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Metric Correlation Plot

The metric correlation plot is used to compare how various input parameters effect a selected output metric across a number of experiments. Users can dynamically choose between available metrics to anchor the visualization on and rearrange and highlight the plot as desired.

The plot itself is a Plotly parallel coordinates plot. More information can be found in the Plotly documentation.

import random

from rubicon_ml import Rubicon
from rubicon_ml.viz import MetricCorrelationPlot

First, we’ll create a few experiments and log some parameters and metrics to them.

rubicon = Rubicon(persistence="memory", auto_git_enabled=True)
project = rubicon.get_or_create_project("metric correlation plot")

for i in range(0, 100):
    experiment = project.log_experiment()

        value=random.choice([True, False]),
    experiment.log_parameter(name="n_estimators", value=random.randrange(2, 10, 2))
        value=random.choice(["A", "B", "C", "D", "E"]),

    experiment.log_metric(name="accuracy", value=random.random())
    experiment.log_metric(name="confidence", value=random.random())

Now, we can instantiate the MetricCorrelationPlot object with the experiments we just logged and view the plot right in the notebook with show. The Dash application itself will be running on when running locally. Use the serve command to launch the server directly without rendering the widget in the current Python interpreter.

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