abacusai.project

Module Contents

Classes

Project

A project is a container which holds datasets, models and deployments

class abacusai.project.Project(client, projectId=None, name=None, useCase=None, problemType=None, createdAt=None, featureGroupsEnabled=None)

Bases: abacusai.return_class.AbstractApiClass

A project is a container which holds datasets, models and deployments

Parameters:
  • client (ApiClient) – An authenticated API Client instance

  • projectId (str) – The ID of the project.

  • name (str) – The name of the project.

  • useCase (str) – The Use Case associated with the project.

  • problemType (str) –

  • createdAt (str) – The date and time when the project was created.

  • featureGroupsEnabled (bool) – Project uses feature groups instead of datasets.

__repr__()

Return repr(self).

to_dict()

Get a dict representation of the parameters in this class

Returns:

The dict value representation of the class parameters

Return type:

dict

refresh()

Calls describe and refreshes the current object’s fields

Returns:

The current object

Return type:

Project

describe()

Returns a description of a project.

Parameters:

project_id (str) – The unique project ID

Returns:

The project description is returned.

Return type:

Project

list_datasets()

Retrieves all dataset(s) attached to a specified project. This API returns all attributes of each dataset, such as its name, type, and ID.

Parameters:

project_id (str) – The unique ID associated with the project.

Returns:

An array representing all of the datasets attached to the project.

Return type:

ProjectDataset

get_schema(dataset_id)

[DEPRECATED] Returns a schema given a specific dataset in a project. The schema of the dataset consists of the columns in the dataset, the data type of the column, and the column’s column mapping.

Parameters:

dataset_id (str) – The unique ID associated with the dataset.

Returns:

An array of objects for each column in the specified dataset.

Return type:

Schema

rename(name)

This method renames a project after it is created.

Parameters:

name (str) – The new name for the project.

delete()

Deletes a specified project from your organization.

This method deletes the project, trained models and deployments in the specified project. The datasets attached to the specified project remain available for use with other projects in the organization.

This method will not delete a project that contains active deployments. Be sure to stop all active deployments before you use the delete option.

Note: All projects, models, and deployments cannot be recovered once they are deleted.

Parameters:

project_id (str) – The unique ID of the project to delete.

set_feature_mapping(feature_group_id, feature_name, feature_mapping, nested_column_name=None)

Set a column’s feature mapping. If the column mapping is single-use and already set in another column in this feature group, this call will first remove the other column’s mapping and move it to this column.

Parameters:
  • feature_group_id (str) – The unique ID associated with the feature group.

  • feature_name (str) – The name of the feature.

  • feature_mapping (str) – The mapping of the feature in the feature group.

  • nested_column_name (str) – The name of the nested column.

Returns:

A list of objects that describes the resulting feature group’s schema after the feature’s featureMapping is set.

Return type:

Feature

validate(feature_group_ids=None)

Validates that the specified project has all required feature group types for its use case and that all required feature columns are set.

Parameters:

feature_group_ids (list) – The feature group IDS to validate

Returns:

The project validation. If the specified project is missing required columns or feature groups, the response includes an array of objects for each missing required feature group and the missing required features in each feature group.

Return type:

ProjectValidation

set_column_data_type(dataset_id, column, data_type)

Set a dataset’s column type.

Parameters:
  • dataset_id (str) – The unique ID associated with the dataset.

  • column (str) – The name of the column.

  • data_type (str) – The type of the data in the column. CATEGORICAL, CATEGORICAL_LIST, NUMERICAL, TIMESTAMP, TEXT, EMAIL, LABEL_LIST, JSON, OBJECT_REFERENCE Refer to the (guide on feature types)[https://api.abacus.ai/app/help/class/FeatureType] for more information. Note: Some ColumnMappings will restrict the options or explicity set the DataType.

Returns:

A list of objects that describes the resulting dataset’s schema after the column’s dataType is set.

Return type:

Schema

set_column_mapping(dataset_id, column, column_mapping)

Set a dataset’s column mapping. If the column mapping is single-use and already set in another column in this dataset, this call will first remove the other column’s mapping and move it to this column.

Parameters:
  • dataset_id (str) – The unique ID associated with the dataset.

  • column (str) – The name of the column.

  • column_mapping (str) – The mapping of the column in the dataset. See a list of columns mapping enums here.

Returns:

A list of columns that describes the resulting dataset’s schema after the column’s columnMapping is set.

Return type:

Schema

remove_column_mapping(dataset_id, column)

Removes a column mapping from a column in the dataset. Returns a list of all columns with their mappings once the change is made.

Parameters:
  • dataset_id (str) – The unique ID associated with the dataset.

  • column (str) – The name of the column.

Returns:

A list of objects that describes the resulting dataset’s schema after the column’s columnMapping is set.

