| Type: | Package |
| Title: | 'Prevision.io' R SDK |
| Version: | 11.7.0 |
| Description: | For working with the 'Prevision.io' AI model management platform's API https://prevision.io/. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Imports: | data.table, futile.logger, httr, jsonlite, Metrics, graphics, stats, utils, XML, magrittr, plotly |
| Suggests: | testthat |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2022-03-09 13:34:45 UTC; Florian |
| Author: | Florian Laroumagne [aut, cre], Prevision.io Inc [cph] |
| Maintainer: | Florian Laroumagne <florian.laroumagne@prevision.io> |
| Repository: | CRAN |
| Date/Publication: | 2022-03-09 13:50:02 UTC |
Create a new connector of a supported type (among: "SQL", "FTP", "SFTP", "S3", "GCP"). If check_if_exist is enabled, the function will check if a connector with the same name already exists. If yes, it will return a message and the information of the existing connector instead of creating a new one.
Description
Create a new connector of a supported type (among: "SQL", "FTP", "SFTP", "S3", "GCP"). If check_if_exist is enabled, the function will check if a connector with the same name already exists. If yes, it will return a message and the information of the existing connector instead of creating a new one.
Usage
create_connector(
project_id,
type,
name,
host,
port,
username,
password,
google_credentials = NULL,
check_if_exist = FALSE
)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
type |
connector type. |
name |
connector name. |
host |
connector host. |
port |
connector port. |
username |
connector username. |
password |
connector password. |
google_credentials |
google credentials JSON (for GCP only). |
check_if_exist |
boolean (FALSE by default). If TRUE, makes extra checks to see if a connector with the same name is already existing. |
Value
list - parsed content of the connector.
Create a new contact point of a supported type (among: "email", "slack").
Description
Create a new contact point of a supported type (among: "email", "slack").
Usage
create_contact_point(
project_id,
type,
name,
addresses = NULL,
webhook_url = NULL
)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
type |
contact point type among "email" or "slack". |
name |
contact point name. |
addresses |
contact point addresses. |
webhook_url |
contact point webhook_url. |
Value
list - parsed content of the contact point.
Create a dataframe from a dataset_id.
Description
Create a dataframe from a dataset_id.
Usage
create_dataframe_from_dataset(dataset_id)
Arguments
dataset_id |
dataset id. |
Value
data.frame - a R dataframe matching the dataset.
Create a dataset embedding from a dataset_id.
Description
Create a dataset embedding from a dataset_id.
Usage
create_dataset_embedding(dataset_id)
Arguments
dataset_id |
dataset id. |
Value
integer - 200 on success.
Upload dataset from data frame.
Description
Upload dataset from data frame.
Usage
create_dataset_from_dataframe(project_id, dataset_name, dataframe, zip = FALSE)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
dataset_name |
given name of the dataset on the platform. |
dataframe |
data.frame to upload. |
zip |
is the temp file zipped before sending it to Prevision.io (default = FALSE). |
Value
list - parsed content of the dataset.
Create a dataset from an existing datasource.
Description
Create a dataset from an existing datasource.
Usage
create_dataset_from_datasource(project_id, dataset_name, datasource_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
dataset_name |
given name of the dataset on the platform. |
datasource_id |
datasource id. |
Value
list - parsed content of the dataset.
Upload dataset from file name.
Description
Upload dataset from file name.
Usage
create_dataset_from_file(
project_id,
dataset_name,
file,
separator = ",",
decimal = "."
)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
dataset_name |
given name of the dataset on the platform. |
file |
path to the dataset. |
separator |
column separator in the file (default: ",") |
decimal |
decimal separator in the file (default: ".") |
Value
list - parsed content of the dataset.
Create a new datasource If check_if_exist is enabled, the function will check if a datasource with the same name already exists. If yes, it will return a message and the information of the existing datasource instead of creating a new one.
Description
Create a new datasource If check_if_exist is enabled, the function will check if a datasource with the same name already exists. If yes, it will return a message and the information of the existing datasource instead of creating a new one.
Usage
create_datasource(
project_id,
connector_id,
name,
path = "",
database = "",
table = "",
bucket = "",
request = "",
check_if_exist = FALSE
)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
connector_id |
connector_id linked to the datasource. |
name |
datasource name. |
path |
datasource path (for SFTP & FTP connector). |
database |
datasource database (for SQL connector). |
table |
datasource table (for SQL connector). |
bucket |
datasource bucket (for S3 connector). |
request |
datasource request (for SQLconnector). |
check_if_exist |
boolean (FALSE by default). If TRUE, makes extra checks to see if a datasource with the same name is already existing. |
Value
list - parsed content of the datasource.
Create a new API key for a deployed model.
Description
Create a new API key for a deployed model.
Usage
create_deployment_api_key(deployment_id)
Arguments
deployment_id |
id of the deployment to create an API key on, can be obtained with get_deployments(). |
Value
list - API key information.
Create a new deployment for a model.
Description
Create a new deployment for a model.
