CivisML Sparse Logistic

civis_ml_sparse_logistic(x, dependent_variable, primary_key = NULL,
excluded_columns = NULL, penalty = c("l2", "l1"), dual = FALSE,
tol = 1e-08, C = 499999950, fit_intercept = TRUE,
intercept_scaling = 1, class_weight = NULL, random_state = 42,
solver = c("liblinear", "newton-cg", "lbfgs", "sag"), max_iter = 100,
multi_class = c("ovr", "multinomial"), fit_params = NULL,
cross_validation_parameters = NULL, calibration = 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)

Arguments

x 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.

Value

A civis_ml object, a list containing the following elements:

job

job metadata from scripts_get_custom.

run

run metadata from scripts_get_custom_runs.

outputs

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.

metrics

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

• 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.

model_info

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

• model list, the fitted scikit-learn model.

• metrics empty list.

• warnings list.

• data_platform list, training data location.

Data Sources

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.frame

civis_ml(x = df, ...)

local csv file

civis_ml(x = "path/to/data.csv", ...)

file in Civis Platform

civis_ml(x = civis_file(1234))

table in Civis Platform

civis_ml(x = civis_table(table_name = "schema.table", database_name = "database"))

Examples

# NOT RUN {
df <- iris
names(df) <- gsub("\\.", "_", names(df))

m <- civis_ml_sparse_logistic(df, "Species")
yhat <- fetch_oos_scores(m)

# Grid Search
cv_params <- list(C = c(.01, 1, 10, 100, 1000))

m <- civis_ml_sparse_logistic(df, "Species",
cross_validation_parameters = cv_params)

# 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")

# }