文件列表:

LICENSE (11357, 2023-03-08)
MANIFEST.in (72, 2023-03-08)
Makefile (401, 2023-03-08)
examples (0, 2023-03-08)
examples\k_selection (0, 2023-03-08)
examples\k_selection\K-BIC.png (26020, 2023-03-08)
examples\performance_test (0, 2023-03-08)
examples\performance_test\log.txt (37796, 2023-03-08)
examples\pipeline_draw (0, 2023-03-08)
examples\pipeline_draw\kmeans(kmeans++)_cluster_centers.png (335339, 2023-03-08)
examples\pipeline_draw\kmeans(kmeans++)_y_pred.png (332485, 2023-03-08)
examples\pipeline_draw\kmeans(random)_cluster_centers.png (335775, 2023-03-08)
examples\pipeline_draw\kmeans(random)_y_pred.png (331997, 2023-03-08)
examples\pipeline_draw\rph_kmeans_cluster_centers.png (336076, 2023-03-08)
examples\pipeline_draw\rph_kmeans_reduced_points.png (88442, 2023-03-08)
examples\pipeline_draw\rph_kmeans_y_pred.png (330419, 2023-03-08)
examples\pipeline_draw\y_true.png (326827, 2023-03-08)
examples\simulate.py (9138, 2023-03-08)
requirements.txt (89, 2023-03-08)
rph_kmeans (0, 2023-03-08)
rph_kmeans\__init__.py (207, 2023-03-08)
rph_kmeans\_point_reducer_cy.by_cython.cpp (1248556, 2023-03-08)
rph_kmeans\_point_reducer_cy.pyx (3748, 2023-03-08)
rph_kmeans\_point_reducer_cy_lib.cpp (876, 2023-03-08)
rph_kmeans\_point_reducer_cy_lib.h (4591, 2023-03-08)
rph_kmeans\k_selection.py (6804, 2023-03-08)
rph_kmeans\point_reducer_base.py (2373, 2023-03-08)
rph_kmeans\point_reducer_cy.py (6104, 2023-03-08)
rph_kmeans\point_reducer_py.py (7096, 2023-03-08)
rph_kmeans\rph_kmeans_.py (7599, 2023-03-08)
rph_kmeans\utils.py (798, 2023-03-08)
setup.py (1760, 2023-03-08)

# RPH-KMeans **RPH-KMeans** is a variant of kmeans algorithm in which the initial centers are produced by point reduction process using one of the **local sensitive hashing (LSH)** techniques called **random projection (RP)**. # Installation To install RPH-Kmeans, simply run the setup script: ``` python3 setup.py install ``` Or use `pip`: ``` pip3 install rph_kmeans ``` Or install from git repo directly: ``` pip3 install git+https://github.com/tinglabs/rph_kmeans.git ``` To run basic clustering: ```python # Note: # type(X) = np.ndarray or scipy.sparse.csr_matrix; # X.shape = (n_samples, n_features) from rph_kmeans import RPHKMeans clt = RPHKMeans() labels = clt.fit_predict(X) ``` To estimate the number of clusters: ```python # Note: # type(X) = np.ndarray or scipy.sparse.csr_matrix; # X.shape = (n_samples, n_features) # type(kmax) = int; # Let k_ be the possible cluster number, it's recommended to set kmax = k_ * 3 from rph_kmeans import select_k_with_bic optimal_k, _, _ = select_k_with_bic(X, kmax=kmax) ``` ## Note For **Mac OSX**, it maybe help to run the command first before running `setup.py`: ``` export MACOSX_DEPLOYMENT_TARGET=10.9 ``` which can fix - `ld: library not found for -lstdc++` - `C++ STL headers not found` If the installation fails when compiling c++ extension, you can just add the path of `rph_kmeans` to `PYTHONPATH` and use **python** version (`point_reducer_version = "py"`) instead of **cython** version (`point_reducer_version = "cy"`). # Demo The experiments show that **RPH-KMeans** can deal with imblanced data much better than **KMeans (k-means++ initialization)** and **KMeans (random initialization)** . ## Simulation Run the script `examples/simulate.py`: ``` python3 simulate.py ``` ### Simlated Data 2-d simulated data is generated: - clusters number: 5 - gaussian distribution - label `0`: `mean=(0, 0); cov=1.0` - label `1`: `mean=(5, 5); cov=1.0` - label `2`: `mean=(-5, -5); cov=1.0` - label `3`: `mean=(5, -5); cov=1.0` - label `4`: `mean=(-5, 5); cov=1.0` - samples number: - label `0`: 5000 - label `1`: 100 - label `2`: 100 - label `3`: 100 - label `4`: 100 It looks like:  ### Performance of RPH-KMeans Run rph-kmeans with default config, we get - **ARI**: 0.99 - **NMI**: 0.*** The final cluster centers:  The predicted label:  The reduced points generated by random projection and the initial centers:  ### Performance of KMeans (kmeans++) Run Kmeans (init='kmeans++'; n_init=10), we get - **ARI**: 0.13 - **NMI**: 0.37 The final cluster centers: _cluster_centers.png) The predicted label: _y_pred.png) Use KMeans (kmeans++; n_init=10) to cluster: ### Performance of KMeans (random) Run Kmeans (init='random'; n_init=10), we get - **ARI**: 0.23 - **NMI**: 0.51 The final cluster centers: _cluster_centers.png) The predicted label: _y_pred.png) ### Summary A more detailed result is as follow (Metric mean and standard deviation of 10 repeat). The running log are in `examples/performance_test/log.txt`. | Method | Real Time (s) | CPU Time (s) | ARI | NMI | | ---- | ---- | ---- | ---- | ---- | | RPH-KMeans (n_init=1) | 0.208 (0.105) | 0.206 (0.105) | 0.997 (0.000) | 0.992 (0.000) | | KMeans (kmeans++; n_init=1) | 0.074 (0.042) | 0.074 (0.042) | 0.175 (0.062) | 0.441 (0.079) | | KMeans (kmeans++; n_init=5) | 0.325 (0.168) | 0.325 (0.168) | 0.316 (0.227) | 0.570 (0.141) | | KMeans (kmeans++; n_init=10) | 0.636 (0.318) | 0.636 (0.318) | 0.693 (0.373) | 0.805 (0.230) | | KMeans (random; n_init=1) | 0.089 (0.052) | 0.089 (0.052) | 0.202 (0.039) | 0.470 (0.067) | | KMeans (random; n_init=5) | 0.390 (0.207) | 0.390 (0.207) | 0.200 (0.055) | 0.463 (0.073) | | KMeans (random; n_init=10) | 0.800 (0.399) | 0.800 (0.399) | 0.215 (0.047) | 0.491 (0.063) | ### Estimation of cluster number The cluster number can be correctly estimated by finding the knee point of **BIC** curve: 

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