Usage

Installation

To use Besimilarity, first install it using pip:

(.venv) $ pip install besimilarity

Quickstart

• besimilarity.AgglomerativeBestSimilarity

  You can use the besimilarity.AgglomerativeBestSimilarity() class to fit your data with best similarity/distance equation based on Agglomerative Clustering. This class also can find best linkage method based on the used similarity/distance.

  Make sure your target column is in the last column.:

  >> from besimilarity import AgglomerativeBestSimilarity
  
  >> aggbs = AgglomerativeBestSimilarity()
  
  >> df = pd.DataFrame(data={
  'A': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
  'B': [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
  'C': [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
  'D': [0, 0, 0, 0, 0, 0, 1, 1, 0, 0],
  'E': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
  'F': [0, 0, 0, 0, 1, 1, 1, 1, 1, 0],
  'G': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
  'H': [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
  'I': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
  'Target': [0, 0, 0, 1, 1, 1, 1, 1, 1, 0]})``
  
  >> aggbs.fit(df, n_clusters=2)
  
  df shape:  (10, 10)
  n_clusters:  2
  affinity:  all
  linkage:  all
  
  100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.06s/it]
  
  >> result = aggbs.get_result()
  
  >> print(result.head())
     linkage           equation  homogeneity  completeness  v_measure
  0  complete          sim gower     0.628236      0.609987   0.618977
  1  complete         sim stiles     0.573438      0.631777   0.601196
  2   average         sim peirce     0.432538      0.432538   0.432538
  3  complete  sim fager_mcgowan     0.331560      0.445928   0.380332
  4   average  sim fager_mcgowan     0.331560      0.445928   0.380332
  
  Or, if you want to calculate based on pair of your target, you can use ``besimilarity.PairBestSimilarity()`` class instead.
  

• besimilarity.PairBestSimilarity

  Class ini menggunakan pasangan dari kombinasi setiap baris untuk mengetahui apakah kombinasi memiliki nilai kesamaan yang besar atau tidak dan dengan target yang sesuai atau tidak. Jika target sama, maka sudah sepatutnya kedua baris tersebut memiliki nilai kesamaan yang besar.:

  >> from besimilarity import PairBestSimilarity
  
  >> pbs = PairBestSimilarity()
  
  >> df = pd.DataFrame(data={
  'A': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
  'B': [0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
  'C': [0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
  'D': [0, 0, 0, 0, 0, 0, 1, 1, 0, 0],
  'E': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
  'F': [0, 0, 0, 0, 1, 1, 1, 1, 1, 0],
  'G': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
  'H': [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
  'I': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
  'Target': [0, 0, 0, 1, 1, 1, 1, 1, 1, 0]})``
  
  >> bs.fit(df, use_seed=True, num_sample=2)
  use_seed:  True
  num_sample:  2
  
  1it [00:00, 96.36it/s]
  100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 21.34it/s]
  
  final 5 best similarity:
                 sim/dis name  mean_auc
  0          jaccard similarity       1.0
  1            gower similarity       1.0
  2     hellinger dissimilarity       1.0
  3  pearson_heron_1 similarity       1.0
  4           dice_2 similarity       1.0