Project

class previsionio.project.Project(_id: str, name: str, description: str = None, color: previsionio.project.ProjectColor = None, created_by: str = None, admins=[], contributors=[], viewers=[], pipelines_count: int = 0, experiments_count: int = 0, dataset_count: int = 0, **kwargs)

Bases: previsionio.api_resource.ApiResource, previsionio.api_resource.UniqueResourceMixin

A Project

Parameters:
  • _id (str) – Unique id of the project
  • name (str) – Name of the project
  • description (str, optional) – Description of the project
  • color (ProjectColor, optional) – Color of the project
create_dataset(name: str, datasource: previsionio.datasource.DataSource = None, file_name: str = None, dataframe: pandas.core.frame.DataFrame = None, **kwargs)

Register a new dataset in the workspace for further processing. You need to provide either a datasource, a file name or a dataframe (only one can be specified).

Note

To start a new experiment version on a dataset, it has to be already registred in your workspace.

Parameters:
  • name (str) – Registration name for the dataset
  • datasource (DataSource, optional) – A DataSource object used to import a remote dataset (if you want to import a specific dataset from an existent database, you need a datasource connector (Connector object) designed to point to the related data source)
  • file_name (str, optional) – Path to a file to upload as dataset
  • dataframe (pd.DataFrame, optional) – A pandas dataframe containing the data to upload
Raises:
  • Exception – If more than one of the keyword arguments datasource, file_name, dataframe was specified
  • PrevisionException – Error while creating the dataset on the platform
Returns:

The registered dataset object in the current workspace.

Return type:

Dataset

create_datasource(connector: previsionio.connector.Connector, name: str, path: str = None, database: str = None, table: str = None, bucket: str = None, request: str = None, gCloud: previsionio.connector.GCloud = None)

Create a new datasource object on the platform.

Parameters:
  • connector (Connector) – Reference to the associated connector (the resource to go through to get a data snapshot)
  • name (str) – Name of the datasource
  • path (str, optional) – Path to the file to fetch via the connector
  • database (str, optional) – Name of the database to fetch data from via the connector
  • table (str, optional) – Name of the table to fetch data from via the connector
  • bucket (str, optional) – Name of the bucket to fetch data from via the connector
  • gCloud (GCloud, optional) – Type of google cloud service
  • request (str, optional) – Direct SQL request to use with the connector to fetch data
Returns:

The registered datasource object in the current project

Return type:

DataSource

Raises:
  • PrevisionException – Any error while uploading data to the platform or parsing the result
  • Exception – For any other unknown error
create_experiment_deployment(name: str, main_model: previsionio.model.Model, challenger_model: previsionio.model.Model = None, access_type: str = 'public') → previsionio.experiment_deployment.ExperimentDeployment

Create a new experiment deployment in the current project.

Parameters:
  • name (str) – experiment deployment name
  • main_model (Model) – main model
  • challenger_model (Model, optional) – challenger model (main and challenger models should come from the same experiment)
  • access_type (str, optional) – public/ fine_grained/ private
Returns:

Fetched experiment deployment object

Return type:

ExperimentDeployment

create_exporter(connector: previsionio.connector.Connector, name: str, description: str = None, path: str = None, bucket: str = None, database: str = None, table: str = None, g_cloud: previsionio.connector.GCloud = None, write_mode: previsionio.exporter.ExporterWriteMode = <ExporterWriteMode.safe: 'safe'>)

Create a new exporter object on the platform.

