https://github.com/azukds/model_interpreter
https://github.com/azukds/model_interpreter
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/azukds/model_interpreter
- Owner: azukds
- License: bsd-3-clause
- Created: 2024-08-06T10:52:22.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-11-28T14:55:18.000Z (6 months ago)
- Last Synced: 2025-04-05T07:49:22.006Z (about 1 month ago)
- Language: Python
- Size: 73.2 KB
- Stars: 5
- Watchers: 4
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.rst
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README
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](https://github.com/shap/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;
```python
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](https://pypi.org/project/model_interpreter/) with;
`pip install model_interpreter`
## Examples
To help get started there are example notebooks in the [examples](https://github.com/lvgig/model_interpreter/tree/main/examples) folder in the repo that show how to use each transformer.
To open the example notebooks in [binder](https://mybinder.org/) click [here](https://mybinder.org/v2/gh/lvgig/model_interpreter/HEAD?labpath=examples) 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](https://github.com/lvgig/model_interpreter/issues).
## Build and test
The test framework we are using for this project is [pytest](https://docs.pytest.org/en/stable/). To build the package locally and run the tests follow the steps below.
First clone the repo and move to the root directory;
```shell
git clone https://github.com/lvgig/model_interpreter.git
cd model_interpreter
```
Next install `model_interpreter` and development dependencies;
```shell
pip install . -r requirements-dev.txt
```
Finally run the test suite with `pytest`;
```shell
pytest
```
## Contribute
`model_interpreter` is under active development, we're super excited if you're interested in contributing!