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acvt45-1.1.3
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مشاهده بیشترتوضیحات
ACV is a library that provides robust and accurate explanations for machine learning models
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | acvt45-1.1.3 |
| نام | acvt45 |
| نسخه کتابخانه | 1.1.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Salim I. Amoukou |
| ایمیل نویسنده | salim.ibrahim-amoukou@universite-paris-saclay.fr |
| آدرس صفحه اصلی | https://github.com/salimamoukou/acv00 |
| آدرس اینترنتی | https://pypi.org/project/acvt45/ |
| مجوز | MIT |
# Active Coalition of Variables (ACV):
ACV is a python library that aims to explain **any machine learning models** or **data**.
* It gives **local rule-based** explanations for any model or data.
* It provides **a better estimation of Shapley Values for tree-based model** (more accurate than [path-dependent TreeSHAP](https://github.com/slundberg/shap])).
It also proposes new Shapley Values that have better local fidelity.
We can regroup the different explanations in two groups: **Agnostic Explanations** and **Tree-based Explanations**.
### Installation
##### Requirements
Python 3.6+
**OSX**: ACV uses Cython extensions that need to be compiled with multi-threading support enabled.
The default Apple Clang compiler does not support OpenMP.
To solve this issue, obtain the lastest gcc version with Homebrew that has multi-threading enabled:
see for example [pysteps installation for OSX.](https://pypi.org/project/pysteps/1.0.0/)
**Windows**: Install MinGW (a Windows distribution of gcc) or Microsoft’s Visual C
Install the acv package:
```
$ pip install acv-exp
```
## A. Agnostic explanations
The Agnostic approaches explain any data (**X**, **Y**) or model (**X**, **f(X)**) using the following
explanation methods:
* Same Decision Probability (SDP) and **Sufficient Explanations**
* **Sufficient Rules**
See the paper [Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models]
for more details.
**I. First, we need to fit our explainer (ACXplainers) to input-output of the data **(X, Y)** or model
**(X, f(X))** if we want to explain the data or the model respectively.**
```python
from acv_explainers import ACXplainer
# It has the same params as a Random Forest, and it should be tuned to maximize the performance.
acv_xplainer = ACXplainer(classifier=True, n_estimators=50, max_depth=5)
acv_xplainer.fit(X_train, y_train)
roc = roc_auc_score(acv_xplainer.predict(X_test), y_test)
```
**II. Then, we can load all the explanations in a webApp as follow:**
```python
import acv_app
import os
# compile the ACXplainer
acv_app.compile_ACXplainers(acv_xplainer, X_train, y_train, X_test, y_test, path=os.getcwd())
# Launch the webApp
acv_app.run_webapp(pickle_path=os.getcwd())
```
**III. Or we can compute each explanation separately as follow:**
#### Same Decision Probability (SDP)
The main tool of our explanations is the Same Decision Probability (SDP). Given <img src="https://latex.codecogs.com/gif.latex?x%20%3D%20%28x_S%2C%20x_%7B%5Cbar%7BS%7D%7D%29" />, the same decision probability <img src="https://latex.codecogs.com/gif.latex?SDP_S%28x%2C%20f%29" /> of variables <img src="https://latex.codecogs.com/gif.latex?x_S" /> is the probabilty that the prediction remains the same when we fixed variables
<img src="https://latex.codecogs.com/gif.latex?X_S=x_S" /> or when the variables <img src="https://latex.codecogs.com/gif.latex?X_{\bar{S}}" /> are missing.
* **How to compute <img src="https://latex.codecogs.com/gif.latex?SDP_S%28x%2C%20f%29" /> ?**
```python
sdp = acv_xplainer.compute_sdp_rf(X, S, data_bground) # data_bground is the background dataset that is used for the estimation. It should be the training samples.
```
#### Minimal Sufficient Explanations
The Sufficient Explanations is the Minimal Subset S such that fixing the values <img src="https://latex.codecogs.com/gif.latex?X_S=x_S" />
permit to maintain the prediction with high probability <img src="https://latex.codecogs.com/gif.latex?\pi" />.
See the paper [ref] for more details.
* **How to compute all the Sufficient Explanations ?**
Since the Sufficient Explanation is not unique for a given instance, we can compute all of them.
```python
sufficient_expl, sdp_expl, sdp_global = acv_xplainer.sufficient_expl_rf(X, y, X_train, y_train, pi_level=0.9)
```
* **How to compute the Minimal Sufficient Explanation <img src="https://latex.codecogs.com/gif.latex?S^\star" /> ?**
The following code return the Sufficient Explanation with minimal cardinality.
```python
sdp_importance, sufficient_expl, size, sdp = acv_xplainer.importance_sdp_rf(X, y, X_train, y_train, pi_level=0.9)
```
#### Local Explanatory Importance
For a given instance, the local explanatory importance of each variable corresponds to the frequency of
apparition of the given variable in the Sufficient Explanations. See the paper [ref] for more details.
* **How to compute the Local Explanatory Importance ?**
```python
lximp = acv_xplainer.compute_local_sdp(d=X_train.shape[1], sufficient_expl)
```
#### Local rule-based explanations
For a given instance **(x, y)** and its Sufficient Explanation S, we compute a local minimal rule which contains *x* such
that every observation **z** that satisfies this rule has <img src="https://latex.codecogs.com/gif.latex?SDP_S(\boldsymbol{x};&space;y)&space;\geq&space;\pi" title="SDP_S(\boldsymbol{x}; y) \geq \pi" />
* **How to compute the local rule explanations ?**
```python
sdp, rules, _, _, _ = acv_xplainer.compute_sdp_maxrules(X, y, data_bground, y_bground, S) # data_bground is the background dataset that is used for the estimation. It should be the training samples.