Parameters:
  • connector (Connector) – Reference to the associated connector (the resource to go through to get a data snapshot)
  • name (str) – Name of the exporter
  • description (str, optional) – Description of the exporter
  • bucket (str, optional) – Bucket of the file to write on via the exporter
  • path (str, optional) – Path to the file to write on via the exporter
  • database (str, optional) – Name of the database to write on via the exporter
  • table (str, optional) – Name of the table to write on via the exporter
  • g_cloud (GCloud, optional) – Type of google cloud service
  • write_mode (ExporterWriteMode, optional) – Write mode
Returns:

The registered exporter object in the current project

Return type:

Exporter

Raises:
  • PrevisionException – Any error while uploading data to the platform or parsing the result
  • Exception – For any other unknown error
create_external_classification()

Create a tabular classification experiment version from external models

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • holdout_dataset (Dataset) – Reference to the holdout dataset object to use for as holdout dataset
  • target_column (str) – The name of the target column for this experiment version
  • external_models (list(tuple)) –

    The external models to add in the experiment version to create. Each tuple contains 3 items describing an external model as follows:

    1. The name you want to give to the model
    2. The path to the model in onnx format
    3. The path to a yaml file containing metadata about the model
  • metric (metrics.Classification, optional) – Specific metric to use for the experiment (default: metrics.Classification.AUC)
  • dataset (Dataset, optional) – Reference to the dataset object that has been used to train the model (default: None)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created ExternalExperimentVersion object

Return type:

external_models.ExternalExperimentVersion

create_external_multiclassification()

Create a tabular multiclassification experiment version from external models

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • holdout_dataset (Dataset) – Reference to the holdout dataset object to use for as holdout dataset
  • target_column (str) – The name of the target column for this experiment version
  • external_models (list(tuple)) –

    The external models to add in the experiment version to create. Each tuple contains 3 items describing an external model as follows:

    1. The name you want to give to the model
    2. The path to the model in onnx format
    3. The path to a yaml file containing metadata about the model
  • metric (metrics.MultiClassification, optional) – Specific metric to use for the experiment (default: metrics.MultiClassification.log_loss)
  • dataset (Dataset, optional) – Reference to the dataset object that has been used to train the model (default: None)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created ExternalExperimentVersion object

Return type:

external_models.ExternalExperimentVersion

create_external_regression()

Create a tabular regression experiment version from external models

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • holdout_dataset (Dataset) – Reference to the holdout dataset object to use for as holdout dataset
  • target_column (str) – The name of the target column for this experiment version
  • external_models (list(tuple)) –

    The external models to add in the experiment version to create. Each tuple contains 3 items describing an external model as follows:

    1. The name you want to give to the model
    2. The path to the model in onnx format
    3. The path to a yaml file containing metadata about the model
  • metric (metrics.Regression, optional) – Specific metric to use for the experiment (default: metrics.Regression.RMSE)
  • dataset (Dataset, optional) – Reference to the dataset object that has been used to train the model (default: None)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created ExternalExperimentVersion object

Return type:

external_models.ExternalExperimentVersion

create_ftp_connector(name: str, host: str, port: int, username: str, password: str)

A connector to interact with a distant source of data (and easily get data snapshots using an associated DataSource resource).

Parameters:
  • name (str) – Name of the connector
  • host (str) – Url of the connector
  • port (int) – Port of the connector
  • username (str) – Username to use connect to the remote data source
  • password (str) – Password to use connect to the remote data source
Returns:

The registered connector object in the current project.

Return type:

FTPConnector

create_gcp_connector(name: str, googleCredentials: Dict)

A connector to interact with a distant source of data (and easily get data snapshots using an associated DataSource resource).

Parameters:
  • name (str) – Name of the connector
  • googleCredentials (dict) – google credentials
Returns:

The registered connector object in the current project.

Return type:

GCPConnector

create_image_folder(name: str, file_name: str)

Register a new image dataset in the workspace for further processing (in the image folders group).

Note

To start a new experiment version on a dataset image, it has to be already registred in your workspace.

Parameters:
  • name (str) – Registration name for the dataset
  • file_name (str) – Path to the zip file to upload as image dataset
Raises:

PrevisionException – Error while creating the dataset on the platform

Returns:

The registered dataset object in the current workspace.

