def load_parameters(self):
self.amount_prediction_method = self.load_data_from_file(data_type=self.SAVE_TYPE_MODEL,
file_name='amount_method')
self.trend_prediction_method = self.load_data_from_file(data_type=self.SAVE_TYPE_MODEL,
file_name='trend_method')
self.data_features = self.load_data_from_file(data_type=self.SAVE_TYPE_MODEL, file_name='features')
self.stock_symbol = self.load_data_from_file(data_type=self.SAVE_TYPE_MODEL, file_name='symbol')
self.data_parser = self.load_data_from_file(data_type=self.SAVE_TYPE_MODEL, file_name='data_parser')
amount_model_path = os.path.join(os.path.abspath(self.model_path), 'amount_model')
trend_model_path = os.path.join(os.path.abspath(self.model_path), 'trend_model')
if self.amount_prediction_method == self.RANDOM_FOREST:
amount_model = RandomForestModel.load(sc=self.sc, path=amount_model_path)
elif self.amount_prediction_method == self.LINEAR_REGRESSION:
amount_model = LinearRegressionModel.load(sc=self.sc, path=amount_model_path)
else:
amount_model = self.load_data_from_file(data_type=self.SAVE_TYPE_MODEL, file_name='amount_model')
if self.trend_prediction_method == self.RANDOM_FOREST:
trend_model = RandomForestModel.load(sc=self.sc, path=trend_model_path)
elif self.trend_prediction_method == self.LOGISTIC_REGRESSION:
trend_model = LogisticRegressionModel.load(sc=self.sc, path=trend_model_path)
elif self.trend_prediction_method == self.NAIVE_BAYES:
trend_model = NaiveBayesModel.load(sc=self.sc, path=trend_model_path)
elif self.trend_prediction_method == self.SVM:
trend_model = SVMModel.load(sc=self.sc, path=trend_model_path)
else:
trend_model = self.load_data_from_file(data_type=self.SAVE_TYPE_MODEL, file_name='trend_model')
return trend_model, amount_model
def predict():
# Make prediction and test accuracy.
sc = SparkContext(appName= 'nb_test')
sameModel = NaiveBayesModel.load(sc, "../../target/myNaiveBayesModel")
data = sc.textFile('../../data/mllib/sample_naive_bayes_data.txt').map(parseLine)
# Split data aproximately into training (60%) and test (40%)
training, test = data.randomSplit([0.1, 0.9], seed=0)
print test.collect()
predictionAndLabel = test.map(lambda p: (sameModel.predict(p.features), p.label))
print predictionAndLabel.collect()
accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
print accuracy
def main():
sc = SparkContext(appName="BayesClassifer")
htf = HashingTF(50000)
data = sc.textFile('/home/varshav/work/PycharmProjects/Sentiment/cleaned_bayes_labels.csv')
data_cleaned = data.map(lambda line : line.split(","))
# Create an RDD of LabeledPoints using category labels as labels and tokenized, hashed text as feature vectors
data_hashed = data_cleaned.map(lambda (label, text): LabeledPoint(label, htf.transform(text)))
data_hashed.persist()
# data = sc.textFile('/home/admin/work/spark-1.4.1-bin-hadoop2.4/data/mllib/sample_naive_bayes_data.txt').map(parseLine)
#print data
# Split data aproximately into training (60%) and test (40%)
training, test = data_hashed.randomSplit([0.70, 0.30], seed=0)
sameModel = NaiveBayesModel.load(sc, "/home/varshav/work/PycharmProjects/StockAnalysis/myModel")
print "----------"
print sameModel.predict(htf.transform("posts jump in net profit"))
predictionAndLabel = test.map(lambda p: (sameModel.predict(p.features), p.label))
predictionAndLabel1 = training.map(lambda p: (sameModel.predict(p.features), p.label))
prediction = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
prediction1 = 1.0 * predictionAndLabel1.filter(lambda (x, v): x == v).count() / training.count()
buy_buy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == 1 and v ==1).count()
# Instantiate metrics object
# Instantiate metrics object
metrics = MulticlassMetrics(predictionAndLabel)
# Overall statistics
precision = metrics.precision()
precision = normalize(precision)
recall = metrics.recall()
recall = normalize(recall)