```
## B. Tree-based explanations
ACV gives Shapley Values explanations for XGBoost, LightGBM, CatBoostClassifier, scikit-learn and pyspark tree models. It
provides the following Shapley Values:
* Classic local Shapley Values (The value function is the conditional expectation <img src="https://latex.codecogs.com/gif.latex?E[f(x)&space;|&space;\boldsymbol{X}_S&space;=&space;\boldsymbol{x}_S]" title="E[f(x) | \boldsymbol{X}_S = \boldsymbol{x}_S]" />)
* Active Shapley values (Local fidelity and Sparse by design)
* Swing Shapley Values (The Shapley values are interpretable by design) *(Coming soon)*
In addition, we use the coalitional version of SV to properly handle categorical variables in the computation of SV.
See the papers [here](https://github.com/salimamoukou/acv00/blob/main/papers/)
To explain the tree-based models above, we need to transform our model into ACVTree.
```python
from acv_explainers import ACVTree
forest = XGBClassifier() # or any Tree Based models
#...trained the model
acvtree = ACVTree(forest, data_bground) # data_bground is the background dataset that is used for the estimation. It should be the training samples.
```
#### Accurate Shapley Values
```python
sv = acvtree.shap_values(X)
```
Note that it provides a better estimation of the [tree-path dependent of TreeSHAP](https://github.com/slundberg/shap]) when the variables are dependent.
#### Accurate Shapley Values with encoded categorical variables
Let assume we have a categorical variable Y with k modalities that we encoded by introducing the dummy variables <img src="https://latex.codecogs.com/gif.latex?Y_1%2C%5Cdots%2C%20Y_%7Bk-1%7D" />. As shown in the paper, we must take the coalition of the dummy variables to correctly compute the Shapley values.
```python
# cat_index := list[list[int]] that contains the column indices of the dummies or one-hot variables grouped
# together for each variable. For example, if we have only 2 categorical variables Y, Z
# transformed into [Y_0, Y_1, Y_2] and [Z_0, Z_1, Z_2]
cat_index = [[0, 1, 2], [3, 4, 5]]
forest_sv = acvtree.shap_values(X, C=cat_index)
```
In addition, we can compute the SV given any coalitions. For example, let assume we have 10 variables
<img src="https://latex.codecogs.com/gif.latex?(X_0, X_1, \dots , X_{10})" /> and we want the following coalition <img src="https://latex.codecogs.com/gif.latex?C_0%20%3D%20%28X_0%2C%20X_1%2C%20X_2%29%2C%20C_1%3D%28X_3%2C%20X_4%29%2C%20C_2%3D%28X_5%2C%20X_6%29" />
```python
coalition = [[0, 1, 2], [3, 4], [5, 6]]
forest_sv = acvtree.shap_values(X, C=coalition)
```
#### **How to compute <img src="https://latex.codecogs.com/gif.latex?SDP_S%28x%2C%20f%29" /> for tree-based classifier ?**
Recall that the <img src="https://latex.codecogs.com/gif.latex?SDP_S%28x%2C%20f%29" /> is the probability that the prediction remains the same when we fixed variables
<img src="https://latex.codecogs.com/gif.latex?X_S=x_S" /> given the subset S.
```python
sdp = acvtree.compute_sdp_clf(X, S, data_bground) # data_bground is the background dataset that is used for the estimation. It should be the training samples.
```
#### **How to compute the Sufficient Coalition <img src="https://latex.codecogs.com/gif.latex?S^\star" /> and the Global SDP importance for tree-based classifier ?**
Recall that the Minimal Sufficient Explanations is the Minimal Subset S such that fixing the values <img src="https://latex.codecogs.com/gif.latex?X_S=x_S" />
permit to maintain the prediction with high probability <img src="https://latex.codecogs.com/gif.latex?\pi" />.
```python
sdp_importance, sdp_index, size, sdp = acvtree.importance_sdp_clf(X, data_bground) # data_bground is the background dataset that is used for the estimation. It should be the training samples.
```
#### Active Shapley values
The Active Shapley values is a SV based on a new game defined in the Paper ([Accurate and robust Shapley Values for explaining predictions and focusing on local important variables](https://github.com/salimamoukou/acv00/blob/main/papers/) such that null (non-important) variables has zero SV and the "payout" is fairly distribute among active variables.
* **How to compute Active Shapley values ?**
```python
import acv_explainers
# First, we need to compute the Active and Null coalition
sdp_importance, sdp_index, size, sdp = acvtree.importance_sdp_clf(X, data_bground)
S_star, N_star = acv_explainers.utils.get_active_null_coalition_list(sdp_index, size)
# Then, we used the active coalition found to compute the Active Shapley values.
forest_asv_adap = acvtree.shap_values_acv_adap(X, C, S_star, N_star, size)
```
##### Remarks for tree-based explanations:
If you don't want to use multi-threaded (due to scaling or memory problem), you have to add "_nopa" to each function (e.g. compute_sdp_clf ==> compute_sdp_clf_nopa).
You can also compute the different values needed in cache by setting cache=True
in ACVTree initialization e.g. ACVTree(model, data_bground, cache=True).
## Examples and tutorials (a lot more to come...)
We can find a tutorial of the usages of ACV in [demo_acv](https://github.com/salimamoukou/acv00/blob/main/notebooks/demo_acv_explainer) and
the notebooks below demonstrate different use cases for ACV. Look inside the notebook directory of the repository if you want to try playing with the original notebooks yourself.
نحوه نصب
نصب پکیج whl acvt45-1.1.3:
pip install acvt45-1.1.3.whl
نصب پکیج tar.gz acvt45-1.1.3:
pip install acvt45-1.1.3.tar.gz