Clustering

pycaret.clustering.setup(data, preprocess: bool = True, imputation_type: str = 'simple', iterative_imputation_iters: int = 5, categorical_features: Optional[List[str]] = None, categorical_imputation: str = 'mode', categorical_iterative_imputer: Union[str, Any] = 'lightgbm', ordinal_features: Optional[Dict[str, list]] = None, high_cardinality_features: Optional[List[str]] = None, high_cardinality_method: str = 'frequency', numeric_features: Optional[List[str]] = None, numeric_imputation: str = 'mean', numeric_iterative_imputer: Union[str, Any] = 'lightgbm', date_features: Optional[List[str]] = None, ignore_features: Optional[List[str]] = None, normalize: bool = False, normalize_method: str = 'zscore', transformation: bool = False, transformation_method: str = 'yeo-johnson', handle_unknown_categorical: bool = True, unknown_categorical_method: str = 'least_frequent', pca: bool = False, pca_method: str = 'linear', pca_components: Optional[float] = None, ignore_low_variance: bool = False, combine_rare_levels: bool = False, rare_level_threshold: float = 0.1, bin_numeric_features: Optional[List[str]] = None, remove_multicollinearity: bool = False, multicollinearity_threshold: float = 0.9, remove_perfect_collinearity: bool = False, group_features: Optional[List[str]] = None, group_names: Optional[List[str]] = None, n_jobs: Optional[int] = -1, use_gpu: bool = False, custom_pipeline: Optional[Union[Any, Tuple[str, Any], List[Any], List[Tuple[str, Any]]]] = None, html: bool = True, session_id: Optional[int] = None, log_experiment: Union[bool, str, BaseLogger, List[Union[str, BaseLogger]]] = False, experiment_name: Optional[str] = None, experiment_custom_tags: Optional[Dict[str, Any]] = None, log_plots: Union[bool, list] = False, log_profile: bool = False, log_data: bool = False, silent: bool = False, verbose: bool = True, profile: bool = False, profile_kwargs: Optional[Dict[str, Any]] = None)

This function initializes the training environment and creates the transformation pipeline. Setup function must be called before executing any other function. It takes one mandatory parameter: data. All the other parameters are optional.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
data: pandas.DataFrame

Shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features.

preprocess: bool, default = True

When set to False, no transformations are applied except for 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.

imputation_type: str, default = ‘simple’

The type of imputation to use. Can be either ‘simple’ or ‘iterative’.

iterative_imputation_iters: int, default = 5

Number of iterations. Ignored when imputation_type is not ‘iterative’.

categorical_features: list of str, default = None

If the inferred data types are not correct or the silent param is set to True, categorical_features param can be used to overwrite or define the data types. It takes a list of strings with column names that are categorical.

categorical_imputation: str, default = ‘constant’

Missing values in categorical features are imputed with a constant ‘not_available’ value. The other available option is ‘mode’.

categorical_iterative_imputer: str, default = ‘lightgbm’

Estimator for iterative imputation of missing values in categorical features. Ignored when imputation_type is not ‘iterative’.

ordinal_features: dict, default = None

Encode categorical features as ordinal. For example, a categorical feature with ‘low’, ‘medium’, ‘high’ values where low < medium < high can be passed as ordinal_features = { ‘column_name’ : [‘low’, ‘medium’, ‘high’] }.

high_cardinality_features: list of str, default = None

When categorical features contains many levels, it can be compressed into fewer levels using this parameter. It takes a list of strings with column names that are categorical.

high_cardinality_method: str, default = ‘frequency’

Categorical features with high cardinality are replaced with the frequency of values in each level occurring in the training dataset. Other available method is ‘clustering’ which trains the K-Means clustering algorithm on the statistical attribute of the training data and replaces the original value of feature with the cluster label. The number of clusters is determined by optimizing Calinski-Harabasz and Silhouette criterion.

numeric_features: list of str, default = None

If the inferred data types are not correct or the silent param is set to True, numeric_features param can be used to overwrite or define the data types. It takes a list of strings with column names that are numeric.

numeric_imputation: str, default = ‘mean’

Missing values in numeric features are imputed with ‘mean’ value of the feature in the training dataset. The other available option is ‘median’ or ‘zero’.

numeric_iterative_imputer: str, default = ‘lightgbm’

