An R expression.

The environment from where global objects should be identified.

If TRUE, argument expr is substitute():ed, otherwise not.

(optional) a logical, a character vector, or a named list to control how globals are handled. For details, see section 'Globals used by future expressions' in the help for future().

(optional) a character vector specifying packages to be attached in the R environment evaluating the future.

If FALSE (default), the future is resolved eagerly (starting immediately), otherwise not.

deprecated as of civis v3.0.1

If TRUE, the garbage collector run (in the process that evaluated the future) only after the value of the future is collected. Exactly when the values are collected may depend on various factors such as number of free workers and whether earlySignal is TRUE (more frequently) or FALSE (less frequently). Some future backends may ignore this argument.

Specified whether conditions should be signaled as soon as possible or not.

A character string label attached to the future.

resources, see scripts_post_containers

the image for the container script.

the tag for the Docker image.

arguments to scripts_post_containers

CivisFuture object.

CivisFuture

array optional. The status of the organization (active/trial/inactive).

array optional. The organization type (platform/ads/survey_vendor/other).

integer, The ID of this organization.

string, The name of this organization.

string, The slug of this organization.

integer, The user ID of the Account Manager.

integer, The user ID of the Client Success Specialist.

string, The status of the organization (active/trial/inactive).

string, The organization type (platform/ads/survey_vendor/other).

string, The custom branding settings.

integer, The monthly contract size.

integer, The max number of full platform users for the org.

integer, The max number of report-only platform users for the org.

string, The business vertical that the organization belongs to.

string, Additional metadata about the organization in JSON format.

boolean, If true, emails sent by platform will not include Civis text.

string, The Salesforce Account ID for this organization.

string, The Tableau Site ID for this organization.

boolean, Flag denoting whether this organization is FedRAMP compliant.

integer, The ID of the user who created this organization

integer, The ID of the user who last updated this organization

list, A list containing the following elements:

• dedicatedDjPoolEnabled boolean, If true, the Organization has a dedicated delayed jobs pool. Defaults to false.

array, The number of tableau refreshes used this month.

integer required. The id of the Alias object.

integer required. The ID of the resource that is shared.

integer required. The ID of the group.

integer required. The ID of the resource that is shared.

integer required. The ID of the user.

integer required.

integer, The id of the Alias object.

integer, The id of the object

string, The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string, The alias of the object

integer, The id of the user who created the alias

string, The display name of the Alias object. Defaults to object name if not provided.

string required. The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string required. The alias of the object

integer, The id of the Alias object.

integer, The id of the object

string, The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string, The alias of the object

integer, The id of the user who created the alias

string, The display name of the Alias object. Defaults to object name if not provided.

string optional. Filter results by object type. Pass multiple object types with a comma-separatedlist. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

integer optional. Number of results to return. Defaults to 50. Maximum allowed is 1000.

integer optional. Page number of the results to return. Defaults to the first page, 1.

string optional. The field on which to order the result set. Defaults to id. Must be one of: id, object_type.

string optional. Direction in which to sort, either asc (ascending) or desc (descending) defaulting to asc.

integer, The id of the Alias object.

integer, The id of the object

string, The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string, The alias of the object

integer, The id of the user who created the alias

string, The display name of the Alias object. Defaults to object name if not provided.

integer required. The ID of the resource that is shared.

integer optional. ID of target user

string, Dependent object type

string, Human readable dependent object type

integer, Dependent object ID

string, Dependent object name, or nil if the requesting user cannot read this object

string, Permission level of target user (not user's groups) for dependent object. Null if no target user or not shareable (e.g. a database table).

string, Additional information about the dependency, if relevant

boolean, Whether or not the requesting user can share this object.

integer required. The ID of the resource that is shared.

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

integer, For owners, the number of total users shared. For writers and readers, the number of visible users shared.

integer, For owners, the number of total groups shared. For writers and readers, the number of visible groups shared.

integer required. The id of the Alias object.

integer optional. The id of the object

string optional. The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string optional. The alias of the object

string optional. The display name of the Alias object. Defaults to object name if not provided.

integer, The id of the Alias object.

integer, The id of the object

string, The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string, The alias of the object

integer, The id of the user who created the alias

string, The display name of the Alias object. Defaults to object name if not provided.

integer required. The id of the object

string required. The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string required. The alias of the object

string optional. The display name of the Alias object. Defaults to object name if not provided.

integer, The id of the Alias object.

integer, The id of the object

string, The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string, The alias of the object

integer, The id of the user who created the alias

string, The display name of the Alias object. Defaults to object name if not provided.

integer required. The id of the Alias object.

integer required. The id of the object

string required. The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string required. The alias of the object

string optional. The display name of the Alias object. Defaults to object name if not provided.

integer, The id of the Alias object.

integer, The id of the object

string, The type of the object. Valid types include: cass_ncoa, container_script, geocode, identity_resolution, dbt_script, python_script, r_script, javascript_script, sql_script, project, notebook, workflow, template_script, template_report, service, report, tableau and service_report.

string, The alias of the object

integer, The id of the user who created the alias

string, The display name of the Alias object. Defaults to object name if not provided.

integer required. The ID of the resource that is shared.

array required. An array of one or more group IDs.

string required. Options are: "read", "write", or "manage".

string optional. Custom body text for e-mail sent on a share.

boolean optional. Send email to the recipients of a share.

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

integer, For owners, the number of total users shared. For writers and readers, the number of visible users shared.

integer, For owners, the number of total groups shared. For writers and readers, the number of visible groups shared.

integer required. The ID of the resource that is shared.

array required. An array of one or more user IDs.

string required. Options are: "read", "write", or "manage".

string optional. Custom body text for e-mail sent on a share.

boolean optional. Send email to the recipients of a share.

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

integer, For owners, the number of total users shared. For writers and readers, the number of visible users shared.

integer, For owners, the number of total groups shared. For writers and readers, the number of visible groups shared.

integer required. The ID of the resource that is shared.

integer required. ID of target user

boolean required. Whether or not to give manage permissions on all dependencies

string optional. Custom body text for e-mail sent on transfer.

boolean optional. Send email to the target user of the transfer?

array, An array containing the following fields:

• objectType string, Dependent object type

• fcoType string, Human readable dependent object type

• id integer, Dependent object ID

• name string, Dependent object name, or nil if the requesting user cannot read this object

• permissionLevel string, Permission level of target user (not user's groups) for dependent object. Null if no target user or not shareable (e.g. a database table).

