[< ✏️ Examples](examples/examples)

# Smartphone Brand Preferences Example


This example highlights the most important features smartphones from certain brands have, to predict the most likely smartphone-brand purchase. The [`Smartphone Brand Preferences Dataset`](../user_guide/datasets.md#smartphone-brand-preferences) is used to capture the characteristics a smartphone has and
why people choose to buy a determined brand

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

```python
>>> from xaiographs.datasets import load_phone_brand_preferences
>>> df_dataset = load_phone_brand_preferences()
>>> df_dataset.head(5)
      brand  internal_memory  performance  main_camera  selfie_camera  battery_size  screen size  weight  price  age   gender    occupation   
0  Samsung              128         8.81           50             10          3700          6.1     167    528   38   Female  Data analyst
1    Apple              256         7.94           12             12          3065          6.1     204    999   38   Female  Data analyst
2   Google              128         6.76           50              8          4614          6.4     207    499   31   Female         sales
3  Samsung              128         7.22           50             10          4500          6.6     195    899   31   Female         sales
4   Google              128         6.88           12              8          4410          6.1     178    449   27   Female   Team leader

```

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


```python
>>> from xaiographs.datasets import load_phone_brand_preferences_discretized
>>> df_dataset, features_cols, target_cols, y_true, y_predict = load_phone_brand_preferences_discretized()
>>> df_dataset.head(5)
   id internal_memory performance main_camera selfie_camera   battery_size     screen_size     weight            price          age  gender      occupation   y_true  y_predict  Apple  Google  Motorola  Samsung  Xiaomi
0  0          128_GB   Ultra top    15_50_MP        <10_MP      <4000_mAh     <6.4_inches     <190_g  450_700_dollars  35_45_years  Female      Technology  Samsung      Apple      1       0         0        0       0     
1  1        >=256_GB         Top      <15_MP      10_30_MP      <4000_mAh     <6.4_inches  190_205_g     >700_dollars  35_45_years  Female      Technology    Apple      Apple      1       0         0        0       0     
2  2          128_GB         Mid    15_50_MP        <10_MP  4000_4700_mAh     <6.4_inches     >205_g  450_700_dollars  25_35_years  Female        Business   Google     Google      0       1         0        0       0     
3  3          128_GB         Mid    15_50_MP        <10_MP  4000_4700_mAh  6.4_6.6_inches  190_205_g     >700_dollars  25_35_years  Female        Business  Samsung    Samsung      0       0         0        1       0     
4  4          128_GB         Mid      <15_MP        <10_MP  4000_4700_mAh     <6.4_inches     <190_g  200_450_dollars  25_35_years  Female  Administration   Google     Google      0       1         0        0       0     

```


&nbsp;
## Code Example

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

```python
>> smartphone_brand_preferences
```

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_phone_brand_preferences_discretized, load_phone_brand_preferences_why

LANG = 'en'

# LOAD DATASETS & SEMANTICS
df_phone_brand_pref, feature_cols, target_cols, y_true, y_predict = load_phone_brand_preferences_discretized()
df_values_semantics, df_target_values_semantics = load_phone_brand_preferences_why(language=LANG)

# EXPLAINER
explainer = Explainer(importance_engine='LIDE', verbose=1)
explainer.fit(df=df_phone_brand_pref, 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_phone_brand_pref[feature_cols + [y_true] + [y_predict]],
        sensitive_cols=['gender', 'age'],
        target_col=y_true,
        predict_col=y_predict)
```

&nbsp;
## XAIoWeb Smartphone Brand Preferences

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/smartphone_brand_preferences_example/XaioWeb_Global_Explainability.png
:alt: Global Explainability
:class: bg-primary
:width: 600px
:align: center
```

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

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


[< ✏️ Examples](examples/examples)