header.py

from __future__ import print_function, division

TEST_MODEL = 0
SHOW_TIME = 1
ALL_CLASSIFIERS = 0
DEBUG = 1
SHOW_PLOTS = 1
USE_UNIFORM_NOISE = 0  # 0 means that we will be using Normal distribution of Noise
ESTIMATE_CLASSIFIERS = 0


NOISE_SIZE = 100


PLOT_AFTER_EPOCH = 1

import matplotlib as mpl

mpl.rcParams["axes.linewidth"] = 0.05  # set the value globally

# ------------------------------------------------------
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# %matplotlib inline
plt.style.use("ggplot")
import xgboost as xgb
import pickle
import gc
import os
import sys
import sklearn.cluster as cluster

global sess
global graph
# importing pandas module
import pandas as pd

from matplotlib import pyplot

# importing regex module
import re
from keras import applications
from keras import backend as K
from keras import layers
from keras import models
from keras import optimizers
import tensorflow as tf
from tensorflow.python.keras.backend import set_session
from tensorflow.python.keras.models import load_model
import time
import pandas as pd
from keras.layers import BatchNormalization
from keras.layers import LeakyReLU
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier

from sklearn.tree import DecisionTreeRegressor

# Import Gaussian Naive Bayes model
from sklearn.naive_bayes import GaussianNB

# Import `Sequential` from `keras.models`
from keras.models import Sequential

# Import `Dense` from `keras.layers`
from keras.layers import Dense

# Load libraries
from sklearn.tree import DecisionTreeClassifier  # Import Decision Tree Classifier
from sklearn.model_selection import train_test_split  # Import train_test_split function
from sklearn import (
    metrics,
)  # Import scikit-learn metrics module for accuracy calculation
from sklearn.linear_model import LogisticRegression
from tabulate import tabulate

# from __future__ import print_function, division
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply, Multiply
from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model

from tensorflow.keras.optimizers import Adam

# from keras.optimizers import Adam
from scipy import stats

# from tensorflow.keras import backend
from tensorflow.python.keras import backend

# import matplotlib.pyplot as plt
# plt.style.use('ggplot')
gc.collect()
# Load custom functions
import timeit
from xgboost import XGBClassifier

# code you want to evaluate
TEST_XGB = 1
from numpy import loadtxt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import recall_score, precision_score, roc_auc_score, f1_score
from sklearn.metrics import recall_score, precision_score, roc_auc_score
import smote_variants as sv
from pylab import *

# tf.disable_v2_behavior()
from tensorflow.compat.v1.keras.backend import get_session
import seaborn as sns

# sns.set(style="ticks")
# try to replace tf.compat.v1.keras.backend.get_session() with tf.compat.v1.keras.backend.get_session()
# from sklearn.svm import SVC
import datetime
import os
from IPython.display import display
from sklearn.metrics import hamming_loss
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from tensorflow.keras.optimizers import RMSprop
import keras.backend as K

# ===============================================================================================================================
no_aug_accu_list = []
real_aug_accu_list = []
SMOTE_IPF_aug_accu_list = []
ProWSyn_aug_accu_list = []
polynom_fit_SMOTE_aug_accu_list = []
uGAN_aug_accu_list = []
GAN_aug_accu_list = []


# ======================================
no_aug_rcl_list = []
real_aug_rcl_list = []
SMOTE_IPF_aug_rcl_list = []
ProWSyn_aug_rcl_list = []
polynom_fit_SMOTE_aug_rcl_list = []
uGAN_aug_rcl_list = []
GAN_aug_rcl_list = []


no_aug_prec_list = []
real_aug_prec_list = []
SMOTE_IPF_aug_prec_list = []
ProWSyn_aug_prec_list = []
polynom_fit_SMOTE_aug_prec_list = []
uGAN_aug_prec_list = []
GAN_aug_prec_list = []


no_aug_f1_list = []
real_aug_f1_list = []
SMOTE_IPF_aug_f1_list = []
ProWSyn_aug_f1_list = []
polynom_fit_SMOTE_aug_f1_list = []
uGAN_aug_f1_list = []
GAN_aug_f1_list = []


# import plot_data
# import importlib

# importlib.reload(plot_data)  # For reloading after making changes
# from plot_data import *

import preprocess
import importlib

importlib.reload(preprocess)  # For reloading after making changes
from preprocess import *

import classifiers
import importlib

importlib.reload(classifiers)  # For reloading after making changes
from classifiers import *


import acgan_cv
import importlib

importlib.reload(acgan_cv)  # For reloading after making changes
from acgan_cv import *

import acgan_cc
import importlib

importlib.reload(acgan_cc)  # For reloading after making changes
from acgan_cc import *

import evagan_cc
import importlib

importlib.reload(evagan_cc)  # For reloading after making changes
from evagan_cc import *

import evagan_cv
import importlib

importlib.reload(evagan_cv)  # For reloading after making changes
from evagan_cv import *


from sklearn.impute import SimpleImputer

plt.style.use("seaborn-white")


def save_losses(
    list_log_iteration=[],
    xgb_acc=[],
    dt_acc=[],
    nb_acc=[],
    knn_acc=[],
    rf_acc=[],
    lr_acc=[],
    xgb_rcl=[],
    dt_rcl=[],
    nb_rcl=[],
    rf_rcl=[],
    lr_rcl=[],
    knn_rcl=[],
    best_xgb_acc_index=[],
    best_xgb_rcl_index=[],
    best_dt_acc_index=[],
    best_dt_rcl_index=[],
    best_nb_acc_index=[],
    best_nb_rcl_index=[],
    best_rf_acc_index=[],
    best_rf_rcl_index=[],
    best_lr_acc_index=[],
    best_lr_rcl_index=[],
    best_knn_acc_index=[],
    best_knn_rcl_index=[],
    epoch_list_disc_loss_real=[],
    epoch_list_disc_loss_generated=[],
    epoch_list_comb_loss=[],
    GAN_type="",
):
    # dictionary of lists
    dict = {
        "Epoch": list_log_iteration,
        "xgb_acc": xgb_acc,
        "dt_acc": dt_acc,
        "nb_acc": nb_acc,
        "rf_acc": rf_acc,
        "lr_acc": lr_acc,
        "knn_acc": knn_acc,
        "xgb_rcl": xgb_rcl,
        "dt_rcl": dt_rcl,
        "nb_rcl": nb_rcl,
        "rf_rcl": rf_rcl,
        "lr_rcl": lr_rcl,
        "knn_rcl": knn_rcl,
        "best_xgb_acc_index": best_xgb_acc_index,
        "best_xgb_rcl_index": best_xgb_rcl_index,
        "best_dt_acc_index": best_dt_acc_index,
        "best_dt_rcl_index": best_dt_rcl_index,
        "best_nb_acc_index": best_nb_acc_index,
        "best_nb_rcl_index": best_nb_rcl_index,
        "best_rf_acc_index": best_rf_acc_index,
        "best_rf_rcl_index": best_rf_rcl_index,
        "best_lr_acc_index": best_lr_acc_index,
        "best_lr_rcl_index": best_lr_rcl_index,
        "best_knn_acc_index": best_knn_acc_index,
        "best_knn_rcl_index": best_knn_rcl_index,
        "dlr": epoch_list_disc_loss_real,
        "dlg": epoch_list_disc_loss_generated,
        "comb_loss": epoch_list_comb_loss,
    }

    df = pd.DataFrame(dict)

    # saving the dataframe
    df.to_csv(GAN_type + "losses.csv")

    print("Losses file saved")