Usage
create_deployment_model(
project_id,
name,
experiment_id,
main_model_experiment_version_id,
challenger_model_experiment_version_id = NULL,
access_type = c("fine_grained"),
type_violation_policy = c("best_effort"),
description = NULL,
main_model_id,
challenger_model_id = NULL
)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
name |
name of the deployment. |
experiment_id |
id of the experiment to deploy, can be obtained with get_experiment_id_from_name(). |
main_model_experiment_version_id |
id of the experiment_version to deploy, can be obtained with get_experiment_version_id(). |
challenger_model_experiment_version_id |
id of the challenger experiment_version to deploy, can be obtained with get_experiment_version_id(). |
access_type |
type of access of the deployment among "fine_grained" (project defined, default), "private" (instance) or "public" (everyone). |
type_violation_policy |
handling of type violation when making predictions among "best_effort" (default) or "strict" (stops the prediction if there is a type violation). |
description |
description of the deployment. |
main_model_id |
id of the model to deploy |
challenger_model_id |
id of the challenger model to deploy |
Value
list - parsed content of the deployment.
Create predictions on a deployed model using a dataset.
Description
Create predictions on a deployed model using a dataset.
Usage
create_deployment_predictions(deployment_id, dataset_id)
Arguments
deployment_id |
id of the deployment, can be obtained with get_deployments(). |
dataset_id |
id of the dataset to predict, can be obtained with get_dataset_id_from_name(). |
Value
integer - 200 on success.
Create a new experiment. If check_if_exist is enabled, the function will check if an experiment with the same name already exists. If yes, it will return a message and the information of the existing experiment instead of creating a new one.
Description
Create a new experiment. If check_if_exist is enabled, the function will check if an experiment with the same name already exists. If yes, it will return a message and the information of the existing experiment instead of creating a new one.
Usage
create_experiment(
project_id,
name,
provider,
data_type,
training_type,
check_if_exist = FALSE
)
Arguments
project_id |
id of the project in which we create the experiment. |
name |
name of the experiment. |
provider |
provider of the experiment ("prevision-auto-ml" or "external") |
data_type |
type of data ("tabular", "images" or "timeseries"). |
training_type |
type of the training you want to achieve ("regression", "classification", "multiclassification", "clustering", "object-detection" or "text-similarity"). |
check_if_exist |
boolean (FALSE by default). If TRUE, makes extra checks to see if an experiment with the same name is already existing. |
Value
list - experiment information.
Create a new version of an existing experiment.
Description
Create a new version of an existing experiment.
Usage
create_experiment_version(
experiment_id,
dataset_id = NULL,
target_column = NULL,
holdout_dataset_id = NULL,
id_column = NULL,
drop_list = NULL,
profile = NULL,
experiment_description = NULL,
metric = NULL,
fold_column = NULL,
normal_models = NULL,
lite_models = NULL,
simple_models = NULL,
with_blend = NULL,
weight_column = NULL,
features_engineering_selected_list = NULL,
features_selection_count = NULL,
features_selection_time = NULL,
folder_dataset_id = NULL,
filename_column = NULL,
ymin = NULL,
ymax = NULL,
xmin = NULL,
xmax = NULL,
time_column = NULL,
start_dw = NULL,
end_dw = NULL,
start_fw = NULL,
end_fw = NULL,
group_list = NULL,
apriori_list = NULL,
content_column = NULL,
queries_dataset_id = NULL,
queries_dataset_content_column = NULL,
queries_dataset_id_column = NULL,
queries_dataset_matching_id_description_column = NULL,
top_k = NULL,
lang = NULL,
models_params = NULL,
name = NULL,
onnx_file = NULL,
yaml_file = NULL
)
Arguments
experiment_id |
id of the experiment that will host the new version. |
dataset_id |
id of the dataset used for the training phase. |
target_column |
name of the TARGET column. |
holdout_dataset_id |
id of the holdout dataset. |
id_column |
name of the id column. |
drop_list |
list of names of features to drop. |
profile |
chosen profil among "quick", "normal", "advanced". |
experiment_description |
experiment description. |
metric |
name of the metric to optimise. |
fold_column |
name of the fold column. |
normal_models |
list of (normal) models to select with full FE & hyperparameters search (among "LR", "RF", "ET", "XGB", "LGB", "NN", "CB"). |
lite_models |
list of (lite) models to select with lite FE & default hyperparameters (among "LR", "RF", "ET", "XGB", "LGB", "NN", "CB", "NBC"). |
simple_models |
list of simple models to select (among "LR", "DT"). |
with_blend |
boolean, do we allow to include blend in the modelisation. |
weight_column |
name of the weight columns. |
features_engineering_selected_list |
list of feature engineering to select (among "Counter", "Date", "freq", "text_tfidf", "text_word2vec", "text_embedding", "tenc", "poly", "pca", "kmean"). |
features_selection_count |
number of features to keep after the feature selection process. |
features_selection_time |
time budget in minutes of the feature selection process. |
folder_dataset_id |
id of the dataset folder (images). |
filename_column |
name of the file name path (images). |
ymin |
name of the column matching the lower y value of the image (object detection). |
ymax |
name of the column matching the higher y value of the image (object detection). |
xmin |
name of the column matching the lower x value of the image (object detection). |
xmax |
name of the column matching the higher x value of the image (object detection). |
time_column |
name of column containing the timestamp (time series). |
start_dw |
value of the start of derivative window (time series), should be a strict negative integer. |
end_dw |
value of the end of derivative window (time series), should be a negative integer greater than start_dw. |
start_fw |
value of the start of forecast window (time series), should be a strict positive integer. |
end_fw |
value of the end of forecast window (time series), should be a strict positive integer greater than start_fw. |
group_list |
list of name of feature that describes groups (time series). |
apriori_list |
list of name of feature that are a priori (time series). |
content_column |
content column name (text-similarity). |
queries_dataset_id |
id of the dataset containing queries (text-similarity). |
queries_dataset_content_column |
name of the column containing queries in the query dataset (text-similarity). |
queries_dataset_id_column |
name of the ID column in the query dataset (text-similarity). |
queries_dataset_matching_id_description_column |
name of the column matching id in the description dataset (text-similarity). |
top_k |
top k individual to find (text-similarity). |
lang |
lang of the text (text-similarity). |
models_params |
parameters of the model (text-similarity). |
name |
name of the external model (external model). |
onnx_file |
path to the onnx file (external model). |
yaml_file |
path to the yaml file (external model). |
Value
list - experiment information.
