from FastExplain import model_data
from FastExplain.datasets import load_titanic_data
df = load_titanic_data()
classification = model_data(df, 'Survived', hypertune=True)

from FastExplain.explain import plot_one_way_analysis, plot_ale

plot_one_way_analysis(classification.data.df, "Age", "Survived", filter = "Sex == 1")

plot_ale(classification.m, classification.data.xs, "Age", filter = "Sex == 1", dep_name = "Survived")

classification_1 = model_data(df, 'Survived', cont_names=['Age'], cat_names = [])
models = [classification.m, classification_1.m]
data = [classification.data.xs, classification_1.data.xs]
plot_ale(models, data, 'Age', dep_name = "Survived")

classification.plot_one_way_analysis("Age", filter = "Sex == 1")
classification.plot_ale("Age", filter = "Sex == 1")

classification.shap_dependence_plot("Age", filter = "Sex == 1")

classification.error
# {'auc': {'model': {'train': 0.9934332941166654,
# 'val': 0.8421607378129118,
# 'overall': 0.9665739941840028}},
# 'cross_entropy': {'model': {'train': 0.19279692001978943,
# 'val': 0.4600233891109683,
# 'overall': 0.24648214781700722}}}