import models
import pandas as pd
import preprocessing as pp
from sklearn.metrics import accuracy_score

# Load Data
credit_risk = pd.read_csv("credit_risk_dataset.csv")

# Feature Addition


# Feature Conversion

person_home_ownership_values = {
    "RENT": 1,
    "MORTGAGE": 2,
    "OWN": 3,
    "OTHER": 4,
}
loan_intent_values = {
    "EDUCATIONAL": 1,
    "MEDICAL": 2,
    "VENTURE": 3,
    "PERSONAL": 4,
    "DEBTCONSOLIDATION": 5
}
loan_grade_values = {
    "A": 1,
    "B": 2,
    "C": 3,
    "D": 4,
    "E": 5
}
cb_person_default_on_file_values = {
    "Y": 1,
    "N": 0,
}

# Convert categorical column to a numerical column
credit_risk["person_home_ownership"] = credit_risk["person_home_ownership"].map(person_home_ownership_values)
credit_risk["loan_intent"] = credit_risk["loan_intent"].map(loan_intent_values)
credit_risk["loan_grade"] = credit_risk["loan_grade"].map(loan_grade_values)
credit_risk["cb_person_default_on_file"] = credit_risk["cb_person_default_on_file"].map(cb_person_default_on_file_values)

print("Feature Conversion Complete")

# Feature Removal
# columns_for_removal = ["housing_median_age", "total_rooms", "total_bedrooms"]
# for column in columns_for_removal:
#    housing.drop(column, axis=1, inplace=True)

# Preprocessing
credit_risk = pp.impute_missing_values(credit_risk)  # Handle missing values
print("Missing Values handling Complete")
# housing = pp.remove_outliers(housing) #Remove outliers

# Training and Testing Preperation
training_features, training_target_value, test_features, test_target_value = pp.training_test_split(credit_risk, "loan_status")  # Split the data into Training and Test sets
print("Training and Test features split Complete")

# Normalise the data
training_features, test_features = pp.normalise(training_features,
                                                test_features)
print("Normalisation Complete")
# Init Models
rf_model = models.random_forest_classifier(training_features,
                                           training_target_value)
print("Model Init Complete")

# Get Predictions
rf_predictions = rf_model.predict(test_features)
print("Predictions Complete")

# Compare Results
accuracy = accuracy_score(test_target_value, rf_predictions)
print(f"Accuracy: {accuracy}")
print(rf_predictions)