Export data using an existing exporter and the resource to export
Description
Export data using an existing exporter and the resource to export
Usage
create_export(exporter_id, type, dataset_id = NULL, prediction_id = NULL)
Arguments
exporter_id |
id of the exporter, can be obtained with get_exporters(). |
type |
type of data to export among \"dataset"\, \"validation-prediction\" or \"deployment-prediction\" |
dataset_id |
id of the dataset to export (only for type == \"dataset\") |
prediction_id |
id of the prediction to export (only for type == \"validation_prediction\" or type == \"deployment-prediction\") |
Value
list - parsed content of the export.
Create a new exporter
Description
Create a new exporter
Usage
create_exporter(
project_id,
connector_id,
name,
description = "",
filepath = "",
file_write_mode = "timestamp",
database = "",
table = "",
database_write_mode = "append",
bucket = ""
)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
connector_id |
connector_id linked to the exporter. |
name |
exporter name. |
description |
description of the exporter. |
filepath |
exporter path (for SFTP & FTP connector). |
file_write_mode |
writing type when exporting a file (for SFT & FTP connector, among \"timestamp\", \"safe\" or \"replace\") |
database |
exporter database (for SQL connector). |
table |
exporter table (for SQL connector). |
database_write_mode |
writing type when exporting data within a database (for SQL connector, among \"append\" or \"replace\"). |
bucket |
exporter bucket (for S3 connector). |
Value
list - parsed content of the exporter.
Upload folder from a local file.
Description
Upload folder from a local file.
Usage
create_folder(project_id, folder_name, file)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
folder_name |
given name of the folder on the platform. |
file |
path to the folder. |
Value
list - parsed content of the folder.
Trigger an existing pipeline run.
Description
Trigger an existing pipeline run.
Usage
create_pipeline_trigger(pipeline_id)
Arguments
pipeline_id |
id of the pipeline run to trigger, can be obtained with get_pipelines(). |
Value
integer - 200 on success.
Create a prediction on a specified experiment_version
Description
Create a prediction on a specified experiment_version
Usage
create_prediction(
experiment_version_id,
dataset_id = NULL,
folder_dataset_id = NULL,
confidence = FALSE,
best_single = FALSE,
model_id = NULL,
queries_dataset_id = NULL,
queries_dataset_content_column = NULL,
queries_dataset_id_column = NULL,
queries_dataset_matching_id_description_column = NULL,
top_k = NULL
)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
dataset_id |
id of the dataset to start the prediction on, can be obtained with get_datasets(). |
folder_dataset_id |
id of the folder dataset to start prediction on, can be obtained with get_folders(). Only usefull for images use cases. |
confidence |
boolean. If enable, confidence interval will be added to predictions. |
best_single |
boolean. If enable, best single model (non blend) will be used for making predictions other wise, best model will be used unless if model_id is fed. |
model_id |
id of the model to start the prediction on. If provided, it will overwrite the "best single" params. |
queries_dataset_id |
id of the dataset containing queries (text-similarity). |
queries_dataset_content_column |
name of the content column in the queries dataset (text-similarity). |
queries_dataset_id_column |
name of the id column in the queries dataset (text-similarity). |
queries_dataset_matching_id_description_column |
name of the column matching the id (text-similarity). |
top_k |
number of class to retrieve (text-similarity). |
Value
list - parsed prediction information.
Create a new project. If check_if_exist is enabled, the function will check if a project with the same name already exists. If yes, it will return a message and the information of the existing project instead of creating a new one.
Description
Create a new project. If check_if_exist is enabled, the function will check if a project with the same name already exists. If yes, it will return a message and the information of the existing project instead of creating a new one.
Usage
create_project(
name,
description = NULL,
color = "#a748f5",
check_if_exist = FALSE
)
Arguments
name |
name of the project. |
description |
description of the project. |
color |
color of the project among \"#4876be\", \"#4ab6eb\", \"#49cf7d\", \"#dc8218\", \"#ecba35\", \"#f45b69\", \"#a748f5\", \"#b34ca2\" or \"#2fe6d0\" (#a748f5 by default). |
check_if_exist |
boolean (FALSE by default). If TRUE, makes extra checks to see if a project with the same name is already existing. |
Value
list - information of the created project.
