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PyCaret 2.0
on Python PyPI

latest releases: 3.0.0, 3.0.0rc9, 3.0.0rc8...
3 years ago

Release: PyCaret 2.0 | Release Date: July 31, 2020

Summary of Changes

  • Experiment Logging MLFlow logging backend added. New parameters log_experiment experiment_name log_profile log_data added in setup. Available in pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp
  • Save / Load Experiment save_experiment and load_experiment function from pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp is removed in PyCaret 2.0
  • System Logging System log files now generated when setup is executed. logs.log file is saved in current working directory. Function get_system_logs can be used to access log file in notebook.
  • Command Line Support When using PyCaret 2.0 outside of Notebook, html parameter in setup must be set to False.
  • Imbalance Dataset fix_imbalance and fix_imbalance_method parameter added in setup for pycaret.classification. When set to True, SMOTE is applied by default to create synthetic datapoints for minority class. To change the method pass any class from imblearn that supports fit_resample method in fix_imbalance_method parameter.
  • Save Plot save parameter added in plot_model. When set to True, it saves the plot as png or html in current working directory.
  • kwargs kwargs** added in create_model for pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly
  • choose_better choose_better and optimize parameter added in tune_model ensemble_model blend_models stack_models create_stacknet in pycaret.classification and pycaret.regression. Read the details below to learn more about thi added in create_model for pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly
  • Training Time TT (Sec) added in compare_models function for pycaret.classification and pycaret.regression
  • New Metric: MCC MCC metric added in score grid for pycaret.classification
  • NEW FUNCTION: automl() New function automl added in pycaret.classification pycaret.regression
  • NEW FUNCTION: pull() New function pull added in pycaret.classification pycaret.regression
  • NEW FUNCTION: models() New function models added in pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp
  • NEW FUNCTION: get_logs() New function get_logs added in pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp
  • NEW FUNCTION: get_config() New function get_config added in pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp
  • NEW FUNCTION: set_config() New function set_config added in pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp
  • NEW FUNCTION: get_system_logs New function get_logs added in pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp
  • CHANGE IN BEHAVIOR: compare_models compare_models now returns top_n models defined by n_select parameter, by default set to 1.
  • CHANGE IN BEHAVIOR: tune_model tune_model function in pycaret.classification and pycaret.regression now requires trained model object to be passed as estimator instead of string abbreviation / ID.
  • REMOVED DEPENDENCIES awscli and shap removed from requirements.txt. To use interpret_model function in pycaret.classification pycaret.regression and deploy_model function in pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly, these libraries will have to be installed separately.

setup

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp

  • remove_perfect_collinearity parameter added in setup(). Default set to False.

    When set to True, perfect collinearity (features with correlation = 1) is removed from the dataset, When two features are 100% correlated, one of it is randomly dropped from the dataset.

  • fix_imbalance parameter added in setup(). Default set to False.

    When dataset has unequal distribution of target class it can be fixed using fix_imbalance parameter. When set to True, SMOTE (Synthetic Minority Over-sampling Technique) is applied by default to create synthetic datapoints for minority class.

  • fix_imbalance_method parameter added in setup(). Default set to None.

    When fix_imbalance is set to True and fix_imbalance_method is None, 'smote' is applied by default to oversample minority class during cross validation. This parameter accepts any module from 'imblearn' that supports 'fit_resample' method.

  • data_split_shuffle parameter added in setup(). Default set to True.

    If set to False, prevents shuffling of rows when splitting data.

  • folds_shuffle parameter added in setup(). Default set to False.

    If set to False, prevents shuffling of rows when using cross validation.

  • n_jobs parameter added in setup(). Default set to -1.

    The number of jobs to run in parallel (for functions that supports parallel processing) -1 means using all processors. To run all functions on single processor set n_jobs to None.

  • html parameter added in setup(). Default set to True.

    If set to False, prevents runtime display of monitor. This must be set to False when using environment that doesnt support HTML.

  • log_experiment parameter added in setup(). Default set to False.

    When set to True, all metrics and parameters are logged on MLFlow server.

  • experiment_name parameter added in setup(). Default set to None.

    Name of experiment for logging. When set to None, 'clf' is by default used as alias for the experiment name.

  • log_plots parameter added in setup(). Default set to False.

    When set to True, specific plots are logged in MLflow as a png file.

  • log_profile parameter added in setup(). Default set to False.

    When set to True, data profile is also logged on MLflow as a html file.

  • log_data parameter added in setup(). Default set to False.

    When set to True, train and test dataset are logged as csv.

  • verbose parameter added in setup(). Default set to True.

    Information grid is not printed when verbose is set to False.

compare_models

pycaret.classification pycaret.regression

  • whitelist parameter added in compare_models. Default set to None.

    In order to run only certain models for the comparison, the model ID's can be passed as a list of strings in whitelist param.

  • n_select parameter added in compare_models. Default set to 1.

    Number of top_n models to return. use negative argument for bottom selection. For example, n_select = -3 means bottom 3 models.

  • verbose parameter added in compare_models. Default set to True.

    Score grid is not printed when verbose is set to False.

create_model

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly

  • cross_validation parameter added in create_model. Default set to True.

    When cross_validation set to False fold parameter is ignored and model is trained on entire training dataset. No metric evaluation is returned. Only applicable in pycaret.classification and pycaret.regression

  • system parameter added in create_model. Default set to True.

    Must remain True all times. Only to be changed by internal functions.

  • ground_truth parameter added in create_model. Default set to None.

    When ground_truth is provided, Homogeneity Score, Rand Index, and Completeness Score is evaluated and printer along with other metrics. This is only available in pycaret.clustering

  • kwargs parameter added in create_model.

