truera 12.1.1

Truera Python SDK.

Overview

As you build and deploy ML models, TruEra plugs into your ML stack to let you test, debug and monitor your projects to ensure each model is doing what it's supposed to be doing — and, if not, why not? From feature development that helps you refine your data to efficiently training and evaluating your models to validating a final model for production, TruEra has you covered.

Quickstart

This guide will help you explain, test, and debug your machine learning models for issues with performance, drift or fairness.

Start improving your machine learning models with the following steps:

1. Create your account

Sign up for a free account here!

2. Get your authentication token

When you sign up, we will generate an authentication token. Find it using the link below:

Retrieve your authentication token

3. Install with pypi

You can install the TruEra SDK from pypi directly in your command line or in a notebook.

pip install truera

4. Intialize your TruEra workspace

from truera.client.truera_workspace import TrueraWorkspace #import truera
from truera.client.truera_authentication import TokenAuthentication # import authentication
tru = TrueraWorkspace("https://app.truera.net", TokenAuthentication("ADD YOUR AUTH TOKEN")) 
tru.set_environment("local") # set the workspace environment

5. Create your project, add data and models

tru.add_project("Project 1", score_type="CHOOSE A SCORE TYPE: regression, classification, probits or logits")
tru.add_data_collection("Data Collection 1") # think of this as the schema that will hold your data "splits"
tru.add_data_split("data_split_1", X, label_data=y, id_col_name = "id") # X must be a dataframe, X and y must have ID
tru.add_python_model("model_1", my_model_object) #add python model
tru.upload_project() # sync to remote

5. Test, debug and explain your models!

Test for Performance

# Create performance tests
tru.tester.add_performance_test(
    test_name="Accuracy Test 1",
    data_split_names=["split1_name", "split2_name"],
    metric="CLASSIFICATION_ACCURACY",
    warn_if_less_than=0.85,
    fail_if_less_than=0.82
)

Find error hotspots in the web app

Or find hotspots in the python SDK

# find error hotspots
explainer = tru.get_explainer("split2_name")
explainer.find_hotspots(metric_of_interest="MSE")

Test for drift

# Create a drift/stability test
tru.tester.add_stability_test(
    test_name="Stability Test",
    comparison_data_split_names=["split1_name", "split2_name"],
    base_data_split_name="reference_split_name",
    metric="DIFFERENCE_OF_MEAN",
    warn_if_outside=[-1, 1],
    fail_if_outside=[-2, 2]
)

Find contributors to drift in the web app

Or find contributors to drift in the python SDK

# Find contributors to drift
explainer = tru.get_explainer("split1")
explainer.set_comparison_data_splits(["split2", "split3"])
explainer.compute_feature_contributors_to_instability()

Test for fairness

# Create fairness tests
tru.tester.add_fairness_test(
    test_name="Fairness Test",
    data_split_names=["split1_name", "split2_name"],
    protected_segments=[("segment_group_name", "protected_segment_name")],
    metric="DISPARATE_IMPACT_RATIO",
    warn_if_outside=[0.9, 1.15],
    fail_if_outside=[0.8, 1.25]
)

Analyze fairness in the web app

Understand your model features in the web app

Or explain your model in the python SDK

# Plot Influence Sensitivity Plots (ISPs) and Partial Dependance Plots (PDPs)
explainer = tru.get_explainer("split1")
explainer.plot_isps()
explainer.plot_pdps()

# Create feature importance tests
tru.tester.add_feature_importance_test(
    test_name="Feature Importance Test",
    data_split_names=["split1_name", "split2_name"],
    min_importance_value=0.01,
    background_split_name="background split name",
    score_type=<score_type>, # (e.g., "regression", or "logits"/"probits"
                            # for the classification project)
    warn_if_greater_than=5, # warn if number of features with global importance values lower than `min_importance_value` is > 5
    fail_if_greater_than=10
)

More Resources

• AI Quality Slack Community
• TruEra Documentation
• AI Quality Blog