Add user in and existing project.
Description
Add user in and existing project.
Usage
create_project_user(project_id, user_mail, user_role)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
user_mail |
email of the user to be add. |
user_role |
role to grand to the user among "admin", "contributor", "viewer" or "end_user". |
Value
list - information of project's users.
Delete an existing connector.
Description
Delete an existing connector.
Usage
delete_connector(connector_id)
Arguments
connector_id |
id of the connector to be deleted, can be obtained with get_connectors(). |
Value
integer - 200 on success.
Delete an existing contact_point
Description
Delete an existing contact_point
Usage
delete_contact_point(contact_point_id)
Arguments
contact_point_id |
id of the contact point to be deleted, can be obtained with get_contact_points(). |
Value
integer - 204 on success.
Delete an existing dataset.
Description
Delete an existing dataset.
Usage
delete_dataset(dataset_id)
Arguments
dataset_id |
id of the dataset, can be obtained with get_datasets(). |
Value
integer - 204 on success.
Delete a datasource
Description
Delete a datasource
Usage
delete_datasource(datasource_id)
Arguments
datasource_id |
id of the datasource to be deleted, can be obtained with get_datasources(). |
Value
integer - 200 on success.
Delete an existing deployment.
Description
Delete an existing deployment.
Usage
delete_deployment(deployment_id)
Arguments
deployment_id |
id of the deployment, can be obtained with get_deployments(). |
Value
integer - 204 on success.
Delete a experiment on the platform.
Description
Delete a experiment on the platform.
Usage
delete_experiment(experiment_id)
Arguments
experiment_id |
id of the experiment, can be obtained with get_experiments(). |
Value
integer - 204 on success.
Delete an exporter
Description
Delete an exporter
Usage
delete_exporter(exporter_id)
Arguments
exporter_id |
id of the exporter to be deleted, can be obtained with get_exporters(). |
Value
integer - 204 on success.
Delete an existing folder.
Description
Delete an existing folder.
Usage
delete_folder(folder_id)
Arguments
folder_id |
id of the folder to be deleted. |
Value
integer - 200 on success.
Delete an existing pipeline
Description
Delete an existing pipeline
Usage
delete_pipeline(pipeline_id, type)
Arguments
pipeline_id |
id of the pipeline to be retrieved, can be obtained with get_pipelines(). |
type |
type of the pipeline to be retrieved among "component", "template", "run". |
Value
integer - 204 on success.
Delete a prediction.
Description
Delete a prediction.
Usage
delete_prediction(prediction_id)
Arguments
prediction_id |
id of the prediction to be deleted, can be obtained with get_experiment_version_predictions(). |
Value
integer - 204 on success.
list of predictions of experiment_id.
Delete an existing project.
Description
Delete an existing project.
Usage
delete_project(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
integer - 204 on success.
Delete user in and existing project.
Description
Delete user in and existing project.
Usage
delete_project_user(project_id, user_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
user_id |
user_id of the user to be delete, can be obtained with get_project_users(). |
Value
integer - 200 on success.
Get the model_id that provide the best predictive performance given experiment_version_id. If include_blend is false, it will return the model_id from the best "non blended" model.
Description
Get the model_id that provide the best predictive performance given experiment_version_id. If include_blend is false, it will return the model_id from the best "non blended" model.
Usage
get_best_model_id(experiment_version_id, include_blend = TRUE)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
include_blend |
boolean, indicating if you want to retrieve the best model among blended models too. |
Value
character - model_id.
Get a connector_id from a connector_name for a given project_id. If duplicated name, the first connector_id that match it is retrieved.
Description
Get a connector_id from a connector_name for a given project_id. If duplicated name, the first connector_id that match it is retrieved.
Usage
get_connector_id_from_name(project_id, connector_name)
Arguments
project_id |
id of the project, can be obtained with get_projects(project_id). |
connector_name |
name of the connector we are searching its id from. |
Value
character - id of the connector if found.
Get information about connector from its id.
Description
Get information about connector from its id.
Usage
get_connector_info(connector_id)
Arguments
connector_id |
id of the connector to be retrieved, can be obtained with get_connectors(). |
Value
list - parsed content of the connector.
Get information of all connectors available for a given project_id.
Description
Get information of all connectors available for a given project_id.
Usage
get_connectors(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - parsed content of all connectors for the supplied project_id.
Get a contact point information from its contact_point_id.
Description
Get a contact point information from its contact_point_id.
Usage
get_contact_point_info(contact_point_id)
Arguments
contact_point_id |
id of the contact point, can be obtained with get_contact_points(). |
Value
list - information of the contact point.
Get information of all contact points available for a given project_id.
Description
Get information of all contact points available for a given project_id.
Usage
get_contact_points(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - parsed content of all contact points for the supplied project_id.
Get a dataset embedding from a dataset_id.
Description
Get a dataset embedding from a dataset_id.
Usage
get_dataset_embedding(dataset_id)
Arguments
dataset_id |
dataset id. |
Value
integer - 200 on success.
Show the head of a dataset from its id.
Description
Show the head of a dataset from its id.
