watex.utils.plot_elbow#

watex.utils.plot_elbow(X, n_clusters, n_init=10, max_iter=300, random_state=42, fig_size=(10, 4), marker='o', savefig=None, **kwd)[source]#

Plot elbow method to find the optimal number of cluster, k’, for a given data.

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.
n_clusters (int, default=8) – The number of clusters to form as well as the number of centroids to generate.
n_init (int, default=10) – Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.
max_iter (int, default=300) – Maximum number of iterations of the k-means algorithm for a single run.
tol (float, default=1e-4) – Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence.
verbose (int, default=0) – Verbosity mode.
random_state (int, RandomState instance or None, default=42) – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic.
savefig (str, default =None ,) – the path to save the figure. Argument is passed to matplotlib.Figure class.
marker (str, default='o',) – cluster marker point.
kwd (dict) – Addionnal keywords arguments passed to matplotlib.pyplot.plot()

Returns:

Return type:

Matplotlib.pyplot axes objects

Example

>>> from watex.datasets import load_hlogs
>>> from watex.utils.plotutils import plot_elbow
>>> # get the only resistivy and gamma-gama values for example
>>> res_gamma = load_hlogs ().frame[['resistivity', 'gamma_gamma']]
>>> plot_elbow(res_gamma, n_clusters=11)