#!/usr/bin/env python

"""

train_SVM.py

VARPA, University of Coruna

Mondejar Guerra, Victor M.

15 Dec 2017

"""

import os

import csv

import gc

import cPickle as pickle

import time

from imblearn.over_sampling import SMOTE, ADASYN

from imblearn.combine import SMOTEENN, SMOTETomek

import collections

from sklearn import svm

import numpy as np

cpu_threads = 7

# http://contrib.scikit-learn.org/imbalanced-learn/stable/auto_examples/combine/plot_comparison_combine.html#sphx-glr-auto-examples-combine-plot-comparison-combine-py

# Perform the oversampling method over the descriptor data

def perform_oversampling(oversamp_method, db_path, oversamp_features_name, tr_features, tr_labels):

    start = time.time()

    oversamp_features_pickle_name = db_path + oversamp_features_name + '_' + oversamp_method + '.p'

    print(oversamp_features_pickle_name)

    if True:

        print("Oversampling method:\t" + oversamp_method + " ...")

        # 1 SMOTE

        if oversamp_method == 'SMOTE':  

            #kind={'borderline1', 'borderline2', 'svm'}

            svm_model = svm.SVC(C=0.001, kernel='rbf', degree=3, gamma='auto', decision_function_shape='ovo')

            oversamp = SMOTE(ratio='auto', random_state=None, k_neighbors=5, m_neighbors=10, out_step=0.5, kind='svm', svm_estimator=svm_model, n_jobs=1)

            # PROBAR SMOTE CON OTRO KIND

        elif oversamp_method == 'SMOTE_regular_min':

            oversamp = SMOTE(ratio='minority', random_state=None, k_neighbors=5, m_neighbors=10, out_step=0.5, kind='regular', svm_estimator=None, n_jobs=1)

        elif oversamp_method == 'SMOTE_regular':

            oversamp = SMOTE(ratio='auto', random_state=None, k_neighbors=5, m_neighbors=10, out_step=0.5, kind='regular', svm_estimator=None, n_jobs=1)

        elif oversamp_method == 'SMOTE_border':

            oversamp = SMOTE(ratio='auto', random_state=None, k_neighbors=5, m_neighbors=10, out_step=0.5, kind='borderline1', svm_estimator=None, n_jobs=1)

        # 2 SMOTEENN

        elif oversamp_method == 'SMOTEENN':    

            oversamp = SMOTEENN()

        # 3 SMOTE TOMEK

        # NOTE: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.65.3904&rep=rep1&type=pdf

        elif oversamp_method == 'SMOTETomek':

            oversamp = SMOTETomek()

        # 4 ADASYN

        elif oversamp_method == 'ADASYN':

            oversamp = ADASYN(ratio='auto', random_state=None, k=None, n_neighbors=5, n_jobs=cpu_threads)

        tr_features_balanced, tr_labels_balanced  = oversamp.fit_sample(tr_features, tr_labels)

        # TODO Write data oversampled!

        print("Writing oversampled data at: " + oversamp_features_pickle_name + " ...")

        np.savetxt('mit_db/' + oversamp_features_name + '_DS1_labels.csv', tr_labels_balanced.astype(int), '%.0f') 

        f = open(oversamp_features_pickle_name, 'wb')

        pickle.dump(tr_features_balanced, f, 2)

        f.close

    end = time.time()

    count = collections.Counter(tr_labels_balanced)

    print("Oversampling balance")

    print(count)

    print("Time required: " + str(format(end - start, '.2f')) + " sec" )

    return tr_features_balanced, tr_labels_balanced