model-interpreter 1.1.0

this package contains functionality for reporting on how your ML model is making decisions

MODEL INTERPRETER

---

model_interpreter returns feature importance values for a single row model prediction with functionality to:

• handle regression and binary / multiclass classification models
• sort features by importance
• map feature names to more interpretable names
• aggregate feature importance's across features
• handle categorical data within the input features

'model_interpreter' uses SHAP to calculate the single row feature importance values.

The package tries to fit one of the three SHAP explainers below in the following order;

• TreeExplainer
• LinearExplainer
• KernelExplainer

Here is a simple example of generating single row feature importance's for a classification model;

from sklearn.ensemble import RandomForestClassifier

from sklearn.datasets import make_classification
from model_interpreter.interpreter import ModelInterpreter

# generate a classification dataset
X, y = make_classification(
n_samples=1000, 
n_features=4, n_informative=2,
 n_redundant=0, random_state=0, 
shuffle=False
)

# fit a model
clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X, y)

# fit model interpreter to model
feature_names = ["feature1", "feature2", "feature3", "feature4"]
model_interpreter = ModelInterpreter(feature_names)
model_interpreter.fit(clf)

# return feature contribution importances for a single row
single_row = X.head(1)
contribution_list = single_model_contribution.transform(
single_row, return_type="name_value_dicts"
)

print(contribution_list)

Which will return the following output:

[{'Name': 'feature2', 'Value': -0.349129583}, {'Name': 'feature1', 'Value': -0.0039231513}, {'Name': 'feature4', 'Value': 0.0031653932}, {'Name': 'feature3', 'Value': 0.0013787609}]

Installation

The easiest way to get model_interpreter is directly from pypi with;

pip install model_interpreter

Examples

To help get started there are example notebooks in the examples folder in the repo that show how to use each transformer.

To open the example notebooks in binder click here or click on the launch binder shield above and then click on the directory button in the side bar to the left to navigate to the specific notebook.

Issues

For bugs and feature requests please open an issue.

Build and test

The test framework we are using for this project is pytest. To build the package locally and run the tests follow the steps below.

First clone the repo and move to the root directory;

git clone https://github.com/lvgig/model_interpreter.git
cd model_interpreter

Next install model_interpreter and development dependencies;

pip install . -r requirements-dev.txt

Finally run the test suite with pytest;

pytest

Contribute

model_interpreter is under active development, we're super excited if you're interested in contributing!