Return type:

DatasetImages

create_s3_connector(name: str, username: str, password: str)

A connector to interact with a distant source of data (and easily get data snapshots using an associated DataSource resource).

Parameters:
  • name (str) – Name of the connector
  • host (str) – Url of the connector
  • port (int) – Port of the connector
  • username (str, optional) – Username to use connect to the remote data source
  • password (str, optional) – Password to use connect to the remote data source
Returns:

The registered connector object in the current project.

Return type:

S3Connector

create_sftp_connector(name: str, host: str, port: int, username: str, password: str)

A connector to interact with a distant source of data (and easily get data snapshots using an associated DataSource resource).

Parameters:
  • name (str) – Name of the connector
  • host (str) – Url of the connector
  • port (int) – Port of the connector
  • username (str) – Username to use connect to the remote data source
  • password (str) – Password to use connect to the remote data source
Returns:

The registered connector object in the current project.

Return type:

SFTPConnector

create_sql_connector(name: str, host: str, port: int, username: str, password: str)

A connector to interact with a distant source of data (and easily get data snapshots using an associated DataSource resource).

Parameters:
  • name (str) – Name of the connector
  • host (str) – Url of the connector
  • port (int) – Port of the connector
  • username (str) – Username to use connect to the remote data source
  • password (str) – Password to use connect to the remote data source
Returns:

The registered connector object in the current project.

Return type:

SQLConnector

delete()

Delete a project from the actual [client] workspace.

Raises:
  • PrevisionException – If the project does not exist
  • requests.exceptions.ConnectionError – Error processing the request
fit_classification(experiment_name: str, dataset: previsionio.dataset.Dataset, column_config: previsionio.experiment_config.ColumnConfig, metric: previsionio.metrics.Classification = <Classification.AUC: 'auc'>, holdout_dataset: previsionio.dataset.Dataset = None, training_config: previsionio.experiment_config.TrainingConfig = <previsionio.experiment_config.TrainingConfig object>, experiment_version_description: str = None) → previsionio.supervised.Supervised

Start a tabular classification experiment version training

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • dataset (Dataset) – Reference to the dataset object to use for as training dataset
  • column_config (ColumnConfig) – Column configuration for the experiment (see the documentation of the ColumnConfig resource for more details on each possible column types)
  • metric (metrics.Classification, optional) – Specific metric to use for the experiment (default: metrics.Classification.AUC)
  • holdout_dataset (Dataset, optional) – Reference to a dataset object to use as a holdout dataset (default: None)
  • training_config (TrainingConfig) – Specific training configuration (see the documentation of the TrainingConfig resource for more details on all the parameters)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created Supervised experiment version object

Return type:

supervised.Supervised

fit_image_classification(experiment_name: str, dataset: previsionio.dataset.Dataset, dataset_images: previsionio.dataset.DatasetImages, column_config: previsionio.experiment_config.ColumnConfig, metric: previsionio.metrics.Classification = <Classification.AUC: 'auc'>, holdout_dataset: previsionio.dataset.Dataset = None, training_config: previsionio.experiment_config.TrainingConfig = <previsionio.experiment_config.TrainingConfig object>, experiment_version_description: str = None) → previsionio.supervised.Supervised

Start an image classification experiment version training

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • dataset (Dataset) – Reference to the dataset object to use for as training dataset
  • dataset_images (DatasetImages) – Reference to the images dataset object to use for as training dataset
  • column_config (ColumnConfig) – Column configuration for the experiment (see the documentation of the ColumnConfig resource for more details on each possible column types)
  • metric (metrics.Classification, optional) – Specific metric to use for the experiment (default: metrics.Classification.AUC)
  • holdout_dataset (Dataset, optional) – Reference to a dataset object to use as a holdout dataset (default: None)
  • training_config (TrainingConfig) – Specific training configuration (see the documentation of the TrainingConfig resource for more details on all the parameters)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created Supervised experiment version object

