Release: PyCaret 2.2 | Release Date: October 28, 2020
Summary of Changes
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Modules Impacted: pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly
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Separate Train and Test Set: New parameter test_data has been added in the setup function of pycaret.classification and pycaret.regression. When a DataFrame is passed into the test_data, it is used as a holdout set and the train_size parameter is ignored. test_data must be labeled and the shape of test_data must match with the shape of data.
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Disable Default Preprocessing: A new parameter preprocess has been added into the setup function. When preprocess is set to False, no transformations are applied except for train_test_split and custom transformations passed in the custom_pipeline param. Data must be ready for modeling (no missing values, no dates, categorical data encoding) when preprocess is set to False.
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Custom Metrics: New functions get_metric, add_metric and remove_metric is now added in pycaret.classification, pycaret.regression, and pycaret.clustering, that can be used to add / remove metrics used in model evaluation.
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Custom Transformations: A new parameter custom_pipeline has been added into the setup function. It takes a tuple of (str, transformer) or a list of tuples. When passed, it will append the custom transformers in the preprocessing pipeline and are applied on each CV fold separately and on the final fit. All the custom transformations are applied after train_test_split and before pycaret's internal transformations.
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GPU enabled Training: To use GPU for training use_gpu parameter in the setup function can be set to True or force. When set to True, it will use GPU with algorithms that support it and fall back on CPU for remaining. When set to force it will only use GPU-enabled algorithms and raise exceptions if they are unavailable for use. The following algorithms are supported on GPU:
- Extreme Gradient Boosting
pycaret.classification pycaret.regression
- LightGBM
pycaret.classification pycaret.regression
- CatBoost
pycaret.classification pycaret.regression
- Random Forest
pycaret.classification pycaret.regression
- K-Nearest Neighbors
pycaret.classification pycaret.regression
- Support Vector Machine
pycaret.classification pycaret.regression
- Logistic Regression
pycaret.classification
- Ridge Classifier
pycaret.classification
- Linear Regression
pycaret.regression
- Lasso Regression
pycaret.regression
- Ridge Regression
pycaret.regression
- Elastic Net (Regression)
pycaret.regression
- K-Means
pycaret.clustering
- Density-Based Spatial Clustering
pycaret.clustering
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Hyperparameter Tuning: New methods for hyperparameter tuning has been added in the tune_model function for pycaret.classification and pycaret.regression. New parameter search_library and search_algorithm in the tune_model function is added. search_library can be scikit-learn, scikit-optimize, tune-sklearn, and optuna. The search_algorithm param can take the following values based on its search_library:
- scikit-learn:
random grid
- scikit-optimize:
bayesian
- tune-sklearn:
random grid bayesian hyperopt bohb
- optuna:
random tpe
Except for scikit-learn, all the other search libraries are not hard dependencies of pycaret and must be installed separately.
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Early Stopping: Early stopping now supported for hyperparameter tuning. A new parameter early_stopping is added in the tune_model function for pycaret.classification and pycaret.regression. It is ignored when search_library is scikit-learn, or if the estimator doesn't have a 'partial_fit' attribute. It can be either an object accepted by the search library or one of the following:
asha for Asynchronous Successive Halving Algorithmhyperband for Hyperbandmedian for median stopping rule- When
False or None, early stopping will not be used.
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Iterative Imputation: Iterative imputation type for numeric and categorical missing values is now implemented. New parameters imputation_type, iterative_imptutation_iters, categorical_iterative_imputer, and numeric_iterative_imputer added in the setup function. Read the blog post for more details: https://www.linkedin.com/pulse/iterative-imputation-pycaret-22-antoni-baum/?trackingId=Shg1zF%2F%2FR5BE7XFpzfTHkA%3D%3D
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New Plots: Following new plots have been added:
- lift
pycaret.classification
- gain
pycaret.classification
- tree
pycaret.classification pycaret.regression
- feature_all
pycaret.classification pycaret.regression
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CatBoost Compatibility: CatBoostClassifier and CatBoostRegressor is now compatible with plot_model. It requires catboost>=0.23.2.
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Log Plots in MLFlow Server: You can now log any plot in the MLFlow tracking server that is available in the plot_model function. To log specific plots, pass a list containing plot IDs in the log_plots parameter. Check the documentation of the plot_model to see all available plots.
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Data Split Stratification: A new parameter data_split_stratify is added in the setup function of pycaret.classification and pycaret.regression. It controls stratification during train_test_split. When set to True, will stratify by target column. To stratify on any other columns, pass a list of column names.
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Fold Strategy: A new parameter fold_strategy is added in the setup function for pycaret.classification and pycaret.regression. By default, it is 'stratifiedkfold' for pycaret.classification and 'kfold' for pycaret.regression. Possible values are:
kfold for KFold CV;stratifiedkfold for Stratified KFold CV;groupkfold for Group KFold CV;timeseries for TimeSeriesSplit CV; or- a custom CV generator object compatible with scikit-learn.