Estimator for iterative imputation of missing values in numeric features. Ignored when imputation_type is set to ‘simple’.

date_features: list of str, default = None

If the inferred data types are not correct or the silent param is set to True, date_features param can be used to overwrite or define the data types. It takes a list of strings with column names that are DateTime.

ignore_features: list of str, default = None

ignore_features param can be used to ignore features during model training. It takes a list of strings with column names that are to be ignored.

normalize: bool, default = False

When set to True, it transforms the numeric features by scaling them to a given range. Type of scaling is defined by the normalize_method parameter.

normalize_method: str, default = ‘zscore’

Defines the method for scaling. By default, normalize method is set to ‘zscore’ The standard zscore is calculated as z = (x - u) / s. Ignored when normalize is not True. The other options are:

  • minmax: scales and translates each feature individually such that it is in the range of 0 - 1.

  • maxabs: scales and translates each feature individually such that the maximal absolute value of each feature will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.

  • robust: scales and translates each feature according to the Interquartile range. When the dataset contains outliers, robust scaler often gives better results.

transformation: bool, default = False

When set to True, it applies the power transform to make data more Gaussian-like. Type of transformation is defined by the transformation_method parameter.

transformation_method: str, default = ‘yeo-johnson’

Defines the method for transformation. By default, the transformation method is set to ‘yeo-johnson’. The other available option for transformation is ‘quantile’. Ignored when transformation is not True.

handle_unknown_categorical: bool, default = True

When set to True, unknown categorical levels in unseen data are replaced by the most or least frequent level as learned in the training dataset.

unknown_categorical_method: str, default = ‘least_frequent’

Method used to replace unknown categorical levels in unseen data. Method can be set to ‘least_frequent’ or ‘most_frequent’.

pca: bool, default = False

When set to True, dimensionality reduction is applied to project the data into a lower dimensional space using the method defined in pca_method parameter.

pca_method: str, default = ‘linear’

The ‘linear’ method performs uses Singular Value Decomposition. Other options are:

  • kernel: dimensionality reduction through the use of RVF kernel.

  • incremental: replacement for ‘linear’ pca when the dataset is too large.

pca_components: int or float, default = None

Number of components to keep. if pca_components is a float, it is treated as a target percentage for information retention. When pca_components is an integer it is treated as the number of features to be kept. pca_components must be less than the original number of features. Ignored when pca is not True.

ignore_low_variance: bool, default = False

When set to True, all categorical features with insignificant variances are removed from the data. The variance is calculated using the ratio of unique values to the number of samples, and the ratio of the most common value to the frequency of the second most common value.

combine_rare_levels: bool, default = False

When set to True, frequency percentile for levels in categorical features below a certain threshold is combined into a single level.

rare_level_threshold: float, default = 0.1

Percentile distribution below which rare categories are combined. Ignored when combine_rare_levels is not True.

bin_numeric_features: list of str, default = None

To convert numeric features into categorical, bin_numeric_features parameter can be used. It takes a list of strings with column names to be discretized. It does so by using ‘sturges’ rule to determine the number of clusters and then apply KMeans algorithm. Original values of the feature are then replaced by the cluster label.

remove_multicollinearity: bool, default = False

When set to True, features with the inter-correlations higher than the defined threshold are removed. Only considers numeric features.

multicollinearity_threshold: float, default = 0.9

Threshold for correlated features. Ignored when remove_multicollinearity is not True.

remove_perfect_collinearity: bool, default = True

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 removed from the dataset.

group_features: list or list of list, default = None

When the dataset contains features with related characteristics, group_features parameter can be used for feature extraction. It takes a list of strings with column names that are related.

group_names: list, default = None

Group names to be used in naming new features. When the length of group_names does not match with the length of group_features, new features are named sequentially group_1, group_2, etc. It is ignored when group_features is None.

n_jobs: int, default = -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.

use_gpu: bool or str, default = False

When set to True, it will use GPU for training with algorithms that support it, and fall back to CPU if they are unavailable. When set to ‘force’, it will only use GPU-enabled algorithms and raise exceptions when they are unavailable. When False, all algorithms are trained using CPU only.