Return type:

Schema

list_feature_groups(filter_feature_group_use=None)

List all the feature groups associated with a project

Parameters:

filter_feature_group_use (str) – The feature group use filter, when given as an argument, only allows feature groups in this project to be returned if they are of the given use. DATA_WRANGLING, TRAINING_INPUT, BATCH_PREDICTION_INPUT, BATCH_PREDICTION_OUTPUT

Returns:

All the Feature Groups in the Organization

Return type:

FeatureGroup

list_feature_group_templates(limit=100, start_after_id=None, should_include_all_system_templates=False)

List feature group templates for feature groups associated with the project.

Parameters:
  • limit (int) – The maximum number of templates to be retrieved.

  • start_after_id (str) – An offset parameter to exclude all templates till the specified feature group template ID.

  • should_include_all_system_templates (bool) –

Returns:

All the feature groups in the organization, optionally limited by the feature group that created the template(s).

Return type:

FeatureGroupTemplate

get_training_config_options(feature_group_ids=None, for_retrain=False)

Retrieves the full description of the model training configuration options available for the specified project.

The configuration options available are determined by the use case associated with the specified project. Refer to the (Use Case Documentation)[https://api.abacus.ai/app/help/useCases] for more information on use cases and use case specific configuration options.

Parameters:
  • feature_group_ids (list) – The feature group IDs to be used for training

  • for_retrain (bool) – If training config options are used for retrain

Returns:

An array of options that can be specified when training a model in this project.

Return type:

TrainingConfigOptions

create_train_test_data_split_feature_group(training_config, feature_group_ids)

Get the train and test data split without training the model. Only supported for models with custom algorithms.

Parameters:
  • training_config (dict) – The training config key/value pairs used to influence how split is calculated.

  • feature_group_ids (list) – List of feature group ids provided by the user, including the required one for data split and others to influence how to split.

Returns:

The feature group containing the training data and folds information.

Return type:

FeatureGroup

train_model(name=None, training_config=None, feature_group_ids=None, refresh_schedule=None, custom_algorithms=None, custom_algorithms_only=False, custom_algorithm_configs=None, cpu_size=None, memory=None)

Trains a model for the specified project.

Use this method to train a model in this project. This method supports user-specified training configurations defined in the getTrainingConfigOptions method.

Parameters:
  • name (str) – The name you want your model to have. Defaults to “<Project Name> Model”.

  • training_config (dict) – The training config key/value pairs used to train this model.

  • feature_group_ids (list) – List of feature group ids provided by the user to train the model on.

  • refresh_schedule (str) – A cron-style string that describes a schedule in UTC to automatically retrain the created model.

  • custom_algorithms (list) – List of user-defined algorithms to train.

  • custom_algorithms_only (bool) – Whether only run custom algorithms.

  • custom_algorithm_configs (dict) – Configs for each user-defined algorithm, key is algorithm name, value is the config serialized to json

  • cpu_size (str) – Size of the cpu for the user-defined algorithms during train.

  • memory (int) – Memory (in GB) for the user-defined algorithms during train.

Returns:

The new model which is being trained.

Return type:

Model

create_model_from_python(function_source_code, train_function_name, training_input_tables, predict_function_name=None, predict_many_function_name=None, initialize_function_name=None, name=None, cpu_size=None, memory=None, training_config=None, exclusive_run=False, package_requirements=None)

Initializes a new Model from user provided Python code. If a list of input feature groups are supplied,

we will provide as arguments to the train and predict functions with the materialized feature groups for those input feature groups.

This method expects functionSourceCode to be a valid language source file which contains the functions named trainFunctionName and predictFunctionName. trainFunctionName returns the ModelVersion that is the result of training the model using trainFunctionName and predictFunctionName has no well defined return type, as it returns the prediction made by the predictFunctionName, which can be anything

Parameters:
  • function_source_code (str) – Contents of a valid python source code file. The source code should contain the functions named trainFunctionName and predictFunctionName. A list of allowed import and system libraries for each language is specified in the user functions documentation section.

  • train_function_name (str) – Name of the function found in the source code that will be executed to train the model. It is not executed when this function is run.

  • training_input_tables (list) – List of feature groups that are supplied to the train function as parameters. Each of the parameters are materialized Dataframes (same type as the functions return value).

  • predict_function_name (str) – Name of the function found in the source code that will be executed run predictions through model. It is not executed when this function is run.

  • predict_many_function_name (str) – Name of the function found in the source code that will be executed for batch prediction of the model. It is not executed when this function is run.

  • initialize_function_name (str) – Name of the function found in the source code to initialize the trained model before using it to make predictions using the model

  • name (str) – The name you want your model to have. Defaults to “<Project Name> Model”

  • cpu_size (str) – Size of the cpu for the model training function

  • memory (int) – Memory (in GB) for the model training function

  • training_config (dict) – Training configuration

  • exclusive_run (bool) – Decides if this model will be run exclusively OR along with other Abacus.ai algorithms

  • package_requirements (dict) – Json with key value pairs corresponding to package: version for each dependency

Returns:

The new model, which has not been trained.