• description string, Additional information about the dependency, if relevant

• shared boolean, Whether dependent object was successfully shared with target user

integer optional. Number of results to return. Defaults to 10. Maximum allowed is 50.

integer optional. Page number of the results to return. Defaults to the first page, 1.

string optional. The field on which to order the result set. Defaults to released_at. Must be one of: released_at.

string optional. Direction in which to sort, either asc (ascending) or desc (descending) defaulting to desc.

integer, The ID of this announcement

string, The subject of this announcement.

string, The body of this announcement.

string, The date and time this announcement was released.

string,

string,

function to be called repeatedly until a status is reached.

arguments to f

The name of the element of the list returned by f containing the status. For most Civis API endpoints, this is the default, "state".

list of states indicating remote work has completed successfully. For most Civis API endpoints, this set of states is the default, c("succeeded", "success").

list of states indicating remote work is in an error state. For most Civis API endpoints, this set of states is the default, c("failed", "cancelled").

Number of seconds after which to timeout.

The interval for retries (in seconds). If NULL (default), use exponentially increasing intervals with jitter (see 'Details')

Print the status of f at a given retry with the retry time (default FALSE)

a vector of values to be passed to f

a vector of values to be passed to f (default NULL)

CivisFuture object.

unused for CivisFuture.

The id of a Civis file.

The id of a Civis file.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

The name of the CivisML workflow. See the Workflows section below.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

Optional, parameters for the final stage estimator in a predefined model, e.g. list(C = 2) for a "sparse_logistic" model.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if not NULL, calibrate output probabilities with the selected method, sigmoid, or isotonic. Valid only with classification models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, source for validation data. There are currently two options: train (the default), which uses training data for validation, and skip, which skips the validation step.

Number of concurrent Platform jobs to use for training and validation, or multi-file / large table prediction. Defaults to NULL, which allows CivisML to dynamically calculate an appropriate number of workers to use (in general, as many as possible without using all resources in the cluster).

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

The id of CivisML model built previously.

Optional, the id of a CivisML model run. If NULL, defaults to fetching the latest run.

A civis_ml object.

See the Data Sources section below.

The table in which to put predictions.

The database containing output_table. If not provided, this will default to the database_name specified when the model was built.

Action to take if the prediction table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, For scoring, this should be a vector of column names of dependent variables to include in the output table. It must be a subset of the dependent_variable vector provided for training. The scores for the returned subset will be identical to the scores which those outputs would have had if all outputs were written, but ignoring some of the model's outputs will let predictions complete faster and use less disk space. If not provided, the entire model output will be written to the output table.

Unused

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting, so a large number usually results in better predictive performance.

The function to measure the quality of a split. Supported criteria are gini for the Gini impurity and entropy for the information gain.

Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance. The best value depends on the interaction of the input variables.

The minimum number of samples required to split an internal node. If an integer, then consider min_samples_split as the minimum number. If a float, then min_samples_split is a percentage and ceiling(min_samples_split * n_samples) are the minimum number of samples for each split.

The minimum number of samples required to be in a leaf node. If an integer, then consider min_samples_leaf as the minimum number. If a float, the min_samples_leaf is a percentage and ceiling(min_samples_leaf * n_samples) are the minimum number of samples for each leaf node.

The minimum weighted fraction of the sum total of weights required to be at a leaf node.

The number of features to consider when looking for the best split.

integer

consider max_features at each split.

float

then max_features is a percentage and max_features * n_features are considered at each split.

auto

then max_features = sqrt(n_features)

sqrt

then max_features = sqrt(n_features)

log2

then max_features = log2(n_features)

NULL

then max_features = n_features

Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction to impurity. If max_leaf_nodes = NULL then unlimited number of leaf nodes.

Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

Whether bootstrap samples are used when building trees.

The seed of the random number generator.

A list with class_label = value pairs, or balanced. When class_weight = "balanced", the class weights will be inversely proportional to the class frequencies in the input data as:

\frac{n_samples}{n_classes * table(y)}

Note, the class weights are multiplied with sample_weight (passed via fit_params) if sample_weight is specified.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if not NULL, calibrate output probabilities with the selected method, sigmoid, or isotonic. Valid only with classification models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting, so a large number usually results in better predictive performance.

The function used to measure the quality of a split. Supported criteria are mse for the mean squared error, and mae for the mean absolute error.

Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance. The best value depends on the interaction of the input variables.

The minimum number of samples required to split an internal node. If an integer, then consider min_samples_split as the minimum number. If a float, then min_samples_split is a percentage and ceiling(min_samples_split * n_samples) are the minimum number of samples for each split.

The minimum number of samples required to be in a leaf node. If an integer, then consider min_samples_leaf as the minimum number. If a float, the min_samples_leaf is a percentage and ceiling(min_samples_leaf * n_samples) are the minimum number of samples for each leaf node.

The minimum weighted fraction of the sum total of weights required to be at a leaf node.

The number of features to consider when looking for the best split.

integer

consider max_features at each split.

float

then max_features is a percentage and max_features * n_features are considered at each split.

auto

then max_features = sqrt(n_features)

sqrt

then max_features = sqrt(n_features)

log2

then max_features = log2(n_features)

NULL

then max_features = n_features

Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction to impurity. If max_leaf_nodes = NULL then unlimited number of leaf nodes.

Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

Whether bootstrap samples are used when building trees.

The seed of the random number generator.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

The loss function to be optimized. deviance refers to deviance (logistic regression) for classification with probabilistic outputs. For exponential, gradient boosting recovers the AdaBoost algorithm.

The learning rate shrinks the contribution of each tree by learning_rate. There is a trade-off between learning_rate and n_estimators.

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting, so a large number usually results in better predictive performance.

The fraction of samples to be used for fitting individual base learners. If smaller than 1.0, this results in Stochastic Gradient Boosting. subsample interacts with the parameter n_estimators. Choosing subsample < 1.0 leads to a reduction of variance and an increase in bias.

The function to measure the quality of a split. The default value criterion = "friedman_mse" is generally the best as it can provide a better approximation in some cases.

The minimum number of samples required to split an internal node. If an integer, then consider min_samples_split as the minimum number. If a float, then min_samples_split is a percentage and ceiling(min_samples_split * n_samples) are the minimum number of samples for each split.

The minimum number of samples required to be in a leaf node. If an integer, then consider min_samples_leaf as the minimum number. If a float, the min_samples_leaf is a percentage and ceiling(min_samples_leaf * n_samples) are the minimum number of samples for each leaf node.

The minimum weighted fraction of the sum total of weights required to be at a leaf node.

Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance. The best value depends on the interaction of the input variables.

Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

The seed of the random number generator.

The number of features to consider when looking for the best split.

integer

consider max_features at each split.

float

then max_features is a percentage and max_features * n_features are considered at each split.

auto

then max_features = sqrt(n_features)

sqrt

then max_features = sqrt(n_features)

log2

then max_features = log2(n_features)

NULL

then max_features = n_features

Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction to impurity. If max_leaf_nodes = NULL then unlimited number of leaf nodes.

Whether to presort the data to speed up the finding of best splits in fitting.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if not NULL, calibrate output probabilities with the selected method, sigmoid, or isotonic. Valid only with classification models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

The loss function to be optimized. ls refers to least squares regression. lad (least absolute deviation) is a highly robust loss function solely based on order information of the input variables. huber is a combination of the two. quantile allows quantile regression (use alpha to specify the quantile).

