[< ✏️ Examples](examples/examples)

# COMPAS Example

This example highlights the most important features that the AI model considered while predicting the likelihood of 
criminal recidivism. The [`COMPAS Dataset`](../user_guide/datasets.md#compas) is used to capture the characteristics 
of criminals in Broward (Florida) between years 2013 and 2014.


This dataset can be obtained using the [`load_compas()`](../api_reference/datasets.md#xaiographs.datasets.load_compas) 
function:

```python
>>> from xaiographs.datasets import load_compas
>>> df_dataset = load_compas()
>>> df_dataset.head(3)
   id FirstName   LastName Gender        Age_range         Ethnicity  days_b_screening_arrest     c_jail_in    c_jail_out  Days_in_jail c_charge_degree                   c_charge_desc  is_recid  is_violent_recid score_risk_recidivism score_text_risk_recidivism  score_risk_violence score_text_risk_violence  Low_Recid  Medium_Recid  High_Recid  No_Recid  score_text_risk_violence  Low_Recid  Medium_Recid  High_Recid  No_Recid  Recid  predict_two_year_recid  real_two_year_recid
0   1    miguel  hernandez   Male  Greater than 45             Other                     -1.0  13/8/13 6:03  14/8/13 5:41             1               F    Aggravated Assault w/Firearm         0                 0                     1                        Low                    1                      Low          1             0           0         1                       Low          1             0           0         1      0                       0                    0
1   3     kevon      dixon   Male          25 - 45  African-American                     -1.0  26/1/13 3:45   5/2/13 5:36            10               F  Felony Battery w/Prior Convict         1                 1                     3                        Low                    1                      Low          1             0           0         0                       Low          1             0           0         0      1                       0                    1
2   5     marcu      brown   Male     Less than 25  African-American                      NaN           NaN           NaN             0               F          Possession of Cannabis         0                 0                     8                       High                    6                   Medium          0             0           1         1                    Medium          0             0           1         1      0                       1                    0
```

To determine the explainability of this dataset, XAIoGraphs provides a dataset that has already been discretized and 
columns with targets probabilities using 
[`load_compas_discretized()`](../api_reference/datasets.md#xaiographs.datasets.load_compas_discretized) function:


```python
>>> from xaiographs.datasets import load_compas_discretized
>>> df_dataset, features_cols, target_cols, y_true, y_predict = load_compas_discretized()
>>> df_dataset.head(3)
  id Gender        Age_range         Ethnicity MaritalStatus c_charge_degree is_recid is_violent_recid  High_Recid  Medium_Recid  Low_Recid    y_true y_predict
0  1   Male  Greater than 45             Other        Single               F       NO               NO           0             0          1  No_Recid  No_Recid
1  3   Male          25 - 45  African-American        Single               F      YES              YES           0             0          1     Recid  No_Recid
2  5   Male          25 - 45             Other     Separated               M       NO               NO           0             0          1  No_Recid  No_Recid
```


&nbsp;
## Code Example

The following entry point (with Python virtual environment enabled) is used to demonstrate this example.

```python
>> compas_example
```

Alternatively, you may run the code below to view a full implementation of all XAIoGraphs functionalities with this Dataset:

```python
from xaiographs import Explainer
from xaiographs import Why
from xaiographs import Fairness
from xaiographs.datasets import load_compas_discretized, load_compas_why

LANG = 'en'

# LOAD DATASETS & SEMANTICS
df_compas, feature_cols, target_cols, y_true, y_predict = load_compas_discretized()
df_values_semantics, df_target_values_semantics = load_compas_why(language=LANG)

# EXPLAINER
explainer = Explainer(importance_engine='LIDE', verbose=1)
explainer.fit(df=df_compas, feature_cols=feature_cols, target_cols=target_cols)

# WHY
why = Why(language=LANG,
          explainer=explainer,
          why_values_semantics=df_values_semantics,
          why_target_values_semantics=df_target_values_semantics,
          verbose=1)
why.fit()

# FAIRNESS
f = Fairness(verbose=1)
f.fit(df=df_compas[feature_cols + [y_true] + [y_predict]],
      sensitive_cols=['Ethnicity', 'Gender', 'Age_range'],
      target_col=y_true,
      predict_col=y_predict)
```

&nbsp;
## XAIoWeb COMPAS


After running the `.fit()` methods of each of the classes (one, two, or all three), a sequence of JSON files are 
generated in the `xaioweb_files` folder to visualized in XAIoWeb interface.


To launch the web (with the virtual environment enabled), run the following entry point:

```python
>> xaioweb -d xaioweb_files -o -f
```

And the results seen in XAIoWeb are the following:

&nbsp;
#### Global Explainability
&nbsp;
```{image} ../../imgs/compas_example/XaioWeb_Global_Explainability.png
:alt: Global Explainability
:class: bg-primary
:width: 600px
:align: center
```

&nbsp;
#### Local Explainability
&nbsp;
```{image} ../../imgs/compas_example/XaioWeb_Local_Explainability.png
:alt: Local Explainability
:class: bg-primary
:width: 600px
:align: center
```

&nbsp;
#### Fairness
&nbsp;
```{image} ../../imgs/compas_example/XaioWeb_Fairness.png
:alt: Fairness
:class: bg-primary
:width: 600px
:align: center
```
&nbsp;

[< ✏️ Examples](examples/examples)