Return type:

Model

list_models()

Retrieves the list of models in the specified project.

Parameters:

project_id (str) – The unique ID associated with the project.

Returns:

An array of models.

Return type:

Model

get_custom_train_function_info(feature_group_names_for_training=None, training_data_parameter_name_override=None, training_config=None, custom_algorithm_config=None)

Returns the information about how to call the custom train function.

Parameters:
  • feature_group_names_for_training (list) – A list of feature group table names that will be used for training

  • training_data_parameter_name_override (dict) – Override from feature group type to parameter name in train function.

  • training_config (dict) – Training config names to values for the options supported by Abacus.ai platform.

  • custom_algorithm_config (dict) – User-defined config that can be serialized by JSON.

Returns:

Information about how to call the customer provided train function.

Return type:

CustomTrainFunctionInfo

create_model_monitor(prediction_feature_group_id, training_feature_group_id=None, name=None, refresh_schedule=None, target_value=None, feature_mappings=None, model_id=None, training_feature_mappings=None)

Runs a model monitor for the specified project.

Parameters:
  • prediction_feature_group_id (str) – The unique ID of the prediction data feature group

  • training_feature_group_id (str) – The unique ID of the training data feature group

  • name (str) – The name you want your model monitor to have. Defaults to “<Project Name> Model Monitor”.

  • refresh_schedule (str) – A cron-style string that describes a schedule in UTC to automatically retrain the created model monitor

  • target_value (str) – A target positive value for the label to compute bias for

  • feature_mappings (dict) – A json map to override features for prediction_feature_group, where keys are column names and the values are feature data use types.

  • model_id (str) – The Unique ID of the Model

  • training_feature_mappings (dict) – “ A json map to override features for training_fature_group, where keys are column names and the values are feature data use types.

Returns:

The new model monitor that was created.

Return type:

ModelMonitor

list_model_monitors()

Retrieves the list of models monitors in the specified project.

Parameters:

project_id (str) – The unique ID associated with the project.

Returns:

An array of model monitors.

Return type:

ModelMonitor

create_deployment_token()

Creates a deployment token for the specified project.

Deployment tokens are used to authenticate requests to the prediction APIs and are scoped on the project level.

Parameters:

project_id (str) – The unique ID associated with the project.

Returns:

The deployment token.

Return type:

DeploymentAuthToken

list_deployments()

Retrieves a list of all deployments in the specified project.

Parameters:

project_id (str) – The unique ID associated with the project.

Returns:

An array of deployments.

Return type:

Deployment

list_deployment_tokens()

Retrieves a list of all deployment tokens in the specified project.

Parameters:

project_id (str) – The unique ID associated with the project.

Returns:

An array of deployment tokens.

Return type:

DeploymentAuthToken

list_refresh_policies(dataset_ids=[], model_ids=[], deployment_ids=[], batch_prediction_ids=[], model_monitor_ids=[], prediction_metric_ids=[])

List the refresh policies for the organization

Parameters:
  • dataset_ids (list) – Comma separated list of Dataset IDs

  • model_ids (list) – Comma separated list of Model IDs

  • deployment_ids (list) – Comma separated list of Deployment IDs

  • batch_prediction_ids (list) – Comma separated list of Batch Prediction IDs

  • model_monitor_ids (list) – Comma separated list of Model Monitor IDs.

  • prediction_metric_ids (list) – Comma separated list of Prediction Metric IDs,

Returns:

List of all refresh policies in the organization

Return type:

RefreshPolicy

list_batch_predictions()

Retrieves a list for the batch predictions in the project

Parameters:

project_id (str) – The unique identifier of the project

Returns:

A list of batch prediction jobs.

Return type:

BatchPrediction

attach_dataset(dataset_id, project_dataset_type)

Attaches dataset to the project.

Parameters:
  • dataset_id (unique string identifier) – A unique identifier for the dataset.

  • project_dataset_type (enum of type string) – The unique use case specific dataset type that might be required or recommended for the specific use case.

Returns:

The schema of the attached dataset.

Return type:

Schema

remove_dataset(dataset_id)

Removes dataset from the project.

Parameters:

dataset_id (unique string identifier) – A unique identifier for the dataset.

create_model_from_functions(train_function, predict_function=None, training_input_tables=None, predict_many_function=None, initialize_function=None, cpu_size=None, memory=None, training_config=None, exclusive_run=False)

Creates a model using python.

Parameters:
  • train_function (callable) – The train function is passed.

  • predict_function (callable) – The prediction function is passed.

  • training_input_tables (list, optional) – The input tables to be used for training the model. Defaults to None.

  • predict_many_function (callable) – Prediction function for batch input

  • cpu_size (str) – Size of the cpu for the feature group function

  • memory (int) – Memory (in GB) for the feature group function

  • initialize_function (callable) –

  • training_config (dict) –

  • exclusive_run (bool) –

Returns:

The model object.

Return type:

Model