GPU enabled algorithms:

custom_pipeline: (str, transformer) or list of (str, transformer), 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 before pycaret’s internal transformations.

html: bool, default = True

When set to False, prevents runtime display of monitor. This must be set to False when the environment does not support IPython. For example, command line terminal, Databricks Notebook, Spyder and other similar IDEs.

session_id: int, default = None

Controls the randomness of experiment. It is equivalent to ‘random_state’ in scikit-learn. When None, a pseudo random number is generated. This can be used for later reproducibility of the entire experiment.

log_experiment: bool, default = False

A (list of) PyCaret BaseLogger or str (one of ‘mlflow’, ‘wandb’) corresponding to a logger to determine which experiment loggers to use. Setting to True will use just MLFlow. If wandb (Weights & Biases) is installed, will also log there.

experiment_name: str, default = None

Name of the experiment for logging. Ignored when log_experiment is False.

experiment_custom_tags: dict, default = None

Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

log_plots: bool or list, default = False

When set to True, certain plots are logged automatically in the MLFlow server. To change the type of plots to be logged, pass a list containing plot IDs. Refer to documentation of plot_model. Ignored when log_experiment is False.

log_profile: bool, default = False

When set to True, data profile is logged on the MLflow server as a html file. Ignored when log_experiment is False.

log_data: bool, default = False

When set to True, dataset is logged on the MLflow server as a csv file. Ignored when log_experiment is False.

silent: bool, default = False

Controls the confirmation input of data types when setup is executed. When executing in completely automated mode or on a remote kernel, this must be True.

verbose: bool, default = True

When set to False, Information grid is not printed.

profile: bool, default = False

When set to True, an interactive EDA report is displayed.

profile_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the ProfileReport method used to create the EDA report. Ignored if profile is False.

Returns

Global variables that can be changed using the set_config function.

pycaret.clustering.create_model(model: Union[str, Any], num_clusters: int = 4, ground_truth: Optional[str] = None, round: int = 4, fit_kwargs: Optional[dict] = None, verbose: bool = True, experiment_custom_tags: Optional[Dict[str, Any]] = None, **kwargs)

This function trains and evaluates the performance of a given model. Metrics evaluated can be accessed using the get_metrics function. Custom metrics can be added or removed using the add_metric and remove_metric function. All the available models can be accessed using the models function.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
model: str or scikit-learn compatible object

ID of an model available in the model library or pass an untrained model object consistent with scikit-learn API. Models available in the model library (ID - Name):

  • ‘kmeans’ - K-Means Clustering

  • ‘ap’ - Affinity Propagation

  • ‘meanshift’ - Mean shift Clustering

  • ‘sc’ - Spectral Clustering

  • ‘hclust’ - Agglomerative Clustering

  • ‘dbscan’ - Density-Based Spatial Clustering

  • ‘optics’ - OPTICS Clustering

  • ‘birch’ - Birch Clustering

  • ‘kmodes’ - K-Modes Clustering

num_clusters: int, default = 4

The number of clusters to form.

ground_truth: str, default = None

ground_truth to be provided to evaluate metrics that require true labels. When None, such metrics are returned as 0.0. All metrics evaluated can be accessed using get_metrics function.

round: int, default = 4

Number of decimal places the metrics in the score grid will be rounded to.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

verbose: bool, default = True

Status update is not printed when verbose is set to False.

experiment_custom_tags: dict, default = None

Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.

**kwargs:

Additional keyword arguments to pass to the estimator.

Returns

Trained Model

Warning

  • num_clusters param not required for Affinity Propagation (‘ap’), Mean shift (‘meanshift’), Density-Based Spatial Clustering (‘dbscan’) and OPTICS Clustering (‘optics’).

  • When fit doesn’t converge in Affinity Propagation (‘ap’) model, all datapoints are labelled as -1.

  • Noisy samples are given the label -1, when using Density-Based Spatial (‘dbscan’) or OPTICS Clustering (‘optics’).

  • OPTICS (‘optics’) clustering may take longer training times on large datasets.

pycaret.clustering.assign_model(model, transformation: bool = False, verbose: bool = True) → DataFrame

This function assigns cluster labels to the dataset for a given model.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
>>> kmeans_df = assign_model(kmeans)
model: scikit-learn compatible object

Trained model object

transformation: bool, default = False

Whether to apply cluster labels on the transformed dataset.

verbose: bool, default = True

Status update is not printed when verbose is set to False.