The learning rate shrinks the contribution of each tree by learning_rate. There is a trade-off between learning_rate and n_estimators.

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting, so a large number usually results in better predictive performance.

The fraction of samples to be used for fitting individual base learners. If smaller than 1.0, this results in Stochastic Gradient Boosting. subsample interacts with the parameter n_estimators. Choosing subsample < 1.0 leads to a reduction of variance and an increase in bias.

The function to measure the quality of a split. The default value criterion = "friedman_mse" is generally the best as it can provide a better approximation in some cases.

The minimum number of samples required to split an internal node. If an integer, then consider min_samples_split as the minimum number. If a float, then min_samples_split is a percentage and ceiling(min_samples_split * n_samples) are the minimum number of samples for each split.

The minimum number of samples required to be in a leaf node. If an integer, then consider min_samples_leaf as the minimum number. If a float, the min_samples_leaf is a percentage and ceiling(min_samples_leaf * n_samples) are the minimum number of samples for each leaf node.

The minimum weighted fraction of the sum total of weights required to be at a leaf node.

Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance. The best value depends on the interaction of the input variables.

Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

The seed of the random number generator.

The number of features to consider when looking for the best split.

integer

consider max_features at each split.

float

then max_features is a percentage and max_features * n_features are considered at each split.

auto

then max_features = sqrt(n_features)

sqrt

then max_features = sqrt(n_features)

log2

then max_features = log2(n_features)

NULL

then max_features = n_features

The alpha-quantile of the huber loss function and the quantile loss function. Ignored unless loss = "huber" or loss = "quantile"

Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction to impurity. If max_leaf_nodes = NULL then unlimited number of leaf nodes.

Whether to presort the data to speed up the finding of best splits in fitting.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting, so a large number usually results in better predictive performance.

The function to measure the quality of a split. Supported criteria are gini for the Gini impurity and entropy for the information gain.

Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance. The best value depends on the interaction of the input variables.

The minimum number of samples required to split an internal node. If an integer, then consider min_samples_split as the minimum number. If a float, then min_samples_split is a percentage and ceiling(min_samples_split * n_samples) are the minimum number of samples for each split.

The minimum number of samples required to be in a leaf node. If an integer, then consider min_samples_leaf as the minimum number. If a float, the min_samples_leaf is a percentage and ceiling(min_samples_leaf * n_samples) are the minimum number of samples for each leaf node.

The minimum weighted fraction of the sum total of weights required to be at a leaf node.

The number of features to consider when looking for the best split.

integer

consider max_features at each split.

float

then max_features is a percentage and max_features * n_features are considered at each split.

auto

then max_features = sqrt(n_features)

sqrt

then max_features = sqrt(n_features)

log2

then max_features = log2(n_features)

NULL

then max_features = n_features

Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction to impurity. If max_leaf_nodes = NULL then unlimited number of leaf nodes.

Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

Whether bootstrap samples are used when building trees.

The seed of the random number generator.

A list with class_label = value pairs, or balanced. When class_weight = "balanced", the class weights will be inversely proportional to the class frequencies in the input data as:

\frac{n_samples}{n_classes * table(y)}

Note, the class weights are multiplied with sample_weight (passed via fit_params) if sample_weight is specified.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if not NULL, calibrate output probabilities with the selected method, sigmoid, or isotonic. Valid only with classification models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting, so a large number usually results in better predictive performance.

The function used to measure the quality of a split. Supported criteria are mse for the mean squared error, and mae for the mean absolute error.

Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance. The best value depends on the interaction of the input variables.

The minimum number of samples required to split an internal node. If an integer, then consider min_samples_split as the minimum number. If a float, then min_samples_split is a percentage and ceiling(min_samples_split * n_samples) are the minimum number of samples for each split.

The minimum number of samples required to be in a leaf node. If an integer, then consider min_samples_leaf as the minimum number. If a float, the min_samples_leaf is a percentage and ceiling(min_samples_leaf * n_samples) are the minimum number of samples for each leaf node.

The minimum weighted fraction of the sum total of weights required to be at a leaf node.

The number of features to consider when looking for the best split.

integer

consider max_features at each split.

float

then max_features is a percentage and max_features * n_features are considered at each split.

auto

then max_features = sqrt(n_features)

sqrt

then max_features = sqrt(n_features)

log2

then max_features = log2(n_features)

NULL

then max_features = n_features

Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction to impurity. If max_leaf_nodes = NULL then unlimited number of leaf nodes.

Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

Whether bootstrap samples are used when building trees.

The seed of the random number generator.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

Should an intercept term be included in the model. If FALSE, no intercept will be included, in this case the data are expected to already be centered.

If TRUE, the regressors will be normalized before fitting the model. normalize is ignored when fit_intercept = FALSE.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

Used to specify the norm used in the penalization. The newton-cg, sag, and lbfgs solvers support only l2 penalties.

Dual or primal formulation. Dual formulation is only implemented for l2 penalty with the liblinear solver. dual = FALSE should be preferred when n_samples > n_features.

Tolerance for stopping criteria.

Inverse of regularization strength, must be a positive float. Smaller values specify stronger regularization.

Should a constant or intercept term be included in the model.

Useful only when the solver = "liblinear" and fit_intercept = TRUE. In this case, a constant term with the value intercept_scaling is added to the design matrix.

A list with class_label = value pairs, or balanced. When class_weight = "balanced", the class weights will be inversely proportional to the class frequencies in the input data as:

\frac{n_samples}{n_classes * table(y)}

Note, the class weights are multiplied with sample_weight (passed via fit_params) if sample_weight is specified.

The seed of the random number generator to use when shuffling the data. Used only in solver = "sag" and solver = "liblinear".

Algorithm to use in the optimization problem. For small data liblinear is a good choice. sag is faster for larger problems. For multiclass problems, only newton-cg, sag, and lbfgs handle multinomial loss. liblinear is limited to one-versus-rest schemes. newton-cg, lbfgs, and sag only handle the l2 penalty.

Note that sag fast convergence is only guaranteed on features with approximately the same scale.

The maximum number of iterations taken for the solvers to converge. Useful for the newton-cg, sag, and lbfgs solvers.

The scheme for multi-class problems. When ovr, then a binary problem is fit for each label. When multinomial, a single model is fit minimizing the multinomial loss. Note, multinomial only works with the newton-cg, sag, and lbfgs solvers.

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if not NULL, calibrate output probabilities with the selected method, sigmoid, or isotonic. Valid only with classification models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

See the Data Sources section below.

The dependent variable of the training dataset. For a multi-target problem, this should be a vector of column names of dependent variables. Nulls in a single dependent variable will automatically be dropped.

Optional, the unique ID (primary key) of the training dataset. This will be used to index the out-of-sample scores. In predict.civis_ml, the primary_key of the training task is used by default primary_key = NA. Use primary_key = NULL to explicitly indicate the data have no primary_key.