Usage
get_dataset_head(dataset_id)
Arguments
dataset_id |
id of the dataset, can be obtained with get_datasets(). |
Value
data.frame - head of the dataset.
Get a dataset_id from a dataset_name. If duplicated name, the first dataset_id that match it is retrieved.
Description
Get a dataset_id from a dataset_name. If duplicated name, the first dataset_id that match it is retrieved.
Usage
get_dataset_id_from_name(project_id, dataset_name)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
dataset_name |
name of the dataset we are searching its id from. Can be obtained with get_datasets(). |
Value
character - id of the dataset if found.
Get a dataset from its id.
Description
Get a dataset from its id.
Usage
get_dataset_info(dataset_id)
Arguments
dataset_id |
id of the dataset, can be obtained with get_datasets(). |
Value
list - parsed content of the dataset.
Get information of all datasets available for a given project_id.
Description
Get information of all datasets available for a given project_id.
Usage
get_datasets(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - parsed content of all datasets for the suppled project_id.
Get a datasource_id from a datasource_name If duplicated name, the first datasource_id that match it is retrieved
Description
Get a datasource_id from a datasource_name If duplicated name, the first datasource_id that match it is retrieved
Usage
get_datasource_id_from_name(project_id, datasource_name)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
datasource_name |
name of the datasource we are searching its id from. Can be obtained with get_datasources(). |
Value
character - id of the datasource if found.
Get a datasource from its id.
Description
Get a datasource from its id.
Usage
get_datasource_info(datasource_id)
Arguments
datasource_id |
id of the data_sources to be retrieved, can be obtained with get_datasources(). |
Value
list - parsed content of the data_sources.
Get information of all data sources available for a given project_id.
Description
Get information of all data sources available for a given project_id.
Usage
get_datasources(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - parsed content of all data_sources for the supplied project_id.
Get a deployment_alert_id from a name and type for a given deployment_id.
Description
Get a deployment_alert_id from a name and type for a given deployment_id.
Usage
get_deployment_alert_id_from_name(deployment_id, name)
Arguments
deployment_id |
id of the deployment, can be obtained with get_deployments(). |
name |
name of the deployment_alert we are searching its id from. |
Value
character - id of the deployment_alert if found.
Get information about a deployment_alert for a given deployed model.
Description
Get information about a deployment_alert for a given deployed model.
Usage
get_deployment_alert_info(deployment_id, deployment_alert_id)
Arguments
deployment_id |
id of the deployment, can be obtained with get_deployments(). |
deployment_alert_id |
id of the deployment_alert to be retrieved, can be obtained with get_deployment_alerts(). |
Value
list - parsed content of the deployment_alert.
Get information of all alerts related to a deployment_id.
Description
Get information of all alerts related to a deployment_id.
Usage
get_deployment_alerts(deployment_id)
Arguments
deployment_id |
id of the project, can be obtained with get_deployments(). |
Value
list - parsed content of all alerts for the supplied deployment_id
Get API keys for a deployed model.
Description
Get API keys for a deployed model.
Usage
get_deployment_api_keys(deployment_id)
Arguments
deployment_id |
id of the deployment to get API keys, can be obtained with get_deployments(). |
Value
data.frame - API keys available for deployment_id.
Get logs from a deployed app.
Description
Get logs from a deployed app.
Usage
get_deployment_app_logs(deployment_id, log_type)
Arguments
deployment_id |
id of the deployment to get the log, can be obtained with get_deployments(). |
log_type |
type of logs we want to get among "build", "deploy" or "run". |
Value
list - logs from deployed apps.
Get a deployment_id from a name and type for a given project_id. If duplicated name, the first deployment_id that match it is retrieved.
Description
Get a deployment_id from a name and type for a given project_id. If duplicated name, the first deployment_id that match it is retrieved.
Usage
get_deployment_id_from_name(project_id, name, type)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
name |
name of the deployment we are searching its id from. |
type |
type of the deployment to be retrieved among "model" or "app". |
Value
character - id of the deployment if found.
Get information about a deployment from its id.
Description
Get information about a deployment from its id.
Usage
get_deployment_info(deployment_id)
Arguments
deployment_id |
id of the deployment to be retrieved, can be obtained with get_deployments(). |
Value
list - parsed content of the deployment.
Get information related to predictions of a prediction_id.
Description
Get information related to predictions of a prediction_id.
Usage
get_deployment_prediction_info(prediction_id)
Arguments
prediction_id |
id of the prediction returned by create_deployment_predictions or that can be obtained with get_deployment_predictions(). |
Value
list - prediction information for a deployed model.
Get listing of predictions related to a deployment_id.
Description
Get listing of predictions related to a deployment_id.
Usage
get_deployment_predictions(deployment_id)
Arguments
deployment_id |
id of the deployment, can be obtained with get_deployments(). |
Value
list - predictions available for a deployed model.
Get usage (calls, errors and response time) of the last version of a deployed model.
Description
Get usage (calls, errors and response time) of the last version of a deployed model.
Usage
get_deployment_usage(deployment_id, usage_type)
Arguments
deployment_id |
id of the deployment to get usage, can be obtained with get_deployments(). |
usage_type |
type of usage to get, among "calls", "errors", "response_time". |
Value
list - plotly object.
Get information of all deployments of a given type available for a given project_id.
