diff --git a/MANIFEST.in b/MANIFEST.in index 9f9fae17..98eae1c2 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1,3 @@ recursive-include kmapper/templates * recursive-include kmapper/static * +include README.md \ No newline at end of file diff --git a/examples/breast-cancer/breast-cancer.html b/examples/breast-cancer/breast-cancer.html index b48f4e1c..10078079 100644 --- a/examples/breast-cancer/breast-cancer.html +++ b/examples/breast-cancer/breast-cancer.html @@ -1,98 +1,592 @@ - - - Wisconsin Breast Cancer Dataset | KeplerMapper - - - -
-

Wisconsin Breast Cancer Dataset

-

- Lens
l2norm

- Cubes per dimension
15

- Overlap percentage
70.0%

- Color Function
average_signal_cluster( l2norm )

- Clusterer
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300, - n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto', - random_state=1618033, tol=0.0001, verbose=0)

- Scaler
MinMaxScaler(copy=True, feature_range=(0, 1)) -

+ + + + + + + Wisconsin Breast Cancer Dataset | KeplerMapper + + + + + + + + + +
+ +
+
+
+ [-] +
+

Cluster Meta

+
+
- - \ No newline at end of file +
+
+
+ [-] +
+

Graph Meta

Nodes

150

Edges

308

Total Samples

1549

Unique Samples

562

Distribution

+
+
+
0.3%
+
+
+
0.5%
+
+
+
1.6%
+
+
+
9.4%
+
+
+
17.1%
+
+
+
55.1%
+
+
+
13.2%
+
+
+
2.1%
+
+
+
0.7%
+
+
+
0.7%
+
+
+
+ + + + + + + \ No newline at end of file diff --git a/examples/breast-cancer/breast-cancer.py b/examples/breast-cancer/breast-cancer.py index c5edada7..a6690fbe 100644 --- a/examples/breast-cancer/breast-cancer.py +++ b/examples/breast-cancer/breast-cancer.py @@ -42,5 +42,4 @@ mapper.visualize(graph, path_html="breast-cancer.html", title="Wisconsin Breast Cancer Dataset", - custom_tooltips=y, - color_function="average_signal_cluster") + custom_tooltips=y) diff --git a/setup.py b/setup.py index c5793188..1b8bee57 100644 --- a/setup.py +++ b/setup.py @@ -2,16 +2,14 @@ from setuptools import setup +with open('README.md') as f: + long_description = f.read() + setup(name='kmapper', version='1.1.3', description='Python implementation of Mapper algorithm for Topological Data Analysis.', - long_description=""" -This is a Python implementation of the TDA Mapper algorithm for visualization of high-dimensional data. For complete documentation, see https://MLWave.github.io/kepler-mapper. - -KeplerMapper employs approaches based on the Mapper algorithm (Singh et al.) as first described in the paper "Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition". - -KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms. -""", + long_description=long_description, + long_description_content_type="text/markdown", author='HJ van Veen, Nathaniel Saul', author_email='info@mlwave.com, nat@saulgill.com', url='https://MLWave.github.io/kepler-mapper',