Line plots

• 1: Line plot reference
• 2: Point aggregation
• 3: Smooth line plots

Line plots show up by default when you plot metrics over time with wandb.log(). Customize with chart settings to compare multiple lines on the same plot, calculate custom axes, and rename labels.

Edit line plot settings

This section shows how to edit the settings for an individual line plot panel, all line plot panels in a section, or all line plot panels in a workspace.

If you’d like to use a custom x-axis, make sure it’s logged in the same call to wandb.log() that you use to log the y-axis.

Individual line plot

A line plot’s individual settings override the line plot settings for the section or the workspace. To customize a line plot:

1. Hover your mouse over the panel, then click the gear icon.
2. Within the modal that appears, select a tab to edit its settings.
3. Click Apply.

Line plot settings

You can configure these settings for a line plot:

Date: Configure the plot’s data-display details.

• X: Select the value to use for the X axis (defaults to Step). You can change the x-axis to Relative Time or select a custom axis based on values you log with W&B.
  • Relative Time (Wall) is clock time since the process started, so if you started a run and resumed it a day later and logged something that would be plotted a 24hrs.
  • Relative Time (Process) is time inside the running process, so if you started a run and ran for 10 seconds and resumed a day later that point would be plotted at 10s.
  • Wall Time is minutes elapsed since the start of the first run on the graph.
  • Step increments by default each time wandb.log() is called, and is supposed to reflect the number of training steps you’ve logged from your model.
• Y: Select one or more y-axes from the logged values, including metrics and hyperparameters that change over time.
• X Axis and Y Axis minimum and maximum values (optional).
• Point aggregation method. Either Random sampling (the default) or Full fidelity. Refer to Sampling.
• Smoothing: Change the smoothing on the line plot. Defaults to Time weighted EMA. Other values include No smoothing, Running average, and Gaussian.
• Outliers: Rescale to exclude outliers from the default plot min and max scale.
• Max number of runs or groups: Show more lines on the line plot at once by increasing this number, which defaults to 10 runs. You’ll see the message “Showing first 10 runs” on the top of the chart if there are more than 10 runs available but the chart is constraining the number visible.
• Chart type: Change between a line plot, an area plot, and a percentage area plot.

Grouping: Configure whether and how to group and aggregate runs in the plot.

• Group by: Select a column, and all the runs with the same value in that column will be grouped together.
• Agg: Aggregation— the value of the line on the graph. The options are mean, median, min, and max of the group.

Chart: Specify titles for the panel, the X axis, and the Y axis, and the -axis, hide or show the legend, and configure its position.

Legend: Customize the appearance of the panel’s legend, if it is enabled.

• Legend: The field in the legend for each line in the plot in the legend of the plot for each line.
• Legend template: Define a fully customizable template for the legend, specifying exactly what text and variables you want to show up in the template at the top of the line plot as well as the legend that appears when you hover your mouse over the plot.

Expressions: Add custom calculated expressions to the panel.

• Y Axis Expressions: Add calculated metrics to your graph. You can use any of the logged metrics as well as configuration values like hyperparameters to calculate custom lines.
• X Axis Expressions: Rescale the x-axis to use calculated values using custom expressions. Useful variables include**_step** for the default x-axis, and the syntax for referencing summary values is ${summary:value}

All line plots in a section

To customize the default settings for all line plots in a section, overriding workspace settings for line plots:

1. Click the section’s gear icon to open its settings.
2. Within the modal that appears, select the Data or Display preferences tabs to configure the default settings for the section. For details about each Data setting, refer to the preceding section, Individual line plot. For details about each display preference, refer to Configure section layout.

All line plots in a workspace

To customize the default settings for all line plots in a workspace:

1. Click the workspace’s settings, which has a gear with the label Settings.
2. Click Line plots.
3. Within the modal that appears, select the Data or Display preferences tabs to configure the default settings for the workspace.

   • For details about each Data setting, refer to the preceding section, Individual line plot.

   • For details about each Display preferences section, refer to Workspace display preferences. At the workspace level, you can configure the default Zooming behavior for line plots. This setting controls whether to synchronize zooming across line plots with a matching x-axis key. Disabled by default.

Visualize average values on a plot

If you have several different experiments and you’d like to see the average of their values on a plot, you can use the Grouping feature in the table. Click “Group” above the run table and select “All” to show averaged values in your graphs.

Here is what the graph looks like before averaging:

The proceeding image shows a graph that represents average values across runs using grouped lines.

Visualize NaN value on a plot

You can also plot NaN values including PyTorch tensors on a line plot with wandb.log. For example:

wandb.log({"test": [..., float("nan"), ...]})

Compare two metrics on one chart

1. Select the Add panels button in the top right corner of the page.
2. From the left panel that appears, expand the Evaluation dropdown.
3. Select Run comparer

Change the color of the line plots

Sometimes the default color of runs is not helpful for comparison. To help overcome this, wandb provides two instances with which one can manually change the colors.

• From the run table
• From the chart legend settings

Each run is given a random color by default upon initialization.

