create_experiment_version {previsionio}

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.