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import XPER

import XPER
from XPER.datasets.load_data import loan_status
import pandas as pd
from sklearn.model_selection import train_test_split

loan = loan_status().iloc[:, :6]

X = loan.drop(columns='Loan_Status')
Y = pd.DataFrame(loan['Loan_Status'])

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=3)

from xgboost import XGBClassifier
import xgboost as xgb

# Create an XGBoost classifier object
gridXGBOOST = xgb.XGBClassifier(eval_metric="error")

# Train the XGBoost classifier on the training data
model = gridXGBOOST.fit(X_train, y_train)

from XPER.compute.Performance import ModelPerformance

# Define the evaluation metric(s) to be used
XPER = ModelPerformance(X_train, y_train, X_test, y_test, model)

# Evaluate the model performance using the specified metric(s)
PM = XPER.evaluate(["AUC"])

# Print the performance metrics
print("Performance Metrics: ", round(PM, 3))

# Option 1 - Kernel True
# Calculate XPER values for the model's performance
XPER_values = XPER.calculate_XPER_values(["AUC"])

# Option 2 - Kernel False
# Calculate XPER values for the model's performance
XPER_values = XPER.calculate_XPER_values(["AUC"],kernel=False)

import pandas as pd
from XPER.viz.Visualisation import visualizationClass as viz

labels = list(loan.drop(columns='Loan_Status').columns)

viz.bar_plot(XPER_values=XPER_values, X_test=pd.DataFrame(X_test), labels=labels, p=6,percentage=True)

viz.beeswarn_plot(XPER_values=XPER_values,X_test=pd.DataFrame(X_test),labels=labels)

viz.force_plot(XPER_values=XPER_values, instance=1, X_test=X_test, variable_name=labels, figsize=(16,4))