Create an Experiment Evaluator

How to write the functions to evaluate your task outputs in experiments

You have created your task, and now you'd like to write an evaluator to grade the task outputs in your experiments. If you don't know what these concepts are, read our overview of datasets and experiments.

There are three types of evaluators you can build — LLM, code, and annotations. We support all three, (if you're looking for guidance on how to decide, we got you).

Here's the simplest version of an evaluation function:

def is_true(output):
    # output is the task output
    return output == True

You can define a simple function to read the output of a task and check it.

Evaluation Inputs

The evaluator function can take the following optional arguments:

Parameter name	Description	Example
dataset_row	the entire row of the data, including every column as dictionary key	def eval(dataset_row): ...
input	experiment run input, which is mapped to attributes.input.value	def eval(input): ...
output	experiment run output	def eval(output): ...
dataset_output	the expected output if available, mapped to attributes.output.value	def eval(dataset_output): ...
metadata	dataset_row metadata, which is mapped to attributes.metadata	def eval(metadata): ...

Evaluation Outputs

We support several types of evaluation outputs. Label must be a string. Score must range from 0.0 to 1.0. Explanation must be a string.

Evaluator Output Type	Example	How it appears in Arize
boolean	True	label = 'True' score = 1.0
float	1.0	score = 1.0
string	"reasonable"	label = 'reasonable'
tuple	(1.0, "my explanation notes")	score = 1.0 explanation = 'my explanation notes'
tuple	("True", 1.0, "my explanation")	label = 'True' score = 1.0 explanation = "my explanation"
EvaluationResult	EvaluationResult( score=1, label='reasonable', explanation='explanation' metadata={} )	score = 1.0 label='reasonable' explanation = 'explanation' metadata={}

To use EvaluationResult class, use the following import statement:

• from arize.experimental.datasets.experiments.types import EvaluationResult

• One of label or score must be supplied (you can't have an evaluation with no result).

Here's another example which compares the output to a value in the dataset_row.

"""Example dataset
dataframe = pd.DataFrame({
    "expected": [2]
})
"""

def is_equal(dataset_row, output):
    expected = dataset_row.get("expected")
    return expected == output

To run the experiment, you can load the evaluator into run_experiment as following:

arize_client.run_experiment(
    space_id="",
    dataset_name="", 
    task="", 
    evaluators=[is_equal], 
    experiment_name=""
)

Learn more

1. Write an LLM Evaluator for experiments
2. Customize your code based evaluator for experiments