make_prediction.py

import sys, os, math, re, unicodedata
from timeit import default_timer as timer
from nltk.tag import pos_tag 
from nltk import word_tokenize
from nltk.stem.porter import *
from nltk.stem import RSLPStemmer
from nltk.corpus import stopwords
from pymongo import MongoClient
from bson.objectid import ObjectId
from pyspark import SparkConf, SparkContext
#from langdetect import detect
#from base import *
# encoding=utf8  

from pyspark.ml.feature import HashingTF, IDF
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.linalg import SparseVector
from pyspark.sql import Row, SQLContext
from pyspark.sql.functions import col

from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
from pyspark.mllib.classification import SVMWithSGD, SVMModel

def removeAccents(s):
  s = ''.join((c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn'))
  return re.sub(r'[^\w]', ' ', s)
    
def createMongoDBConnection(host, port, username, password, db):
	client = MongoClient(host, port)
	return client[db]

def findUserById(userId):
    db = createMongoDBConnection(host, port, username, password, database)
    return db.users.find_one({'_id': ObjectId(userId)})

def findPosts(user):
    posts = []

    if user['facebook']['posts'] is not None:
        for post in user['facebook']['posts']:
            if 'message' in post.keys():
                posts.append((post['id_post'], removeAccents(post['message']), u'Post', u'Facebook'))

    if user['twitter'] is not None:
        for post in user['twitter']:
            if 'text' in post.keys():
                posts.append((post['id'], removeAccents(post['text']), u'Post', u'Twitter'))  

    return posts


def findProductById(prodId):
    db = createMongoDBConnection(host, port, username, password, database)
    prod = db.produto_novo.find_one({'_id': ObjectId(prodId)})
    prod['idprod'] = str(prod['_id'])
    return prod

def updateUser(user):
    db = createMongoDBConnection(host, port, username, password, database)
    return db.usuario.save(user)

def getTokensAndCategories():  
    db = createMongoDBConnection(host, port, username, password, database)
    model = db.model
    
    tokens_dict = db.model.find({"_type": "token"}).limit(1).next()
    del tokens_dict['_type']
    del tokens_dict['_id']
    del tokens_dict['_datetime']
    tokens_list = [None] * (max(tokens_dict.values()) + 1)

    for key, value in tokens_dict.iteritems():
        tokens_list[value] = key

    categories_dict = db.model.find({"_type": "category"}).limit(1).next()
    del categories_dict['_type']
    del categories_dict['_id']
    del categories_dict['_datetime']
    categories_list = [None] * (max(categories_dict.values()) + 1)

    for key, value in categories_dict.iteritems():
        categories_list[value] = key

    categories_and_subcategories_dict = db.model.find({"_type": "category and subcategory"}).limit(1).next()
    del categories_and_subcategories_dict['_type']
    del categories_and_subcategories_dict['_id']
    del categories_and_subcategories_dict['_datetime']
    categories_and_subcategories_list = [None] * (max(categories_and_subcategories_dict.values()) + 1)

    for key, value in categories_and_subcategories_dict.iteritems():
        pre_string = key.split(",")
        categories_and_subcategories_list[value] = (pre_string[0], pre_string[1])

    return tokens_list, categories_list, categories_and_subcategories_list
    
def insertSuggestions(suggestions, iduser, posts):   
    recomendations = dict()
    recomendations['recomendacoes'] = []    
    for post in suggestions:

        suggestions_dict = dict()

        suggestions_dict['postId'] = post[0][0]
        suggestions_dict['products'] = []

        for post_base in posts:
            #isso nao esta funcionando, verificar o pq
            if int(post_base[0]) == int(post[0][0]):
                suggestions_dict['post'] = post_base

        for product in post:
            if len(product) > 0:
                prod = findProductById(product[1])
                if len(prod) > 0:
                    prod['cosineSimilarity'] = product[2]
                    suggestions_dict['products'].append(prod)            

        recomendations['recomendacoes'].append(suggestions_dict)            

    db = createMongoDBConnection(host, port, username, password, database)
    db.users.update_one({"_id": ObjectId(iduser)}, {"$set" : recomendations})

    return True

def cossine(v1, v2):
    if (v1.dot(v1)*v2.dot(v2)) != 0:
        return v1.dot(v2)/(v1.dot(v1)*v2.dot(v2))
    else:
        return 0

def main(sc, sqlContext):

    #start = timer()