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Global Fold Parameter: A new parameter fold has been added in the setup function for pycaret.classification and pycaret.regression. It controls the number of folds to be used in cross validation. This is a global setting that can be over-written at function level by using fold parameter within each function. Ignored when fold_strategy is a custom object.
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Fold Groups: Optional Group labels when fold_strategy is groupkfold. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing the group label.
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Transformation Pipeline: All transformations are now applied after train_test_split.
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Data Type Handling: All data types handling internally has been changed from int64 and float64 to int32 and float32 respectively in order to improve memory usage and performance, as well as for better compatibility with GPU-based algorithms.
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AutoML Behavior Change: automl function in pycaret.classification and pycaret.regression is no more re-fitting the model on the entire dataset. As such, if the model needs to be fitted on the entire dataset including the holdout set, finalize_model must be explicitly used.
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Default Tuning Grid: Default hyperparameter tuning grid for RandomForest, XGBoost, CatBoost, and LightGBM has been amended to remove extreme values for max_depth and other training intense parameters to speed up the tuning process.
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Random Forest Default Values: Default value of n_estimators for RandomForestClassifier and RandomForestRegressor has been changed from 10 to 100 to make it consistent with the default behavior of scikit-learn.
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AUC for Multiclass Classification: AUC for Multiclass target is now available in the metric evaluation.
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Google Colab Display: All output printed on screen (information grid, score grids) is now format compatible with Google Colab resulting in semantic improvements.
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Sampling Parameter Removed: sampling parameter is now removed from the setup function of pycaret.classification and pycaret.regression.
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Type Hinting: In order to make both the usage and development easier, type hints have been added to all updated pycaret functions, in accordance with best practices. Users can leverage those by using an IDE with support for type hints.
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Documentation: All Modules documentation on the website is now retired. Updated documentation is available here: https://pycaret.readthedocs.io/en/latest/
Function Level Changes
New Functions Introduced in PyCaret 2.2
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get_metrics: Returns table of available metrics used for CV.
pycaret.classification pycaret.regression pycaret.clustering
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add_metric: Adds a custom metric for model evaluation.
pycaret.classification pycaret.regression pycaret.clustering
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remove_metric: Remove custom metrics.
pycaret.classification pycaret.regression pycaret.clustering
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save_config: save all global variables to a pickle file, allowing to later resume without rerunning the setup function.
pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly
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load_config: Load global variables from pickle file into Python environment.
pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly
setup
pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly
Following new parameters have been added:
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test_data: pandas.DataFrame, default = None
If not None, test_data is used as a hold-out set, and the train_size parameter is ignored. test_data must be labeled and the shape of data and test_data must match.
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preprocess: bool, default = True
When set to False, no transformations are applied except for train_test_split and custom transformations passed in custom_pipeline param. Data must be ready for modeling (no missing values, no dates, categorical data encoding) when preprocess is set to False.
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imputation_type: str, default = 'simple'
The type of imputation to use. Can be either 'simple' or 'iterative'.
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iterative_imputation_iters: int, default = 5
The number of iterations. Ignored when imputation_type is not 'iterative'.
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categorical_iterative_imputer: str, default = 'lightgbm'
Estimator for iterative imputation of missing values in categorical features. Ignored when imputation_type is not 'iterative'.
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numeric_iterative_imputer: str, default = 'lightgbm'
Estimator for iterative imputation of missing values in numeric features. Ignored when imputation_type is set to 'simple'.
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data_split_stratify: bool or list, default = False
Controls stratification during 'train_test_split'. When set to True, will stratify by target column. To stratify on any other columns, pass a list of column names. Ignored when data_split_shuffle is False.
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fold_strategy: str or sklearn CV generator object, default = 'stratifiedkfold' / 'kfold'
Choice of cross validation strategy. Possible values are:
- 'kfold'
- 'stratifiedkfold'
- 'groupkfold'
- 'timeseries'
- a custom CV generator object compatible with scikit-learn.
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fold: int, default = 10
The number of folds to be used in cross-validation. Must be at least 2. This is a global setting that can be over-written at the function level by using the fold parameter. Ignored when fold_strategy is a custom object.
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fold_shuffle: bool, default = False
Controls the shuffle parameter of CV. Only applicable when fold_strategy is 'kfold' or 'stratifiedkfold'. Ignored when fold_strategy is a custom object.
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fold_groups: str or array-like, with shape (n_samples,), default = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
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use_gpu: str or bool, default = False
When set to 'force', will try to use GPU with all algorithms that support it, and raise exceptions if they are unavailable. When set to True, will use GPU with algorithms that support it, and fall back to CPU if they are unavailable. When False, all algorithms are trained using CPU only.
