Job and Latest Model Metrics

POST `/api/v2/metrics/job_and_latest_model`

Description

The job_and_latest_model endpoint retrieves combined metrics for a job and its currently deployed model. This endpoint provides comprehensive performance data that includes both job-level statistics and the active model's performance metrics. As part of the request, the metrics of the model will be also recomputed and updated.

Quick Start Examples

Get metrics for a job and its active model

curl -X POST "https://your.arcanna.ai/api/v2/metrics/job_and_latest_model?job_id=1234" \
  -H "X-Arcanna-Api-Key: your-api-key-here" \
  -H "Content-Type: application/json" \
  -d '{}'

Get metrics with custom time range

curl -X POST "https://your.arcanna.ai/api/v2/metrics/job_and_latest_model?job_id=1234&start_datetime=2025-01-01T00:00:00Z&end_datetime=2025-01-02T00:00:00Z" \
  -H "X-Arcanna-Api-Key: your-api-key-here" \
  -H "Content-Type: application/json" \
  -d '{}'

Get metrics with custom timeout

curl -X POST "https://your.arcanna.ai/api/v2/metrics/job_and_latest_model?job_id=1234&timeout_s=300" \
  -H "X-Arcanna-Api-Key: your-api-key-here" \
  -H "Content-Type: application/json" \
  -d '{}'

Get metrics with filters

curl -X POST "https://your.arcanna.ai/api/v2/metrics/job_and_latest_model?job_id=1234" \
  -H "X-Arcanna-Api-Key: your-api-key-here" \
  -H "Content-Type: application/json" \
  -d '{
    "filters": [
      {
        "field": "arcanna.result_label",
        "operator": "is",
        "value": "Escalate"
      }
    ]
  }'

Request Parameters

Query Parameters

Parameter	Type	Required	Description
`job_id`	integer or string	Yes	The ID of the job to retrieve metrics for
`start_datetime`	string	No	Start date for metrics (ISO 8601 format)
`end_datetime`	string	No	End date for metrics (ISO 8601 format)
`timeout_s`	integer	No	Maximum wait time (in seconds) for refreshing model metrics. Default is 120

Request Body (Optional)

{
  "filters": [
    {
      "field": "string",
      "operator": "string", 
      "value": "string"
    }
  ]
}

Filter Parameters:

field: The specific data field to apply the filter to
operator: Comparison method ('is', 'is not', 'contains', 'exists', 'gt', 'lt', etc.)
value: The criteria used by the operator to filter the field

Response

Success Response (200 OK)

{
  "job_id": 1234,
  "model_id": "generic-1-1234-model-1747385873.hdf5",
  "job_metrics": {
    "confusion_matrix": [
      [4490, 0, 0],
      [0, 749, 0], 
      [0, 0, 37]
    ],
    "overall_accuracy": 1.0,
    "overall_f1_score": 1.0,
    "overall_recall": 1.0,
    "overall_precision": 1.0,
    "metrics_per_decision": {
      "Drop": {
        "precision": 1.0,
        "recall": 1.0,
        "f1_score": 1.0,
        "true_positives": 4490,
        "true_negatives": 786,
        "false_positives": 0,
        "false_negatives": 0
      },
      "Investigate": {
        "precision": 1.0,
        "recall": 1.0,
        "f1_score": 1.0,
        "true_positives": 749,
        "true_negatives": 4527,
        "false_positives": 0,
        "false_negatives": 0
      },
      "Escalate": {
        "precision": 1.0,
        "recall": 1.0,
        "f1_score": 1.0,
        "true_positives": 37,
        "true_negatives": 5239,
        "false_positives": 0,
        "false_negatives": 0
      }
    },
    "start_time": "2025-01-01T00:00:00Z",
    "end_time": "2025-01-02T00:00:00Z",
    "time_saved_minutes": 1250.5,
    "active_model_id": "generic-1-1234-model-1747385873.hdf5",
    "all_model_ids": ["generic-1-1234-model-1747385873.hdf5"],
    "total_events": 5276,
    "total_events_in_knowledge_base": 2516,
    "total_events_with_consensus": 5,
    "changed_consensus_after_training": {
      "total_events": 0,
      "top_events_ids": [],
      "top_buckets_ids": []
    }
  },
  "model_metrics": {
    "confusion_matrix": [
      [4490, 0, 0],
      [0, 749, 0],
      [0, 0, 37]
    ],
    "overall_accuracy": 1.0,
    "overall_f1_score": 1.0,
    "overall_recall": 1.0,
    "overall_precision": 1.0,
    "metrics_per_decision": {
      "Drop": {
        "precision": 1.0,
        "recall": 1.0,
        "f1_score": 1.0,
        "true_positives": 4490,
        "true_negatives": 786,
        "false_positives": 0,
        "false_negatives": 0
      },
      "Investigate": {
        "precision": 1.0,
        "recall": 1.0,
        "f1_score": 1.0,
        "true_positives": 749,
        "true_negatives": 4527,
        "false_positives": 0,
        "false_negatives": 0
      },
      "Escalate": {
        "precision": 1.0,
        "recall": 1.0,
        "f1_score": 1.0,
        "true_positives": 37,
        "true_negatives": 5239,
        "false_positives": 0,
        "false_negatives": 0
      }
    },
    "model_id": "generic-1-1234-model-1747385873.hdf5",
    "is_recomputing_metrics": false,
    "last_recomputed_timestamp": "2025-01-15T10:30:00Z",
    "kb_count_per_decision": {
      "Drop": {
        "alerts_count": 2066,
        "buckets_count": 10
      },
      "Investigate": {
        "alerts_count": 429,
        "buckets_count": 5
      },
      "Escalate": {
        "alerts_count": 21,
        "buckets_count": 4
      }
    },
    "buckets_in_kb": 19,
    "events_in_kb": 2516
  }
}