Optional, a vector of columns which will be considered ineligible to be independent variables.

The regularization strength, must be a vector of floats of length n_targets or a single float. Larger values specify stronger regularization.

Should an intercept term be included in the model. If FALSE, no intercept will be included, in this case the data are expected to already be centered.

If TRUE, the regressors will be normalized before fitting the model. normalize is ignored when fit_intercept = FALSE.

Maximum number of iterations for conjugate gradient solver. For sparse_cg and lsqr solvers, the default value is predetermined. For the sag solver, the default value is 1000.

Precision of the solution.

Solver to use for the optimization problem.

auto

chooses the solver automatically based on the type of data.

svd

uses Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than cholesky.

cholesky

uses the standard decomposition to obtain a closed-form solution.

sparse_cg

uses the conjugate gradient solver. As an iterative algorithm, this solver is more appropriate than cholesky for large-scale data.

lsqr

uses the dedicated regularized least-squares routine.

sag

uses Stochastic Average Gradient descent. It also uses an iterative procedure, and is often faster than other solvers when both n_samples and n_features are large. Note that sag fast convergence is only guaranteed on features with approximately the same scale

The seed of the pseudo random number generator to use when shuffling the data. Used only when solver = "sag".

Optional, a mapping from parameter names in the model's fit method to the column names which hold the data, e.g. list(sample_weight = 'survey_weight_column').

Optional, parameter grid for learner parameters, e.g. list(n_estimators = c(100, 200, 500), learning_rate = c(0.01, 0.1), max_depth = c(2, 3)) or "hyperband" for supported models.

Optional, if provided, store out-of-sample predictions on training set data to this Redshift "schema.tablename".

Optional, the name of the database where the oos_scores_table will be created. If not provided, this will default to database_name.

Optional, action to take if oos_scores_table already exists. One of "fail", "append", "drop", or "truncate". The default is "fail".

Optional, the prefix of the Platform modeling jobs. It will have " Train" or " Predict" added to become the Script title.

Optional, the number of CPU shares requested in the Civis Platform for training jobs or prediction child jobs. 1024 shares = 1 CPU.

Optional, the memory requested from Civis Platform for training jobs or prediction child jobs, in MiB.

Optional, the disk space requested on Civis Platform for training jobs or prediction child jobs, in GB.

Optional, model status notifications. See scripts_post_custom for further documentation about email and URL notification.

Check for job completion every this number of seconds.

Optional, If TRUE, supply debug outputs in Platform logs and make prediction child jobs visible.

Optional, a one-length character vector of the CivisML version. The default is "prod", the latest version in production

job metadata from scripts_get_custom.

run metadata from scripts_get_custom_runs.

CivisML metadata from scripts_list_custom_runs_outputs containing the locations of files produced by CivisML e.g. files, projects, metrics, model_info, logs, predictions, and estimators.

Parsed CivisML output from metrics.json containing metadata from validation. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model with CV results.

• metrics list, validation metrics (accuracy, confusion, ROC, AUC, etc).

• warnings list.

• data_platform list, training data location.

Parsed CivisML output from model_info.json containing metadata from training. A list containing the following elements:

• run list, metadata about the run.

• data list, metadata about the training data.

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

Arguments to CivisFuture and then scripts_post_containers

integer, job id of the script.

integer, id of the run. If NULL (default), the most recent run will be used.

The table name, as "schema.table".

The name of the database holding table_name.

A SQL WHERE clause used to scope the rows of the training or prediction table. Note, the WHERE keyword is added at runtime.

A SQL_LIMIT clause for querying the training or prediction set. Note, the LIMIT keyword is added at runtime.

string, The SQL select string to be executed.

string, Name of database where query is run.

string, optional. Name to identify scripted sql job.

logical, Whether to hide the query in platform.

logical, optional. Whether to include headers as an element in the returned list.

string, optional, Type of compression to use, if any. One of 'none', 'zip', or 'gzip'.

string, optional. Which delimiter to use. One of ',', '\t' or '|'.

logical, optional. Whether or not to quote fields.

string, optional. A user specified filename prefix for the output files to have.

integer required. The ID of the cluster which this partition belongs to.

integer required. The ID of this cluster partition.

integer required.

boolean optional. When true, usage stats are returned in instance config objects. Defaults to false.

integer, The ID of this cluster.

string, The id of this cluster's organization.

string, The name of this cluster's organization.

string, The slug of this cluster's organization.

string, The slug of this cluster's raw configuration.

boolean, Whether this cluster has a custom partition configuration.

array, An array containing the following fields:

• clusterPartitionId integer, The ID of this cluster partition.

• name string, The name of the cluster partition.

• labels array, Labels associated with this partition.

• instanceConfigs array, The instances configured for this cluster partition.

• defaultInstanceConfigId integer, The id of the InstanceConfig that is the default for this partition.

boolean, Whether this cluster needs a NAT gateway or not.

number, The number of hours used this month for this cluster.

integer required. The ID of this instance config.

boolean optional. When true, usage stats are returned in instance config objects. Defaults to false.

integer, The ID of this InstanceConfig.

string, An EC2 instance type. Possible values include t2.large, m4.xlarge, m4.2xlarge, m4.4xlarge, m5.12xlarge, c5.18xlarge, and g6.2xlarge.

integer, The minimum number of instances of that type in this cluster.

integer, The maximum number of instances of that type in this cluster.

integer, The amount of memory (RAM) available to a single instance of that type in megabytes.

integer, The number of processor shares available to a single instance of that type in millicores.

integer, The amount of disk available to a single instance of that type in gigabytes.

list, A list containing the following elements:

• pendingMemoryRequested integer, The sum of memory requests (in MB) for pending deployments in this instance config.

• pendingCpuRequested integer, The sum of cpu requests (in millicores) for pending deployments in this instance config.

• runningMemoryRequested integer, The sum of memory requests (in MB) for running deployments in this instance config.

• runningCpuRequested integer, The sum of cpu requests (in millicores) for running deployments in this instance config.

• pendingDeployments integer, The number of pending deployments in this instance config.

• runningDeployments integer, The number of running deployments in this instance config.

integer, The ID of this InstanceConfig's cluster partition

string, The name of this InstanceConfig's cluster partition

integer required. The ID of the cluster which this partition belongs to.

integer required. The ID of this cluster partition.

boolean optional. When true, usage stats are returned in instance config objects. Defaults to false.

integer, The ID of this cluster partition.

string, The name of the cluster partition.

array, Labels associated with this partition.

array, An array containing the following fields:

• instanceConfigId integer, The ID of this InstanceConfig.

• instanceType string, An EC2 instance type. Possible values include t2.large, m4.xlarge, m4.2xlarge, m4.4xlarge, m5.12xlarge, c5.18xlarge, and g6.2xlarge.

• minInstances integer, The minimum number of instances of that type in this cluster.

• maxInstances integer, The maximum number of instances of that type in this cluster.