Description
Get information of all deployments of a given type available for a given project_id.
Usage
get_deployments(project_id, type)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
type |
type of the deployment to retrieve among "model" or "app". |
Value
list - parsed content of all deployments of the given type for the supplied project_id.
Get a experiment_id from a experiment_name If duplicated name, the first experiment_id that match it is retrieved.
Description
Get a experiment_id from a experiment_name If duplicated name, the first experiment_id that match it is retrieved.
Usage
get_experiment_id_from_name(project_id, experiment_name)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
experiment_name |
name of the experiment we are searching its id from. Can be obtained with get_experiments(). |
Value
character - id matching the experiment_name if found.
Get a experiment from its experiment_id.
Description
Get a experiment from its experiment_id.
Usage
get_experiment_info(experiment_id)
Arguments
experiment_id |
id of the experiment, can be obtained with get_experiments(). |
Value
list - parsed content of the experiment.
Get features information related to a experiment_version_id.
Description
Get features information related to a experiment_version_id.
Usage
get_experiment_version_features(experiment_version_id)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
Value
list - parsed content of the experiment_version features information.
Get a experiment version id from a experiment_id and its version number.
Description
Get a experiment version id from a experiment_id and its version number.
Usage
get_experiment_version_id(experiment_id, version_number = 1)
Arguments
experiment_id |
id of the experiment, can be obtained with get_experiments(). |
version_number |
number of the version of the experiment. 1 by default |
Value
character - experiment version id.
Get a experiment_version info from its experiment_version_id
Description
Get a experiment_version info from its experiment_version_id
Usage
get_experiment_version_info(experiment_version_id)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
Value
list - parsed content of the experiment_version.
Get a model list related to a experiment_version_id.
Description
Get a model list related to a experiment_version_id.
Usage
get_experiment_version_models(experiment_version_id)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
Value
list - parsed content of models attached to experiment_version_id.
Get a list of prediction from a experiment_version_id.
Description
Get a list of prediction from a experiment_version_id.
Usage
get_experiment_version_predictions(
experiment_version_id,
generating_type = "user"
)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
generating_type |
can be "user" (= user predictions) or "auto" (= hold out predictions). |
Value
list - parsed prediction list items.
Get information of all experiments available for a given project_id.
Description
Get information of all experiments available for a given project_id.
Usage
get_experiments(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - parsed content of all experiments for the supplied project_id.
Get all exports done from an exporter_id
Description
Get all exports done from an exporter_id
Usage
get_exporter_exports(exporter_id)
Arguments
exporter_id |
id of the exporter to retrieve information, can be obtained with get_exporters(). |
Value
list - list of exports of the supplied exporter_id.
Get a exporter_id from a exporter_name. If duplicated name, the first exporter_id that match it is retrieved
Description
Get a exporter_id from a exporter_name. If duplicated name, the first exporter_id that match it is retrieved
Usage
get_exporter_id_from_name(project_id, exporter_name)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
exporter_name |
name of the exporter we are searching its id from. Can be obtained with get_exporters(). |
Value
character - id of the exporter if found.
Get an exporter from its id.
Description
Get an exporter from its id.
Usage
get_exporter_info(exporter_id)
Arguments
exporter_id |
id of the exporter to be retrieved, can be obtained with get_exporters(). |
Value
list - parsed content of the exporter.
Get information of all exporters available for a given project_id.
Description
Get information of all exporters available for a given project_id.
Usage
get_exporters(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - parsed content of all exporters for the supplied project_id.
Get information of a given feature related to a experiment_version_id.
Description
Get information of a given feature related to a experiment_version_id.
Usage
get_features_infos(experiment_version_id, feature_name)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
feature_name |
name of the feature to retrive information. |
Value
list - parsed content of the specific feature.
Get a folder from its id.
Description
Get a folder from its id.
Usage
get_folder(folder_id)
Arguments
folder_id |
id of the image folder, can be obtained with get_folders(). |
Value
list - parsed content of the folder.
Get a folder_id from a folder_name. If duplicated name, the first folder_id that match it is retrieved.
Description
Get a folder_id from a folder_name. If duplicated name, the first folder_id that match it is retrieved.
Usage
get_folder_id_from_name(project_id, folder_name)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
folder_name |
name of the folder we are searching its id from. Can be obtained with get_folders(). |
Value
character - id of the folder if found.
Get information of all image folders available for a given project_id.
Description
Get information of all image folders available for a given project_id.
Usage
get_folders(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - parsed content of all folders.
Get the cross validation file from a specific model.
Description
Get the cross validation file from a specific model.
Usage
get_model_cv(model_id)
Arguments
model_id |
id of the model to get the CV, can be obtained with get_experiment_version_models(). |
Value
data.frame - cross validation data coming from model_id.
Get feature importance corresponding to a model_id.
Description
Get feature importance corresponding to a model_id.
Usage
get_model_feature_importance(model_id, mode = "raw")
Arguments
model_id |
id of the model, can be obtained with get_experiment_models(). |
mode |
character indicating the type of feature importance among "raw" (default) or "engineered". |
Value
data.frame - dataset of the model's feature importance.
Get hyperparameters corresponding to a model_id.