Upon clicking any of the colors, a color palette appears from which we can manually choose the color we want.

1. Hover your mouse over the panel you want to edit its settings for.
2. Select the pencil icon that appears.
3. Choose the Legend tab.

Visualize on different x axes

If you’d like to see the absolute time that an experiment has taken, or see what day an experiment ran, you can switch the x axis. Here’s an example of switching from steps to relative time and then to wall time.

Area plots

In the line plot settings, in the advanced tab, click on different plot styles to get an area plot or a percentage area plot.

Zoom

Click and drag a rectangle to zoom vertically and horizontally at the same time. This changes the x-axis and y-axis zoom.

Hide chart legend

Turn off the legend in the line plot with this simple toggle:

1 - Line plot reference

X-Axis

You can set the x-axis of a line plot to any value that you have logged with W&B.log as long as it’s always logged as a number.

Y-Axis variables

You can set the y-axis variables to any value you have logged with wandb.log as long as you were logging numbers, arrays of numbers or a histogram of numbers. If you logged more than 1500 points for a variable, W&B samples down to 1500 points.

X range and Y range

You can change the maximum and minimum values of X and Y for the plot.

X range default is from the smallest value of your x-axis to the largest.

Y range default is from the smallest value of your metrics and zero to the largest value of your metrics.

Max runs/groups

By default you will only plot 10 runs or groups of runs. The runs will be taken from the top of your runs table or run set, so if you sort your runs table or run set you can change the runs that are shown.

Legend

You can control the legend of your chart to show for any run any config value that you logged and meta data from the runs such as the created at time or the user who created the run.

Example:

${run:displayName} - ${config:dropout} will make the legend name for each run something like royal-sweep - 0.5 where royal-sweep is the run name and 0.5 is the config parameter named dropout.

You can set value inside[[ ]] to display point specific values in the crosshair when hovering over a chart. For example \[\[ $x: $y ($original) ]] would display something like “2: 3 (2.9)”

Supported values inside [[ ]] are as follows:

Grouping

You can aggregate all of the runs by turning on grouping, or group over an individual variable. You can also turn on grouping by grouping inside the table and the groups will automatically populate into the graph.

Smoothing

You can set the smoothing coefficient to be between 0 and 1 where 0 is no smoothing and 1 is maximum smoothing.

Ignore outliers

Rescale the plot to exclude outliers from the default plot min and max scale. The setting’s impact on the plot depends on the plot’s sampling mode.

• For plots that use random sampling mode, when you enable Ignore outliers, only points from 5% to 95% are shown. When outliers are shown, they are not formatted differently from other points.
• For plots that use full fidelity mode, all points are always shown, condensed down to the last value in each bucket. When Ignore outliers is enabled, the minimum and maximum bounds of each bucket are shaded. Otherwise, no area is shaded.

Expression

Expression lets you plot values derived from metrics like 1-accuracy. It currently only works if you are plotting a single metric. You can do simple arithmetic expressions, +, -, *, / and % as well as ** for powers.

Plot style

Select a style for your line plot.

Line plot:

Area plot:

Percentage area plot:

2 - Point aggregation

Use point aggregation methods within your line plots for improved data visualization accuracy and performance. There are two types of point aggregation modes: full fidelity and random sampling. W&B uses full fidelity mode by default.

Full fidelity

When you use full fidelity mode, W&B breaks the x-axis into dynamic buckets based on the number of data points. It then calculates the minimum, maximum, and average values within each bucket while rendering a point aggregation for the line plot.

There are three main advantages to using full fidelity mode for point aggregation:

• Preserve extreme values and spikes: retain extreme values and spikes in your data
• Configure how minimum and maximum points render: use the W&B App to interactively decide whether you want to show extreme (min/max) values as a shaded area.
• Explore your data without losing data fidelity: W&B recalculates x-axis bucket sizes when you zoom into specific data points. This helps ensure that you can explore your data without losing accuracy. Caching is used to store previously computed aggregations to help reduce loading times which is particularly useful if you are navigating through large datasets.

Configure how minimum and maximum points render

Show or hide minimum and maximum values with shaded areas around your line plots.

The proceeding image shows a blue line plot. The light blue shaded area represents the minimum and maximum values for each bucket.

There are three ways to render minimum and maximum values in your line plots:

• Never: The min/max values are not displayed as a shaded area. Only show the aggregated line across the x-axis bucket.
• On hover: The shaded area for min/max values appears dynamically when you hover over the chart. This option keeps the view uncluttered while allowing you to inspect ranges interactively.
• Always: The min/max shaded area is consistently displayed for every bucket in the chart, helping you visualize the full range of values at all times. This can introduce visual noise if there are many runs visualized in the chart.

By default, the minimum and maximum values are not displayed as shaded areas. To view one of the shaded area options, follow these steps:

• All charts in a workspace
• Individual chart in a workspace

Explore your data without losing data fidelity

Analyze specific regions of the dataset without missing critical points like extreme values or spikes. When you zoom in on a line plot, W&B adjusts the buckets sizes used to calculate the minimum, maximum, and average values within each bucket.