Error Response (404 Not Found)

{
  "error": "Job or model not found"
}

Response Fields

Top-Level Fields

job_id: The ID of the job
model_id: The ID of the currently active model
job_metrics: Object containing job-level performance metrics
model_metrics: Object containing model-specific performance metrics

Job Metrics Object

confusion_matrix: 2D array representing the confusion matrix of model decisions
overall_accuracy: Mean accuracy across all decisions (0-1)
overall_f1_score: Mean F1 score across all decisions (0-1)
overall_recall: Mean recall across all decisions (0-1)
overall_precision: Mean precision across all decisions (0-1)
metrics_per_decision: Object containing metrics for each decision type:
- precision: Precision score for this decision type
- recall: Recall score for this decision type
- f1_score: F1 score for this decision type
- true_positives: Number of true positive predictions
- true_negatives: Number of true negative predictions
- false_positives: Number of false positive predictions
- false_negatives: Number of false negative predictions
start_time: Start time for the computed metrics period
end_time: End time for the computed metrics period
time_saved_minutes: Estimated time saved by Arcanna (in minutes)
active_model_id: ID of the currently active model
all_model_ids: List of all model IDs associated with this job
total_events: Total number of events processed
total_events_in_knowledge_base: Number of events in the knowledge base
total_events_with_consensus: Number of events with consensus
changed_consensus_after_training: Object containing information about events that changed consensus after training
- total_events: Total number of events that changed consensus
- top_events_ids: List of top 10 events IDs that changed consensus
- top_buckets_ids: List of top 10 buckets IDs that changed consensus

Model Metrics Object

confusion_matrix: 2D array representing the confusion matrix of model decisions
overall_accuracy: Mean accuracy across all decisions (0-1)
overall_f1_score: Mean F1 score across all decisions (0-1)
overall_recall: Mean recall across all decisions (0-1)
overall_precision: Mean precision across all decisions (0-1)
metrics_per_decision: Object containing detailed metrics for each decision type
model_id: The unique identifier of the model
is_recomputing_metrics: Boolean indicating if metrics are currently being recomputed
last_recomputed_timestamp: Timestamp of the last metrics recomputation
kb_count_per_decision: Object containing knowledge base counts for each decision type:
- alerts_count: Number of alerts in the knowledge base for this decision
- buckets_count: Number of buckets in the knowledge base for this decision
buckets_in_kb: Total number of buckets in the knowledge base
events_in_kb: Total number of events in the knowledge base

Important Notes

Automatic Model Metrics Refresh

This endpoint automatically triggers a refresh of the model metrics as part of the request
The timeout_s parameter controls how long to wait for the refresh to complete
If the refresh takes longer than the timeout, the endpoint returns the last known metrics

Combined View Benefits

Provides both job-level and model-level metrics in a single request
Ensures model metrics are up-to-date through automatic refresh
Ideal for comprehensive performance analysis and reporting

Use Job Metrics for job-only performance data
Use Model Metrics for model-only performance data
Use Request Recompute Metrics for manual model metric refresh

POST /api/v2/metrics/job_and_latest_model​

Description​

Quick Start Examples​

Get metrics for a job and its active model​

Get metrics with custom time range​

Get metrics with custom timeout​

Get metrics with filters​

Request Parameters​

Query Parameters​

Request Body (Optional)​

Response​

Success Response (200 OK)​

Error Response (404 Not Found)​

Response Fields​

Top-Level Fields​

Job Metrics Object​

Model Metrics Object​

Important Notes​

Automatic Model Metrics Refresh​

Combined View Benefits​

Related Endpoints​