#AUTHOR: RAHUL VERMA and SPIRO RAZIS

import sys

import pprint

import numpy

from sklearn import svm

from sklearn import linear_model

import time

start_time = time.time()

numpy.set_printoptions(threshold=numpy.nan)

def parseTextViaPMCID(textFile, pmcidFeatureList, uniqueWordsDictionary,lim):

    if textFile.startswith("randomBeni"):

        print("beneficial")

        fileType = "beneficial".encode('utf-8')

    elif textFile.startswith("randomHarm"):

        print("harmful")

        fileType = "harmful".encode('utf-8')

    else:

        if textFile.startswith("b"):

            fileType = "beneficial".encode('utf-8')

        else:

            fileType = "harmful".encode('utf-8')

    limit = 0 

    entryCount       = 0

    disease          = ""

    causeOrTreatment = ""

    relation         = ""

    newEntry = False    

    lindex = 0

    with open(textFile, "r") as openedTextFile:

        for line in openedTextFile:

            lindex+=1

            if line.startswith("pmcid   : "): #it's the idNumber

                entryCount += 1

                newEntry = True

            elif line.startswith("sentence: "): #it's a sentence

                pass

            elif line.startswith("entities: "): #it's the two in a relationship

                disease = line[11:line.index(",")].lower().encode('utf-8')

                causeOrTreatment = line[(line.index(",")+2):-2].lower().encode('utf-8')

                if disease not in uniqueWordsDictionary:

                    if limit < lim:

                        uniqueWordsDictionary[disease] = {}

                if causeOrTreatment not in uniqueWordsDictionary:

                    if limit < lim:

                        uniqueWordsDictionary[causeOrTreatment] = {}

            elif line.startswith("offsets : "): #the position of the entities

                pass

            elif line.startswith("relation: "): #the actual relationship

                relation = line[10:-1].lower().encode('utf-8')                   

            else:             

                if line.startswith("\n") and (newEntry == True):

                    pmcidFeatureList.append([disease, causeOrTreatment, relation, fileType])

                    disease          = ""

                    causeOrTreatment = ""

                    relation         = ""

                    newEntry = False

                    limit += 1

                else:

                    print("invalid line: %s" %(line))

                    print(textFile,lindex)

                    sys.exit(2)

    return (pmcidFeatureList, entryCount, uniqueWordsDictionary)

def printFeatureWithCellValue(numpyRow, featureRow):

    for index, feature in enumerate(featureRow):

        print("%s: %d" %(feature, numpyRow[index]))

    print("harmfulOrBeneficial: %d" %(numpyRow[-1]))

    return

def printFeaturesWithValuesEqualOne(numpyRow, featureRow):

    for index, feature in enumerate(featureRow):

        if numpyRow[index] == 1:

            print("%s: %d" %(feature, numpyRow[index]))

    print("harmfulOrBeneficial: %d" %(numpyRow[-1]))

    return

def main(argv):

    #Python3 training.py beneficial.txt harmful.txt

    if len(argv) != 3:

        print("invalid number of arguments")

        sys.exit(2)

    #two separate lists because don't know how many entries in each, so dividing one list will be difficult

    pmcidBeneficialData = []

    pmcidHarmfulData = []

    uniqueWordsDictionary = {}

    (pmcidBeneficialData, beneficialCount, uniqueWordsDictionary)    = parseTextViaPMCID(argv[1], pmcidBeneficialData, uniqueWordsDictionary,10356)

    (pmcidHarmfulData, harmfulCount, uniqueWordsDictionary)       = parseTextViaPMCID(argv[2], pmcidHarmfulData, uniqueWordsDictionary,9797)

    benprec = 10356/beneficialCount

    harmprec = 9797/harmfulCount

    uniqueFeaturesArray = numpy.empty(shape = (1, len(uniqueWordsDictionary)), dtype="S128")

    #place the dictionary words into the array

    for index, feature in enumerate(uniqueWordsDictionary):

        uniqueFeaturesArray[0, index] = feature

    uniqueFeaturesArray.sort()

    #now to create the three individual arrays    

    numFeatures = len(uniqueWordsDictionary) + 1  #plus 1 for harmful or beneficial

    #from 20 to 80%:

    #beneficial60Percent = int(beneficialCount * 0.6)

    beneficial80Percent = int(beneficialCount * benprec)-1

    beneficial20Percent = int(beneficialCount - beneficial80Percent)

    #harmful60Percent    = int(harmfulCount * 0.6)

    harmful80Percent    = int(harmfulCount * harmprec)-1

    harmful20Percent    = int(harmfulCount - harmful80Percent)

    #shape = (rows, columns)    

    trainArray = numpy.empty(shape=((beneficial80Percent + harmful80Percent), numFeatures), dtype=numpy.int8) #Default is numpy.float64

    testArray   = numpy.empty(shape=((beneficial20Percent + harmful20Percent), numFeatures), dtype=numpy.int8)

    #

    #training data

    for entry in range(0, beneficial80Percent):

        #for each entry, find the index of the given feature

        for index, feature in enumerate(pmcidBeneficialData[entry]):

            if index < 2:

