get logs and get config function in clustering model in pycaret
This recipe explains the get logs and get config function in clustering model in pycaret. The get logs function returns a table of experiment logs and the get config function retrieves the global variables created when initializing the setup function.
Recipe Objective - What are the get_logs and get_config functions in the clustering model in pycaret?
PyCaret provides the get_logs and get_config function in the clustering module.
Table of Contents
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get_logs function with Example:-
PyCaret provides "pycaret.clustering.get_logs()" function.
The get_logs function returns a table of experiment logs. Only works when log_experiment is True when initializing the setup function.
from pycaret.datasets import get_data
jewll = get_data('jewellery')
# importing clustering module
from pycaret.clustering import *
# initialize the setup
setup_name = setup(data = jewll, log_experiment = True)
# k_means clustering
k_m = create_model('kmeans')
get_log = get_logs()
get_log
get_config function with Example:-
PyCaret provides "pycaret.clustering.get_config()" function. The get_config function retrieves the global variables created when initializing the setup function.
X: Transformed dataset (X)
data_before_preprocess: data before preprocessing
seed: random state set through session_id
prep_pipe: Transformation pipeline configured through the setup
n_jobs_param: n_jobs parameter used in model training
html_param: html_param configured through setup
create_model_container: results grid storage container
from pycaret.datasets import get_data
jewll = get_data('jewellery')
# importing clustering module
from pycaret.clustering import *
# initialize the setup
setup_name = setup(data = jewll)
X = get_config('X')
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