Last updated: 16 Feb 2023

👋 PyCaret Clustering Tutorial¶

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that exponentially speeds up the experiment cycle and makes you more productive.

Compared with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks, such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more.

The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise.

💻 Installation¶

PyCaret is tested and supported on the following 64-bit systems:

Python 3.7 – 3.10
Python 3.9 for Ubuntu only
Ubuntu 16.04 or later
Windows 7 or later

You can install PyCaret with Python's pip package manager:

pip install pycaret

PyCaret's default installation will not install all the extra dependencies automatically. For that you will have to install the full version:

pip install pycaret[full]

or depending on your use-case you may install one of the following variant:

pip install pycaret[analysis]
pip install pycaret[models]
pip install pycaret[tuner]
pip install pycaret[mlops]
pip install pycaret[parallel]
pip install pycaret[test]

In [1]:

# check installed version
import pycaret
pycaret.__version__

Out[1]:

'3.0.0'

🚀 Quick start¶

PyCaret's Clustering Module is an unsupervised machine learning module that performs the task of grouping a set of objects in such a way that objects in the same group (also known as a cluster) are more similar to each other than to those in other groups.

It provides several pre-processing features that prepare the data for modeling through the setup function. It has over 10 ready-to-use algorithms and several plots to analyze the performance of trained models.

A typical workflow in PyCaret's unsupervised module consist of following 6 steps in this order:

Setup ➡️ Create Model ➡️ Assign Labels ➡️ Analyze Model ➡️ Prediction ➡️ Save Model¶

In [2]:

# loading sample dataset from pycaret dataset module
from pycaret.datasets import get_data
data = get_data('jewellery')

	Age	Income	SpendingScore	Savings
0	58	77769	0.791329	6559.829923
1	59	81799	0.791082	5417.661426
2	62	74751	0.702657	9258.992965
3	59	74373	0.765680	7346.334504
4	87	17760	0.348778	16869.507130

Setup¶

This function initializes the training environment and creates the transformation pipeline. Setup function must be called before executing any other function in PyCaret. It only has one required parameter i.e. data. All the other parameters are optional.

In [3]:

# import pycaret clustering and init setup
from pycaret.clustering import *
s = setup(data, session_id = 123)

	Description	Value
0	Session id	123
1	Original data shape	(505, 4)
2	Transformed data shape	(505, 4)
3	Numeric features	4
4	Preprocess	True
5	Imputation type	simple
6	Numeric imputation	mean
7	Categorical imputation	mode
8	CPU Jobs	-1
9	Use GPU	False
10	Log Experiment	False
11	Experiment Name	cluster-default-name
12	USI	3c6c

Once the setup has been successfully executed it shows the information grid containing experiment level information.

Session id: A pseudo-random number distributed as a seed in all functions for later reproducibility. If no session_id is passed, a random number is automatically generated that is distributed to all functions.

- **Original data shape:** Shape of the original data prior to any transformations.

- **Transformed data shape:** Shape of data after transformations

- **Numeric features :** The number of features considered as numerical.

- **Categorical features :** The number of features considered as categorical.

PyCaret has two set of API's that you can work with. (1) Functional (as seen above) and (2) Object Oriented API.

With Object Oriented API instead of executing functions directly you will import a class and execute methods of class.

In [4]:

# import ClusteringExperiment and init the class
from pycaret.clustering import ClusteringExperiment
exp = ClusteringExperiment()

In [5]:

# check the type of exp
type(exp)

Out[5]:

pycaret.clustering.oop.ClusteringExperiment

In [6]:

# init setup on exp
exp.setup(data, session_id = 123)

	Description	Value
0	Session id	123
1	Original data shape	(505, 4)
2	Transformed data shape	(505, 4)
3	Numeric features	4
4	Preprocess	True
5	Imputation type	simple
6	Numeric imputation	mean
7	Categorical imputation	mode
8	CPU Jobs	-1
9	Use GPU	False
10	Log Experiment	False
11	Experiment Name	cluster-default-name
12	USI	6c6d

Out[6]:

<pycaret.clustering.oop.ClusteringExperiment at 0x16a6fe44e50>

You can use any of the two method i.e. Functional or OOP and even switch back and forth between two set of API's. The choice of method will not impact the results and has been tested for consistency. ___

Create Model¶

This function trains and evaluates the performance of a given model. Metrics evaluated can be accessed using the get_metrics function. Custom metrics can be added or removed using the add_metric and remove_metric function. All the available models can be accessed using the models function.

In [7]:

# train kmeans model
kmeans = create_model('kmeans')

	Silhouette	Calinski-Harabasz	Davies-Bouldin	Homogeneity	Rand Index	Completeness
0	0.7207	5011.8115	0.4114	0	0	0

Processing:   0%|          | 0/3 [00:00<?, ?it/s]

In [8]:

# to check all the available models
models()

Out[8]:

	Name	Reference
ID
kmeans	K-Means Clustering	sklearn.cluster._kmeans.KMeans
ap	Affinity Propagation	sklearn.cluster._affinity_propagation.Affinity...
meanshift	Mean Shift Clustering	sklearn.cluster._mean_shift.MeanShift
sc	Spectral Clustering	sklearn.cluster._spectral.SpectralClustering
hclust	Agglomerative Clustering	sklearn.cluster._agglomerative.AgglomerativeCl...
dbscan	Density-Based Spatial Clustering	sklearn.cluster._dbscan.DBSCAN
optics	OPTICS Clustering	sklearn.cluster._optics.OPTICS
birch	Birch Clustering	sklearn.cluster._birch.Birch
kmodes	K-Modes Clustering	kmodes.kmodes.KModes

In [9]:

# train meanshift model
meanshift = create_model('meanshift')

	Silhouette	Calinski-Harabasz	Davies-Bouldin	Homogeneity	Rand Index	Completeness
0	0.7393	3567.5370	0.3435	0	0	0

Processing:   0%|          | 0/3 [00:00<?, ?it/s]

Assign Model¶

This function assigns cluster labels to the training data, given a trained model.

In [10]:

kmeans_cluster = assign_model(kmeans)
kmeans_cluster

Out[10]:

	Age	Income	SpendingScore	Savings	Cluster
0	58	77769	0.791329	6559.830078	Cluster 2
1	59	81799	0.791082	5417.661621	Cluster 2
2	62	74751	0.702657	9258.993164	Cluster 2
3	59	74373	0.765680	7346.334473	Cluster 2
4	87	17760	0.348778	16869.507812	Cluster 0
...	...	...	...	...	...
500	28	101206	0.387441	14936.775391	Cluster 1
501	93	19934	0.203140	17969.693359	Cluster 0
502	90	35297	0.355149	16091.402344	Cluster 0
503	91	20681	0.354679	18401.087891	Cluster 0
504	89	30267	0.289310	14386.351562	Cluster 0

505 rows × 5 columns

Analyze Model¶

You can use the plot_model function to analyzes the performance of a trained model on the test set. It may require re-training the model in certain cases.

In [11]:

# plot pca cluster plot 
plot_model(kmeans, plot = 'cluster')