• instanceMaxMemory integer, The amount of memory (RAM) available to a single instance of that type in megabytes.

• instanceMaxCpu integer, The number of processor shares available to a single instance of that type in millicores.

• instanceMaxDisk integer, The amount of disk available to a single instance of that type in gigabytes.

• usageStats object,

integer, The id of the InstanceConfig that is the default for this partition.

integer optional. The ID of this cluster's organization. Cannot be used along with the organization slug.

string optional. The slug of this cluster's organization. Cannot be used along with the organization ID.

string optional. The slug of this cluster's raw configuration.

boolean optional. When true, excludes KubeClusters associated with inactive orgs. Defaults to false.

integer optional. Number of results to return. Defaults to its maximum of 50.

integer optional. Page number of the results to return. Defaults to the first page, 1.

string optional. The field on which to order the result set. Defaults to organization_id. Must be one of: organization_id, created_at.

string optional. Direction in which to sort, either asc (ascending) or desc (descending) defaulting to asc.

integer, The ID of this cluster.

string, The id of this cluster's organization.

string, The name of this cluster's organization.

string, The slug of this cluster's organization.

string, The slug of this cluster's raw configuration.

boolean, Whether this cluster has a custom partition configuration.

array, An array containing the following fields:

• clusterPartitionId integer, The ID of this cluster partition.

• name string, The name of the cluster partition.

• labels array, Labels associated with this partition.

• instanceConfigs array, The instances configured for this cluster partition.

• defaultInstanceConfigId integer, The id of the InstanceConfig that is the default for this partition.

boolean, Whether this cluster needs a NAT gateway or not.

integer required.

boolean optional. When true, usage stats are returned in instance config objects. Defaults to false.

integer, The total number of normalized hours used by this cluster.

string, Denotes the instance type the normalized hours are attributed to.

string,

string, The month and year the normalized hours are attributed to.

integer required. The ID of this cluster.

string, The base type of this deployment

string, State of the deployment

integer, Number of deployments of base type and state

integer, Total amount of CPU in millicores for deployments of base type and state

integer, Total amount of Memory in megabytes for deployments of base type and state

integer required. The id of the cluster.

string optional. If specified, return deployments of these base types. It accepts a comma-separated list, possible values are 'Notebook', 'Service', 'Run'.

string optional. If specified, return deployments in these states. It accepts a comma-separated list, possible values are pending, running, terminated, sleeping

string optional. If specified, return deployments created after this date. Must be 31 days or less before end date. Defaults to 7 days prior to end date. The date must be provided in the format YYYY-MM-DD.

string optional. If specified, return deployments created before this date. Defaults to 7 days after start date, or today if start date is not specified. The date must be provided in the format YYYY-MM-DD.

integer optional. Number of results to return. Defaults to its maximum of 50.

integer optional. Page number of the results to return. Defaults to the first page, 1.

string optional. The field on which to order the result set. Defaults to created_at. Must be one of: created_at.

string optional. Direction in which to sort, either asc (ascending) or desc (descending) defaulting to asc.

integer, The id of this deployment.

string, The name of the deployment.

integer, The id of the base object associated with the deployment.

string, The base type of this deployment.

string, The state of the deployment.

integer, The CPU in millicores required by the deployment.

integer, The memory in MB required by the deployment.

integer, The disk space in GB required by the deployment.

string, The EC2 instance type requested for the deployment.

list, A list containing the following elements:

• id integer, The ID of this user.

• name string, This user's name.

• username string, This user's username.

• initials string, This user's initials.

• online boolean, Whether this user is online.

number, If the deployment has finished, the maximum amount of memory used during the deployment, in MB.

number, If the deployment has finished, the maximum amount of cpu used during the deployment, in millicores.

string,

string,

integer required. The id of the instance config.

string optional. If specified, return workloads in these states. It accepts a comma-separated list, possible values are pending, running

integer, The id of this deployment.

string, The base type of this deployment.

integer, The id of the base object associated with this deployment.

string, The name of the base object associated with this deployment. Null if you do not have permission to read the object.

string, If the base object is a job run you have permission to read, the type of the job. One of "python_script", "r_script", "container_script", or "custom_script".

integer, If the base object is a job run you have permission to read, the id of the job.

string, If the base object is a job run you have permission to read, and it was requested to be cancelled, the timestamp of that request.

string, The state of this deployment.

integer, The CPU in millicores requested by this deployment.

integer, The memory in MB requested by this deployment.

integer, The disk space in GB requested by this deployment.

list, A list containing the following elements:

• id integer, The ID of this user.

• name string, This user's name.

• username string, This user's username.

• initials string, This user's initials.

• online boolean, Whether this user is online.

string, The timestamp of when the deployment began.

boolean, True if you have permission to cancel this deployment.

integer required. The ID of this instance config.

string optional. The span of time that the graphs cover. Must be one of 1_day, 1_week.

string, URL for the graph of historical CPU usage in this instance config.

string, URL for the graph of historical memory usage in this instance config.

integer required. The ID of this instance config.

string optional. The span of time that the graphs cover. Must be one of 1_day, 1_week.

string optional. The metric to retrieve. Must be one of cpu, memory.

integer, The ID of this instance config.

string, URL for the graph of historical CPU usage in this instance config.

string, The span of time that the graphs cover. Must be one of 1_day, 1_week.

string, The unit of the values.

list, A list containing the following elements:

• used list . A list containing the following elements:

  • times array, The times associated with data points, in seconds since epoch.

  • values array, The values of the data points.

• requested list . A list containing the following elements:

  • times array, The times associated with data points, in seconds since epoch.

  • values array, The values of the data points.

• capacity list . A list containing the following elements:

  • times array, The times associated with data points, in seconds since epoch.

  • values array, The values of the data points.

integer required. The ID of this instance config.

string optional. The field on which to order the result set. Defaults to running_deployments. Must be one of pending_memory_requested, pending_cpu_requested, running_memory_requested, running_cpu_requested, pending_deployments, running_deployments.

string optional. Direction in which to sort, either asc (ascending) or desc (descending). Defaults to desc.

string, The owning user's ID

string, The owning user's name

integer, The number of deployments belonging to the owning user in "pending" state

integer, The sum of memory requests (in MB) for deployments belonging to the owning user in "pending" state

integer, The sum of CPU requests (in millicores) for deployments belonging to the owning user in "pending" state

integer, The number of deployments belonging to the owning user in "running" state

integer, The sum of memory requests (in MB) for deployments belonging to the owning user in "running" state

integer, The sum of CPU requests (in millicores) for deployments belonging to the owning user in "running" state

integer required.

boolean optional. When true, usage stats are returned in instance config objects. Defaults to false.

integer, The ID of this cluster partition.

string, The name of the cluster partition.

array, Labels associated with this partition.

array, An array containing the following fields:

• instanceConfigId integer, The ID of this InstanceConfig.

