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توضیحات

easy interface for ensemble clustering

ویژگی	مقدار
سیستم عامل	-
نام فایل	flexible-clustering-tree-0.21
نام	flexible-clustering-tree
نسخه کتابخانه	0.21
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Kensuke Mitsuzawa
ایمیل نویسنده	kensuke.mit@gmail.com
آدرس صفحه اصلی	https://github.com/Kensuke-Mitsuzawa/flexible_clustering_tree
آدرس اینترنتی	https://pypi.org/project/flexible-clustering-tree/
مجوز	-

flexible-clustering-tree ======================== -------------- What’s this? ============ In the context of **clustering** task, ``flexible-clustering-tree`` provides you **easy** and **controllable** clustering framework. |image0| Background ========== Let’s suppose, you have huge data. You’d like to observe data as easy as possible. Hierarchical clustering is a way to see clustering tree. However, hierarchical clustering tends to fall into local optimization. So, you need other clustering method. But at the same time, you wanna observe your data with tree structure style. here, ``flexible-clustering-tree`` could give you simple way from data into tree viewer(d3 based) You could set any kinds of clustering algorithm such as Kmeans, DBSCAN, Spectral-Clustering. Multi feature and Multi clustering ---------------------------------- During making a tree, you might want use various kind of clustering algorithm. For example, you use Kmeans for the 1st later of a tree, and DBSCAN for the 2nd layer of a tree. And you might also use various kind of feature type depending on a layer of a tree. For example, in the context of document clustering, “title” of news for the 1st layer, and “whole text” for the 2nd layer. The example below, this is a clustering tree of 20-news data set. - 1st layer(red highlight) is done with HDBSCAN clustering, and feature is dense vector of ``Subject`` text, which is converted by word2vec model. - 2nd layer(blue highlight) is done with Kmeans, and feature is sparse vector of whole text(BOW). You could design your clustering tree as you want! |image1| Both are possible ``flexible-clustering-tree``! Contribution ============ - Easy interface(scikit-learn way) from data(feature matrix) into a tree viewer - Possible to make various clustering algorithms ensemble - Possible to set various feature types How to use? =========== .. code:: python from flexible_clustering_tree import FeatureMatrixObject, MultiFeatureMatrixObject from flexible_clustering_tree import ClusteringOperator, MultiClusteringOperator from flexible_clustering_tree import FlexibleClustering import numpy import codecs # set feature matrix # an input of 1st layer is list of string input_string = ['d'] * 10 + ['e'] * 10 + ['c'] * 10 + ['a'] * 10 + ['b'] * 10 + ['f'] * 50 f_obj_1st = FeatureMatrixObject(0, feature_strings=input_string) # an input of 2nd layer is the dense matrix (100, 300) f_obj_2nd = FeatureMatrixObject(1, matrix_object=numpy.random.rand(100, 300)) # an input of 3rd layer is the dense matrix (100, 50) f_obj_3rd = FeatureMatrixObject(2, matrix_object=numpy.random.rand(100, 50)) dict_index2label = {i: "label-{}".format(i) for i in range(0, 100)} multi_feature_matrix = MultiFeatureMatrixObject( [f_obj_1st, f_obj_2nd, f_obj_3rd], dict_index2label=dict_index2label ) # set clustering operation from sklearn.cluster.k_means_ import KMeans from hdbscan.hdbscan_ import HDBSCAN from flexible_clustering_tree import StringAggregation c_operation_1st = ClusteringOperator(0, -1, StringAggregation()) c_operation_2nd = ClusteringOperator(1, 10, KMeans(10)) c_operation_3rd = ClusteringOperator(2, -1, HDBSCAN()) multi_clustering = MultiClusteringOperator([c_operation_1st, c_operation_2nd]) # run flexible clustering clustering_runner = FlexibleClustering(max_depth=5) index2cluster_no = clustering_runner.fit_transform(multi_feature_matrix, multi_clustering) html = clustering_runner.clustering_tree.to_html() # output to html with codecs.open("out.html", "w", "utf-8") as f: f.write(html) # you're also able to generate tables via Pandas. import pandas table_objects = clustering_runner.clustering_tree.to_objects() print(pandas.DataFrame(table_objects['cluster_information'])) print(pandas.DataFrame(table_objects['leaf_information'])) The output of pandas table is below. The relation-table of clusters is in ``cluster_information``. :: cluster_id parent_id depth_level clustering_method 0 0 -1 1 StringAggregation 1 1 -1 1 StringAggregation 2 2 -1 1 StringAggregation 3 3 -1 1 StringAggregation 4 4 -1 1 StringAggregation 5 5 -1 1 StringAggregation 6 6 5 2 KMeans 7 7 5 2 KMeans 8 8 5 2 KMeans 9 9 5 2 KMeans 10 10 5 2 KMeans 11 11 5 2 KMeans 12 12 5 2 KMeans 13 13 5 2 KMeans 14 14 5 2 KMeans 15 15 5 2 KMeans The relation-table of leaf nodes is in ``leaf_information``. :: leaf_id cluster_id label args 0 0 0 label-0 None 1 1 0 label-1 None 2 2 0 label-2 None 3 3 0 label-3 None 4 4 0 label-4 None .. ... ... ... ... 95 95 14 label-95 None 96 96 8 label-96 None 97 97 13 label-97 None 98 98 10 label-98 None 99 99 12 label-99 None [100 rows x 4 columns] You could see examples at ``/examples``. setup ===== .. code:: bash pip install flexible_clustering_tree or close this repository .. code:: bash python setup.py install For Developers ============== Environment ----------- - Python >= 3.x Dev/Test environment by Docker ------------------------------ You build dev/test environment with Docker container. Here is simple procedure, 1. build docker image 2. start docker container 3. run test in the container .. code:: bash $ cd tests $ docker-compose build $ docker-compose up $ docker run --name test-container -v `pwd`:/codes/flexible-clustering-tree/ -dt tests_dev_env $ docker exec -it test-container python /codes/flexible-clustering-tree/setup.py test If you’re using pycharm professional edition, you could call a docker-compose file as Python interpreter. .. |image0| image:: https://user-images.githubusercontent.com/1772712/47308081-9980cd00-d66b-11e8-98c0-a275db021cd7.gif .. |image1| image:: https://user-images.githubusercontent.com/1772712/47308468-abaf3b00-d66c-11e8-9a08-26facc39e80e.png

نیازمندی

مقدار	نام
-	sqlitedict
-	scipy
-	numpy
-	scikit-learn
-	hdbscan
-	pyexcelerate
-	jinja2
-	pypandoc

نحوه نصب

نصب پکیج whl flexible-clustering-tree-0.21:

pip install flexible-clustering-tree-0.21.whl

نصب پکیج tar.gz flexible-clustering-tree-0.21:

pip install flexible-clustering-tree-0.21.tar.gz