Description
Get hyperparameters corresponding to a model_id.
Usage
get_model_hyperparameters(model_id)
Arguments
model_id |
id of the model, can be obtained with experimentModels(experiment_id). |
Value
list - parsed content of the model's hyperparameters.
Get model information corresponding to a model_id.
Description
Get model information corresponding to a model_id.
Usage
get_model_infos(model_id)
Arguments
model_id |
id of the model, can be obtained with get_experiment_models(). |
Value
list - parsed content of the model.
Get a pipeline_id from a pipeline_name and type for a given project_id. If duplicated name, the first pipeline_id that match it is retrieved.
Description
Get a pipeline_id from a pipeline_name and type for a given project_id. If duplicated name, the first pipeline_id that match it is retrieved.
Usage
get_pipeline_id_from_name(project_id, name, type)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
name |
name of the pipeline we are searching its id from. |
type |
type of the pipeline to be retrieved among "component", "template", "run". |
Value
character - id of the connector if found.
Get information about a pipeline from its id and its type.
Description
Get information about a pipeline from its id and its type.
Usage
get_pipeline_info(pipeline_id, type)
Arguments
pipeline_id |
id of the pipeline to be retrieved, can be obtained with get_pipelines(). |
type |
type of the pipeline to be retrieved among "component", "template", "run". |
Value
list - parsed content of the pipeline.
Get information of all pipelines of a given type available for a given project_id.
Description
Get information of all pipelines of a given type available for a given project_id.
Usage
get_pipelines(project_id, type)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
type |
type of the pipeline to retrieve among "component", "template", or "run". |
Value
list - parsed content of all pipelines of the given type for the supplied project_id.
Get a specific prediction from a prediction_id. Wait up until time_out is reached and wait wait_time between each retry.
Description
Get a specific prediction from a prediction_id. Wait up until time_out is reached and wait wait_time between each retry.
Usage
get_prediction(prediction_id, prediction_type, time_out = 3600, wait_time = 10)
Arguments
prediction_id |
id of the prediction to be retrieved, can be obtained with get_experiment_version_predictions(). |
prediction_type |
type of prediction among "validation" (not deployed model) and "deployment" (deployed model). |
time_out |
maximum number of seconds to wait for the prediction. 3 600 by default. |
wait_time |
number of seconds to wait between each retry. 10 by default. |
Value
data.frame - predictions coming from prediction_id.
Get a information about a prediction_id.
Description
Get a information about a prediction_id.
Usage
get_prediction_infos(prediction_id)
Arguments
prediction_id |
id of the prediction to be retrieved, can be obtained with get_experiment_version_predictions(). |
Value
list - parsed prediction information.
Get a project_id from a project_name If duplicated name, the first project_id that match it is retrieved.
Description
Get a project_id from a project_name If duplicated name, the first project_id that match it is retrieved.
Usage
get_project_id_from_name(project_name)
Arguments
project_name |
name of the project we are searching its id from. Can be obtained with get_projects(). |
Value
character - project_id of the project_name if found.
Get a project from its project_id.
Description
Get a project from its project_id.
Usage
get_project_info(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - information of the project.
Get users from a project.
Description
Get users from a project.
Usage
get_project_users(project_id)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
Value
list - information of project's users.
Retrieves all projects.
Description
Retrieves all projects.
Usage
get_projects()
Value
list - list of existing projects.
Get metrics on a CV file retrieved by Prevision.io for a binary classification use case
Description
Get metrics on a CV file retrieved by Prevision.io for a binary classification use case
Usage
helper_cv_classif_analysis(actual, predicted, fold, thresh = NULL, step = 1000)
Arguments
actual |
target comming from the cross Validation dataframe retrieved by Prevision.io |
predicted |
prediction comming from the cross Validation dataframe retrieved by Prevision.io |
fold |
fold number comming from the cross Validation dataframe retrieved by Prevision.io |
thresh |
threshold to use. If not provided optimal threshold given F1 score will be computed |
step |
number of iteration done to find optimal thresh (1000 by default = 0.1% resolution per fold) |
Value
data.frame - metrics computed between actual and predicted vectors.
[BETA] Return a data.frame that contains features, a boolean indicating if the feature may have a different distribution between the submitted datasets (if p-value < threshold), their exact p-value and the test used to compute it.
Description
[BETA] Return a data.frame that contains features, a boolean indicating if the feature may have a different distribution between the submitted datasets (if p-value < threshold), their exact p-value and the test used to compute it.
Usage
helper_drift_analysis(dataset_1, dataset_2, p_value = 0.05, features = NULL)
Arguments
dataset_1 |
the first data set |
dataset_2 |
the second data set |
p_value |
a p-value that will be the decision criteria for deciding if a feature is suspicious 5% by default |
features |
a vector of features names that should be tested. If NULL, only the intersection of the names() will be kept |
Value
vector - a vector of suspicious features.
[BETA] Compute the optimal prediction for each rows in a data frame, for a given model, a list of actionable features and a number of samples for each features to be tested.
Description
[BETA] Compute the optimal prediction for each rows in a data frame, for a given model, a list of actionable features and a number of samples for each features to be tested.
