enas_convELM_simp.py

'''
Created on April , 2021
@author:
'''

## Import libraries in python
import argparse
import time
import json
import logging
import sys
import os
import math
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import random
import importlib
from scipy.stats import randint, expon, uniform
import glob
import tensorflow as tf
import sklearn as sk
from sklearn import svm
from sklearn.utils import shuffle
from sklearn import metrics
from sklearn import preprocessing
from sklearn import pipeline
from sklearn.metrics import mean_squared_error
from math import sqrt


from utils.elm_network import network_fit

from utils.hpelm import ELM, HPELM
from utils.convELM_task_simp import SimpleNeuroEvolutionTask
from utils.ea_multi import GeneticAlgorithm

import torch


# random seed predictable
jobs = 1

current_dir = os.path.dirname(os.path.abspath(__file__))
data_filedir = os.path.join(current_dir, 'N-CMAPSS')
data_filepath = os.path.join(current_dir, 'N-CMAPSS', 'N-CMAPSS_DS02-006.h5')
sample_dir_path = os.path.join(data_filedir, 'Samples_whole')

model_temp_path = os.path.join(current_dir, 'Models', 'convELM_rep.h5')
torch_temp_path = os.path.join(current_dir, 'torch_model')

pic_dir = os.path.join(current_dir, 'Figures')


# Log file path of EA in csv
# directory_path = current_dir + '/EA_log'
directory_path = os.path.join(current_dir, 'EA_log')

if not os.path.exists(pic_dir):
    os.makedirs(pic_dir)

if not os.path.exists(directory_path):
    os.makedirs(directory_path)    


'''
load array from npz files
'''

def array_tensorlst_data (arry, bs, device):
    # arry = arry.reshape(arry.shape[0],arry.shape[2],arry.shape[1])

    if bs > arry.shape[0]:
        bs = arry.shape[0]

    arry = arry.transpose((0,2,1))

    print ("arry.shape[0]//bs", arry.shape[0]//bs)
    num_train_batch = arry.shape[0]//bs
    print (arry.shape)
    arry_cut = arry[:num_train_batch*bs]
    arrt_rem = arry[num_train_batch*bs:]
    print (arry.shape)
    arry4d = arry_cut.reshape(int(arry_cut.shape[0]/bs), bs, arry_cut.shape[1], arry_cut.shape[2])
    print (arry4d.shape)

    arry_lst = list(arry4d)

    arry_lst.append(arrt_rem)

    print (len(arry_lst))
    print (arry_lst[0].shape)

    train_batch_lst = []
    for batch_sample in arry_lst:
        arr_tensor = torch.from_numpy(batch_sample)
        if torch.cuda.is_available():
            arr_tensor = arr_tensor.to(device)
        train_batch_lst.append(arr_tensor)


    
    return train_batch_lst



def array_tensorlst_label (arry, bs, device):

    if bs > arry.shape[0]:
        bs = arry.shape[0]  

    print ("arry.shape[0]//bs", arry.shape[0]//bs)
    num_train_batch = arry.shape[0]//bs
    arry_cut = arry[:num_train_batch*bs]
    arrt_rem = arry[num_train_batch*bs:]
    arry2d = arry_cut.reshape(int(arry_cut.shape[0]/bs), bs)

    arry_lst = list(arry2d)

    arry_lst.append(arrt_rem)

    print (len(arry_lst))
    print (arry_lst[0].shape)

    train_batch_lst = []
    for batch_sample in arry_lst:
        arr_tensor = torch.from_numpy(batch_sample)
        if torch.cuda.is_available():
            arr_tensor = arr_tensor.to(device)
        train_batch_lst.append(arr_tensor)

    return train_batch_lst



def load_part_array (sample_dir_path, unit_num, win_len, stride, part_num):
    filename =  'Unit%s_win%s_str%s_part%s.npz' %(str(int(unit_num)), win_len, stride, part_num)
    filepath =  os.path.join(sample_dir_path, filename)
    loaded = np.load(filepath)
    return loaded['sample'], loaded['label']

def load_part_array_merge (sample_dir_path, unit_num, win_len, win_stride, partition):
    sample_array_lst = []
    label_array_lst = []
    print ("Unit: ", unit_num)
    for part in range(partition):
      print ("Part.", part+1)
      sample_array, label_array = load_part_array (sample_dir_path, unit_num, win_len, win_stride, part+1)
      sample_array_lst.append(sample_array)
      label_array_lst.append(label_array)
    sample_array = np.dstack(sample_array_lst)
    label_array = np.concatenate(label_array_lst)
    sample_array = sample_array.transpose(2, 0, 1)
    print ("sample_array.shape", sample_array.shape)
    print ("label_array.shape", label_array.shape)
    return sample_array, label_array


def load_array (sample_dir_path, unit_num, win_len, stride):
    filename =  'Unit%s_win%s_str%s.npz' %(str(int(unit_num)), win_len, stride)
    filepath =  os.path.join(sample_dir_path, filename)
    loaded = np.load(filepath)

    return loaded['sample'].transpose(2, 0, 1), loaded['label']


def shuffle_array(sample_array, label_array):
    ind_list = list(range(len(sample_array)))
    print("ind_list befor: ", ind_list[:10])
    print("ind_list befor: ", ind_list[-10:])
    ind_list = shuffle(ind_list)
    print("ind_list after: ", ind_list[:10])
    print("ind_list after: ", ind_list[-10:])
    print("Shuffeling in progress")
    shuffle_sample = sample_array[ind_list, :, :]
    shuffle_label = label_array[ind_list,]
    return shuffle_sample, shuffle_label

def figsave(history, h1,h2,h3,h4, bs, lr, sub):
    fig_acc = plt.figure(figsize=(15, 8))
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('Training', fontsize=24)
    plt.ylabel('loss', fontdict={'fontsize': 18})
    plt.xlabel('epoch', fontdict={'fontsize': 18})
    plt.legend(['Training loss', 'Validation loss'], loc='upper left', fontsize=18)
    plt.show()
    print ("saving file:training loss figure")
    fig_acc.savefig(pic_dir + "/elm_enas_training_h1%s_h2%s_h3%s_h4%s_bs%s_sub%s_lr%s.png" %(int(h1), int(h2), int(h3), int(h4), int(bs), int(sub), str(lr)))
    return


def score_calculator(y_predicted, y_actual):
    # Score metric
    h_array = y_predicted - y_actual
    s_array = np.zeros(len(h_array))
    for j, h_j in enumerate(h_array):
        if h_j < 0:
            s_array[j] = math.exp(-(h_j / 13)) - 1

        else:
            s_array[j] = math.exp(h_j / 10) - 1
    score = np.sum(s_array)
    return score


def release_list(a):
   del a[:]
   del a


def recursive_clean(directory_path):
    """clean the whole content of :directory_path:"""
    if os.path.isdir(directory_path) and os.path.exists(directory_path):
        files = glob.glob(directory_path + '*')
        for file_ in files:
            if os.path.isdir(file_):
                recursive_clean(file_ + '/')
            else:
                os.remove(file_)

units_index_train = [2.0, 5.0, 10.0, 16.0, 18.0, 20.0]
units_index_test = [11.0, 14.0, 15.0]



def tensor_type_checker(tensor, device):
    if torch.cuda.is_available():
        tensor = tensor.to(device)
    print(f"Shape of tensor: {tensor.shape}")
    print(f"Datatype of tensor: {tensor.dtype}")
    print(f"Device tensor is stored on: {tensor.device}")
    return tensor


def main():
    # current_dir = os.path.dirname(os.path.abspath(__file__))
    parser = argparse.ArgumentParser(description='NAS CNN')
    parser.add_argument('-w', type=int, default=50, help='sequence length', required=True)
    parser.add_argument('-s', type=int, default=1, help='stride of filter')
    parser.add_argument('-bs', type=int, default=512, help='batch size')
    parser.add_argument('-pt', type=int, default=30, help='patience')
    parser.add_argument('-ep', type=int, default=100, help='epochs')
    parser.add_argument('-vs', type=float, default=0.2, help='validation split')
    parser.add_argument('-lr', type=float, default=10**(-1*4), help='learning rate')
    parser.add_argument('-sub', type=int, default=10, help='subsampling stride')
    parser.add_argument('-t', type=int, required=True, help='trial')
    parser.add_argument('--pop', type=int, default=20, required=False, help='population size of EA')
    parser.add_argument('--gen', type=int, default=20, required=False, help='generations of evolution')
    parser.add_argument('--device', type=str, default="cuda", help='Use "basic" if GPU with cuda is not available')
    parser.add_argument('--obj', type=str, default="soo", help='Use "soo" for single objective and "moo" for multiobjective')

    args = parser.parse_args()

    win_len = args.w
    win_stride = args.s

    lr = args.lr
    bs = args.bs
    ep = args.ep
    pt = args.pt
    vs = args.vs
    sub = args.sub

    device = args.device
    print(f"Using {device} device")



    obj = args.obj
    trial = args.t

    # random seed predictable
    jobs = 1
    seed = trial

    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)



    train_units_samples_lst =[]
    train_units_labels_lst = []

    for index in units_index_train:
        print("Load data index: ", index)
        sample_array, label_array = load_array (sample_dir_path, index, win_len, win_stride)
        #sample_array, label_array = shuffle_array(sample_array, label_array)

        sample_array = sample_array[::sub]
        label_array = label_array[::sub]

        # sample_array = sample_array.astype(np.float32)
        # label_array = label_array.astype(np.float32)

        train_units_samples_lst.append(sample_array)
        train_units_labels_lst.append(label_array)

    sample_array = np.concatenate(train_units_samples_lst)
    label_array = np.concatenate(train_units_labels_lst)
    print ("samples are aggregated")

    release_list(train_units_samples_lst)
    release_list(train_units_labels_lst)
    train_units_samples_lst =[]
    train_units_labels_lst = []
    print("Memory released")

    sample_array, label_array = shuffle_array(sample_array, label_array)
    print("samples are shuffled")

    # sample_array = sample_array.reshape(sample_array.shape[0], sample_array.shape[2])
    print("sample_array_reshape.shape", sample_array.shape)
    print("label_array_reshape.shape", label_array.shape)
    window_length = sample_array.shape[1]
    feat_len = sample_array.shape[2]
    num_samples = sample_array.shape[0]
    print ("window_length", window_length)
    print("feat_len", feat_len)

    train_sample_array = sample_array[:int(num_samples*(1-vs))]
    train_label_array = label_array[:int(num_samples*(1-vs))]
    val_sample_array = sample_array[int(num_samples*(1-vs))+1:]
    val_label_array = label_array[int(num_samples*(1-vs))+1:]

    print ("train_sample_array.shape", train_sample_array.shape)
    print ("train_label_array.shape", train_label_array.shape)
    print ("val_sample_array.shape", val_sample_array.shape)
    print ("val_label_array.shape", val_label_array.shape)

    sample_array = []
    label_array = []


    # train_sample_array.shape (84212, 50, 20) = # samples, win_len, feature_len
    # train_label_array.shape (84212,)
    # val_sample_array.shape (21053, 50, 20)
    # val_label_array.shape (21053,)    


    train_sample_array = array_tensorlst_data(train_sample_array, bs, device)[0]
    val_sample_array = array_tensorlst_data(val_sample_array, bs, device)[0]
    train_label_array = array_tensorlst_label(train_label_array, bs, device)[0]
    val_label_array = array_tensorlst_label(val_label_array, bs, device)[0]

    bs = train_sample_array.shape[0]

    # tensor_type_checker(train_sample_array[0], device) 
    # tensor_type_checker(train_label_array[0], device) 
    # tensor_type_checker(val_sample_array[0], device) 
    # tensor_type_checker(val_label_array[0], device) 



    ## Parameters for the GA
    pop_size = args.pop
    n_generations = args.gen
    cx_prob = 0.5  # 0.25
    mut_prob = 0.5  # 0.7
    cx_op = "one_point"
    mut_op = "uniform"

    if obj == "soo":
        sel_op = "best"
        other_args = {
            'mut_gene_probability': 0.3  # 0.1
        }

        mutate_log_path = os.path.join(directory_path, 'mute_log_ori_%s_%s_%s_%s.csv' % (pop_size, n_generations, obj, trial))
        mutate_log_col = ['idx', 'params_1', 'params_2', 'params_3', 'params_4', 'params_5', 'fitness_1',
                          'gen']
        mutate_log_df = pd.DataFrame(columns=mutate_log_col, index=None)
        mutate_log_df.to_csv(mutate_log_path, index=False)

        def log_function(population, gen, hv=None, mutate_log_path=mutate_log_path):
            for i in range(len(population)):
                indiv = population[i]
                if indiv == []:
                    "non_mutated empty"
                    pass
                else:
                    # print ("i: ", i)
                    indiv.append(indiv.fitness.values[0])
                    indiv.append(gen)

            temp_df = pd.DataFrame(np.array(population), index=None)
            temp_df.to_csv(mutate_log_path, mode='a', header=None)
            print("population saved")
            return


    # elif obj == "moo":
    else:
        sel_op = "nsga2"
        other_args = {
            'mut_gene_probability': 0.4  # 0.1
        }
        mutate_log_path = os.path.join(directory_path, 'mute_log_ori_%s_%s_%s_%s.csv' % (pop_size, n_generations, obj, trial ))
        mutate_log_col = ['idx', 'params_1', 'params_2', 'params_3', 'params_4', 
                          'fitness_1', 'fitness_2', 'hypervolume', 'gen']
        mutate_log_df = pd.DataFrame(columns=mutate_log_col, index=None)
        mutate_log_df.to_csv(mutate_log_path, index=False)

        def log_function(population, gen, hv=None, mutate_log_path=mutate_log_path):
            for i in range(len(population)):
                indiv = population[i]
                if indiv == []:
                    "non_mutated empty"
                    pass
                else:
                    # print ("i: ", i)
                    indiv.append(indiv.fitness.values[0])
                    indiv.append(indiv.fitness.values[1])
                    # append val_rmse
                    indiv.append(hv)
                    indiv.append(gen)

            temp_df = pd.DataFrame(np.array(population), index=None)
            temp_df.to_csv(mutate_log_path, mode='a', header=None)
            print("population saved")
            return



    prft_path = os.path.join(directory_path, 'prft_out_ori_%s_%s_%s.csv' % (pop_size, n_generations, trial))



    start = time.time()

    cs = 0.0001

    # Assign & run EA
    task = SimpleNeuroEvolutionTask(
        train_sample_array = train_sample_array,
        train_label_array = train_label_array,
        val_sample_array = val_sample_array,
        val_label_array = val_label_array,
        constant = cs,
        epochs = ep,
        batch=bs,
        model_path = model_temp_path,
        device = device,
        obj = obj
    )

    # aic = task.evaluate(individual_seed)

    ga = GeneticAlgorithm(
        task=task,
        population_size=pop_size,
        n_generations=n_generations,
        cx_probability=cx_prob,
        mut_probability=mut_prob,
        crossover_operator=cx_op,
        mutation_operator=mut_op,
        selection_operator=sel_op,
        jobs=jobs,
        log_function=log_function,
        cs = cs,
        **other_args
    )

    pop, log, hof, prtf = ga.run()

    print("Best individual:")
    print(hof[0])
    print(prtf)

    # Save to the txt file
    # hof_filepath = tmp_path + "hof/best_params_fn-%s_ps-%s_ng-%s.txt" % (csv_filename, pop_size, n_generations)
    # with open(hof_filepath, 'w') as f:
    #     f.write(json.dumps(hof[0]))

    print("Best individual is saved")
    end = time.time()
    print("EA time: ", end - start)
    print ("####################  EA COMPLETE / HOF TEST   ##############################")



if __name__ == '__main__':
    main()