set config and save config function in anomaly detection pycaret

This recipe explains the set config and save config function in anomaly detection using pycaret. The set config function resets the global variables and save config function saves all global variables to a pickle file

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Recipe Objective - What is the set_config, save_config function in anomaly detection in pycaret?

PyCaret provides the set_config and save_config function in the anomaly detection module.

 

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set_config function with Example:-

PyCaret provides "pycaret.anomaly.set_config()" function. The set_config function resets the global variables.

Accessible variables:-

X: Transformed dataset (X)

data_before_preprocess: data before preprocessing

seed: random state set through session_id

prep_pipe: Transformation pipeline configured through 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

master_model_container: model storage container

display_container: results display container

exp_name_log: Name of experiment set through setup

logging_param: log_experiment param set through setup

log_plots_param: log_plots param set through setup

USI: Unique session ID parameter set through setup

gpu_param: use_gpu param configured through setup

from pycaret.datasets import get_data
anmly = get_data('anomaly')
# importing anomaly module
from pycaret.anomaly import *
# initialize the setup
setup_name = setup(data = anmly)
set_config('seed', 123)

save_config function with Example:-

PyCaret provides "pycaret.anomaly.save_config()" function.

The save_config function saves all global variables to a pickle file, allowing to resume without rerunning the setup later.

from pycaret.datasets import get_data
anmly = get_data('anomaly')
# importing anomaly module
from pycaret.anomaly import *
# initialize the setup
setup_name = setup(data = anmly)
# saving our model
save_config('model_knn.pkl')

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