Usage
helper_optimal_prediction(
project_id,
experiment_id,
model_id,
df,
actionable_features,
nb_sample,
maximize,
zip = FALSE,
version = 1
)
Arguments
project_id |
id of the project containing the use case. |
experiment_id |
id of the experiment to be predicted on. |
model_id |
id of the model to be predicted on. |
df |
a data frame to be predicted on. |
actionable_features |
a list of actionable_featuress features contained in the names of the data frame. |
nb_sample |
a vector of number of sample for each actionable_features features. |
maximize |
a boolean indicating if we maximize or minimize the predicted target. |
zip |
a boolean indicating if the data frame to predict should be zipped prior sending to the instance. |
version |
version of the use case we want to make the prediction on. |
Value
data.frame - optimal vector and the prediction associated with for each rows in the original data frame.
Plot RECALL, PRECISION & F1 SCORE versus top n predictions for a binary classification use case
Description
Plot RECALL, PRECISION & F1 SCORE versus top n predictions for a binary classification use case
Usage
helper_plot_classif_analysis(actual, predicted, top, compute_every_n = 1)
Arguments
actual |
true value (0 or 1 only) |
predicted |
prediction vector (probability) |
top |
top individual to analyse |
compute_every_n |
compute indicators every n individuals (1 by default) |
Value
data.frame - metrics computed between actual and predicted vectors.
Pause a running experiment_version on the platform.
Description
Pause a running experiment_version on the platform.
Usage
pause_experiment_version(experiment_version_id)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
Value
integer - 200 on success.
Download resources according specific parameters.
Description
Download resources according specific parameters.
Usage
pio_download(endpoint, tempFile)
Arguments
endpoint |
end of the url of the API call. |
tempFile |
temporary file to download. |
Value
list - response from the request.
Initialization of the connection to your instance Prevision.io.
Description
Initialization of the connection to your instance Prevision.io.
Usage
pio_init(token, url)
Arguments
token |
your master token, can be found on your instance on the "API KEY" page. |
url |
the url of your instance. |
Value
list - url and token needed for connecting to the Prevision.io environment.
Examples
## Not run: pio_init('eyJhbGciOiJIUz', 'https://xxx.prevision.io')
Convert a list returned from APIs to a dataframe. Only working for consistent list (same naming and number of columns).
Description
Convert a list returned from APIs to a dataframe. Only working for consistent list (same naming and number of columns).
Usage
pio_list_to_df(list)
Arguments
list |
named list comming from an API call. |
Value
data.frame - cast a consistent list to a data.frame.
Request the platform. Thanks to an endpoint, the url and the API, you can create request.
Description
Request the platform. Thanks to an endpoint, the url and the API, you can create request.
Usage
pio_request(endpoint, method, data = NULL, upload = FALSE)
Arguments
endpoint |
end of the url of the API call. |
method |
the method needed according the API (Available: POST, GET, DELETE). |
data |
object to upload when using method POST. |
upload |
used parameter when uploading dataset (for encoding in API call), don't use it. |
Value
list - response from the request.
Examples
## Not run: pio_request(paste0('/jobs/', experiment$jobId), DELETE)
Resume a paused experiment_version on the platform.
Description
Resume a paused experiment_version on the platform.
Usage
resume_experiment_version(experiment_version_id)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
Value
integer - 200 on success.
Stop a running or paused experiment_version on the platform.
Description
Stop a running or paused experiment_version on the platform.
Usage
stop_experiment_version(experiment_version_id)
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
Value
integer - 200 on success.
Test an existing connector.
Description
Test an existing connector.
Usage
test_connector(connector_id)
Arguments
connector_id |
id of the connector to be tested, can be obtained with get_connectors(). |
Value
integer - 200 on success.
Test an existing contact point
Description
Test an existing contact point
Usage
test_contact_point(contact_point_id)
Arguments
contact_point_id |
id of the contact point to be tested, can be obtained with get_contact_points(). |
Value
integer - 200 on success.
Test a datasource
Description
Test a datasource
Usage
test_datasource(datasource_id)
Arguments
datasource_id |
id of the datasource to be tested, can be obtained with get_datasources(). |
Value
integer - 200 on success.
Check if a type of a deployment is supported
Description
Check if a type of a deployment is supported
Usage
test_deployment_type(type)
Arguments
type |
type of the deployment among "model" or "app". |
Value
no return value, called for side effects.
Check if a type of a pipeline is supported
Description
Check if a type of a pipeline is supported
Usage
test_pipeline_type(type)
Arguments
type |
type of the pipeline among "component", "template", "run". |
Value
no return value, called for side effects.
Update the description of a given experiment_version_id.
Description
Update the description of a given experiment_version_id.
Usage
update_experiment_version_description(experiment_version_id, description = "")
Arguments
experiment_version_id |
id of the experiment_version, can be obtained with get_experiment_version_id(). |
description |
Description of the experiment. |
Value
integer - 200 on success.
Update user role in and existing project.
Description
Update user role in and existing project.
Usage
update_project_user_role(project_id, user_id, user_role)
Arguments
project_id |
id of the project, can be obtained with get_projects(). |
user_id |
user_id of the user to be delete, can be obtained with get_project_users(). |
user_role |
role to grand to the user among "admin", "contributor", "viewer" and "end_user". |
Value
list - information of project's users.