Client$log()

Batch Logging - Designed for sending batches of data to Arize

Overview

The Client$log() is designed for training, validation or production environment where batches of data are processed. These environments may be either a R Studio Notebook or a R server that is batch processing lots of backend data.

Import and initialize Arize R client from the Arize Client$new() to call Client$log() with a R data.frame() containing inference data.

Initializing Client Examples

ORGANIZATION_KEY <- 'ORGANIZATION_KEY'
API_KEY <- 'API_KEY'
arize_client <- Client$new(
    organization_key = ORGANIZATION_KEY, 
    api_key = API_KEY)

Parameters & Returns

schema <- create_schema(
  prediction_id_column_name = "prediction_id",
  prediction_label_column_name = "prediction_label",
  prediction_score_column_name = "prediction_score",
  actual_label_column_name = "actual_label",
  actual_score_column_name = "actual_score",
  feature_column_names = features,
  timestamp_column_name = "prediction_ts"
)


# send training data
arize_client$log(
  .data_frame = df_train,
  .schema = schema
  .model_id = model_id,
  .model_version = model_version,
  .model_type = model_types$SCORE_CATEGORICAL,
  .environment = environments$TRAINING,
)

Parameter	Data Type	Description	Required
.data_frame	data.frame	data.frame to log	Required
.schema	arize::create_schema	the schema (see ?arize::create_schema)	Required
.model_id	character	character, id for the model	Required
.model_type	integer	1 for binary, 2 for numeric, 3 for categorical, 4 for score-categorical	Required
.environment	environment	1 for production, 2 for validation, 3 for training	Required
.model_version	character	character, the model version	Optional
.batch_id	character	character, the batch id	Optional
.sync	logical	logical, whether to sync	Optional
.validate	logical	logical, whether to run validation checks	Optional
.path	character	character, path to use for serialization	Optional

Schema Attributes

Attribute	Data Type	Description	Required
prediction_id_column_name	character	Column name for prediction_id	Required
feature_column_names	List[character]	List of column names for features	Optional
prediction_label_column_name	character	Column name for prediction label	Optional
prediction_score_column_name	character	Column name for prediction scores	Optional
actual_label_column_name	character	Column name for actual label	Optional
actual_score_column_name	str	Column name for numeric sequences. Used for NDCG calculations in ranking models	Optional
timestamp_column_name	character	Column name for timestamps	Optional

Examples

Check out the Example Tutorial

Example 1: Logging Features, Predictions, & Actuals

model_id <- "click_through_rate_categorical_vignette_R"  # This is the model name that will show up in Arize
model_version <- "v1.0"  # Version of model - can be any string

schema <- create_schema(
  prediction_id_column_name = "id",
  feature_column_names = features,
  prediction_label_column_name = "predictions",
  prediction_score_column_name = "CTR_predicted",
  actual_label_column_name = "actuals",
  actual_score_column_name = "CTR",
  timestamp_column_name = "model_date"
)

arize_client$log(
  .data_frame = df_train,
  .model_id = model_id,
  .model_version = model_version,
  .model_type = model_types$SCORE_CATEGORICAL,
  .environment = environments$TRAINING,
  .schema = schema
)