CivisML Sparse Linear Regression
civis_ml_sparse_linear_regressor(
x,
dependent_variable,
primary_key = NULL,
excluded_columns = NULL,
fit_intercept = TRUE,
normalize = FALSE,
fit_params = NULL,
cross_validation_parameters = NULL,
oos_scores_table = NULL,
oos_scores_db = NULL,
oos_scores_if_exists = c("fail", "append", "drop", "truncate"),
model_name = NULL,
cpu_requested = NULL,
memory_requested = NULL,
disk_requested = NULL,
notifications = NULL,
polling_interval = NULL,
verbose = FALSE,
civisml_version = "prod"
)A civis_ml object, a list containing the following elements:
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
For building models with civis_ml, the training data can reside in
four different places, a file in the Civis Platform, a CSV or feather-format file
on the local disk, a data.frame resident in local the R environment, and finally,
a table in the Civis Platform. Use the following helpers to specify the
data source when calling civis_ml:
data.framecivis_ml(x = df, ...)
civis_ml(x = "path/to/data.csv", ...)
civis_ml(x = civis_file(1234))
civis_ml(x = civis_table(table_name = "schema.table", database_name = "database"))
if (FALSE) {
data(ChickWeight)
m <- civis_ml_sparse_linear_regressor(ChickWeight, dependent_variable = "weight")
yhat <- fetch_oos_scores(m)
# make a prediction job, storing in a redshift table
pred_info <- predict(m, newdata = civis_table("schema.table", "my_database"),
output_table = "schema.scores_table")
}
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