others.md

description
Other Functions in PyCaret

Others

pull

Returns the last printed scoring grid. Use pull function after any training function to store the scoring grid in pandas.DataFrame.

Example

# loading dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable')

# compare models
best_model = compare_models()

# get the scoring grid
results = pull()

type(results)
>>> pandas.core.frame.DataFrame

models

Return a table containing all the models available in the imported module of the model library.

Example

# loading dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable')

# check model library
models()

If you want to see a little more information than this, you can pass internal=True.

# loading dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable')

# check model library
models(internal = True)

get_config

This function retrieves the global variables created when initializing the setup function.

Example

# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable')

# get X_train
get_config('X_train')

Variables accessible by get_config function:

• X: Transformed dataset (X)
• y: Transformed dataset (y)
• X_train: Transformed train dataset (X)
• X_test: Transformed test/holdout dataset (X)
• y_train: Transformed train dataset (y)
• y_test: Transformed test/holdout dataset (y)
• seed: random state set through session_id
• prep_pipe: Transformation pipeline
• fold_shuffle_param: shuffle parameter used in Kfolds
• 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
• logging_param: log_experiment param
• log_plots_param: log_plots param
• USI: Unique session ID parameter
• fix_imbalance_param: fix_imbalance param
• fix_imbalance_method_param: fix_imbalance_method param
• data_before_preprocess: data before preprocessing
• target_param: name of target variable
• gpu_param: use_gpu param configured through setup
• fold_generator: CV splitter configured in fold_strategy
• fold_param: fold params defined in the setup
• fold_groups_param: fold groups defined in the setup
• stratify_param: stratify parameter defined in the setup

set_config

This function resets the global variables.

Example

# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', session_id = 123)

# reset environment seed
set_config('seed', 999) 

get_metrics

Returns the table of all the available metrics in the metric container. All these metrics are used for cross-validation.

# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', session_id = 123)

# get metrics
get_metrics()

add_metric

Adds a custom metric to the metric container.

# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', session_id = 123)

# add metric
from sklearn.metrics import log_loss
add_metric('logloss', 'Log Loss', log_loss, greater_is_better = False)

Now if you check metric container:

get_metrics()

remove_metric

Removes a metric from the metric container.

# remove metric
remove_metric('logloss')

No Output. Let's check the metric container again.

get_metrics()

automl

This function returns the best model out of all trained models in the current setup based on the optimize parameter. Metrics evaluated can be accessed using the get_metrics function.

Example

# load dataset 
from pycaret.datasets import get_data 
data = get_data('diabetes') 

# init setup 
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable') 

# compare models
top5 = compare_models(n_select = 5) 

# tune models
tuned_top5 = [tune_model(i) for i in top5]

# ensemble models
bagged_top5 = [ensemble_model(i) for i in tuned_top5]

# blend models
blender = blend_models(estimator_list = top5) 

# stack models
stacker = stack_models(estimator_list = top5) 

# automl 
best = automl(optimize = 'Recall')
print(best)

get_logs

Returns a table of experiment logs. Only works when log_experiment = True when initializing the setup function.

Example

# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', log_experiment = True, experiment_name = 'diabetes1')

# compare models
top5 = compare_models()

# check ML logs
get_logs()

get_system_logs

Read and print logs.log file from current active directory.

Example

# loading dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', session_id = 123)

# check system logs
from pycaret.utils import get_system_logs
get_system_logs()