Batches are sets of predictions grouped with a specific name and optional description. The name paremeter lets you record the purpose of the predictions, for example “Developer satisfaction”.

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Basic usage

Create a prediction batch by passing an object to the batch field in a predictions create request. The create method’s predictions field accepts both single prediction objects or a list of objects, but only if batch details are set will a batch get created.
Predictions are asynchronous and take ~20 seconds each to run. Please see the section on handling async results for more details.
from semilattice import Semilattice

semilattice = Semilattice()

response = semilattice.predictions.create(
    population_id="population-id", # Replace with specific ID
    batch={
        "name": "Developer satisfaction",
        "description": "Questions peering into the day-to-day of deveopers",
    },
    predictions=[
        {
            "question": "Which is worse?",
            "answer_options": ["Tech debt", "Confusing error messages"],
            "question_options": {"question_type": "single-choice"}
        },
        {
            "question": "Which brings you most joy?",
            "answer_options": ["Plentiful code examples", "Consistent method naming"],
            "question_options": {"question_type": "single-choice"}
        }
    ]
)

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Fetch a batch

Prediction responses always contain a batch field, but only if batch details were provided will a batch be created. 
# Each prediction response will have a batch field containing the batch id
predictions = response.data
batch_id = predictions[0].batch
If batch details were provided, each prediction in the batch will have the same batch ID in its batch field. Grab this ID and then fetch the batch. The batch response will contain both the batch object and the batch’s predictions. 
response = semilattice.predictions.get_batch(batch_id=batch_id)
batch = response.data.batch
batch_predictions = response.data.predictions

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Choosing population models

require a specific population model ID. Call the list method on populations to get a list of population models available for simulation. 
response = semilattice.populations.list()
populations = response.data
Alternatively, you can navigate to the populations page on your dashboard and select a population model to use. Copy the ID from the population’s metadata or from the address bar.

Click to copy the population model's ID

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Handling async batch results

Batch prediction simulations run asynchronously. A batch object has a status field which captures the overall prediction status of the batch. In addition, each individual prediction within the batch has its own status field which captures its individual prediction status. The initial batch status will be "Queued", and you need to poll for completion.

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Initial response

{
    "id": "ac9c798e-87b8-46d0-b824-8d8d8d5dca03",
    "status": "Queued",
    "name": "Developer satisfaction",
    "description": "Questions peering into the day-to-day of deveopers",
    // ... other fields
}

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Polling for results

The batch will progress through these statuses: Queued → Running → Predicted (or potentially Failed). Predictions typically take less than 20 seconds, so a batch should take N * ~20 seconds, where N is the number of predictions in the batch. 
import time

while batch.status != "Predicted":
    time.sleep(1)
    response = semilattice.predictions.get_batch(batch_id=batch.id)
    batch = response.data.batch

print("Batch complete!")