Custom Charts overview - CoreWeave Docs

Custom charts in W&B are programmable through a group of functions in the wandb.plot namespace. These functions create interactive visualizations in W&B project dashboards, and support common ML visualizations such as confusion matrices, ROC curves, and distribution plots.

Available chart functions

Function	Description
`confusion_matrix()`	Generate confusion matrices for classification performance visualization.
`roc_curve()`	Create Receiver Operating Characteristic curves for binary and multi-class classifiers.
`pr_curve()`	Build Precision-Recall curves for classifier evaluation.
`line()`	Construct line charts from tabular data.
`scatter()`	Create scatter plots for variable relationships.
`bar()`	Generate bar charts for categorical data.
`histogram()`	Build histograms for data distribution analysis.
`line_series()`	Plot multiple line series on a single chart.
`plot_table()`	Create custom charts using Vega-Lite specifications.

Common use cases

Model evaluation

Classification: confusion_matrix(), roc_curve(), and pr_curve() for classifier evaluation
Regression: scatter() for prediction vs. actual plots and histogram() for residual analysis
Vega-Lite Charts: plot_table() for domain-specific visualizations

Training monitoring

Learning Curves: line() or line_series() for tracking metrics over epochs
Hyperparameter Comparison: bar() charts for comparing configurations

Data analysis

Distribution Analysis: histogram() for feature distributions
Correlation Analysis: scatter() plots for variable relationships

Getting started

Log a confusion matrix

import wandb

y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 2, 0, 1, 1]
class_names = ["class_0", "class_1", "class_2"]

# Initialize a run
with wandb.init(project="custom-charts-demo") as run:
    run.log({
        "conf_mat": wandb.plot.confusion_matrix(
            y_true=y_true, 
            preds=y_pred,
            class_names=class_names
        )
    })

Build a scatter plot for feature analysis

import numpy as np

# Generate synthetic data
data_table = wandb.Table(columns=["feature_1", "feature_2", "label"])

with wandb.init(project="custom-charts-demo") as run:

    for _ in range(100):
        data_table.add_data(
            np.random.randn(), 
            np.random.randn(), 
            np.random.choice(["A", "B"])
        )

    run.log({
        "feature_scatter": wandb.plot.scatter(
            data_table, x="feature_1", y="feature_2",
            title="Feature Distribution"
        )
    })

​Available chart functions

​Common use cases

​Model evaluation

​Training monitoring

​Data analysis

​Getting started

​Log a confusion matrix

​Build a scatter plot for feature analysis