import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.metrics import mean_absolute_error


sns.set_theme()

def read_results(filename):
    with open(filename, "r") as f:
        lines = f.readlines()

        preds_values = []
        actual_values = []
        mae_values = []

        for line in lines:
            if line.startswith("Preds:"):
                preds = line.replace("[", "")
                preds = preds.replace("]", "")
                preds = preds.strip("Preds:")
                preds = preds.strip()
                preds = preds.split(",")
                preds = [p.strip() for p in preds]
                preds = np.asarray([float(p) for p in preds])
                preds_values.append(preds)

            if line.startswith("Actual:"):
                actual = line.replace("[", "")
                actual = actual.replace("]", "")
                actual = actual.strip("Actual values:")
                actual = actual.strip()
                actual = actual.split(",")
                actual = [a.strip() for a in actual]
                actual = np.asarray([float(a) for a in actual])
                actual_values.append(actual)

            if line.startswith("MAE"):
                mae = float(line.split()[-1])
                mae_values.append(mae)
    return preds_values, actual_values, mae_values


def plot_distribution(preds_values, actual_values, mae_values, model_name, threshold, oversampled):
    for i in range(2):
        if i == 0:
            input_type = "BoW"
        else:
            input_type = "TF-IDF"
        preds = preds_values[i]
        actual = actual_values[i]
        mae = mae_values[i]

        res = pd.DataFrame()
        res["Prediction"] = preds
        res["Actual"] = actual

        sns.displot(res, kind="kde")
        plt.xlabel("Home standard score")
        plt.title(f"Model: {model_name}, Input type: {input_type}, MAE: {mae}, Threshold:{threshold}",
                  fontsize = 10)
        plt.ylim(-0.03, 2.5)
        plt.tight_layout()
        plt.savefig(f"figs/{model_name}_{input_type}_{threshold[0]}_{threshold[1]}_{oversampled}.png")
        plt.close()


def print_category_errors(actual, preds):
    for i in range(2):
        if i == 0:
            input_type = "BoW"
        else:
            input_type = "TF-IDF"

        preds = preds_values[i]
        actual = actual_values[i]
        mae = mae_values[i]

        print(input_type)
        actual1 = list(actual[np.where(actual < 0.98)])
        preds1 = list(preds[np.where(actual < 0.98)])
        print(f"Category 1 MAE: {mean_absolute_error(actual1, preds1):.4f}")
        print(f"Category 1 correlation: {np.corrcoef(actual1, preds1)[0][1]:.4f}")
        print()

        actual2 = list(actual[np.where((actual >= 0.98) & (actual < 1.5))])
        preds2 = list(preds[np.where((actual >= 0.98) & (actual < 1.5))])
        print(f"Category 2 MAE: {mean_absolute_error(actual2, preds2):.4f}")
        print(f"Category 2 correlation: {np.corrcoef(actual2, preds2)[0][1]:.4f}")
        print()

        actual3 = list(actual[np.where((actual >= 1.5) & (actual < 2))])
        preds3 = list(preds[np.where((actual >= 1.5) & (actual < 2))])
        print(f"Category 3 MAE: {mean_absolute_error(actual3, preds3):.4f}")
        print(f"Category 3 correlation: {np.corrcoef(actual3, preds3)[0][1]:.4f}")
        print()

        actual4 = list(actual[np.where(actual >= 2)])
        preds4 = list(preds[np.where(actual >= 2)])
        print(f"Category 4 MAE: {mean_absolute_error(actual4, preds4):.4f}")
        print(f"Category 4 correlation: {np.corrcoef(actual4, preds4)[0][1]:.4f}")
        print()
        print(f"Overall corr: {np.corrcoef(actual, preds)[0][1]:.4f}")


if __name__ == "__main__":
    filename = "linear_models/lasso_0.01_0.99.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "Lasso", [0.01, 0.99], False)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "linear_models/lin_reg_0.01_0.99.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "Linear regression", [0.01, 0.99], False)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "linear_models/sgd_reg_0.01_0.99.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "SGD Regressor", [0.01, 0.99], False)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "oversampled_False_catboost_reg_0.01_0.99.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "CatBoostRegressor", [0.01, 0.99], False)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "linear_models/lasso_0.2_0.8.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "Lasso", [0.2, 0.8], False)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "linear_models/oversampled_lin_reg_0.01_0.99.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "Linear regression", [0.01, 0.99], True)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "linear_models/oversampled_lasso_0.01_0.99.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "Lasso", [0.01, 0.99], True)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "linear_models/oversampled_sgd_reg_0.01_0.99.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "SGD Regressor", [0.01, 0.99], True)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "oversampled_True_catboost_reg_0.01_0.99.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "CatBoostRegressor", [0.01, 0.99], True)
    print_category_errors(actual_values, preds_values)
    print("============================")

    filename = "linear_models/oversampled_lasso_0.15_0.85.txt"
    print(filename)
    preds_values, actual_values, mae_values = read_results(filename)
    #plot_distribution(preds_values, actual_values, mae_values, "Lasso", [0.15, 0.85], True)
    print_category_errors(actual_values, preds_values)
    print("============================")