EvaluationLogger

The EvaluationLogger provides a flexible, incremental way to log evaluation data directly from your Python code. You don't need deep knowledge of Weave's internal data types; simply instantiate a logger and use its methods (log_prediction, log_score, log_summary) to record evaluation steps.

This approach is particularly helpful in complex workflows where the entire dataset or all scorers might not be defined upfront.

In contrast to the standard Evaluation object, which requires a predefined Dataset and list of Scorer objects, the EvaluationLogger allows you to log individual predictions and their associated scores incrementally as they become available.

Prefer a more structured evaluation?

If you prefer a more opinionated evaluation framework with predefined datasets and scorers, see Weave's standard Evaluation framework.

The EvaluationLogger offers flexibility while the standard framework offers structure and guidance.

Basic workflow

1. Initialize the logger: Create an instance of EvaluationLogger, optionally providing metadata about the model and dataset. Defaults will be used if omitted.
2. Log predictions: Call log_prediction for each input/output pair from your system.
3. Log scores: Use the returned ScoreLogger to log_score for the prediction. Multiple scores per prediction are supported.
4. Finish prediction: Always call finish() after logging scores for a prediction to finalize it.
5. Log summary: After all predictions are processed, call log_summary to aggregate scores and add optional custom metrics.

important

After calling finish() on a prediction, no more scores can be logged for it.

For a Python code demonstrating the described workflow, see the Basic example.

Basic example

The following example shows how to use EvaluationLogger to log predictions and scores inline with your existing Python code.

The user_model model function is defined and applied to a list of inputs. For each example:

• The input and output are logged using log_prediction.
• A simple correctness score (correctness_score) is logged via log_score.
• finish() finalizes logging for that prediction. Finally, log_summary records any aggregate metrics and triggers automatic score summarization in Weave.

import weave
from openai import OpenAI
from weave.flow.eval_imperative import EvaluationLogger

# Initialize the logger (model/dataset names are optional metadata)
eval_logger = EvaluationLogger(
    model="my_model",
    dataset="my_dataset"
)

# Example input data (this can be any data structure you want)
eval_samples = [
    {'inputs': {'a': 1, 'b': 2}, 'expected': 3},
    {'inputs': {'a': 2, 'b': 3}, 'expected': 5},
    {'inputs': {'a': 3, 'b': 4}, 'expected': 7},
]

# Example model logic.  This does not have to be decorated with @weave.op,
# but if you do, it will be traced and logged.
@weave.op
def user_model(a: int, b: int) -> int:
    oai = OpenAI()
    _ = oai.chat.completions.create(messages=[{"role": "user", "content": f"What is {a}+{b}?"}], model="gpt-4o-mini")
    return a + b

# Iterate through examples, predict, and log
for sample in eval_samples:
    inputs = sample["inputs"]
    model_output = user_model(**inputs) # Pass inputs as kwargs

    # Log the prediction input and output
    pred_logger = eval_logger.log_prediction(
        inputs=inputs,
        output=model_output
    )

    # Calculate and log a score for this prediction
    expected = sample["expected"]
    correctness_score = model_output == expected
    pred_logger.log_score(
        scorer="correctness", # Simple string name for the scorer
        score=correctness_score
    )

    # Finish logging for this specific prediction
    pred_logger.finish()

# Log a final summary for the entire evaluation.
# Weave auto-aggregates the 'correctness' scores logged above.
summary_stats = {"subjective_overall_score": 0.8}
eval_logger.log_summary(summary_stats)

print("Evaluation logging complete. View results in the Weave UI.")

Advanced usage

Get outputs before logging

You can first compute your model outputs, then separately log predictions and scores. This allows for better separation of evaluation and logging logic.

ev = EvaluationLogger(model="example_model", dataset="example_dataset")

outputs = [your_output_generator(**inputs) for inputs in your_dataset]
preds = [ev.log_prediction(inputs, output) for inputs, output in zip(your_dataset, outputs)]
for pred in preds:
    pred.log_score(scorer="greater_than_5_scorer", score=output > 5)
    pred.log_score(scorer="greater_than_7_scorer", score=output > 7)

ev.log_summary()

Log rich media

Inputs, outputs, and scores can include rich media such as images, videos, audio, or structured tables. Simply pass a dict or media object into the log_prediction or log_score methods:

import io
import wave
import struct
from PIL import Image
import random
from typing import Any
import weave

def generate_random_audio_wave_read(duration=2, sample_rate=44100):
    n_samples = duration * sample_rate
    amplitude = 32767  # 16-bit max amplitude

    buffer = io.BytesIO()

    # Write wave data to the buffer
    with wave.open(buffer, 'wb') as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)  # 16-bit
        wf.setframerate(sample_rate)

        for _ in range(n_samples):
            sample = random.randint(-amplitude, amplitude)
            wf.writeframes(struct.pack('<h', sample))

    # Rewind the buffer to the beginning so we can read from it
    buffer.seek(0)

    # Return a Wave_read object
    return wave.open(buffer, 'rb')

rich_media_dataset = [
    {
        'image': Image.new(
            "RGB",
            (100, 100),
            color=(
                random.randint(0, 255),
                random.randint(0, 255),
                random.randint(0, 255),
            ),
        ),
        "audio": generate_random_audio_wave_read(),
    }
    for _ in range(5)
]

@weave.op
def your_output_generator(image: Image.Image, audio) -> dict[str, Any]:
    return {
        "result": random.randint(0, 10),
        "image": image,
        "audio": audio,
    }

ev = EvaluationLogger(model="example_model", dataset="example_dataset")

for inputs in rich_media_dataset:
    output = your_output_generator(**inputs)
    pred = ev.log_prediction(inputs, output)
    pred.log_score(scorer="greater_than_5_scorer", score=output["result"] > 5)
    pred.log_score(scorer="greater_than_7_scorer", score=output["result"] > 7)

ev.log_summary()

Log and compare multiple evaluations

With EvaluationLogger, you can log and compare multiple evaluations.

1. Run the code sample shown below.
2. In the Weave UI, navigate to the Evals tab.
3. Select the evals that you want to compare.
4. Click the Compare button. In the Compare view, you can:
   • Choose which Evals to add or remove
   • Choose which metrics to show or hide
   • Page through specific examples to see how different models performed for the same input on a given dataset
   For more information on comparisons, see Comparisons

import weave

models = [
    "model1",
    "model2",
     {"name": "model3", "metadata": {"coolness": 9001}}
]

for model in models:
    ev = EvaluationLogger(model=model, dataset="example_dataset")
    for inputs in your_dataset:
        output = your_output_generator(**inputs)
        pred = ev.log_prediction(inputs=inputs, output=output)
        pred.log_score(scorer="greater_than_3_scorer", score=output > 3)
        pred.log_score(scorer="greater_than_5_scorer", score=output > 5)
        pred.log_score(scorer="greater_than_7_scorer", score=output > 7)
        pred.finish()

    ev.log_summary()

Usage tips

• Call finish() promptly after each prediction.
• Use log_summary to capture metrics not tied to single predictions (e.g., overall latency).
• Rich media logging is great for qualitative analysis.