f1Score = metrics.fMeasure()
f1Score = normalize(f1Score)
print("Summary Stats")
print("Precision = %s" % precision)
print("Recall = %s" % recall)
print("F1 Score = %s" % f1Score)
'''
# Statistics by class
labels = data_hashed.map(lambda lp: lp.label).distinct().collect()
for label in sorted(labels):
print("Class %s precision = %s" % (label, metrics.precision(label)))
print("Class %s recall = %s" % (label, metrics.recall(label)))
print("Class %s F1 Measure = %s" % (label, metrics.fMeasure(label, beta=1.0)))
'''
'''
def predict_row(sentence, spark_context, model_folder):
"""
:param sentence: a sentence to be analysed
:type sentence: basestring
:param spark_context: the current spark context
:type spark_context: SparkContext
:param model_folder:
:type model_folder: basestring
:return: 0.0 if the sentence is negative, 1 if the sentence is neutral and 2 if the sentence is positive
:rtype: float
"""
htf = HashingTF(50000)
sentence_features = htf.transform(tokenize(sentence))
model = NaiveBayesModel.load(spark_context, model_folder)
prediction = model.predict(sentence_features)
print 'prediction :', prediction
return prediction
def get_naive_bayes_model(spark_context, train_hashed, model_folder):
"""
:param spark_context: the current spark context
:type spark_context: SparkContext
:param train_hashed:
:type train_hashed: DataFrame
:param model_folder:
:type model_folder: basestring
:return: a trained Naive Bayes model
:rtype: NaiveBayesModel
"""
if not path.exists(model_folder):
# Train a Naive Bayes model on the training data
model = NaiveBayes.train(train_hashed)
# Ask Spark to save the model so it won't have to be re-trained later
model.save(spark_context, model_folder)
else:
model = NaiveBayesModel.load(spark_context, model_folder)
return model
def main():
sc = SparkContext(appName="BayesClassifer")
htf = HashingTF(50000)
data = sc.textFile('/home/varshav/work/PycharmProjects/Sentiment/1.csv')
data_cleaned = data.map(lambda line: line.split(","))
# Create an RDD of LabeledPoints using category labels as labels and tokenized, hashed text as feature vectors
data_hashed = data_cleaned.map(
lambda (label, text): LabeledPoint(label, htf.transform(text)))
data_hashed.persist()
# data = sc.textFile('/home/admin/work/spark-1.4.1-bin-hadoop2.4/data/mllib/sample_naive_bayes_data.txt').map(parseLine)
#print data
# Split data aproximately into training (60%) and test (40%)
training, test = data_hashed.randomSplit([0.70, 0.30], seed=0)
# Train a naive Bayes model.
model = NaiveBayes.train(training, 1.0)
# Save and load model
model.save(sc, "/home/varshav/Desktop/Bangalore")
sameModel = NaiveBayesModel.load(sc, "/home/varshav/Desktop/Bangalore")
print "----------"
print model.predict(htf.transform("posts jump in net profit"))
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p:
(sameModel.predict(p.features), p.label))
predictionAndLabel1 = training.map(
lambda p: (sameModel.predict(p.features), p.label))
prediction = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == v).count() / test.count()
#buy_buy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == 1 and v == 1 ).count()
# print buy_buy
prediction1 = 1.0 * predictionAndLabel1.filter(
lambda (x, v): x == v).count() / training.count()
print prediction
print prediction1
sc.stop()
def test_Model():
model = NaiveBayesModel.load(sc, "finalproject/model/NaiveBayesModel")
testFile = sc.textFile("testdata.manual.csv")
testData = testFile.map(transformData)
testData = testData.filter(lambda x: x[0] != '\"2\"')
features = testData.map(lambda x: x[1])
#print(testData.map(lambda x:x[0]).collect())
label = testData.map(
lambda x: x[0]).distinct().zipWithIndex().collectAsMap()
print(label)
preprocessedData = preProcess(features)
featuresData = getFeatures(preprocessedData)
print("train", featuresData.take(5))
testingLabeled = createLabeledData(featuresData, testData)
