Skip to main content

2D Plots Reporting

The scatter_hist_confusion_mat_reporting.py example demonstrates reporting series data in the following 2D formats:

ClearML reports these tables in the ClearML Web UI, experiment details > RESULTS tab > PLOTS sub-tab.

When the script runs, it creates an experiment named 2D plots reporting, which is associated with the examples project.

Histograms#

Report histograms by calling the Logger.report_histogram method. To report more than one series on the same plot, use same the title argument. For different plots, use different title arguments. Specify the type of histogram with the mode parameter. The mode values are group (the default), stack, and relative.

# report a single histogram
histogram = np.random.randint(10, size=10)
Logger.current_logger().report_histogram(
"single_histogram",
"random histogram",
iteration=iteration,
values=histogram,
xaxis="title x",
yaxis="title y",
)
# report two histograms on the same graph (plot)
histogram1 = np.random.randint(13, size=10)
histogram2 = histogram * 0.75
Logger.current_logger().report_histogram(
"two_histogram",
"series 1",
iteration=iteration,
values=histogram1,
xaxis="title x",
yaxis="title y",
)
Logger.current_logger().report_histogram(
"two_histogram",
"series 2",
iteration=iteration,
values=histogram2,
xaxis="title x",
yaxis="title y",
)

image

image

Confusion Matrices#

Report confusion matrices by calling the Logger.report_matrix method.

# report confusion matrix
confusion = np.random.randint(10, size=(10, 10))
Logger.current_logger().report_matrix(
"example_confusion",
"ignored",
iteration=iteration,
matrix=confusion,
xaxis="title X",
yaxis="title Y",
)

image

# report confusion matrix with 0,0 is at the top left
Logger.current_logger().report_matrix(
"example_confusion_0_0_at_top",
"ignored",
iteration=iteration,
matrix=confusion,
xaxis="title X",
yaxis="title Y",
yaxis_reversed=True,
)

2D Scatter Plots#

Report 2D scatter plots by calling the Logger.report_scatter2d method. Use the mode parameter to plot data points with lines (by default), markers, or both lines and markers.

scatter2d = np.hstack(
(np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1)))
)
# report 2d scatter plot with lines
Logger.current_logger().report_scatter2d(
"example_scatter",
"series_xy",
iteration=iteration,
scatter=scatter2d,
xaxis="title x",
yaxis="title y",
)
# report 2d scatter plot with markers
Logger.current_logger().report_scatter2d(
"example_scatter",
"series_markers",
iteration=iteration,
scatter=scatter2d,
xaxis="title x",
yaxis="title y",
mode='markers'
)
# report 2d scatter plot with lines and markers
Logger.current_logger().report_scatter2d(
"example_scatter",
"series_lines+markers",
iteration=iteration,
scatter=scatter2d,
xaxis="title x",
yaxis="title y",
mode='lines+markers'
)

image