Retrieving the value of a metric in a scenario or as project variable

I'm definitely NOT doing it the way the documentation says to do it, but I'm using a python script to toss out partitions in a partitioned dataset that failed to achieve a significant amount of training data. To do this, I compute the metrics on the final partition that would lead to training, and then - as part of the scenario - provide only those partitions to the model training. Most of that is not pertinent to your question, but in the snippet below you can see how I access the metrics to make decisions for the scenario. There seems to be quite a bit of info in the computed metrics, so I distilled it down to just what I needed with a dict comprehension, but for your purposes it would probably be good to spend some time familiarizing yourself with everything in there.

Good Luck, and good hunting!

-J

client = dataiku.api_client()
p = client.get_project("project_name_here")
d = p.get_dataset("your_dataset_here")
    for t in block_targets:  # targets are the partitions
        computed_metrics = d.compute_metrics(str(t)) # partition can be omitted
        metrics = {x["metricId"]: x["value"] for x in computed_metrics["result"]["computed"]}
        print("For partition " + str(t) + " there are " + str(metrics["records:COUNT_RECORDS"]) + " records.")
        if "records:COUNT_RECORDS" not in metrics:
            print("Rejecting partition " + str(t) + " due to missing COUNT_RECORDS")
            # rejected_partitions.append(t)
            continue
        if int(metrics["records:COUNT_RECORDS"]) < v["minimum_training_support"]:  # project variable controls minimum size
            print("Rejecting partition " + str(t) + " due to low COUNT_RECORDS (" + str(v["minimum_training_support"]) + ")")
            # rejected_partitions.append(t)
            continue
    valid_partitions.append(t)  # these partitions will proceed to modeling