Spotted in master build 6892
when using seeds dataset, Inference returned different predicted_value comparing with model prediction. model returns 1,2,3, but inference returns 1.0,2.0,0.0
To reproduce,
- create a classification job.
{
"source": {
"index": [
"seeds"
]
},
"dest": {
"index": "dest_seeds_80",
"results_field": "ml"
},
"analysis": {
"classification": {
"dependent_variable": "seed_class",
"num_top_feature_importance_values": 2,
"class_assignment_objective": "maximize_minimum_recall",
"num_top_classes": 2,
"prediction_field_name": "seed_class_prediction",
"training_percent": 80
}
}
}
- run job, then create a pipeline, then run inference.
PUT _ingest/pipeline/inference_pipeline
{
"processors": [
{
"inference": {
"model_id": "dfa_seeds_1587034738_000_0-1587049148665",
"target_field": "class_prediction_infer",
"field_map": {
},
"inference_config": {"classification": {
"num_top_feature_importance_values": 3
}}
}
}
]
}
- Now, against dest index, run aggregations on both predictions, you will get:
"aggregations" : {
"inference" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 1.0,
"doc_count" : 71
},
{
"key" : 2.0,
"doc_count" : 70
},
{
"key" : 0.0,
"doc_count" : 69
}
]
},
"ml" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 1,
"doc_count" : 71
},
{
"key" : 2,
"doc_count" : 70
},
{
"key" : 3,
"doc_count" : 69
}
]
}
}
Spotted in master build 6892
when using
seedsdataset, Inference returned different predicted_value comparing with model prediction. model returns1,2,3, but inference returns1.0,2.0,0.0To reproduce,