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Logging and visualizing rich metadata
With rich metadata logging and visualization, you can analyze a variety of information about mode performance and dataset distribution for better model debugging.
Attributes enable you to log a comprehensive set of metadata about your training and test datasets, plot feature frequencies, subcategories, etc. You can associate advanced metadata about your experiment run and model quality and generate a variety of visualizations in our Web UI. You can log and visualize histograms, correlation matrix, confusion matrix, line plots, single and/or multiple variable bar charts, log complex tables, etc.
Here are the steps to log and visualize custom attributes:
    When you are logging an experiment run, record additional metadata about your dataset and model metrics using the client library.
Import from a variety of supported Verta data types and start logging key attributes of your experiment run

Log confusion matrix

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run = client.set_experiment_run()
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# log confusion matrix
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from verta.data_types import ConfusionMatrix
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data = ConfusionMatrix(
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value=[
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[650000, 100000],
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[24000, 3330000],
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],
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labels=["High Income", "Low Income"],
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)
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run.log_attribute("Income_Confusion_Matrix", data)
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The chart will be available in the web UI under experiment run detail view.

Log discrete histograms

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run = client.set_experiment_run()
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# log discrete histogram
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from verta.data_types import DiscreteHistogram
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data = DiscreteHistogram(
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buckets=list(itertools.chain(*df.index.tolist())),
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data=df.tolist(),
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)
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run.log_attribute("Response_Histogram", data)
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The chart will be available in the web UI under experiment run detail view.

Log float histograms

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run = client.set_experiment_run()
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# log float histogram
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from verta.data_types import FloatHistogram
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data = FloatHistogram(
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bucket_limits=[1, 13, 25, 37, 49, 61,72,89],
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data=[1500, 5300, 9100, 3400, 700, 1700, 2700],
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)
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run.log_attribute("Age_Histogram", data)
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The chart will be available in the web UI under experiment run detail view.

Log line chart

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run = client.set_experiment_run()
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# log line chart
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from verta.data_types import Line
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data = Line(
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x=[1,2,3,4,5,6,7,8,9,10],
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y=[191100,196000,205128,208568,214462,225251,237034,244088,248728,270002],
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)
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run.log_attribute("Top_Income_Over_Time", data)
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The chart will be available in the web UI under experiment run detail view.

Log Table

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run = client.set_experiment_run()
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# log table
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from verta.data_types import Table
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data = Table(
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data=[["No formal qualification", 1020, "20%", "5%"], ["High school", 6730, "33%", "5%"], ["College", 8440, "39%", "18%"], ["Bachelors", 12100, "20%", "25%"], ["Advanced", 18400, "30%", "50%"]],
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columns=["User-Segment", "Average-Capital-Gain", "Self-Employed", "Occupation-Specialty"],
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)
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run.log_attribute("Measurements", data)
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The chart will be available in the web UI under experiment run detail view.

Log basic key:value strings

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run = client.set_experiment_run()
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# log attributes
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run.log_attributes({
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'library': "scikit-learn",
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'model_type': "logistic regression",
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})
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The chart will be available in the web UI under experiment run detail view.
Last modified 3mo ago