'''
SVM Model.

@author: chunk
chunkplus@gmail.com
2014 Dec
'''

import os, sys

from ...mfeat import *
from ...mmodel import *
from ...mmodel.svm.svmutil import *

from ...mspark import SC2

from ...common import *

import numpy as np

import csv
import json

import pickle

import cv2

from sklearn import svm

package_dir = os.path.dirname(os.path.abspath(__file__))

dict_Train = {}
dict_databuf = {}
dict_tagbuf = {}
dict_featbuf = {}


class ModelSVM(ModelBase):

    def __init__(self, toolset='sklearn', sc=None):

        ModelBase.__init__(self)

        self.toolset = toolset

        self.sparker = sc

    def _train_libsvm(self, X, Y):

        X, Y = list(X), list(Y)

        # X, Y = [float(i) for i in X], [float(i) for i in Y]

        prob = svm_problem(Y, X)

        param = svm_parameter('-t 0 -c 4 -b 1 -h 0')
        # param = svm_parameter(kernel_type=LINEAR, C=10)

        m = svm_train(prob, param)

        svm_save_model(os.path.join(package_dir, '../..', 'res/svm_libsvm.model'), m)

        self.model = m

        return m

    def _predict_libsvm(self, feat, model=None):

        if model is None:
            if self.model != None:
                model = self.model
            else:

                print 'loading model ...'

                model = svm_load_model(os.path.join(package_dir, '../..', 'res/svm_libsvm.model'))

        feat = [list(feat)]
        # print len(feat),[0] * len(feat)
        label, _, _ = svm_predict([0] * len(feat), feat, model)

        return label

    def _test_libsvm(self, X, Y, model=None):

        if model is None:
            if self.model != None:
                model = self.model
            else:

                print 'loading model ...'

                model = svm_load_model(os.path.join(package_dir, '../..', 'res/svm_libsvm.model'))

        X, Y = list(X), list(Y)

        p_labs, p_acc, p_vals = svm_predict(Y, X, model)
        # ACC, MSE, SCC = evaluations(Y, p_labs)

        return p_acc

    def _train_sklearn(self, X, Y):
        clf = svm.SVC(C=4, kernel='linear', shrinking=False, verbose=True)

        clf.fit(X, Y)

        with open(os.path.join(package_dir, '../..', 'res/svm_sklearn.model'), 'wb') as modelfile:

            model = pickle.dump(clf, modelfile)

        self.model = clf

        return clf

    def _predict_sklearn(self, feat, model=None):

        """N.B. sklearn.svm.base.predict :
            Perform classification on samples in X.
                Parameters
                ----------
                X : {array-like, sparse matrix}, shape = [n_samples, n_features]

                Returns
                -------
                y_pred : array, shape = [n_samples]
                    Class labels for samples in X.
        """
        if model is None:
            if self.model != None:
                model = self.model
            else:

                print 'loading model ...'

                with open(os.path.join(package_dir, '../..', 'res/svm_sklearn.model'), 'rb') as modelfile:

                    model = pickle.load(modelfile)

        return model.predict(feat)

    def __test_sklearn(self, X, Y, model=None):

        if model is None:
            if self.model != None:
                model = self.model
            else:

                print 'loading model ...'

                with open(os.path.join(package_dir, '../..', 'res/svm_sklearn.model'), 'rb') as modelfile:

                    model = pickle.load(modelfile)

        result_Y = np.array(self._predict_sklearn(X, model))

        fp = 0
        tp = 0
        sum = np.sum(np.array(Y) == 1)
        positive, negative = np.sum(np.array(Y) == 1), np.sum(np.array(Y) == 0)
        print positive, negative
        for i in range(len(Y)):
            if Y[i] == 0 and result_Y[i] == 1:
                fp += 1
            elif Y[i] == 1 and result_Y[i] == 1:
                tp += 1
        return float(fp) / negative, float(tp) / positive, np.mean(Y == result_Y)

    def _test_sklearn(self, X, Y, model=None):

        if model is None:

            if self.model != None:
                model = self.model
            else:
                print 'loading model ...'

                with open(os.path.join(package_dir, '../..', 'res/svm_sklearn.model'), 'rb') as modelfile:

                    model = pickle.load(modelfile)

        return model.score(X, Y)

    def _train_opencv(self, X, Y):
        svm_params = dict(kernel_type=cv2.SVM_LINEAR,
                          svm_type=cv2.SVM_C_SVC,

                          C=2.67, gamma=5.383)

        X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32)

        svm = cv2.SVM()
        svm.train(X, Y, params=svm_params)

        svm.save(os.path.join(package_dir, '../..', 'res/svm_opencv.model'))

        self.model = svm

        return svm


    def _predict_opencv(self, feat, model=None):
        if model is None:
            if self.model != None:
                model = self.model
            else:
                print 'loading model ...'

                with open(os.path.join(package_dir, '../..', 'res/svm_opencv.model'), 'rb') as modelfile:

                    model = pickle.load(modelfile)
        feat = np.array(feat, dtype=np.float32)

        return model.predict(feat)


    def _test_opencv(self, X, Y, model=None):
        if model is None:
            if self.model != None:
                model = self.model
            else:
                print 'loading model ...'

                with open(os.path.join(package_dir, '../..', 'res/svm_opencv.model'), 'rb') as modelfile:

                    model = pickle.load(modelfile)

        X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32)

        # result_Y = np.array([self._predict_cv(x, model) for x in X])

        result_Y = np.array(model.predict_all(X)).ravel()

        return np.mean(Y == result_Y)

    def _train_spark(self, X, Y=None):

        if self.sparker == None:

            self.sparker = SC2.Sparker(host='HPC-server', appname='ImageCV', master='spark://HPC-server:7077')

        self.model = self.sparker.train_svm(X, Y)

        return svm

    def _predict_spark(self, feat, model=None):
        return self.sparker.predict_svm(feat, model)

    def _test_spark(self, X, Y, model=None):
        return self.sparker.test_svm(X, Y, model)

    def train(self, X, Y=None):

        if self.toolset == 'sklearn':
            return self._train_sklearn(X, Y)
        elif self.toolset == 'opencv':

            return self._train_opencv(X, Y)
        elif self.toolset == 'libsvm':

            return self._train_libsvm(X, Y)
        elif self.toolset == 'spark':
            return self._train_spark(X, Y)
        else:
            raise Exception("Unknown toolset!")

    def predict(self, feat, model=None):

        if self.toolset == 'sklearn':

            return self._predict_sklearn(feat, model)
        elif self.toolset == 'opencv':

            return self._predict_opencv(feat, model)
        elif self.toolset == 'libsvm':

            return self._predict_libsvm(feat, model)
        elif self.toolset == 'spark':

            return self._predict_spark(feat, model)

        else:
            raise Exception("Unknown toolset!")

    def test(self, X, Y=None, model=None):

        if self.toolset == 'sklearn':

            return self.__test_sklearn(X, Y, model)

        elif self.toolset == 'opencv':

            return self._test_opencv(X, Y, model)
        elif self.toolset == 'libsvm':

            return self._test_libsvm(X, Y, model)
        elif self.toolset == 'spark':

            return self._test_spark(X, Y, model)

        else:
            raise Exception("Unknown toolset!")