print("train", testingLabeled.take(5))
labeledTestingData = testingLabeled.map(
lambda record: LabeledPoint(label[record[0][0]], record[1]))
predictionAndLabel = labeledTestingData.map(
lambda p: (model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == v).count() / labeledTestingData.count()
print("Accuracy: ", accuracy)
from pyspark import SparkContext
from pyspark.mllib.feature import HashingTF
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import NaiveBayes
from pyspark import SparkConf
from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
from pyspark.mllib.linalg import Vectors
print("Successfully imported Spark Modules")
except ImportError as e:
print("Can not import Spark Modules", e)
sys.exit(1)
sc = SparkContext(appName="Test")
sameModel = NaiveBayesModel.load(
sc, "/home/sparkCluster/work/PycharmProjects/StockAnalysis/myModel")
htf = HashingTF(50000)
pg_no = 1
company_name = []
company_list = []
companies = []
codes_list = []
comp = []
experts = []
stopwords = []
exclude = []
def getCompanyName():
global comp
# **************************************************************************************************************
# 옷 카테고리 예측
# **************************************************************************************************************
from pyspark.mllib.feature import HashingTF, IDF
from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
from pyspark.mllib.linalg import Vectors
pdf = pd.read_csv('file:///home/ec2-user/data/parseData.csv',encoding='utf-8')
df = sqlContext.createDataFrame(pdf)
#df.show()
htf = HashingTF(10000)
categoryModel = NaiveBayesModel.load(sc, "target/tmp/parseModel")
# **************************************************************************************************************
# 분류
# **************************************************************************************************************
# labelDf.show()
def getGenderLabelCode(rdd, label):
GenderRdd = rdd.map(lambda row: row.gender).distinct()
def getGenderCode(rdd):
dic = {'etc': 'e'}
for feature in rdd.collect():
uniToStr = str(feature)
dic[uniToStr] = uniToStr[0]
average = numpy.average(score)
deviation = numpy.std(score)
return 50 + 10 * ((score - average) / deviation)
# mix-in
NaiveBayesModel.likelihood = likelihood
conf = SparkConf().setAppName("sample").setMaster("local")
sc = SparkContext(conf=conf)
path = os.path.abspath(os.path.dirname(__file__))
texts = pickle.load(open("%s/model/texts.pick" % path))
labels = pickle.load(open("%s/model/labels.pick" % path))
texts = sc.parallelize(texts)
htf = HashingTF(1000) # Warning!! default value is 2^20
htf.transform(texts)
words = sys.argv[1].split()
test_tf = htf.transform(words)
model = NaiveBayesModel.load(sc, "%s/model" % path)
test = model.predict(test_tf)
likelihoods = model.likelihood(test_tf)
print "likelihoods: %s" % likelihoods
print "standard scores: %s" % standard_score(likelihoods)
print "label: %s" % labels[int(test)].encode('utf-8')
# json_data = {"likelihood": likelihoods[int(test)], "label": labels[int(test)].encode('utf-8')}
# print json.dumps(json_data)
def load_model(cls, path="$SPARK_HOME/NaiveBayes"):
"""
"""
return NaiveBayesModel.load(sc, path)
no_stopwords = [w for w in no_punctuation if not w in STOPWORDS]
stemmed = [STEMMER.stem(w) for w in no_stopwords]
result = [w for w in stemmed if w]
if not result:
return [""]
return result
folderpath='hdfs://ec2-54-213-170-202.us-west-2.compute.amazonaws.com:9000/user/root/crawled_data'
sc = SparkContext()
data_raw = sc.wholeTextFiles(folderpath)
data_cleaned = data_raw.map(lambda (filename, text): (filename, tokenize(text)))
htf = HashingTF(50000)
data_hashed = data_cleaned.map(lambda (filename, text): (filename, htf.transform(text)))
data_hashed.persist()
sameModel = NaiveBayesModel.load(sc, 'hdfs://ec2-54-213-170-202.us-west-2.compute.amazonaws.com:9000/user/root/bbcmodel')