    #print '---Pegando usuario, posts, tokens e categorias do MongoDB---'
    #start_i = timer()
    user = findUserById(iduser)
    posts = findPosts(user) 
    
    tokens, category, categoryAndSubcategory = getTokensAndCategories()
    postsRDD = (sc.parallelize(posts).map(lambda s: (s[0], word_tokenize(s[1].lower()), s[2], s[3]))
                    .map(lambda p: (p[0], [x for x in p[1] if x in tokens] ,p[2], p[3]))
                    .cache())

    
    #print '####levou %d segundos' % (timer() - start_i)

    #print '---Pegando produtos do MongoDB---'
    #start_i = timer()

    #print '####levou %d segundos' % (timer() - start_i)
    
    #print '---Criando corpusRDD---'
    #start_i = timer()
    stpwrds = stopwords.words('portuguese')
    corpusRDD = (postsRDD.map(lambda s: (s[0], [PorterStemmer().stem(x) for x in s[1] if x not in stpwrds], s[2], s[3]))
                         .filter(lambda x: len(x[1]) >= 20 or (x[2] == u'Post' and len(x[1])>0))
                         .cache())
    #print '####levou %d segundos' % (timer() - start_i)

    #print '---Calculando TF-IDF---'
    #start_i = timer()
    wordsData = corpusRDD.map(lambda s: Row(label=int(s[0]), words=s[1], type=s[2]))
    wordsDataDF = sqlContext.createDataFrame(wordsData).unionAll(sqlContext.read.parquet("/home/ubuntu/recsys-tcc-ml/parquet/wordsDataDF.parquet"))


    numTokens = len(tokens)
    hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=numTokens)
    idf = IDF(inputCol="rawFeatures", outputCol="features")

    featurizedData = hashingTF.transform(wordsDataDF)

    idfModel = idf.fit(featurizedData)
    tfIDF = idfModel.transform(featurizedData).cache()

    postTFIDF = (tfIDF
                    .filter(tfIDF.type==u'Post')
                    #.map(lambda s: Row(label=s[0], type=s[1], words=s[2], rawFeatures=s[3], features=s[4], sentiment=SVM.predict(s[4])))
                    .cache())

    #postTFIDF = postTFIDF.filter(lambda p: p.sentiment == 1)
    #print '####levou %d segundos' % (timer() - start_i)

    #print '---Carregando modelo---'
    #start_i = timer()
    NB = NaiveBayesModel.load(sc, '/home/ubuntu/recsys-tcc-ml/models/naivebayes/modelo_categoria')
    SVM = SVMModel.load(sc, "/home/ubuntu/recsys-tcc-ml/models/svm")
    #print '####levou %d segundos' % (timer() - start_i)

    #print '---Usando o modelo---'
    #start_i = timer()
    predictions = (postTFIDF
                        .map(lambda p: (NB.predict(p.features), p[0], SVM.predict(p.features)))
                        .filter(lambda p: p[2]==1)
                        .map(lambda p: (p[0], p[1]))
                        .groupByKey()
                        .mapValues(list)
                        .collect())

    #print '####levou %d segundos' % (timer() - start_i)
    #print '---Calculando similaridades---'
    #start_i = timer()
    suggestions = []

    for prediction in predictions:
        category_to_use = category[int(prediction[0])]
        #print ' Calculando similaridades para a categoria: {}'.format(category_to_use)
        tf = tfIDF.filter(tfIDF.type==category_to_use).cache()
        for post in prediction[1]:
            postVector = postTFIDF.filter(postTFIDF.label == post).map(lambda x: x.features).collect()[0]
            sim = (tf
                    .map(lambda x: (post, x.label, cossine(x.features, postVector)))
                    .filter(lambda x: x[2]>=threshold)
                    .collect())
            if len(sim) > 0:
                suggestions.append(sim)

    #print '####levou %d segundos' % (timer() - start_i)

    if len(suggestions) > 0:
        #print '---Inserindo recomendacoes no MongoDB---'
        #start_i = timer()
        insertSuggestions(suggestions, iduser, posts)
        #print '####levou %d segundos' % (timer() - start_i)

if __name__ == '__main__':

    APP_NAME = 'Recomender System - Calculo de recomendacao'
    threshold  = 0.0002
    #numMaxSuggestionsPerPost = 5
    numStarts = 5

    host = 'localhost'
    port = 27017
    username = ''
    password = ''
    database = 'tcc-recsys-mongo'
    
    iduser = sys.argv[1]

    sc = SparkContext(appName=APP_NAME)
    sqlContext = SQLContext(sc)

    main(sc, sqlContext)
    sc.stop()