In this guide we will see how to save and load EvaluationScenario, TestSet and EvaluationTestResult.

​
Client

To track all our evaluation result we use the trusttest.client. Currently, we support two client types:
  • file-system: Save the results in the local filesystem.
  • neuraltrust: Save the results in the remote NeuralTrust server.
To define the client: 
import os
import trusttest

client = trusttest.client(type="neuraltrust", token=os.getenv("NEURALTRUST_TOKEN"))

​
Save a scenario results

First we need to define our scenario, run the evaluation and get the results. 
from trusttest.evaluation_contexts import ExpectedResponseContext
from trusttest.evaluation_scenarios import EvaluationScenario
from trusttest.evaluator_suite import EvaluatorSuite
from trusttest.evaluators import BleuEvaluator
from trusttest.targets.testing import DummyTarget
from trusttest.probes.dataset import DatasetProbe
from trusttest.dataset_builder import Dataset, DatasetItem


target = DummyTarget()
probe = DatasetProbe(
    target=target,
    dataset=Dataset([
        [
            DatasetItem(
                question="What is Python?",
                context=ExpectedResponseContext(
                    expected_response="Python is a high-level, interpreted programming language."
                ),
            )
        ]
    ]),
)

test_set = probe.get_test_set()
scenario = EvaluationScenario(
    name="Quickstart Functional Test",
    description="Functional test example.",
    evaluator_suite=EvaluatorSuite(
        evaluators=[BleuEvaluator(threshold=0.3)],
        criteria="any_fail",
    ),
)

results = scenario.evaluate(test_set)
Then we can save the evaluation scenario with just one line: 
client.save_evaluation_scenario(scenario)
To save the scenario TestSet: 
client.save_evaluation_scenario_test_set(scenario.id, test_set)
Finally, to save the EvaluationTestResult: 
client.save_evaluation_scenario_run(results)
Got to your NeuralTrust dashboard to see the results. Or if you are using the file-system client, you can see the results in the trusttest_db folder.

​
Load a scenario results

We can also load any scenario, test set or evaluation test result from the client. And re-run the evaluation, clone it, etc. 
loaded_scenario = client.get_evaluation_scenario(scenario.id)
loaded_scenario_result = client.get_evaluation_scenario_run(scenario.id)
loaded_test_set = client.get_evaluation_scenario_test_set(scenario.id)

result = loaded_scenario.evaluate(loaded_test_set)
result.display()

​
Complete example

import trusttest
import os
from trusttest.evaluation_contexts import ExpectedResponseContext
from trusttest.evaluation_scenarios import EvaluationScenario
from trusttest.evaluator_suite import EvaluatorSuite
from trusttest.evaluators import BleuEvaluator
from trusttest.targets.testing import DummyTarget
from trusttest.probes.dataset import DatasetProbe
from trusttest.dataset_builder import Dataset, DatasetItem

target = DummyTarget()
probe = DatasetProbe(
    target=target,
    dataset=Dataset([
        [
            DatasetItem(
                question="What is Python?",
                context=ExpectedResponseContext(
                    expected_response="Python is a high-level, interpreted programming language."
                ),
            )
        ]
    ]),
)

test_set = probe.get_test_set()
scenario = EvaluationScenario(
    name="Quickstart Functional Test",
    description="Functional test example.",
    evaluator_suite=EvaluatorSuite(
        evaluators=[BleuEvaluator(threshold=0.3)],
        criteria="any_fail",
    ),
)

results = scenario.evaluate(test_set)

client = trusttest.client(type="neuraltrust", token=os.getenv("NEURALTRUST_TOKEN"))

client.save_evaluation_scenario(scenario)
client.save_evaluation_scenario_test_set(scenario.id, test_set)
client.save_evaluation_scenario_run(results)

loaded_scenario = client.get_evaluation_scenario(scenario.id)
loaded_scenario_result = client.get_evaluation_scenario_run(scenario.id)
loaded_test_set = client.get_evaluation_scenario_test_set(scenario.id)

result = loaded_scenario.evaluate(loaded_test_set)
result.display()