                #get the index of the given feature

                featureColumn = numpy.searchsorted(uniqueFeaturesArray[0], feature)

                if uniqueFeaturesArray[0][featureColumn] == feature:

                    trainArray[entry, featureColumn] = 1

                else: print("trainArray: incorrect beneficial feature match"); sys.exit(0)

            else: break 

        trainArray[entry, -1] = 1

    for entry in range(0, harmful80Percent):

        for index, feature in enumerate(pmcidHarmfulData[entry]):

            if index < 2:

                trainingEntry = entry + beneficial80Percent

                #get the index of the given feature

                featureColumn = numpy.searchsorted(uniqueFeaturesArray[0], feature)

                #set it to 1

                if uniqueFeaturesArray[0][featureColumn] == feature:

                    trainArray[trainingEntry, featureColumn] = 1

                else: print("trainArray: incorrect harmful feature match"); sys.exit(0)

            else: break #beneficialOrHarmful column remains 0

    #test data

    for entry in range(0, beneficial20Percent):

        dataEntry = entry + beneficial80Percent #finding next beneficial entry, starting from 60% until 80%        

        for index, feature in enumerate(pmcidBeneficialData[dataEntry]):

            if index < 2:

                for featureColumn in range(0,len(uniqueFeaturesArray[0])):

                    if uniqueFeaturesArray[0][featureColumn] == feature:

                        testArray[entry, featureColumn] = 1

            else: break #index == 3 and the column should remain 0

        testArray[entry, -1] = 1 

    for entry in range(0, harmful20Percent):

        dataEntry = entry + harmful80Percent # finding the next harmful entry starting from 60% until 80%

        for index, feature in enumerate(pmcidHarmfulData[dataEntry]):

            if index < 2:

                devEntry  = entry + beneficial20Percent #because the prior data entered ended with beneficial20Percent

                for featureColumn in range(0,len(uniqueFeaturesArray[0])):

                    if uniqueFeaturesArray[0][featureColumn] == feature:

                        testArray[devEntry, featureColumn] = 1

            else: break #index == 3 and column should remain 0

    ###########################################CLASSIFICATION SECTION#############################################################

    #Here we set up our list for support vectors and our  list for classes.

    #We will setup lists to hold our support vectors our classes.

    supportVectorsL = []

    classesListL = []

    for row in trainArray:

        y1 = row[len(row)-1]

        supportVectorsL.append(row[:-1])

        classesListL.append(y1)

    #Here we initialize our Linear classifier

    supportVectors = numpy.asarray(supportVectorsL)

    classesList = numpy.asarray(classesListL)

    #Here we try out the linear regresion stuff

    classifier = linear_model.LogisticRegression()

    classifier.fit(supportVectors,classesList)

    ############Test our sets through our logisitc model##################

    print("--------------------LOGISTIC------------------------")

    logistic(classifier,testArray,"TEST")

    print("--------------------SVM------------------------")

    #Here we set up the svm

    classifier = svm.SVC()

    classifier.fit(supportVectors,classesList)

    classifier.kernel="linear"

    ############Test our sets through our SVM model##################

    SVC(classifier,testArray,"TEST")

    sys.exit(0)

def SVC(classifier, testArray,t):

    testpredictionarray = []

    for row in testArray:

        predictionvector = row[:-1]

        if 1 in predictionvector:

            predictionvector = [predictionvector]

            prediction = classifier.predict(predictionvector)

            pre = int(prediction[0])

        else:

            pre = -1

        testpredictionarray.append(pre)

    totalAccuray(testArray,testpredictionarray,t)

    featAccuracy(testArray,testpredictionarray,t,1)

    featAccuracy(testArray,testpredictionarray,t,2)

def logistic(classifier, testArray,t):

    testpredictionarray = []

    for row in testArray:

        predictionvector = row[:-1]

        if 1 in predictionvector:

            predictionvector = [predictionvector]

            prediction = classifier.predict(predictionvector)

            pre = int(prediction[0])

        else:

            pre = -1

        testpredictionarray.append(pre)

    totalAccuray(testArray,testpredictionarray,t)

    featAccuracy(testArray,testpredictionarray,t,1)

    featAccuracy(testArray,testpredictionarray,t,2)

def totalAccuray(testArray,testpredictionarray,t):

    testcounter = 0

    #here we test for accuracy in the test set results.

    for x in range(0,len(testArray)):

        t1= testArray[x][len(testArray[x])-1]

        t1 = int(t1)

        if t1 == testpredictionarray[x]:

            testcounter = testcounter + 1

    accuracy= testcounter/len(testArray)   

    print(t+" set accuracy = " + str(accuracy))        

def featAccuracy(testArray,testpredictionarray,t,y):

    actual = 0

    testcounter = 0

    for x in range(0,len(testArray)):

        l = list(testArray[x])

        c = l.count(1)

        if c == y:

            actual+=1

            t1= testArray[x][len(testArray[x])-1]

            t1 = int(t1)

            if t1 == testpredictionarray[x]:

                testcounter = testcounter + 1

    try:

        accuracy= testcounter/actual

    except ZeroDivisionError:

        print(t+" set accuracy for only "+str(y)+" feature vectors = UNDEFINED")

        return

    print(t+" set accuracy for only "+str(y)+" feature vectors = " + str(accuracy))        

main(sys.argv)

#