W&B divides the x-axis is dynamically into 1000 buckets by default. For each bucket, W&B calculates the following values:

• Minimum: The lowest value in that bucket.
• Maximum: The highest value in that bucket.
• Average: The mean value of all points in that bucket.

W&B plots values in buckets in a way that preserves full data representation and includes extreme values in every plot. When zoomed in to 1,000 points or fewer, full fidelity mode renders every data point without additional aggregation.

To zoom in on a line plot, follow these steps:

1. Navigate to your W&B project
2. Select on the Workspace icon on the left tab
3. Optionally add a line plot panel to your workspace or navigate to an existing line plot panel.
4. Click and drag to select a specific region to zoom in on.

Random sampling

Random sampling uses 1500 randomly sampled points to render line plots. Random sampling is useful for performance reasons when you have a large number of data points.

Enable random sampling

By default, W&B uses full fidelity mode. To enable random sampling, follow these steps:

• All charts in a workspace
• Individual chart in a workspace

Access non sampled data

You can access the complete history of metrics logged during a run using the W&B Run API. The following example demonstrates how to retrieve and process the loss values from a specific run:

# Initialize the W&B API
run = api.run("l2k2/examples-numpy-boston/i0wt6xua")

# Retrieve the history of the 'Loss' metric
history = run.scan_history(keys=["Loss"])

# Extract the loss values from the history
losses = [row["Loss"] for row in history]

3 - Smooth line plots

W&B supports three types of smoothing:

• exponential moving average (default)
• gaussian smoothing
• running average
• exponential moving average - Tensorboard (deprecated)

See these live in an interactive W&B report.

Exponential Moving Average (Default)

Exponential smoothing is a technique for smoothing time series data by exponentially decaying the weight of previous points. The range is 0 to 1. See Exponential Smoothing for background. There is a de-bias term added so that early values in the time series are not biased towards zero.

The EMA algorithm takes the density of points on the line (the number of y values per unit of range on x-axis) into account. This allows consistent smoothing when displaying multiple lines with different characteristics simultaneously.

Here is sample code for how this works under the hood:

const smoothingWeight = Math.min(Math.sqrt(smoothingParam || 0), 0.999);
let lastY = yValues.length > 0 ? 0 : NaN;
let debiasWeight = 0;

return yValues.map((yPoint, index) => {
  const prevX = index > 0 ? index - 1 : 0;
  // VIEWPORT_SCALE scales the result to the chart's x-axis range
  const changeInX =
    ((xValues[index] - xValues[prevX]) / rangeOfX) * VIEWPORT_SCALE;
  const smoothingWeightAdj = Math.pow(smoothingWeight, changeInX);

  lastY = lastY * smoothingWeightAdj + yPoint;
  debiasWeight = debiasWeight * smoothingWeightAdj + 1;
  return lastY / debiasWeight;
});

Here’s what this looks like in the app:

Gaussian Smoothing

Gaussian smoothing (or gaussian kernel smoothing) computes a weighted average of the points, where the weights correspond to a gaussian distribution with the standard deviation specified as the smoothing parameter. See . The smoothed value is calculated for every input x value.

Gaussian smoothing is a good standard choice for smoothing if you are not concerned with matching TensorBoard’s behavior. Unlike an exponential moving average the point will be smoothed based on points occurring both before and after the value.

Here’s what this looks like in the app:

Running Average

Running average is a smoothing algorithm that replaces a point with the average of points in a window before and after the given x value. See “Boxcar Filter” at https://en.wikipedia.org/wiki/Moving_average. The selected parameter for running average tells Weights and Biases the number of points to consider in the moving average.

Consider using Gaussian Smoothing if your points are spaced unevenly on the x-axis.

The following image demonstrates how a running app looks like in the app:

Exponential Moving Average (Deprecated)

The TensorBoard EMA algorithm has been deprecated as it cannot accurately smooth multiple lines on the same chart that do not have a consistent point density (number of points plotted per unit of x-axis).

Exponential moving average is implemented to match TensorBoard’s smoothing algorithm. The range is 0 to 1. See Exponential Smoothing for background. There is a debias term added so that early values in the time series are not biases towards zero.

Here is sample code for how this works under the hood:

  data.forEach(d => {
    const nextVal = d;
    last = last * smoothingWeight + (1 - smoothingWeight) * nextVal;
    numAccum++;
    debiasWeight = 1.0 - Math.pow(smoothingWeight, numAccum);
    smoothedData.push(last / debiasWeight);

Here’s what this looks like in the app:

Implementation Details

All of the smoothing algorithms run on the sampled data, meaning that if you log more than 1500 points, the smoothing algorithm will run after the points are downloaded from the server. The intention of the smoothing algorithms is to help find patterns in data quickly. If you need exact smoothed values on metrics with a large number of logged points, it may be better to download your metrics through the API and run your own smoothing methods.

Hide original data

By default we show the original, unsmoothed data as a faint line in the background. Click the Show Original toggle to turn this off.