• instanceType string, An EC2 instance type. Possible values include t2.large, m4.xlarge, m4.2xlarge, m4.4xlarge, m5.12xlarge, c5.18xlarge, and g6.2xlarge.

• minInstances integer, The minimum number of instances of that type in this cluster.

• maxInstances integer, The maximum number of instances of that type in this cluster.

• instanceMaxMemory integer, The amount of memory (RAM) available to a single instance of that type in megabytes.

• instanceMaxCpu integer, The number of processor shares available to a single instance of that type in millicores.

• instanceMaxDisk integer, The amount of disk available to a single instance of that type in gigabytes.

• usageStats object,

integer, The id of the InstanceConfig that is the default for this partition.

integer required. The ID of the cluster which this partition belongs to.

integer required. The ID of this cluster partition.

array optional. An array containing the following fields:

• instanceType string, An EC2 instance type. Possible values include t2.large, m4.xlarge, m4.2xlarge, m4.4xlarge, m5.12xlarge, c5.18xlarge, and g6.2xlarge.

• minInstances integer, The minimum number of instances of that type in this cluster.

• maxInstances integer, The maximum number of instances of that type in this cluster.

string optional. The name of the cluster partition.

array optional. Labels associated with this partition.

integer, The ID of this cluster partition.

string, The name of the cluster partition.

array, Labels associated with this partition.

array, An array containing the following fields:

• instanceConfigId integer, The ID of this InstanceConfig.

• instanceType string, An EC2 instance type. Possible values include t2.large, m4.xlarge, m4.2xlarge, m4.4xlarge, m5.12xlarge, c5.18xlarge, and g6.2xlarge.

• minInstances integer, The minimum number of instances of that type in this cluster.

• maxInstances integer, The maximum number of instances of that type in this cluster.

• instanceMaxMemory integer, The amount of memory (RAM) available to a single instance of that type in megabytes.

• instanceMaxCpu integer, The number of processor shares available to a single instance of that type in millicores.

• instanceMaxDisk integer, The amount of disk available to a single instance of that type in gigabytes.

• usageStats object,

integer, The id of the InstanceConfig that is the default for this partition.

integer required. The ID of the cluster which this partition belongs to.

array required. An array containing the following fields:

• instanceType string, An EC2 instance type. Possible values include t2.large, m4.xlarge, m4.2xlarge, m4.4xlarge, m5.12xlarge, c5.18xlarge, and g6.2xlarge.

• minInstances integer, The minimum number of instances of that type in this cluster.

• maxInstances integer, The maximum number of instances of that type in this cluster.

string required. The name of the cluster partition.

array required. Labels associated with this partition.

integer, The ID of this cluster partition.

string, The name of the cluster partition.

array, Labels associated with this partition.

array, An array containing the following fields:

• instanceConfigId integer, The ID of this InstanceConfig.

• instanceType string, An EC2 instance type. Possible values include t2.large, m4.xlarge, m4.2xlarge, m4.4xlarge, m5.12xlarge, c5.18xlarge, and g6.2xlarge.

• minInstances integer, The minimum number of instances of that type in this cluster.

• maxInstances integer, The maximum number of instances of that type in this cluster.

• instanceMaxMemory integer, The amount of memory (RAM) available to a single instance of that type in megabytes.

• instanceMaxCpu integer, The number of processor shares available to a single instance of that type in millicores.

• instanceMaxDisk integer, The amount of disk available to a single instance of that type in gigabytes.

• usageStats object,

integer, The id of the InstanceConfig that is the default for this partition.

civis_ml_model

see documentation for generic coef()

other arguments

integer required. The ID of the credential.

integer required. The ID of the resource that is shared.

integer required. The ID of the group.

integer required. The ID of the resource that is shared.

integer required. The ID of the user.

integer required. The ID of the credential.

integer, The ID of the credential.

string, The name identifying the credential

string, The credential's type.

string, The username for the credential.

string, A long description of the credential.

string, The username of the user who this credential belongs to. Using user.username is preferred.

list, A list containing the following elements:

• id integer, The ID of this user.

• name string, This user's name.

• username string, This user's username.

• initials string, This user's initials.

• online boolean, Whether this user is online.

integer, The ID of the remote host associated with this credential.

string, The name of the remote host associated with this credential.

string, The U.S. state for the credential. Only for VAN credentials.

string, The creation time for this credential.

string, The last modification time for this credential.

boolean, Whether or not the credential is a default. Only for Database credentials.

boolean, Whether or not the credential is an OAuth credential.

string optional. The type (or types) of credentials to return. One or more of: Amazon Web Services S3, Bitbucket, CASS/NCOA PAF, Certificate, Civis Platform, Custom, Database, Google, Github, Salesforce User, Salesforce Client, and TableauUser. Specify multiple values as a comma-separated list (e.g., "A,B").

integer optional. The ID of the remote host associated with the credentials to return.

boolean optional. If true, will return a list with a single credential which is the current user's default credential.

boolean optional. If true, will only return system credentials. System credentials can only be created and viewed by Civis Admins.

string optional. A comma-separated list of user ids. If specified, returns set of credentials owned by the users that requesting user has at least read access on.

string optional. If specified, will be used to filter the credentials returned. Will search across name and will return any full name containing the search string.

integer optional. Number of results to return. Defaults to its maximum of 1000.

integer optional. Page number of the results to return. Defaults to the first page, 1.

string optional. The field on which to order the result set. Defaults to updated_at. Must be one of: updated_at, created_at, name.

string optional. Direction in which to sort, either asc (ascending) or desc (descending) defaulting to desc.

integer, The ID of the credential.

string, The name identifying the credential

string, The credential's type.

string, The username for the credential.

string, A long description of the credential.

string, The username of the user who this credential belongs to. Using user.username is preferred.

list, A list containing the following elements:

• id integer, The ID of this user.

• name string, This user's name.

• username string, This user's username.

• initials string, This user's initials.

• online boolean, Whether this user is online.

integer, The ID of the remote host associated with this credential.

string, The name of the remote host associated with this credential.

string, The U.S. state for the credential. Only for VAN credentials.

string, The creation time for this credential.

string, The last modification time for this credential.

boolean, Whether or not the credential is a default. Only for Database credentials.

boolean, Whether or not the credential is an OAuth credential.

integer required. The ID of the resource that is shared.

integer optional. ID of target user

string, Dependent object type

string, Human readable dependent object type

integer, Dependent object ID

string, Dependent object name, or nil if the requesting user cannot read this object

string, Permission level of target user (not user's groups) for dependent object. Null if no target user or not shareable (e.g. a database table).

string, Additional information about the dependency, if relevant

boolean, Whether or not the requesting user can share this object.

integer required. The ID of the resource that is shared.

