View Eval Tables

When you create an Eval Table, specify which columns contain inputs, outputs, and scores. W&B groups the columns into corresponding sections in the Eval Table. The following example shows the basic structure of an EvalTable object:

import wandb

wandb.EvalTable(
    input_columns=["column1", "column2"],
    output_columns=["output_column1", "output_column2"],
    score_columns=["score_column1", "score_column2"],
)

Pass one or more column names to each of the following arguments:

input_columns: Data provided to the model, such as an image, prompt, or ground-truth label.
output_columns: Values produced by the model, such as a prediction or generated response.
score_columns: Metrics or other values used to evaluate the output.

W&B displays these columns in the Inputs, Outputs, and Scores sections of the Eval Table.

The examples on this page use code from Create an Eval Table. Expand View code to view or copy the complete example.

View code

import pandas as pd
import wandb

df = pd.DataFrame(
    [
        {
            "image_id": "img_001",
            "true_label": "cat",
            "predicted_label": "cat",
            "confidence": 0.97,
            "correct": True,
        },
        {
            "image_id": "img_002",
            "true_label": "dog",
            "predicted_label": "cat",
            "confidence": 0.72,
            "correct": False,
        },
        {
            "image_id": "img_003",
            "true_label": "car",
            "predicted_label": "car",
            "confidence": 0.89,
            "correct": True,
        },
    ]
)

df["correct"] = df["correct"].astype(object)  # preserves native Python bools

with wandb.init(project="classifier-eval-table-demo") as run:
    eval_table = wandb.EvalTable(
        dataframe=df,
        input_columns=["image_id", "true_label"],
        output_columns=["predicted_label"],
        score_columns=["correct", "confidence"],
    )
    run.log({"validation_predictions_eval": eval_table})

For example, consider the code example from Create an Eval Table:

import wandb

with wandb.init(project="classifier-eval-table-demo") as run:
    eval_table = wandb.EvalTable(
        dataframe=df,
        input_columns=["image_id", "true_label"],
        output_columns=["predicted_label"],
        score_columns=["correct", "confidence"],
    )
    run.log({"validation_predictions_eval": eval_table})

In that example:

The Inputs section contains the image_id and true_label columns.
The Outputs section contains the predicted_label column.
The Scores section contains the correct and confidence columns.

The following image highlights these sections:

Detail view

Select a row to open a detailed view of that example. The detail view shows the example’s inputs, outputs, and scores. The following image shows the detail view for the first row in the Eval Table:

Use the detail view to inspect examples when the table contains many columns or when you compare results across multiple runs. Select the up or down arrow above the Inputs section to move between examples in the current table view.

Filter data

Use filters to display only rows that match specific conditions. To add a filter:

Select the Filter button above the table.
From the dropdown, select the column to filter.
Select an operator.
Specify a value.

W&B automatically applies the filter to the table and displays only rows that match the condition.

You can apply multiple filters. To remove a filter, select the X next to it.

Show or hide columns

Use the Columns menu to control which columns appear in the Eval Table. The available columns depend on the data logged to the table. To show or hide a column:

Select Columns above the table.
Select a column to show it, or clear the column to hide it.

The following image shows a Columns menu in which all available columns are selected:

Guides

Integrations

Reference

Detail view

Filter data

Show or hide columns

​Detail view

​Filter data

​Show or hide columns

Detail view

Filter data

Show or hide columns