Monitoring Config JSON

The Monitoring Config JSON captures all information required to register a model, a prediction dataset, or a ground truth dataset.This section describes the structure of the Monitoring Config JSON file. The following is a sample config:

{
        "variables": [
                {
                        "name": "age",
                        "valueType": "numerical",
                        "variableType": "feature",
                        "featureImportance": 0.9
                },
                {
                        "name": "y",
                        "valueType": "categorical",
                        "variableType": "prediction"
                },
                {
                        "name": "date",
                        "valueType": "datetime",
                        "variableType": "timestamp"
                },
                {
                        "name": "RowId",
                        "valueType": "string",
                        "variableType": "row_identifier"
                }
        ],
        "datasetDetails": {
                "name": "TrainingData.csv",
                "datasetType": "file",
                "datasetConfig": {
                        "path": "TrainingData.csv",
                        "fileFormat": "csv"
                },
                "datasourceName": "abc-shared-bucket",
                "datasourceType": "s3"
        },
        "modelMetadata": {
                "name": "test_psg",
                "modelType": "classification",
                "version": "2",
                "description": "",
                "author": "testadmin"
        }
}

variables

An array of variables that declare all features and prediction columns that you want to analyze. For each member in the array, specify the name, variableType, and valueType.

name

Name of the column.

variableType

Provides the attribute that identifies the column. Supported types are: +

feature

Can only be of valueType numerical or categorical.
Input feature of the model.
Data drift will be calculated for this data column.
Must be declared while registering the model along with its training data.
The column must be present in all training and prediction datasets registered with the model.

prediction (optional)

When declared, there can only be one Prediction column.
Can only be of valueType numerical or categorical.
Output prediction of the model.
Data drift and model quality metrics are calculated for this data column. Include this column when registering your model to ensure both data drift and model quality analysis can be run. If it isn’t included, model quality metrics won’t be computed.

timestamp (optional)

When present, there can be only one timestamp column.
Can only be of datetime valueType.
Although you can declare this column when adding prediction data for the first time, Domino recommends that it be declared during model registration.
Identifies the column that contains the timestamp for the prediction made. If not declared, the ingestion time of the data in the Model Monitor is used as the timestamp of the prediction.
Must contain the date/time when the prediction was made. Column values must follow the ISO 8601 time format.
When it is not declared, the ingestion time of the prediction dataset into the Model Monitor is substituted as the timestamp of prediction.
To use automatic ingestion for Snowflake, you must include this column. Snowflake’s documentation recommends setting the timezone to UTC for both the Spark cluster and the Snowflake deployment.

row_identifier (optional)

Can only be of string valueType.
Uniquely identifies each prediction row. Typically referred to as prediction ID, transaction ID, and so on.
When present, there can be only one row_identifier column.
Although you can declare this column when adding prediction data for the first time, Domino recommends that it be declared during model registration.
Values are used to match ground truth data to the predictions to calculate model quality metrics. Model quality metrics will not be calculated if this column is not present. If used, must be present in both prediction and ground truth datasets.

ground_truth

Identifies the column that contains the ground truth labels in the ground truth datasets.

sample_weight

Column that contains the weight to be associated with each prediction to calculate the Gini Norm metric.

prediction_probability

Column that contains the probability value for the model’s prediction. Can be a single value (maps to the probability value of the positive class) or a list of values (the length of the list must match the number of unique prediction labels or classes present in the training dataset).

Note	For a field of this type, include `forPredictionOutput` to indicate the prediction column for which you are specifying the probability. This column is required if you want to measure AUC, ROC, Log Loss, and Gini Norm as part of the model quality analysis.

valueType

Identifies the value of the column. Supported types are categorical, numerical, datetime, or string. Can only be set to datetime for one column.

forPredictionOutput

Specifies which prediction column the ground truth variable represents in Ground Truth Config.

datasetDetails

name

The name to associate with this dataset instance. You can use the same name as the file you are selecting.

datasetType

The supported type is “file”.

datasetConfig

Defines the actual location of the file.

path: The name of the file. If you are using Snowflake, the path is the name of the Snowflake table.
fileFormat: .csv and parquet are supported. If you are using Snowflake, the path must be snowflake.

datasourceName

Name you provided when you created the data source.

datasourceType

A supported data source types s3, gcs, azure_blob, azure_data_lake_gen1, azure_data_lake_gen2, hdfs, or snowflake.

modelMetadata

Captures metadata related to the model. Specify the name, modelVersion, modelType, dataset, dateCreated, description. and author attributes. dateCreated must be in a valid UTC format (ISO 8601). Valid values for modelType are classification and regression.

featureImportance

Highlights the overall impact of the feature on predictions made to the model, relative to other features.