Registry / ai-ml / interpret-community

interpret-community

library0.32.0pypypiunverified

Microsoft Interpret Extensions SDK for Python – provides a unified API for model interpretability including SHAP, LIME, and surrogate explainers. Current version: 0.32.0. Release cadence: irregular, ~2-4 per year.

ai-ml

pip install interpret-community

github ↗package ↗

INSTALL

IMPORT

SIG · INTERPRET-COMMUNIT

interpret-community

ai-mlpythonv0.32.0

Install

18.9s avg

Import

6260ms

Disk

616MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.32.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 18.2s

py 3.11

✕ build_error

✓ 17.8s

py 3.12

✕ build_error

✓ 18.7s

py 3.13

✕ build_error

py 3.9

✕ build_error

✓ 20.8s

616MB installed

● package 616MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

TabularExplainer

✓ from interpret.ext.blackbox import TabularExplainer

✗ from interpret.blackbox import TabularExplainer

Old import path without 'ext' - missing subpackage

MimicExplainer

✓ from interpret.ext.blackbox import MimicExplainer

✗ from interpret.community import MimicExplainer

MimicExplainer lives in interpret.ext.blackbox, not interpret.community

Basic usage: train a model, create a TabularExplainer, and get global feature importance.

import pandas as pd from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier from interpret.ext.blackbox import TabularExplainer iris = load_iris() X = pd.DataFrame(iris.data, columns=iris.feature_names) y = iris.target model = RandomForestClassifier().fit(X, y) explainer = TabularExplainer(model, X, features=iris.feature_names) global_explanation = explainer.explain_global() print(global_explanation.feature_importance())

Debug

Known issues

breakingIn v0.29.0, Python 3.6 support was dropped. Upgrade to Python 3.7+.

fix

Use Python 3.7 or higher.

affects: >=0.29.0

breakingIn v0.25.0, the function _get_surrogate_model_replication_measure() was made public as get_surrogate_model_replication_measure(). Private underscore prefix removed.

fix

Use get_surrogate_model_replication_measure() instead of _get_surrogate_model_replication_measure().

affects: >=0.25.0

deprecatedThe old import path from interpret.blackbox is deprecated. Use interpret.ext.blackbox instead.

fix

Use 'from interpret.ext.blackbox import ...'.

affects: >=0.30.0

gotchaPassing numpy arrays without feature names may cause errors in explainers that expect pandas DataFrames.

fix

Wrap input data in a pandas DataFrame with column names when using TabularExplainer.

affects: all

gotchaWhen using MimicExplainer, the surrogate model (e.g., DecisionTree) must be passed as a model object, not a string.

fix

Instantiate the surrogate model first: from sklearn.tree import DecisionTreeRegressor; MimicExplainer(..., augment_data=True, surrogate_model=DecisionTreeRegressor()).

affects: all

Upgrade

Version history

0.32.0latest on PyPI · released Feb 7, 2025

Audit

Dependencies

interpret-corerequiredRequired for core interpretation logic

shapoptionalUsed for SHAP-based explainers

ml-wrappersoptionalWrapper models for scikit-learn, etc.

pandasrequiredData input/output handling

Agent activity

10 hits · last 30 days

node

Resources

githubhttps://github.com/interpretml/interpret-community ↗

packagehttps://pypi.org/project/interpret-community/ ↗