Education Performance Example#
In this example, we use the Education Performace Dataset. This
dataset was collected from the Faculty of Engineering and Faculty of Educational Sciences students in 2019.
The purpose is to predict students’ end-of-term performances using ML techniques.
This dataset can be obtained using the load_education_performance()
function:
>>> from xaiographs.datasets import load_education_performance
>>> df_dataset = load_education_performance()
>>> df_dataset.head(3)
id age sex graduated_h_school_type scholarship_type additional_work activity partner total_salary transport accomodation mother_ed farther_ed siblings parental_status mother_occup father_occup weekly_study_hours reading_non_scientific reading_scientific attendance_seminars_dep impact_of_projects attendances_classes preparation_midterm_company preparation_midterm_time taking_notes listenning discussion_improves_interest flip_classrom course_id grade
0 0 2 1 2 3 2 2 1 3 4 2 1 2 3 1 2 3 2 2 2 1 1 2 1 1 2 2 2 2 1 Fail
1 1 1 1 1 4 1 1 2 4 2 3 4 4 1 1 3 2 3 3 3 1 3 1 3 2 3 1 3 3 1 Fail
2 2 1 1 1 4 2 2 2 1 1 1 3 4 4 2 2 2 3 2 2 1 1 1 1 1 2 2 2 3 1 Fail
To determine the explainability of this dataset, XAIoGraphs provides a dataset that has already been discretized and
columns with targets probabilities using
load_education_performance_discretized() function:
>>> from xaiographs.datasets import load_education_performance_discretized
>>> df_dataset, features_cols, target_cols, y_true, y_predict = load_education_performance_discretized()
>>> df_dataset.head(3)
id age sex graduated_h_school_type scholarship_type additional_work activity partner total_salary transport accomodation mother_ed farther_ed parental_status mother_occup father_occup weekly_study_hours reading_non_scientific reading_scientific attendance_seminars_dep impact_of_projects attendances_classes preparation_midterm_company preparation_midterm_time taking_notes listenning discussion_improves_interest flip_classrom y_true y_predict A B C D Fail
0 0 22-25 female state 50% No No Yes USD 271-340 Other dormitory primary school secondary school married housewife private sector employee <5 hours Sometimes Sometimes Yes positive sometimes alone closest date to the exam sometimes sometimes sometimes useful Fail Fail 0 0 0 0 1
1 1 18-21 female private 75% Yes Yes No USD 341-410 Private car/taxi with family university university married government officer government officer 6-10 hours Often Often Yes neutral always not applicable regularly during the semester always never always not applicable Fail Fail 0 0 0 0 1
2 2 18-21 female private 75% No No No USD 135-200 Bus rental high school university divorced housewife government officer 6-10 hours Sometimes Sometimes Yes positive always alone closest date to the exam sometimes sometimes sometimes not applicable Fail Fail 0 0 0 0 1
Code Example#
The following entry point (with Python virtual environment enabled) is used to demonstrate this example.
>> education_performance_example
Alternatively, you may run the code below to view a full implementation of all XAIoGraphs functionalities with this Dataset:
from xaiographs import Explainer
from xaiographs import Fairness
from xaiographs import Why
from xaiographs.datasets import load_education_performance_discretized, load_education_performance_why
LANG = 'en'
# LOAD DATASETS & SEMANTICS
example_dataset, feature_cols, target_cols, y_true, y_predict = load_education_performance_discretized()
df_values_semantics, df_target_values_semantics = load_education_performance_why(language=LANG)
# EXPLAINER
explainer = Explainer(importance_engine='LIDE', number_of_features=13, verbose=1)
explainer.fit(df=example_dataset, 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=example_dataset[feature_cols + [y_true] + [y_predict]],
sensitive_cols=['age', 'sex', 'total_salary', 'accomodation', 'parental_status'],
target_col=y_true,
predict_col=y_predict)
XAIoWeb Education Performance#
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:
>> xaioweb -d xaioweb_files -o -f
And the results seen in XAIoWeb are the following:
Global Explainability#
Local Explainability#
Fairness#