Return type:

supervised.Supervised

fit_image_multiclassification(experiment_name: str, dataset: previsionio.dataset.Dataset, dataset_images: previsionio.dataset.DatasetImages, column_config: previsionio.experiment_config.ColumnConfig, metric: previsionio.metrics.MultiClassification = <MultiClassification.log_loss: 'log_loss'>, holdout_dataset: previsionio.dataset.Dataset = None, training_config: previsionio.experiment_config.TrainingConfig = <previsionio.experiment_config.TrainingConfig object>, experiment_version_description: str = None) → previsionio.supervised.Supervised

Start an image multiclassification experiment version training

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • dataset (Dataset) – Reference to the dataset object to use for as training dataset
  • dataset_images (DatasetImages) – Reference to the images dataset object to use for as training dataset
  • column_config (ColumnConfig) – Column configuration for the experiment (see the documentation of the ColumnConfig resource for more details on each possible column types)
  • metric (metrics.MultiClassification, optional) – Specific metric to use for the experiment (default: metrics.MultiClassification.log_loss)
  • holdout_dataset (Dataset, optional) – Reference to a dataset object to use as a holdout dataset (default: None)
  • training_config (TrainingConfig) – Specific training configuration (see the documentation of the TrainingConfig resource for more details on all the parameters)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created Supervised experiment version object

Return type:

supervised.Supervised

fit_image_regression(experiment_name: str, dataset: previsionio.dataset.Dataset, dataset_images: previsionio.dataset.DatasetImages, column_config: previsionio.experiment_config.ColumnConfig, metric: previsionio.metrics.Regression = <Regression.RMSE: 'rmse'>, holdout_dataset: previsionio.dataset.Dataset = None, training_config: previsionio.experiment_config.TrainingConfig = <previsionio.experiment_config.TrainingConfig object>, experiment_version_description: str = None) → previsionio.supervised.Supervised

Start an image regression experiment version training

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • dataset (Dataset) – Reference to the dataset object to use for as training dataset
  • dataset_images (DatasetImages) – Reference to the images dataset object to use for as training dataset
  • column_config (ColumnConfig) – Column configuration for the experiment (see the documentation of the ColumnConfig resource for more details on each possible column types)
  • metric (metrics.Regression, optional) – Specific metric to use for the experiment (default: metrics.Regression.RMSE)
  • holdout_dataset (Dataset, optional) – Reference to a dataset object to use as a holdout dataset (default: None)
  • training_config (TrainingConfig) – Specific training configuration (see the documentation of the TrainingConfig resource for more details on all the parameters)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created Supervised experiment version object

Return type:

supervised.Supervised

fit_multiclassification(experiment_name: str, dataset: previsionio.dataset.Dataset, column_config: previsionio.experiment_config.ColumnConfig, metric: previsionio.metrics.MultiClassification = <MultiClassification.log_loss: 'log_loss'>, holdout_dataset: previsionio.dataset.Dataset = None, training_config: previsionio.experiment_config.TrainingConfig = <previsionio.experiment_config.TrainingConfig object>, experiment_version_description: str = None) → previsionio.supervised.Supervised

Start a tabular multiclassification experiment version training

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • dataset (Dataset) – Reference to the dataset object to use for as training dataset
  • column_config (ColumnConfig) – Column configuration for the experiment (see the documentation of the ColumnConfig resource for more details on each possible column types)
  • metric (metrics.MultiClassification, optional) – Specific metric to use for the experiment (default: metrics.MultiClassification.log_loss)
  • holdout_dataset (Dataset, optional) – Reference to a dataset object to use as a holdout dataset (default: None)
  • training_config (TrainingConfig) – Specific training configuration (see the documentation of the TrainingConfig resource for more details on all the parameters)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created Supervised experiment version object

