Tutorial: Perform evaluation using the Python SDK

Set up your credentials. If you are running this quickstart in Colaboratory, run the following:

Import libraries

Import your libraries and set up your project and location.

import pandas as pd

import vertexai
from vertexai.evaluation import EvalTask, PointwiseMetric, PointwiseMetricPromptTemplate
from google.cloud import aiplatform

PROJECT_ID = "PROJECT_ID"
LOCATION = "LOCATION"
EXPERIMENT_NAME = "EXPERIMENT_NAME"

vertexai.init(
    project=PROJECT_ID,
    location=LOCATION,
)

Note that EXPERIMENT_NAME can only contain lowercase alphanumeric characters and hyphens, up to a maximum of 127 characters.

Set up evaluation metrics based on your criteria

The following metric definition evaluates the text quality generated from a large language model based on two criteria: Fluency and Entertaining. The code defines a metric called custom_text_quality using those two criteria:

custom_text_quality = PointwiseMetric(
    metric="custom_text_quality",
    metric_prompt_template=PointwiseMetricPromptTemplate(
        criteria={
            "fluency": (
                "Sentences flow smoothly and are easy to read, avoiding awkward"
                " phrasing or run-on sentences. Ideas and sentences connect"
                " logically, using transitions effectively where needed."
            ),
            "entertaining": (
                "Short, amusing text that incorporates emojis, exclamations and"
                " questions to convey quick and spontaneous communication and"
                " diversion."
            ),
        },
        rating_rubric={
            "1": "The response performs well on both criteria.",
            "0": "The response is somewhat aligned with both criteria",
            "-1": "The response falls short on both criteria",
        },
    ),
)

Prepare your dataset

Add the following code to prepare your dataset:

responses = [
    # An example of good custom_text_quality
    "Life is a rollercoaster, full of ups and downs, but it's the thrill that keeps us coming back for more!",
    # An example of medium custom_text_quality
    "The weather is nice today, not too hot, not too cold.",
    # An example of poor custom_text_quality
    "The weather is, you know, whatever.",
]

eval_dataset = pd.DataFrame({
    "response" : responses,
})

Run evaluation with your dataset

Run the evaluation:

eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=[custom_text_quality],
    experiment=EXPERIMENT_NAME
)

pointwise_result = eval_task.evaluate()

View the evaluation results for each response in the metrics_table Pandas DataFrame:

pointwise_result.metrics_table

Before you begin