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

integer, For owners, the number of total users shared. For writers and readers, the number of visible users shared.

integer, For owners, the number of total groups shared. For writers and readers, the number of visible groups shared.

array, list of acceptable credential types

integer required. The ID of the credential.

string optional. The name identifying the credential.

string optional. The type of credential. Note: only these credentials can be created or edited via this API ["Amazon Web Services S3", "CASS/NCOA PAF", "Certificate", "Civis Platform", "Custom", "Database", "Google", "Salesforce User", "Salesforce Client", "TableauUser"]

string optional. A long description of the credential.

string optional. The username for the credential.

string optional. The password for the credential.

integer optional. The ID of the remote host associated with the credential.

integer optional. The ID of the user the credential is created for. Note: This attribute is only accepted if you are a Civis Admin User.

string optional. The U.S. state for the credential. Only for VAN credentials.

boolean optional. Boolean flag that sets a credential to be a system credential. System credentials can only be created by Civis Admins and will create a credential owned by the Civis Robot user.

boolean optional. Whether or not the credential is a default. Only for Database credentials.

boolean optional. Whether or not the credential is an OAuth credential.

integer, The ID of the credential.

string, The name identifying the credential

string, The credential's type.

string, The username for the credential.

string, A long description of the credential.

string, The username of the user who this credential belongs to. Using user.username is preferred.

list, A list containing the following elements:

• id integer, The ID of this user.

• name string, This user's name.

• username string, This user's username.

• initials string, This user's initials.

• online boolean, Whether this user is online.

integer, The ID of the remote host associated with this credential.

string, The name of the remote host associated with this credential.

string, The U.S. state for the credential. Only for VAN credentials.

string, The creation time for this credential.

string, The last modification time for this credential.

boolean, Whether or not the credential is a default. Only for Database credentials.

boolean, Whether or not the credential is an OAuth credential.

string required. The type of credential. Note: only these credentials can be created or edited via this API ["Amazon Web Services S3", "CASS/NCOA PAF", "Certificate", "Civis Platform", "Custom", "Database", "Google", "Salesforce User", "Salesforce Client", "TableauUser"]

string required. The username for the credential.

string required. The password for the credential.

string optional. The name identifying the credential.

string optional. A long description of the credential.

integer optional. The ID of the remote host associated with the credential.

integer optional. The ID of the user the credential is created for. Note: This attribute is only accepted if you are a Civis Admin User.

string optional. The U.S. state for the credential. Only for VAN credentials.

boolean optional. Boolean flag that sets a credential to be a system credential. System credentials can only be created by Civis Admins and will create a credential owned by the Civis Robot user.

boolean optional. Whether or not the credential is a default. Only for Database credentials.

boolean optional. Whether or not the credential is an OAuth credential.

integer, The ID of the credential.

string, The name identifying the credential

string, The credential's type.

string, The username for the credential.

string, A long description of the credential.

string, The username of the user who this credential belongs to. Using user.username is preferred.

list, A list containing the following elements:

• id integer, The ID of this user.

• name string, This user's name.

• username string, This user's username.

• initials string, This user's initials.

• online boolean, Whether this user is online.

integer, The ID of the remote host associated with this credential.

string, The name of the remote host associated with this credential.

string, The U.S. state for the credential. Only for VAN credentials.

string, The creation time for this credential.

string, The last modification time for this credential.

boolean, Whether or not the credential is a default. Only for Database credentials.

boolean, Whether or not the credential is an OAuth credential.

string required. The URL to your host.

string required. The type of remote host. One of: RemoteHostTypes::Bigquery, RemoteHostTypes::Bitbucket, RemoteHostTypes::GitSSH, RemoteHostTypes::Github, RemoteHostTypes::GoogleDoc, RemoteHostTypes::JDBC, RemoteHostTypes::Postgres, RemoteHostTypes::Redshift, RemoteHostTypes::S3Storage, and RemoteHostTypes::Salesforce

string required. The username for the credential.

string required. The password for the credential.

integer, The ID of the credential.

string, The name identifying the credential

string, The credential's type.

string, The username for the credential.

string, A long description of the credential.

string, The username of the user who this credential belongs to. Using user.username is preferred.

list, A list containing the following elements:

• id integer, The ID of this user.

• name string, This user's name.

• username string, This user's username.

• initials string, This user's initials.

• online boolean, Whether this user is online.

integer, The ID of the remote host associated with this credential.

string, The name of the remote host associated with this credential.

string, The U.S. state for the credential. Only for VAN credentials.

string, The creation time for this credential.

string, The last modification time for this credential.

boolean, Whether or not the credential is a default. Only for Database credentials.

boolean, Whether or not the credential is an OAuth credential.

integer required. The ID of the credential.

integer optional. The number of seconds the temporary credential should be valid. Defaults to 15 minutes. Must not be less than 15 minutes or greater than 36 hours.

string, The identifier of the credential.

string, The secret part of the credential.

string, The session token identifier.

integer required. The ID of the credential.

string required. The type of credential. Note: only these credentials can be created or edited via this API ["Amazon Web Services S3", "CASS/NCOA PAF", "Certificate", "Civis Platform", "Custom", "Database", "Google", "Salesforce User", "Salesforce Client", "TableauUser"]

string required. The username for the credential.

string required. The password for the credential.

string optional. The name identifying the credential.

string optional. A long description of the credential.

integer optional. The ID of the remote host associated with the credential.

integer optional. The ID of the user the credential is created for. Note: This attribute is only accepted if you are a Civis Admin User.

string optional. The U.S. state for the credential. Only for VAN credentials.

boolean optional. Boolean flag that sets a credential to be a system credential. System credentials can only be created by Civis Admins and will create a credential owned by the Civis Robot user.

boolean optional. Whether or not the credential is a default. Only for Database credentials.

boolean optional. Whether or not the credential is an OAuth credential.

integer, The ID of the credential.

string, The name identifying the credential

string, The credential's type.

string, The username for the credential.

string, A long description of the credential.

string, The username of the user who this credential belongs to. Using user.username is preferred.

list, A list containing the following elements:

• id integer, The ID of this user.

• name string, This user's name.

• username string, This user's username.

• initials string, This user's initials.

• online boolean, Whether this user is online.

integer, The ID of the remote host associated with this credential.

string, The name of the remote host associated with this credential.

string, The U.S. state for the credential. Only for VAN credentials.

string, The creation time for this credential.

string, The last modification time for this credential.

boolean, Whether or not the credential is a default. Only for Database credentials.

boolean, Whether or not the credential is an OAuth credential.

integer required. The ID of the resource that is shared.

array required. An array of one or more group IDs.

string required. Options are: "read", "write", or "manage".

string optional. Custom body text for e-mail sent on a share.

boolean optional. Send email to the recipients of a share.

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

integer, For owners, the number of total users shared. For writers and readers, the number of visible users shared.

integer, For owners, the number of total groups shared. For writers and readers, the number of visible groups shared.

integer required. The ID of the resource that is shared.

array required. An array of one or more user IDs.

string required. Options are: "read", "write", or "manage".

string optional. Custom body text for e-mail sent on a share.

boolean optional. Send email to the recipients of a share.