Returns

pandas.DataFrame

pycaret.clustering.plot_model(model, plot: str = 'cluster', feature: Optional[str] = None, label: bool = False, scale: float = 1, save: bool = False, display_format: Optional[str] = None)

This function analyzes the performance of a trained model.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
>>> plot_model(kmeans, plot = 'cluster')
model: scikit-learn compatible object

Trained Model Object

plot: str, default = ‘cluster’

List of available plots (ID - Name):

  • ‘cluster’ - Cluster PCA Plot (2d)

  • ‘tsne’ - Cluster TSnE (3d)

  • ‘elbow’ - Elbow Plot

  • ‘silhouette’ - Silhouette Plot

  • ‘distance’ - Distance Plot

  • ‘distribution’ - Distribution Plot

feature: str, default = None

Feature to be evaluated when plot = ‘distribution’. When plot type is ‘cluster’ or ‘tsne’ feature column is used as a hoverover tooltip and/or label when the label param is set to True. When the plot type is ‘cluster’ or ‘tsne’ and feature is None, first column of the dataset is used.

label: bool, default = False

Name of column to be used as data labels. Ignored when plot is not ‘cluster’ or ‘tsne’.

scale: float, default = 1

The resolution scale of the figure.

save: bool, default = False

When set to True, plot is saved in the current working directory.

display_format: str, default = None

To display plots in Streamlit (https://www.streamlit.io/), set this to ‘streamlit’. Currently, not all plots are supported.

Returns

None

pycaret.clustering.evaluate_model(model, feature: Optional[str] = None, fit_kwargs: Optional[dict] = None)

This function displays a user interface for analyzing performance of a trained model. It calls the plot_model function internally.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
>>> evaluate_model(kmeans)
model: scikit-learn compatible object

Trained model object

feature: str, default = None

Feature to be evaluated when plot = ‘distribution’. When plot type is ‘cluster’ or ‘tsne’ feature column is used as a hoverover tooltip and/or label when the label param is set to True. When the plot type is ‘cluster’ or ‘tsne’ and feature is None, first column of the dataset is used.

fit_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the fit method of the model.

Returns

None

Warning

  • This function only works in IPython enabled Notebook.

pycaret.clustering.tune_model(model, supervised_target: str, supervised_type: Optional[str] = None, supervised_estimator: Union[str, Any] = 'lr', optimize: Optional[str] = None, custom_grid: Optional[List[int]] = None, fold: int = 10, fit_kwargs: Optional[dict] = None, groups: Optional[Union[str, Any]] = None, round: int = 4, verbose: bool = True)

This function tunes the num_clusters parameter of a given model.

Example

>>> from pycaret.datasets import get_data
>>> juice = get_data('juice')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = juice)
>>> tuned_kmeans = tune_model(model = 'kmeans', supervised_target = 'Purchase')
model: str

ID of an model available in the model library. Models that can be tuned in this function (ID - Model):

  • ‘kmeans’ - K-Means Clustering

  • ‘sc’ - Spectral Clustering

  • ‘hclust’ - Agglomerative Clustering

  • ‘birch’ - Birch Clustering

  • ‘kmodes’ - K-Modes Clustering

supervised_target: str

Name of the target column containing labels.

supervised_type: str, default = None

Type of task. ‘classification’ or ‘regression’. Automatically inferred when None.

supervised_estimator: str, default = None
Classification (ID - Name):

  • ‘lr’ - Logistic Regression (Default)

  • ‘knn’ - K Nearest Neighbour

  • ‘nb’ - Naive Bayes

  • ‘dt’ - Decision Tree Classifier

  • ‘svm’ - SVM - Linear Kernel

  • ‘rbfsvm’ - SVM - Radial Kernel

  • ‘gpc’ - Gaussian Process Classifier

  • ‘mlp’ - Multi Level Perceptron

  • ‘ridge’ - Ridge Classifier

  • ‘rf’ - Random Forest Classifier

  • ‘qda’ - Quadratic Discriminant Analysis

  • ‘ada’ - Ada Boost Classifier

  • ‘gbc’ - Gradient Boosting Classifier

  • ‘lda’ - Linear Discriminant Analysis

  • ‘et’ - Extra Trees Classifier

  • ‘xgboost’ - Extreme Gradient Boosting

  • ‘lightgbm’ - Light Gradient Boosting

  • ‘catboost’ - CatBoost Classifier

Regression (ID - Name):

  • ‘lr’ - Linear Regression (Default)

  • ‘lasso’ - Lasso Regression

  • ‘ridge’ - Ridge Regression

  • ‘en’ - Elastic Net

  • ‘lar’ - Least Angle Regression

  • ‘llar’ - Lasso Least Angle Regression

  • ‘omp’ - Orthogonal Matching Pursuit

  • ‘br’ - Bayesian Ridge

  • ‘ard’ - Automatic Relevance Determ.

