Others
Other functions in PyCaret
Returns the last printed scoring grid. Use
pull function after any training function to store the scoring grid in pandas.DataFrame.1
# loading dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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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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# compare models
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best_model = compare_models()
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# get the scoring grid
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results = pull()
Output from pull()
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type(results)
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# >>> pandas.core.frame.DataFrame
Return a table containing all the models available in the imported module of the model library.
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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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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable')
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# check model library
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models()
Output from models()
If you want to see a little more information than this, you can pass
internal=True.1
# loading dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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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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# check model library
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models(internal = True)
Output from models(internal = True)
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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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# init setup
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from pycaret.classification import *
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clf1 = setup(data, target = 'Class variable')
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# get X_train
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get_config('X_train')
Output from get_config('X_train')
To check all accessible parameters with
get_config:1
# check all available param
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get_config()
Variables accessible by
get_config function:- 'USI'
- 'X'
- 'X_test'
- 'X_test_transformed'
- 'X_train'
- 'X_train_transformed'
- 'X_transformed'
- 'data'
- 'dataset'
- 'dataset_transformed'
- 'exp_id'
- 'exp_name_log'
- 'fix_imbalance'
- 'fold_generator'
- 'fold_groups_param'
- 'fold_shuffle_param'
- 'gpu_n_jobs_param'
- 'gpu_param'
- 'html_param'
- 'idx'
- 'is_multiclass'
- 'log_plots_param'
- 'logging_param'
- 'memory'
- 'n_jobs_param'
- 'pipeline'
- 'seed'
- 'target_param'
- 'test'
- 'test_transformed'
- 'train'
- 'train_transformed'
- 'variable_and_property_keys'
- 'variables'
- 'y'
- 'y_test'
- 'y_test_transformed'
- 'y_train'
- 'y_train_transformed'
- 'y_transformed'
This function resets the global variables.
1
# load dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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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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# reset environment seed
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set_config('seed', 999)
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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# 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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# get metrics
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get_metrics()
Output from get_metrics()
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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# 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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# 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)
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()
Output from get_metrics() (after adding log loss metric)
Removes a metric from the metric container.
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# remove metric
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remove_metric('logloss')
No Output. Let's check the metric container again.
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get_metrics()
Output from get_metrics() (after removing log loss metric)
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.1
# load dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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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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# compare models
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top5 = compare_models(n_select = 5)
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# tune models
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tuned_top5 = [tune_model(i) for i in top5]
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# ensemble models
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bagged_top5 = [ensemble_model(i) for i in tuned_top5]
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# blend models
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blender = blend_models(estimator_list = top5)
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# stack models
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stacker = stack_models(estimator_list = top5)
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# automl
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best = automl(optimize = 'Recall')
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print(best)
Output from print(best)
Returns a table of experiment logs. Only works when
log_experiment = True when initializing the setup function.1
# load dataset
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from pycaret.datasets import get_data
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data = get_data('diabetes')
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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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# compare models
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top5 = compare_models()
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# check ML logs
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get_logs()
Output from get_logs()
Obtain the current experiment object and return a class. This is useful when you are using a functional API and want to move to an OOP API.
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# loading dataset
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from pycaret.datasets import get_data
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data = get_data('insurance')
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# init setup using functional API
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from pycaret.regression import *
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s = setup(data, target = 'charges', session_id = 123)
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# compare models
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best = compare_models()
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# return OOP class for current functional experiment
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reg1 = get_current_experiment()
Set the current experiment created using the OOP API to be used with the functional API.
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# loading dataset
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from pycaret.datasets import get_data
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data = get_data('insurance')
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# init setup using OOP API
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from pycaret.regression import RegressionExperiment
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reg1 = RegressionExperiment()
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reg1.setup(data, target = 'charges', session_id = 123)
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# compare models
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best = compare_models()
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# set OOP experiment as functional
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set_current_experiment(reg1)
Last modified 2mo ago