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Other Functions in PyCaret
pull
Returns the last printed scoring grid. Use pull function after any training function to store the scoring grid in pandas.DataFrame.
Example
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# loading dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable')
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​
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# compare models
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best_model = compare_models()
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​
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# get the scoring grid
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results = pull()
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Output from pull()
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type(results)
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>>> pandas.core.frame.DataFrame
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models
Return a table containing all the models available in the imported module of the model library.
Example
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# loading dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable')
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​
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# check model library
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models()
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Output from models()
If you want to see a little more information than this, you can pass internal=True.
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# loading dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable')
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​
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# check model library
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models(internal = True)
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Output from models(internal = True)
get_config
This function retrieves the global variables created when initializing the setup function. 
Example
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# load dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable')
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​
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# get X_train
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get_config('X_train')
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Output from 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
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# load dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable', session_id = 123)
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​
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# reset environment seed
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set_config('seed', 999) 
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get_metrics
Returns the table of all the available metrics in the metric container. All these metrics are used for cross-validation.
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# load dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable', session_id = 123)
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​
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# get metrics
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get_metrics()
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Output from get_metrics()
add_metric
Adds a custom metric to the metric container. 
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# load dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable', session_id = 123)
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​
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# add metric
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from sklearn.metrics import log_loss
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add_metric('logloss', 'Log Loss', log_loss, greater_is_better = False)
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Output from add_metric('logloss', 'Log Loss', log_loss, greater_is_better = False)
Now if you check metric container:
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get_metrics()
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Output from get_metrics() (after adding log loss metric)
remove_metric
Removes a metric from the metric container.
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# remove metric
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remove_metric('logloss')
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No Output. Let's check the metric container again.
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get_metrics()
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Output from get_metrics() (after removing log loss metric)
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
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# load dataset 
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from pycaret.datasets import get_data 
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data = get_data('diabetes') 
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​
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# init setup 
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable') 
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​
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# compare models
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top5 = compare_models(n_select = 5) 
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​
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# tune models
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tuned_top5 = [tune_model(i) for i in top5]
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​
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# ensemble models
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bagged_top5 = [ensemble_model(i) for i in tuned_top5]
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​
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# blend models
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blender = blend_models(estimator_list = top5) 
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​
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# stack models
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stacker = stack_models(estimator_list = top5) 
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​
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# automl 
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best = automl(optimize = 'Recall')
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print(best)
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Output from print(best)
get_logs
Returns a table of experiment logs. Only works when log_experiment = True when initializing the setup function.
Example
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# load dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable', log_experiment = True, experiment_name = 'diabetes1')
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​
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# compare models
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top5 = compare_models()
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​
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# check ML logs
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get_logs()
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Output from get_logs()
get_system_logs
Read and print logs.log file from current active directory.
Example
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# loading dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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​
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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable', session_id = 123)
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​
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# check system logs
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from pycaret.utils import get_system_logs
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get_system_logs()
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