predictedLabel = data_hashed.map(lambda (filename, text): (filename.split("/")[-1][:-4],sameModel.predict(text)))
preds = predictedLabel.collect()
conn = psycopg2.connect(database="NewsSource", user="******", password="******", host="newdb.cnceaogjppz8.us-west-2.rds.amazonaws.com", port="5432")
cur = conn.cursor()
update = 'update articlestable set classifiedcategory=%s where id=%s'
newscategory = {hash('entertainment'):'entertainment',hash('sports'):'sports',hash('politics'):'politics',hash('technology'):'technology',hash('business'):'business'}
for pred in preds:
if(hash('entertainment')== pred[1]):
category = 'entertainment'
elif(hash('sports')== pred[1]):
category = 'sports'
sorted_dict = sorted(dictionary_RDD_IDFs_Weights.items(),
key=operator.itemgetter(1))
# Set to max of N words for corresponding number of features for which the model is trained
Dictionary = []
for key, value in sorted_dict:
Dictionary.append(key)
print(len(Dictionary))
# Create a broadcast variable for the Dictionary
Dictionary_BV = sc.broadcast(sorted(Dictionary))
# Load Naive Bayes Model
model_path = "/Users/path/to/twitter_analytics/NB_model"
sameModel = NaiveBayesModel.load(sc, model_path)
# Start intro Video - make sure to first run "chmod a+x play.sh" otherwise --> permission denied exception
video = "Users:path:to:vids:intro.mp4"
video_1 = subprocess.Popen("osascript runner.scpt " + "'" + video + "'",
shell=True)
# Get user twitter-handle
x = int(
input(
"Do you have a twitter account? \n(1) Yes \n(2) No \nYour choice: "
))
if x == 1:
user_handle = input("Please provide user twitter handle: ")
friends = get_friends(user_handle, api)
terms = tags.split()
# filter words that not exist in the vocabulary
terms = [x for x in list(set(terms)) if x in list(set(vocabulary))]
indices = list(map(lambda x: vocabulary.index(x), list(set(terms))))
indices.sort()
occurrences = list(
map(lambda x: float(terms.count(vocabulary[x])), indices))
return [len(vocabulary), indices, occurrences]
conf = SparkConf()
conf.setAppName("NaiveBaye")
conf.set('spark.driver.memory', '6g')
conf.set('spark.executor.memory', '6g')
conf.set('spark.cores.max', 156)
#load tags passed as parameter
tags = sys.argv[1]
bow = bow(tags) #bag of words of that tags
sc = SparkContext(conf=conf) # SparkContext
model = NaiveBayesModel.load(sc, "model")
result = model.predict(SparseVector(bow[0], bow[1], bow[2]))
print str(classValues[result])
def load_model(self, context, path):
return NaiveBayesModel.load(context, path)
def save_model(model, model_name):
output_dir = model_name
shutil.rmtree(output_dir, ignore_errors=True)
model.save(sc, output_dir)
print('*' * 50, 'MODELS_TRAIN', '*' * 50)
iris = datasets.load_iris()
data_set = iris.data
Y = iris.target
data_set = pd.DataFrame(data_set)
data_set['labels'] = Y
print(data_set.head(5))
print(data_set.shape)
s_df = sqlContext.createDataFrame(data_set)
train_dataset = s_df.rdd.map(lambda x: LabeledPoint(x[-1], x[:4]))
training, test = train_dataset.randomSplit([0.6, 0.4])
model = NaiveBayes.train(training, 0.7)
predictionAndLabel = test.map(lambda p: (model.predict(p.features), p.label))
accuracy(predictionAndLabel)
################################################SAVE_LOAD###############################################################
print('*' * 50, 'SAVE_LOAD', '*' * 50)
save_model(model, 'myNaiveBayesModel')
sameModel = NaiveBayesModel.load(sc, 'myNaiveBayesModel')
predictionAndLabel_1 = test.map(lambda p: (model.predict(p.features), p.label))
accuracy(predictionAndLabel_1)
__author__ = 'ruben'
from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
def parseLine(line):
parts = line.split(',')
label = float(parts[0])
features = Vectors.dense([float(x) for x in parts[1].split(' ')])
return LabeledPoint(label, features)
data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)