  • ‘par’ - Passive Aggressive Regressor

  • ‘ransac’ - Random Sample Consensus

  • ‘tr’ - TheilSen Regressor

  • ‘huber’ - Huber Regressor

  • ‘kr’ - Kernel Ridge

  • ‘svm’ - Support Vector Machine

  • ‘knn’ - K Neighbors Regressor

  • ‘dt’ - Decision Tree

  • ‘rf’ - Random Forest

  • ‘et’ - Extra Trees Regressor

  • ‘ada’ - AdaBoost Regressor

  • ‘gbr’ - Gradient Boosting

  • ‘mlp’ - Multi Level Perceptron

  • ‘xgboost’ - Extreme Gradient Boosting

  • ‘lightgbm’ - Light Gradient Boosting

  • ‘catboost’ - CatBoost Regressor

optimize: str, default = None
For Classification tasks:

Accuracy, AUC, Recall, Precision, F1, Kappa (default = ‘Accuracy’)

For Regression tasks:

MAE, MSE, RMSE, R2, RMSLE, MAPE (default = ‘R2’)

custom_grid: list, default = None

By default, a pre-defined number of clusters is iterated over to optimize the supervised objective. To overwrite default iteration, pass a list of num_clusters to iterate over in custom_grid param.

fold: int, default = 10

Number of folds to be used in Kfold CV. Must be at least 2.

verbose: bool, default = True

Status update is not printed when verbose is set to False.

Returns

Trained Model with optimized num_clusters parameter.

Warning

  • Affinity Propagation, Mean shift, Density-Based Spatial Clustering and OPTICS Clustering cannot be used in this function since they donot support the num_clusters param.

pycaret.clustering.predict_model(model, data: DataFrame) → DataFrame

This function generates cluster labels using a trained model.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
>>> kmeans_predictions = predict_model(model = kmeans, data = unseen_data)
model: scikit-learn compatible object

Trained Model Object.

datapandas.DataFrame

Shape (n_samples, n_features) where n_samples is the number of samples and n_features is the number of features.

Returns

pandas.DataFrame

Warning

  • Models that do not support ‘predict’ method cannot be used in the predict_model.

  • The behavior of the predict_model is changed in version 2.1 without backward compatibility. As such, the pipelines trained using the version (<= 2.0), may not work for inference with version >= 2.1. You can either retrain your models with a newer version or downgrade the version for inference.

pycaret.clustering.deploy_model(model, model_name: str, authentication: dict, platform: str = 'aws')

This function deploys the transformation pipeline and trained model on cloud.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
>>> # sets appropriate credentials for the platform as environment variables
>>> import os
>>> os.environ["AWS_ACCESS_KEY_ID"] = str("foo")
>>> os.environ["AWS_SECRET_ACCESS_KEY"] = str("bar")
>>> deploy_model(model = kmeans, model_name = 'kmeans-for-deployment', platform = 'aws', authentication = {'bucket' : 'S3-bucket-name'})
Amazon Web Service (AWS) users:

To deploy a model on AWS S3 (‘aws’), the credentials have to be passed. The easiest way is to use environment variables in your local environment. Following information from the IAM portal of amazon console account are required:

  • AWS Access Key ID

  • AWS Secret Key Access

More info: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#environment-variables

Google Cloud Platform (GCP) users:

To deploy a model on Google Cloud Platform (‘gcp’), project must be created using command line or GCP console. Once project is created, you must create a service account and download the service account key as a JSON file to set environment variables in your local environment.

More info: https://cloud.google.com/docs/authentication/production

Microsoft Azure (Azure) users:

To deploy a model on Microsoft Azure (‘azure’), environment variables for connection string must be set in your local environment. Go to settings of storage account on Azure portal to access the connection string required.

