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Views

View Examples

Examples of using the Views classes to extend the functionality of Workbench Artifacts are in the Examples section at the bottom of this page.

Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing Workbench Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.

If you're looking to read and pull data from a view please see the Views documentation.

View Constructor Classes

These classes provide APIs for creating Views for DataSources and FeatureSets.

  • DisplayView: The Display View is leveraged by the web views/components and allows fine tuning of the UI for the Workbench Dashboard.
  • ComputationView: The Computation View controls which columns have descriptive stats, outliers, and correlation calculations. Typically the computation view is a superset of the display view.
  • TrainingView: The Training View will add a 'training' column to the data for model training, validation, and testing. Each row will have a 1 or 0 indicated whether is was used in the model training.
  • MDQView: The Model Data Quality View computes various data quality metrics that include modeling inference results.

Examples

All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples

Listing Views

views.py
from workbench.api.data_source import DataSource

# Convert the Data Source to a Feature Set
test_data = DataSource('test_data')
test_data.views()
["display", "training", "computation"]

Getting a Particular View

views.py
from workbench.api.feature_set import FeatureSet

fs = FeatureSet('test_features')

# Grab the columns for the display view
display_view = fs.view("display")
display_view.columns
['id', 'name', 'height', 'weight', 'salary', ...]

# Pull the dataframe for this view
df = display_view.pull_dataframe()
    id       name     height      weight         salary ...
0   58  Person 58  71.781227  275.088196  162053.140625

View Queries

All Workbench Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.

view_query.py
from workbench.api.feature_set import FeatureSet

# Grab a FeatureSet View
fs = FeatureSet("abalone_features")
t_view = fs.view("training")

# Make some queries using the Athena backend
df = t_view(f"select * from {t_view.table} where height > .3")
print(df.head())

df = t_view.query("select * from abalone_features where class_number_of_rings < 3")
print(df.head())

Output

  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings
0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10
1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8

  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings
0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1
1   I   0.150     0.100   0.025         0.015          0.0045          0.0040         0.0050                      2

The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord