#==========================================
# Header
#==========================================
# Copyright (c) Anonymised
# All rights reserved.
# This source code is licensed under the license found in the LICENSE file in the root directory of this source tree.

# Some of this code modified part of the regression_exp.py file in https://github.com/stanfordmlgroup/ngboost that prepares data and performs early stopping of boosting.
# License: Apache License 2.0
# The copy of the original license is attached at the bottom.



#==========================================
# Import Library
#==========================================
from argparse import ArgumentParser

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from joblib import Parallel, delayed
import torch
from torch.func import jacrev

import sys
sys.path.insert(0, '../src')
from swgboost import SWGBoost
from model import Gaussian_Loc_Scale
from datasets import dataset_loader
from learners import learner_loader



#==========================================
# Main Functions
#==========================================
def parse_arguments():
    argparser = ArgumentParser()
    argparser.add_argument("--id", type=str, default="02_uci")
    argparser.add_argument("--n_jobs", type=int, default=10)
    argparser.add_argument("--dataset", type=str, default="housing")
    argparser.add_argument("--k_repeat", type=int, default=20)
    argparser.add_argument("--learner", type=str, default="tree")
    argparser.add_argument("--learning_rate", type=float, default=0.1)
    argparser.add_argument("--n_estimators", type=int, default=4000)
    argparser.add_argument("--n_particles", type=int, default=10)
    argparser.add_argument("--d_particles", type=int, default=2)
    argparser.add_argument("--bandwidth", type=float, default=0.1)
    argparser.add_argument("--subsample", type=float, default=1.0)
    return argparser.parse_args()


def preprocess_standardisation(dat_train, dat_test):
    if dat_train.shape[1] > 1:
        dat_train_mean, dat_train_std = np.mean(dat_train, axis=0), np.std(dat_train, axis=0)
        dat_train_std[dat_train_std == 0] = 1.0
    else:
        dat_train_mean, dat_train_std = np.mean(dat_train), np.std(dat_train)
    dat_train = ( dat_train - dat_train_mean ) / dat_train_std
    dat_test = ( dat_test - dat_train_mean ) / dat_train_std
    return (dat_train, dat_test), (dat_train_mean, dat_train_std)


def create_random_repeat(num_data, num_repeat):
    # Follow thw way in split_data_train_test.py file in https://github.com/yaringal/DropoutUncertaintyExps
    folds = []
    for i in range(num_repeat):
        permutation = np.random.choice(range(num_data), num_data, replace=False)
        train_index = permutation[0:round(num_data*0.9)]
        test_index = permutation[round(num_data*0.9):num_data]
        folds.append((train_index, test_index))
    return folds


def get_grad_funcs():
    alpha = 0.01
    beta = 0.01
    scale = 10
    
    def log_posterior(p0, p1, y):
        return - (1/2) * ( (y - p0)**2 ) / torch.exp(2*p1) - (1/2) * (p0**2) / (scale**2) - ( alpha + 1 ) * p1 - beta / torch.exp(p1)
    
    log_grad_p0 = jacrev(log_posterior, argnums=0)
    log_grad_p1 = jacrev(log_posterior, argnums=1)
    log_hess_p0 = jacrev(jacrev(log_posterior, argnums=0), argnums=0)
    log_hess_p1 = jacrev(jacrev(log_posterior, argnums=1), argnums=1)
    
    def grad_logp(p, y):
        q = p.clone()
        q[0] = log_grad_p0(p[0], p[1], y[0])
        q[1] = log_grad_p1(p[0], p[1], y[0])
        return q

    def hess_logp(p, y):
        q = p.clone()
        q[0] = log_hess_p0(p[0], p[1], y[0])
        q[1] = log_hess_p1(p[0], p[1], y[0])
        return q
    
    return grad_logp, hess_logp


def one_fold(X, Y, kth, fold_kth):
    train_index = fold_kth[0]
    test_index = fold_kth[1]
    X_train, X_test = X[train_index], X[test_index]
    Y_train, Y_test = Y[train_index], Y[test_index]

    # preprocess data by standardisation, where likelihood will be adjusted using change of variable formula
    (X_train, X_test), (X_train_mean, X_train_std) = preprocess_standardisation(X_train, X_test)
    (Y_train, Y_test), (Y_train_mean, Y_train_std) = preprocess_standardisation(Y_train, Y_test)

    X_buf, X_val, Y_buf, Y_val = train_test_split(X_train, Y_train, test_size=0.2, random_state=1)

    model = Gaussian_Loc_Scale()
    grad_logp, hess_logp = get_grad_funcs()

    reg = SWGBoost(grad_logp, hess_logp, learner_loader[args.learner]['class'],
        learner_param = learner_loader[args.learner]['default_param'],
        learning_rate = args.learning_rate,
        n_estimators = args.n_estimators,
        n_particles = args.n_particles,
        d_particles = args.d_particles,
        bandwidth = args.bandwidth,
        subsample = args.subsample)
    reg.fit(X_buf, Y_buf)

    # early stopping for estimation number using the validation set
    P_val_eachitr = reg.predict_eachitr(X_val)
    NLL_val_eachitr = [ model.negative_loglikelihood_with_standardised_output(Y_val, P_val, Y_train_std) for P_val in P_val_eachitr ]
    bestitr = int( np.argmin(NLL_val_eachitr) + 1 )
    
    # train the SWGBoost using all the data
    reg = SWGBoost(grad_logp, hess_logp, learner_loader[args.learner]['class'],
        learner_param = learner_loader[args.learner]['default_param'],
        learning_rate = args.learning_rate,
        n_estimators = bestitr,
        n_particles = args.n_particles,
        d_particles = args.d_particles,
        bandwidth = args.bandwidth,
        subsample = args.subsample)
    reg.fit(X_train, Y_train)
    
    # test prediction
    P_test = reg.predict(X_test)
    NLL_test = model.negative_loglikelihood_with_standardised_output(Y_test, P_test, Y_train_std)
    print("[{:02d}th trial NLL] Test {:.4f} | Val {:.4f} | Bestitr {:d}".format(kth, NLL_test, np.min(NLL_val_eachitr), bestitr))
    return NLL_test, bestitr


def main(args):
    np.random.seed(1)
    torch.manual_seed(1)
    torch.set_num_threads(1)
    
    # load dataset
    data = dataset_loader[args.dataset]()
    X, Y = data.iloc[:, :-1].values, data.iloc[:, -1].values.reshape(-1,1)
    folds = create_random_repeat(X.shape[0], args.k_repeat)
    print("=== Dataset {:s} | Num {:d} | Model {:s} ===".format(args.dataset, X.shape[0], "normal"))
    
    results = Parallel(n_jobs=args.n_jobs, backend="multiprocessing")(delayed(one_fold)(X, Y, kth, folds[kth]) for kth in range(len(folds)))
    NLL_tests = np.array(results)

    print("=== NLL Test Summary {:.4f} +/- {:.4f} ===".format(np.mean(NLL_tests[:,0]), np.std(NLL_tests[:,0])))
    pd.DataFrame(NLL_tests).to_csv("../result/" + args.id + "_test_nll_" + args.dataset + ".csv", index=False, header=["NLL", "bestitr"])



#==========================================
# Execution
#==========================================
if __name__ == "__main__":
    args = parse_arguments()
    main(args)




#==========================================
# Apache License 2.0
#==========================================
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