Return type:

supervised.Supervised

fit_regression(experiment_name: str, dataset: previsionio.dataset.Dataset, column_config: previsionio.experiment_config.ColumnConfig, metric: previsionio.metrics.Regression = <Regression.RMSE: 'rmse'>, holdout_dataset: previsionio.dataset.Dataset = None, training_config: previsionio.experiment_config.TrainingConfig = <previsionio.experiment_config.TrainingConfig object>, experiment_version_description: str = None) → previsionio.supervised.Supervised

Start a tabular regression experiment version training

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • dataset (Dataset) – Reference to the dataset object to use for as training dataset
  • column_config (ColumnConfig) – Column configuration for the experiment (see the documentation of the ColumnConfig resource for more details on each possible column types)
  • metric (metrics.Regression, optional) – Specific metric to use for the experiment (default: metrics.Regression.RMSE)
  • holdout_dataset (Dataset, optional) – Reference to a dataset object to use as a holdout dataset (default: None)
  • training_config (TrainingConfig) – Specific training configuration (see the documentation of the TrainingConfig resource for more details on all the parameters)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created Supervised experiment version object

Return type:

supervised.Supervised

fit_text_similarity(experiment_name: str, dataset: previsionio.dataset.Dataset, description_column_config: previsionio.text_similarity.DescriptionsColumnConfig, metric: previsionio.metrics.TextSimilarity = <TextSimilarity.accuracy_at_k: 'accuracy_at_k'>, top_k: int = 10, lang: previsionio.text_similarity.TextSimilarityLang = <TextSimilarityLang.Auto: 'auto'>, queries_dataset: previsionio.dataset.Dataset = None, queries_column_config: previsionio.text_similarity.QueriesColumnConfig = None, models_parameters: previsionio.text_similarity.ListModelsParameters = <previsionio.text_similarity.ListModelsParameters object>, experiment_version_description: str = None) → previsionio.text_similarity.TextSimilarity

Start a text similarity experiment version training with a specific training configuration.

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • dataset (Dataset) – Reference to the dataset object to use for as training dataset
  • description_column_config (DescriptionsColumnConfig) – Description column configuration (see the documentation of the DescriptionsColumnConfig resource for more details on each possible column types)
  • metric (metrics.TextSimilarity, optional) – Specific metric to use for the experiment (default: metrics.TextSimilarity.accuracy_at_k)
  • top_k (int, optional) – top_k (default: 10)
  • lang (TextSimilarityLang, optional) – lang of the training dataset (default: TextSimilarityLang.Auto)
  • queries_dataset (Dataset, optional) – Reference to a dataset object to use as a queries dataset (default: None)
  • queries_column_config (QueriesColumnConfig) – Queries column configuration (see the documentation of the QueriesColumnConfig resource for more details on each possible column types)
  • models_parameters (ListModelsParameters) – Specific training configuration (see the documentation of the ListModelsParameters resource for more details on all the parameters)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created TextSimilarity experiment version object

Return type:

text_similarity.TextSimilarity

fit_timeseries_regression(experiment_name: str, dataset: previsionio.dataset.Dataset, column_config: previsionio.experiment_config.ColumnConfig, time_window: previsionio.timeseries.TimeWindow, metric: previsionio.metrics.Regression = <Regression.RMSE: 'rmse'>, holdout_dataset: previsionio.dataset.Dataset = None, training_config: previsionio.experiment_config.TrainingConfig = <previsionio.experiment_config.TrainingConfig object>, experiment_version_description: str = None) → previsionio.timeseries.TimeSeries

Start a timeseries regression experiment version training

Parameters:
  • experiment_name (str) – Name of the experiment to create
  • dataset (Dataset) – Reference to the dataset object to use for as training dataset
  • column_config (ColumnConfig) – Column configuration for the experiment version (see the documentation of the ColumnConfig resource for more details on each possible column types)
  • time_window (TimeWindow) – Time configuration (see the documentation of the TimeWindow resource for more details)
  • metric (metrics.Regression, optional) – Specific metric to use for the experiment (default: metrics.Regression.RMSE)
  • holdout_dataset (Dataset, optional) – Reference to a dataset object to use as a holdout dataset (default: None)
  • training_config (TrainingConfig) – Specific training configuration (see the documentation of the TrainingConfig resource for more details on all the parameters)
  • experiment_version_description (str) – Description of the experiment version to create
Returns:

Newly created TimeSeries experiment version object

Return type:

timeseries.TimeSeries

classmethod from_id(_id: str)

Get a project from the instance by its unique id.