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

list, A list containing the following elements:

• users array,

• groups array,

integer, For owners, the number of total users shared. For writers and readers, the number of visible users shared.

integer, For owners, the number of total groups shared. For writers and readers, the number of visible groups shared.

integer required. The ID of the resource that is shared.

integer required. ID of target user

boolean required. Whether or not to give manage permissions on all dependencies

string optional. Custom body text for e-mail sent on transfer.

boolean optional. Send email to the target user of the transfer?

array, An array containing the following fields:

• objectType string, Dependent object type

• fcoType string, Human readable dependent object type

• id integer, Dependent object ID

• name string, Dependent object name, or nil if the requesting user cannot read this object

• permissionLevel string, Permission level of target user (not user's groups) for dependent object. Null if no target user or not shareable (e.g. a database table).

• description string, Additional information about the dependency, if relevant

• shared boolean, Whether dependent object was successfully shared with target user

integer required. The ID of the database

integer required. The ID of the project.

string required. The name of the schema

string required. The name of the table

integer required. The ID for the database.

integer, The ID for the database.

string, The name of the database in Platform.

string, The type of the database.

string, The cluster identifier of the database.

string, The host of the database server.

integer, The port of the database.

string, The internal name of the database.

boolean, True if the database is Civis-managed. False otherwise.

integer required. The ID of the database

string required. The name of the schema

integer optional. If provided, schemas will be filtered based on the given credential.

string, Name of the granted user or group

string, User or group

array, Privileges that the grantee has on this resource

array, Privileges that the grantee can grant to others for this resource

integer required. The ID of the database

string required. The name of the schema

string required. The name of the table

integer optional. If provided, schemas will be filtered based on the given credential.

integer, The ID of the table.

integer, The ID of the database.

string, The name of the schema containing the table.

string, Name of the table.

string, The description of the table, as specified by the table owner

boolean, True if this table represents a view. False if it represents a regular table.

integer, The number of rows in the table.

integer, The number of columns in the table.

number, The size of the table in megabytes.

string, The database username of the table's owner.

string, The column used as the Amazon Redshift distkey.

string, The column used as the Amazon Redshift sortkey.

string, How up-to-date the table's statistics on row counts, null counts, distinct counts, and values distributions are. One of: refreshing, stale, or current.

string, The time of the last statistics refresh.

string, The last time that Civis Platform captured a change in this table.Only applicable for Redshift tables; please see the Civis help desk for more info.

string, The last time that Civis Platform captured a change to the table attributes/structure.Only applicable for Redshift tables; please see the Civis help desk for more info.

string, The ID of the most recent statistics refresh.

list, A list containing the following elements:

• id integer,

• state string,

• createdAt string, The time that the run was queued.

• startedAt string, The time that the run started.

• finishedAt string, The time that the run completed.

• error string, The error message for this run, if present.

array, The primary keys for this table.

array, The columns indicating an entry's modification status for this table.

array, An array containing the following fields:

• id integer, Table Tag ID

• name string, Table Tag Name

list, The ontology-key to column-name mapping. See /ontology for the list of valid ontology keys.

array, An array containing the following fields:

• name string, Name of the column.

• civisDataType string, The generic data type of the column (ex. "string"). Since this is database-agnostic, it may be helpful when loading data to R/Python.

• sqlType string, The database-specific SQL type of the column (ex. "varchar(30)").

• sampleValues array, A sample of values from the column.

• encoding string, The compression encoding for this columnSee: http://docs.aws.amazon.com/redshift/latest/dg/c_Compression_encodings.html

• description string, The description of the column, as specified by the table owner

• order integer, Relative position of the column in the table.

• minValue string, Smallest value in the column.

• maxValue string, Largest value in the column.

• avgValue number, This parameter is deprecated.

• stddev number, This parameter is deprecated.

• valueDistributionPercent object, A mapping between each value in the column and the percentage of rows with that value.Only present for tables with fewer than approximately 25,000,000 rows and for columns with fewer than twenty distinct values.

• coverageCount integer, Number of non-null values in the column.

• nullCount integer, Number of null values in the column.

• possibleDependentVariableTypes array, Possible dependent variable types the column may be used to model. Null if it may not be used as a dependent variable.

• useableAsIndependentVariable boolean, Whether the column may be used as an independent variable to train a model.

• useableAsPrimaryKey boolean, Whether the column may be used as an primary key to identify table rows.

• valueDistribution object, An object mapping distinct values in the column to the number of times they appear in the column

• distinctCount integer, Number of distinct values in the column. NULL values are counted and treated as a single distinct value.

array, An array containing the following fields:

• id integer,

• leftTableId integer,

• leftIdentifier string,

• rightTableId integer,

• rightIdentifier string,

• on string,

• leftJoin boolean,

• createdAt string,

• updatedAt string,

array,

array, An array containing the following fields:

• type string,

• createdAt string,

• updatedAt string,

• joinId integer,

string,

string,

array, An array containing the following fields:

• sourceTableId integer, Source table

• targetType string, Target type

• targetId integer, Target ID

• target object,

• job object,

integer required. The ID of the database.

string required. The span of time that the graphs cover. Must be one of 1_hour, 4_hours, 1_day, 2_days, 1_week.

string, URL for the aws redshift cpu utliization graph.

string, URL for the aws redshift disk usage graph.

string, URL for the aws redshift queue length graph.

string, URL for the aws redshift status graph.

string, URL for the aws redshift maintenance graph.

string, URL for the aws redshift table count graph.

integer required. The ID of the database

string required. The name of the schema

string required. The name of the table

integer optional. If provided, schemas will be filtered based on the given credential.

string, Name of the granted user or group

string, User or group

array, Privileges that the grantee has on this resource

array, Privileges that the grantee can grant to others for this resource

integer required. The ID of the database this rule is applied to.

integer required. The ID of this whitelisted IP address.

integer, The ID of this whitelisted IP address.

integer, The ID of the database this rule is applied to.

string, The ID of the security group this rule is applied to.

string, The subnet mask that is allowed by this rule.

string, The user who authorized this rule.

boolean, True if the rule is applied, false if it has been revoked.

string, The time this rule was created.

string, The time this rule was last updated.

integer, The ID for the database.

string, The name of the database in Platform.

string, The type of the database.

string, The cluster identifier of the database.

string, The host of the database server.

integer, The port of the database.

string, The internal name of the database.

boolean, True if the database is Civis-managed. False otherwise.

integer required. The ID of the database this advanced settings object belongs to.

boolean, Whether or not caching is enabled for export jobs run on this database server.

integer required. The ID for the database.

string, The name of the group.

array, The members of the group.

integer required. The ID of the database.

string optional. If specified, will be used to filter the schemas returned.Substring matching is supported (e.g., "name=schema" will return both "schema1" and "schema2"). Does not apply to BigQuery databases.

integer optional. If provided, schemas will be filtered based on the given credential.