create_experiment_version: Create a new version of an existing experiment.
In previsionio: 'Prevision.io' R SDK

create_experiment_version

R Documentation

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).