pycaret.clustering.ClusteringExperiment#

class pycaret.clustering.ClusteringExperiment[source]#

__init__() → None[source]#

Methods

`__init__`()
`add_metric`(id, name, score_func[, ...])	Adds a custom metric to be used in all functions.
`assign_model`(model[, transformation, score, ...])	This function assigns cluster labels to the dataset for a given model.
`convert_model`(estimator[, language])	This function transpiles trained machine learning models into native inference script in different programming languages (Python, C, Java, Go, JavaScript, Visual Basic, C#, PowerShell, R, PHP, Dart, Haskell, Ruby, F#).
`create_api`(estimator, api_name[, host, port])	This function takes an input `estimator` and creates a POST API for inference.
`create_docker`(api_name[, base_image, ...])	This function creates a `Dockerfile` and `requirements.txt` for productionalizing API end-point.
`create_model`(estimator[, num_clusters, ...])	This function trains and evaluates the performance of a given model.
`deploy_model`(model, model_name, authentication)	(In Preview)
`evaluate_model`(estimator[, fold, ...])	This function displays a user interface for analyzing performance of a trained model.
`finalize_model`()
`get_allowed_engines`(estimator)	Get all the allowed engines for the specified estimator
`get_config`([variable])	This function is used to access global environment variables.
`get_engine`(estimator)	Gets the model engine currently set in the experiment for the specified model.
`get_logs`([experiment_name, save])	Returns a table with experiment logs consisting run details, parameter, metrics and tags.
`get_metrics`([reset, include_custom, ...])	Returns table of metrics available.
`load_experiment`(path_or_file[, data, ...])	Load an experiment saved with `save_experiment` from path or file.
`load_model`(model_name[, platform, ...])	This function loads a previously saved transformation pipeline and model from the current active directory into the current python environment.
`models`([type, internal, raise_errors])	Returns table of models available in model library.
`plot_model`(estimator[, plot, scale, save, ...])	This function analyzes the performance of a trained model.
`plot_model_check_display_format_`(display_format)	Checks if the display format is in the allowed list
`predict_model`(estimator, data[, ml_usecase])	This function generates cluster labels using a trained model.
`pull`([pop])	Returns the latest displayed table.
`remove_metric`(name_or_id)	Removes a metric used for evaluation.
`save_experiment`(path_or_file, ...)	Saves the experiment to a pickle file.
`save_model`(model, model_name[, model_only, ...])	This function saves the transformation pipeline and trained model object into the current active directory as a pickle file for later use.
`set_config`([variable, value])	This function is used to reset global environment variables.
`setup`([data, data_func, index, ...])	This function initializes the training environment and creates the transformation pipeline.

Attributes

`X`	Feature set.
`X_train`	Feature set of the training set.
`X_train_transformed`	Transformed feature set of the training set.
`X_transformed`	Transformed feature set.
`dataset`	Complete dataset without ignored columns.
`dataset_transformed`	Transformed dataset.
`gpu_n_jobs_param`
`is_multiclass`	Method to check if the problem is multiclass.
`train`	Training set.
`train_transformed`	Transformed training set.
`variable_and_property_keys`
`variables`