Cluster Analysis

[1]:

import data_describe as dd

Load dataset from scikit-learn

[2]:

from sklearn.datasets import load_wine
import pandas as pd
df = load_wine(as_frame=True).data
target = load_wine().target  # For supervised clustering

Cluster Defaults

[3]:

c = dd.cluster(df)

[5]:

c.show()

[5]:

<matplotlib.axes._subplots.AxesSubplot at 0x1f82f8ff988>
../_images/examples_cluster_analysis_6_1.png

Using Plotly

[6]:

dd.cluster(df, target=target, viz_backend="plotly")

None

[6]:

<data_describe.core.clusters.KmeansClusterWidget at 0x1f96000eb88>

Show Cluster Search for K-Means

[7]:

cl = dd.cluster(df, target=target)
cl.cluster_search_plot()

[7]:

<matplotlib.axes._subplots.AxesSubplot at 0x1f960c7fcc8>
../_images/examples_cluster_analysis_10_1.png

Visualize using t-SNE for Dimensionality Reduction

[8]:

dd.cluster(df, target=target, dim_method="tsne")

<matplotlib.axes._subplots.AxesSubplot at 0x1f960d24248>

[8]:

<data_describe.core.clusters.KmeansClusterWidget at 0x1f960cf5988>
../_images/examples_cluster_analysis_12_2.png

Return Reduced Data with Cluster Labels

[9]:

cl.viz_data.head()

[9]:
x y clusters
0 3.316751 -1.443463 0
1 2.209465 0.333393 0
2 2.516740 -1.031151 0
3 3.757066 -2.756372 0
4 1.008908 -0.869831 0

Return Cluster Labels Only

[10]:

cl.clusters

[10]:

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2])

KMeans - Specifying number of clusters

[11]:

dd.cluster(df, n_clusters=4)

<matplotlib.axes._subplots.AxesSubplot at 0x1f960dc7588>

[11]:

<data_describe.core.clusters.KmeansClusterWidget at 0x1f960dc2c48>
../_images/examples_cluster_analysis_18_2.png

KMeans - Using Davies-Bouldin for finding optimal n_clusters

[12]:

cl = dd.cluster(df, target=target, metric='davies_bouldin_score')
cl

<matplotlib.axes._subplots.AxesSubplot at 0x1f9610e1a88>

[12]:

<data_describe.core.clusters.KmeansClusterWidget at 0x1f960d9aa88>
../_images/examples_cluster_analysis_20_2.png 
[13]:

cl.cluster_search_plot()

[13]:

<matplotlib.axes._subplots.AxesSubplot at 0x1f96117cd48>
../_images/examples_cluster_analysis_21_1.png

HDBSCAN

[14]:

dd.cluster(df, method="hdbscan", viz_backend="plotly")

None

[14]:

<data_describe.core.clusters.HDBSCANClusterWidget at 0x1f9611fe448>