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custom_pipeline: transformer or list of transformers or tuple, default = None*
When passed, will append the custom transformers in the preprocessing pipeline and are applied on each CV fold separately and on the final fit. All the custom transformations are applied after 'train_test_split' and before pycaret's internal transformations.
compare_models
pycaret.classification pycaret.regression
Following new parameters have been added:
-
cross_validation: bool = True
When set to False, metrics are evaluated on holdout set. fold param is ignored when cross_validation is set to False.
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errors: str = "ignore"
When set to 'ignore', will skip the model with exceptions and continue. If 'raise', will stop the function when exceptions are raised.
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fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
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groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
create_model
pycaret.classification pycaret.regression
Following new parameters have been added:
-
cross_validation: bool = True
When set to False, metrics are evaluated on holdout set. fold param is ignored when cross_validation is set to False.
-
fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
-
groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
Following parameters have been removed:
- ensemble - Deprecated - use
ensemble_model function directly.
- method - Deprecated - use
ensemble_model function directly.
- system - Moved to private API.
tune_model
pycaret.classification pycaret.regression
Following new parameters have been added:
-
search_library: str, default = 'scikit-learn'
The search library used for tuning hyperparameters. Possible values:
'scikit-learn' - default, requires no further installation
https://github.com/scikit-learn/scikit-learn
'scikit-optimize' - pip install scikit-optimize
https://scikit-optimize.github.io/stable/
'tune-sklearn' - pip install tune-sklearn ray[tune]
https://github.com/ray-project/tune-sklearn
'optuna' - pip install optuna
https://optuna.org/
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search_algorithm: str, default = None
The search algorithm depends on the search_library parameter. Some search algorithms require additional libraries to be installed. When None, will use the search library-specific default algorithm.
scikit-learn possible values:
- random (default)
- grid
scikit-optimize possible values:
- bayesian (default)
tune-sklearn possible values:
- random (default)
- grid
- bayesian pip install scikit-optimize
- hyperopt pip install hyperopt
- bohb pip install hpbandster ConfigSpace
optuna possible values:
- tpe (default)
- random
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early_stopping: bool or str or object, default = False
Use early stopping to stop fitting to a hyperparameter configuration if it performs poorly. Ignored when search_library is scikit-learn, or if the estimator does not have 'partial_fit' attribute. If False or None, early stopping will not be used. Can be either an object accepted by the search library or one of the following:
- 'asha' for Asynchronous Successive Halving Algorithm
- 'hyperband' for Hyperband
- 'median' for Median Stopping Rule
- If False or None, early stopping will not be used.
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early_stopping_max_iters: int, default = 10
The maximum number of epochs to run for each sampled configuration. Ignored if early_stopping is False or None.
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fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
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groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
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return_tuner: bool, default = False
When set to True, will return a tuple of (model, tuner_object).
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tuner_verbose: bool or in, default = True
If True or above 0, will print messages from the tuner. Higher values print more messages. Ignored when verbose param is False.
ensemble_model
pycaret.classification pycaret.regression
Following new parameters have been added:
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fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
-
groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
blend_models
pycaret.classification pycaret.regression
Following new parameters have been added:
-
fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
-
groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
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weights: list, default = None
Sequence of weights (float or int) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights when None.
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The default value for the method parameter has been changed from hard to auto.
stack_models
pycaret.classification pycaret.regression
Following new parameters have been added:
-
fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
-
groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
calibrate_model
pycaret.classification
Following new parameters have been added:
-
fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
-
groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
plot_model
pycaret.classification pycaret.regression
Following new parameters have been added:
-
fold: int or scikit-learn compatible CV generator, default = None
Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the 'n_splits' parameter of the CV generator in the setup function.
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fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
-
groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
evaluate_model
pycaret.classification pycaret.regression
Following new parameters have been added:
-
fold: int or scikit-learn compatible CV generator, default = None
Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the 'n_splits' parameter of the CV generator in the setup function.
-
fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
-
groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
finalize_model
pycaret.classification pycaret.regression
Following new parameters have been added:
-
fit_kwargs: Optional[dict] = None
Dictionary of arguments passed to the fit method of the model.
-
groups: Optional[Union[str, Any]] = None
Optional group labels when 'GroupKFold' is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When a string is passed, it is interpreted as the column name in the dataset containing group labels.
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model_only: bool, default = True
When set to False, only the model object is re-trained and all the transformations in Pipeline are ignored.
models
pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly
Following new parameters have been added:
-
internal: bool, default = False
When True, will return extra columns and rows used internally.
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raise_errors: bool, default = True
When False, will suppress all exceptions, ignoring models that couldn't be created.
Source code(tar.gz)
Source code(zip)
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