Parameters:_id (str) – Unique id of the resource to retrieve
Returns:The fetched datasource
Return type:Project
Raises:PrevisionException – Any error while fetching data from the platform or parsing the result
info() → Dict

Get a datasource from the instance by its unique id.

Parameters:_id (str) – Unique id of the resource to retrieve
Returns:
Information about the Project with these entries:
”_id” “name” “description” “color” “created_by” “admins” “contributors” “viewers” “pipelines_count” “experiments_count” “dataset_count” “users”
Return type:Dict
Raises:PrevisionException – Any error while fetching data from the platform or parsing the result
classmethod list(all: bool = False)

List all the available project in the current active [client] workspace.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched project objects
Return type:list(Project)
list_connectors(all: bool = True)

List all the available connectors in the current active project.

Warning

Contrary to the parent list() function, this method returns actual Connector objects rather than plain dictionaries with the corresponding data.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched dataset objects
Return type:list(Connector)
list_datasets(all: bool = True)

List all the available datasets in the current active project.

Warning

Contrary to the parent list() function, this method returns actual Dataset objects rather than plain dictionaries with the corresponding data.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched dataset objects
Return type:list(Dataset)
list_datasource(all: bool = False)

List all the available datasources in the current active project.

Warning

Contrary to the parent list() function, this method returns actual DataSource objects rather than plain dictionaries with the corresponding data.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched dataset objects
Return type:list(DataSource)
list_experiment_deployments(all: bool = True)

List all the available experiment in the current project.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched experiment deployment objects
Return type:list(ExperimentDeployment)
list_experiments(all: bool = True)

List all the available experiment in the current project.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched experiment objects
Return type:list(Experiment)
list_exporter(all: bool = False)

List all the available exporters in the current active project.

Warning

Contrary to the parent list() function, this method returns actual Exporter objects rather than plain dictionaries with the corresponding data.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched dataset objects
Return type:list(Exporter)
list_image_folders(all: bool = True)

List all the available dataset image in the current active [client] workspace.

Warning

Contrary to the parent list() function, this method returns actual DatasetImages objects rather than plain dictionaries with the corresponding data.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched dataset objects
Return type:list(DatasetImages)
list_pipeline_scheduled_runs(all: bool = False)

List all the available pipeline scheduled runs in the current active project.

Warning

Contrary to the parent list() function, this method returns actual Exporter objects rather than plain dictionaries with the corresponding data.

Parameters:all (boolean, optional) – Whether to force the SDK to load all items of the given type (by calling the paginated API several times). Else, the query will only return the first page of result.
Returns:Fetched dataset objects
Return type:list(PipelineScheduledRun)
classmethod new(name: str, description: str = None, color: previsionio.project.ProjectColor = None) → previsionio.project.Project

Create a new project on the platform.

Parameters:
  • name (str) – Name of the project
  • description (str, optional) – Description of the project
  • color (str, optional) – Color of the project
Returns:

The registered project object in the current workspace

Return type:

Project

Raises:
  • PrevisionException – Any error while uploading data to the platform or parsing the result
  • Exception – For any other unknown error
users()

Get a project from the instance by its unique id.

Parameters:_id (str) – Unique id of the resource to retrieve
Returns:The fetched datasource
Return type:Project
Raises:PrevisionException – Any error while fetching data from the platform or parsing the result
class previsionio.project.ProjectColor

Bases: enum.Enum

An enumeration.