  • AZURE_STORAGE_CONNECTION_STRING (required as environment variable)

More info: https://docs.microsoft.com/en-us/azure/storage/blobs/storage-quickstart-blobs-python?toc=%2Fpython%2Fazure%2FTOC.json

model: scikit-learn compatible object

Trained model object

model_name: str

Name of model.

authentication: dict

Dictionary of applicable authentication tokens.

When platform = ‘aws’: {‘bucket’ : ‘S3-bucket-name’, ‘path’: (optional) folder name under the bucket}

When platform = ‘gcp’: {‘project’: ‘gcp-project-name’, ‘bucket’ : ‘gcp-bucket-name’}

When platform = ‘azure’: {‘container’: ‘azure-container-name’}

platform: str, default = ‘aws’

Name of the platform. Currently supported platforms: ‘aws’, ‘gcp’ and ‘azure’.

Returns

None

pycaret.clustering.save_model(model, model_name: str, model_only: bool = False, verbose: bool = True, **kwargs)

This function saves the transformation pipeline and trained model object into the current working directory as a pickle file for later use.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> kmeans = create_model('kmeans')
>>> save_model(lr, 'saved_kmeans_model')
model: scikit-learn compatible object

Trained model object

model_name: str

Name of the model.

model_only: bool, default = False

When set to True, only trained model object is saved instead of the entire pipeline.

verbose: bool, default = True

Success message is not printed when verbose is set to False.

**kwargs:

Additional keyword arguments to pass to joblib.dump().

Returns

Tuple of the model object and the filename.

pycaret.clustering.load_model(model_name, platform: Optional[str] = None, authentication: Optional[Dict[str, str]] = None, verbose: bool = True)

This function loads a previously saved pipeline.

Example

>>> from pycaret.clustering import load_model
>>> saved_kmeans = load_model('saved_kmeans_model')
model_name: str

Name of the model.

platform: str, default = None

Name of the cloud platform. Currently supported platforms: ‘aws’, ‘gcp’ and ‘azure’.

authentication: dict, default = None

dictionary of applicable authentication tokens.

when platform = ‘aws’: {‘bucket’ : ‘Name of Bucket on S3’, ‘path’: (optional) folder name under the bucket}

when platform = ‘gcp’: {‘project’: ‘gcp-project-name’, ‘bucket’ : ‘gcp-bucket-name’}

when platform = ‘azure’: {‘container’: ‘azure-container-name’}

verbose: bool, default = True

Success message is not printed when verbose is set to False.

Returns

Trained Model

pycaret.clustering.pull(pop: bool = False) → DataFrame

Returns last printed score grid. Use pull function after any training function to store the score grid in pandas.DataFrame.

pop: bool, default = False

If True, will pop (remove) the returned dataframe from the display container.

Returns

pandas.DataFrame

pycaret.clustering.models(internal: bool = False, raise_errors: bool = True) → DataFrame

Returns table of models available in the model library.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> all_models = models()
internal: bool, default = False

If True, will return extra columns and rows used internally.

raise_errors: bool, default = True

If False, will suppress all exceptions, ignoring models that couldn’t be created.

Returns

pandas.DataFrame

pycaret.clustering.get_metrics(reset: bool = False, include_custom: bool = True, raise_errors: bool = True) → DataFrame

Returns table of metrics available.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> all_metrics = get_metrics()
reset: bool, default = False

If True, will reset all changes made using add_metric() and get_metric().

include_custom: bool, default = True

Whether to include user added (custom) metrics or not.

raise_errors: bool, default = True

If False, will suppress all exceptions, ignoring models that couldn’t be created.

Returns

pandas.DataFrame

pycaret.clustering.add_metric(id: str, name: str, score_func: type, target: str = 'pred', greater_is_better: bool = True, multiclass: bool = True, **kwargs) → Series

Adds a custom metric to be used in all functions.

id: str

Unique id for the metric.

name: str

Display name of the metric.

score_func: type

Score function (or loss function) with signature score_func(y, y_pred, **kwargs).

target: str, default = ‘pred’

The target of the score function.

  • ‘pred’ for the prediction table

  • ‘pred_proba’ for pred_proba

  • ‘threshold’ for decision_function or predict_proba

greater_is_better: bool, default = True

Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. In the latter case, the scorer object will sign-flip the outcome of the score_func.

multiclass: bool, default = True

Whether the metric supports multiclass problems.

