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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
)