watex.utils.plot_clusters#

watex.utils.plot_clusters(n_clusters, X, y_pred, cluster_centers=None, savefig=None)[source]#

Visualize the cluster that k-means identified in the dataset

Parameters:

  • n_clusters – int, number of cluster to visualize

  • X – NDArray, data containing the features, expect to be a two dimensional data

  • y_pred – array-like, array containing the predicted class labels.

  • cluster_centers – NDArray containg the coordinates of the centroids or the similar points with continous features.

Example:

>>> from watex.exlib.sklearn import KMeans, MinMaxScaler
>>> from watex.utils.plotutils import plot_clusters
>>> from watex.datasets import fetch_data
>>> h= fetch_data('hlogs').frame
>>> # collect two features 'resistivity' and gamma-gamma logging values
>>> h2 = h[['resistivity', 'gamma_gamma']]
>>> km = KMeans (n_clusters =3 , init= 'random' )
>>> # scaled the data with MinMax scaler i.e. between ( 0-1)
>>> h2_scaled = MinMaxScaler().fit_transform(h2)
>>> ykm = km.fit_predict(h2_scaled )
>>> plot_clusters (3 , h2_scaled, ykm , km.cluster_centers_ )