**kwargs:

Arguments to be passed to score function.

Returns

pandas.Series

pycaret.clustering.remove_metric(name_or_id: str)

Removes a metric used for evaluation.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> remove_metric('cs')
name_or_id: str

Display name or ID of the metric.

Returns

None

pycaret.clustering.get_logs(experiment_name: Optional[str] = None, save: bool = False) → DataFrame

Returns a table of experiment logs. Only works when log_experiment is True when initializing the setup function.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery,  log_experiment = True)
>>> kmeans = create_model('kmeans')
>>> exp_logs = get_logs()
experiment_name: str, default = None

When None current active run is used.

save: bool, default = False

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

Returns

pandas.DataFrame

pycaret.clustering.get_config(variable: str)

This function retrieves the global variables created when initializing the setup function. Following variables are accessible:

  • X: Transformed dataset (X)

  • data_before_preprocess: data before preprocessing

  • seed: random state set through session_id

  • prep_pipe: Transformation pipeline configured through setup

  • 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 set through setup

  • logging_param: log_experiment param set through setup

  • log_plots_param: log_plots param set through setup

  • USI: Unique session ID parameter set through setup

  • gpu_param: use_gpu param configured through setup

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> X = get_config('X')
Returns

Global variable

pycaret.clustering.set_config(variable: str, value)

This function resets the global variables. Following variables are accessible:

  • X: Transformed dataset (X)

  • data_before_preprocess: data before preprocessing

  • seed: random state set through session_id

  • prep_pipe: Transformation pipeline configured through setup

  • 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 set through setup

  • logging_param: log_experiment param set through setup

  • log_plots_param: log_plots param set through setup

  • USI: Unique session ID parameter set through setup

  • gpu_param: use_gpu param configured through setup

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> set_config('seed', 123)
Returns

None

pycaret.clustering.save_config(file_name: str)

This function save all global variables to a pickle file, allowing to later resume without rerunning the setup.

Example

>>> from pycaret.datasets import get_data
>>> jewellery = get_data('jewellery')
>>> from pycaret.clustering import *
>>> exp_name = setup(data = jewellery)
>>> save_config('myvars.pkl')
Returns

None

pycaret.clustering.load_config(file_name: str)

This function loads global variables from a pickle file into Python environment.

Example

>>> from pycaret.clustering import load_config
>>> load_config('myvars.pkl')
Returns

Global variables

pycaret.clustering.get_clusters(data, model: Union[str, Any] = 'kmeans', num_clusters: int = 4, ground_truth: Optional[str] = None, round: int = 4, fit_kwargs: Optional[dict] = None, preprocess: bool = True, imputation_type: str = 'simple', iterative_imputation_iters: int = 5, categorical_features: Optional[List[str]] = None, categorical_imputation: str = 'mode', categorical_iterative_imputer: Union[str, Any] = 'lightgbm', ordinal_features: Optional[Dict[str, list]] = None, high_cardinality_features: Optional[List[str]] = None, high_cardinality_method: str = 'frequency', numeric_features: Optional[List[str]] = None, numeric_imputation: str = 'mean', numeric_iterative_imputer: Union[str, Any] = 'lightgbm', date_features: Optional[List[str]] = None, ignore_features: Optional[List[str]] = None, normalize: bool = False, normalize_method: str = 'zscore', transformation: bool = False, transformation_method: str = 'yeo-johnson', handle_unknown_categorical: bool = True, unknown_categorical_method: str = 'least_frequent', pca: bool = False, pca_method: str = 'linear', pca_components: Optional[float] = None, ignore_low_variance: bool = False, combine_rare_levels: bool = False, rare_level_threshold: float = 0.1, bin_numeric_features: Optional[List[str]] = None, remove_multicollinearity: bool = False, multicollinearity_threshold: float = 0.9, remove_perfect_collinearity: bool = False, group_features: Optional[List[str]] = None, group_names: Optional[List[str]] = None, n_jobs: Optional[int] = -1, session_id: Optional[int] = None, log_experiment: Union[bool, str, BaseLogger, List[Union[str, BaseLogger]]] = False, experiment_name: Optional[str] = None, log_plots: Union[bool, list] = False, log_profile: bool = False, log_data: bool = False, profile: bool = False, **kwargs) → DataFrame

Callable from any external environment without requiring setup initialization.