- Plotting module
+- DataClustering module
- Optimal scales module
- I/O module
- Example: Markov Stability with PyGenStability
diff --git a/objects.inv b/objects.inv
index d964f49..05f3b62 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/optimal_scales.html b/optimal_scales.html
index f91ab34..a1d141e 100644
--- a/optimal_scales.html
+++ b/optimal_scales.html
@@ -3,7 +3,7 @@
-
+
Optimal scales module - PyGenStability documentation
@@ -159,6 +159,7 @@
- Constructors module
- The pygenstability cli
- Plotting module
+- DataClustering module
- Optimal scales module
- I/O module
- Example: Markov Stability with PyGenStability
@@ -251,14 +252,14 @@
-
+
Previous
- Plotting module
+ DataClustering module
diff --git a/plotting.html b/plotting.html
index defb2dc..0a55b9d 100644
--- a/plotting.html
+++ b/plotting.html
@@ -3,7 +3,7 @@
-
+
Plotting module - PyGenStability documentation
@@ -159,6 +159,7 @@
- Constructors module
- The pygenstability cli
- Plotting module
+- DataClustering module
- Optimal scales module
- I/O module
- Example: Markov Stability with PyGenStability
@@ -328,12 +329,12 @@
+
+
+ pygenstability.DataClustering
+
+
diff --git a/pygenstability.html b/pygenstability.html
index a529b32..6028fff 100644
--- a/pygenstability.html
+++ b/pygenstability.html
@@ -159,6 +159,7 @@
Constructors module
The pygenstability cli
Plotting module
+DataClustering module
Optimal scales module
I/O module
Example: Markov Stability with PyGenStability
@@ -255,9 +256,12 @@
’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)
Constructors module
The pygenstability cli
Plotting module
+DataClustering module
Optimal scales module
I/O module
Example: Markov Stability with PyGenStability
diff --git a/searchindex.js b/searchindex.js
index 6fad64b..b32202b 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file