    Additional keyword arguments to pass to the estimator.

tune_model

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp

  • custom_grid parameter added in tune_model. Default set to None.

    To use custom hyperparameters for tuning pass a dictionary with parameter name and values to be iterated. When set to None it uses pre-defined tuning grid. For pycaret.clustering pycaret.anomaly pycaret.nlp, custom_grid param must be a list of values to iterate over.

  • choose_better parameter added in tune_model. Default set to False.

    When set to set to True, base estimator is returned when the performance doesn't improve by tune_model. This gurantees the returned object would perform atleast equivalent to base estimator created using create_model or model returned by compare_models.

ensemble_model

pycaret.classification pycaret.regression

  • choose_better parameter added in ensemble_model. Default set to False.

    When set to set to True, base estimator is returned when the performance doesn't improve by tune_model. This gurantees the returned object would perform atleast equivalent to base estimator created using create_model or model returned by compare_models.

  • optimize parameter added in ensemble_model. Default set to Accuracy for pycaret.classification and R2 for pycaret.regression.

    Only used when choose_better is set to True. optimize parameter is used to compare emsembled model with base estimator. Values accepted in optimize parameter for pycaret.classification are 'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC' and for pycaret.regression are 'MAE', 'MSE', 'RMSE' 'R2', 'RMSLE' and 'MAPE'.

blend_models

pycaret.classification pycaret.regression

  • choose_better parameter added in blend_models. Default set to False.

    When set to set to True, base estimator is returned when the performance doesn't improve by tune_model. This gurantees the returned object would perform atleast equivalent to base estimator created using create_model or model returned by compare_models.

  • optimize parameter added in blend_models. Default set to Accuracy for pycaret.classification and R2 for pycaret.regression.

    Only used when choose_better is set to True. optimize parameter is used to compare emsembled model with base estimator. Values accepted in optimize parameter for pycaret.classification are 'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC' and for pycaret.regression are 'MAE', 'MSE', 'RMSE' 'R2', 'RMSLE' and 'MAPE'.

stack_models

pycaret.classification pycaret.regression

  • choose_better parameter added in stack_models. Default set to False.

    When set to set to True, base estimator is returned when the performance doesn't improve by tune_model. This gurantees the returned object would perform atleast equivalent to base estimator created using create_model or model returned by compare_models.

  • optimize parameter added in stack_models. Default set to Accuracy for pycaret.classification and R2 for pycaret.regression.

    Only used when choose_better is set to True. optimize parameter is used to compare emsembled model with base estimator. Values accepted in optimize parameter for pycaret.classification are 'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC' and for pycaret.regression are 'MAE', 'MSE', 'RMSE' 'R2', 'RMSLE' and 'MAPE'.

create_stacknet

pycaret.classification pycaret.regression

  • choose_better parameter added in create_stacknet. Default set to False.

    When set to set to True, base estimator is returned when the performance doesn't improve by tune_model. This gurantees the returned object would perform atleast equivalent to base estimator created using create_model or model returned by compare_models.

  • optimize parameter added in create_stacknet. Default set to Accuracy for pycaret.classification and R2 for pycaret.regression.

    Only used when choose_better is set to True. optimize parameter is used to compare emsembled model with base estimator. Values accepted in optimize parameter for pycaret.classification are 'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC' and for pycaret.regression are 'MAE', 'MSE', 'RMSE' 'R2', 'RMSLE' and 'MAPE'.

predict_model

pycaret.classification pycaret.regression

  • verbose parameter added in predict_model. Default set to True.

    Holdout score grid is not printed when verbose is set to False.

plot_model

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp

  • save parameter added in plot_model. Default set to False.

    When set to True, Plot is saved as a 'png' file in current working directory.

  • verbose parameter added in plot_model. Default set to True.

    Progress bar not shown when verbose set to False.

  • system parameter added in plot_model. Default set to True.

    Must remain True all times. Only to be changed by internal functions.

NEW FUNCTION: automl

pycaret.classification pycaret.regression

  • This function returns the best model out of all models created in current active environment based on metric defined in optimize parameter.

Parameters:

  • optimize string, default = 'Accuracy' for pycaret.classification and 'R2' for pycaret.regression

    Other values you can pass in optimize param are 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', and 'MCC' for pycaret.classification and 'MAE', 'MSE', 'RMSE', 'R2', 'RMSLE', and 'MAPE' for pycaret.regression

  • use_holdout bool, default = False

    When set to True, metrics are evaluated on holdout set instead of CV.

NEW FUNCTION: pull

pycaret.classification pycaret.regression

  • This function returns the last printed score grid as pandas dataframe.

NEW FUNCTION: models

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp

  • This function Returns the table of models available in model library.

Parameters:

  • type string, default = None

    linear : filters and only return linear models

    tree : filters and only return tree based models

    ensemble : filters and only return ensemble models

type parameter only available in pycaret.classification and pycaret.regression

NEW FUNCTION: get_logs

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp

  • This function returns a table with experiment logs consisting run details, parameter, metrics and tags.

Parameters:

  • experiment_name string, default = None

    When set to None current active run is used.

  • save bool, default = False

    When set to True, csv file is saved in current directory.

NEW FUNCTION: get_config

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp

  • This function is used to access global environment variables. Check docstring for the list of global var accessible.

NEW FUNCTION: set_config

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp

  • This function is used to reset global environment variables. Check docstring for the list of global var accessible.

NEW FUNCTION: get_system_logs

pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.nlp

  • This function is reads and print 'logs.log' file from current active directory. logs.log is generated from setup is initialized in any module.
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