# Split data aproximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4], seed = 0)
# Train a naive Bayes model.
model = NaiveBayes.train(training, 1.0)
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p : (model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
# Save and load model
model.save(sc, "myModelPath")
sameModel = NaiveBayesModel.load(sc, "myModelPath")
def loadModelFromDisk(self, sc):
print("Loading pretrained model from disk \n")
model = NaiveBayesModel.load(
sc, "hdfs://192.168.1.33:9000//NaiveBayes.model")
print("Complate \n")
return model
if ascontext:
if ascontext.isComputeDataModelOnly():
ascontext.setSparkOutputSchema(output_schema)
sys.exit(0)
else:
modelpath = ascontext.getModelContentToPath("model")
model_metadata = json.loads(ascontext.getModelContentToString("model.metadata"))
# create a DataModelTools to handle data model and data conversions
datamodel = model_metadata["datamodel"]
dmt = DataModelTools(datamodel)
predictors = model_metadata["predictors"]
DataModelTools.checkPredictors(datamodel,predictors,df)
from pyspark.mllib.classification import NaiveBayesModel
model = NaiveBayesModel.load(sc, modelpath);
# to score the model, we need an RDD of DenseVector (the numeric encoded values of the predictors), use DataModelTools to do this
dv = dmt.extractDenseVector(df,predictors).map(lambda x:x[1])
# scoring generates an RDD of predictions (but not the original features)
predictions = model.predict(dv)
# now we need to zip together the original rows from the DataFrame and the RDD of predictions
# we end up with an RDD containing the list of values from the original dataframe plus the predicted class, converted from the encoded number to the original string
def rowToList(row):
result = []
for idx in range(0, len(row)):
result.append(row[idx])
return result
# $example on$
# Load and parse the data file.
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
# Split data approximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4])
# Train a naive Bayes model.
model = NaiveBayes.train(training, 1.0)
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p:
(model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(
lambda pl: pl[0] == pl[1]).count() / test.count()
print('model accuracy {}'.format(accuracy))
# Save and load model
output_dir = 'target/tmp/myNaiveBayesModel'
shutil.rmtree(output_dir, ignore_errors=True)
model.save(sc, output_dir)
sameModel = NaiveBayesModel.load(sc, output_dir)
predictionAndLabel = test.map(lambda p:
(sameModel.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(
lambda pl: pl[0] == pl[1]).count() / test.count()
print('sameModel accuracy {}'.format(accuracy))
# $example off$
def getModel(self, path):
if self.type == 'NaiveBayes':
return NaiveBayesModel.load(self.sc, path)
elif self.type == 'DecisionTree':
return DecisionTreeModel.load(self.sc, path)
def main():
sc = SparkContext(appName="BayesClassifer")
htf = HashingTF(50000)
data = sc.textFile(
'/home/varshav/work/PycharmProjects/Sentiment/cleaned_bayes_labels1.csv'
)
data_cleaned = data.map(lambda line: line.split(","))
# Create an RDD of LabeledPoints using category labels as labels and tokenized, hashed text as feature vectors
data_hashed = data_cleaned.map(
lambda (label, text): LabeledPoint(label, htf.transform(text)))
data_hashed.persist()
# data = sc.textFile('/home/admin/work/spark-1.4.1-bin-hadoop2.4/data/mllib/sample_naive_bayes_data.txt').map(parseLine)
#print data
# Split data aproximately into training (60%) and test (40%)
training, test = data_hashed.randomSplit([0.70, 0.30], seed=0)
sameModel = NaiveBayesModel.load(
sc, "/home/varshav/work/PycharmProjects/StockAnalysis/myModel")
print "----------"
print sameModel.predict(htf.transform("posts jump in net profit"))
predictionAndLabel = test.map(lambda p:
(sameModel.predict(p.features), p.label))
predictionAndLabel1 = training.map(
lambda p: (sameModel.predict(p.features), p.label))
prediction = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == v).count() / test.count()
prediction1 = 1.0 * predictionAndLabel1.filter(
lambda (x, v): x == v).count() / training.count()
buy_buy = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == 1 and v == 1).count()
sell_sell = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == 2 and v == 2).count()
hold_hold = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == 3 and v == 3).count()
print buy_buy
print sell_sell
print hold_hold
# Statistics by class
labels = data_hashed.map(lambda lp: lp.label).distinct().collect()
print labels
print type(labels[0])
'''
for label in sorted(labels):
print("Class %s precision = %s" % (label, metrics.precision(label)))
print("Class %s recall = %s" % (label, metrics.recall(label)))
print("Class %s F1 Measure = %s" % (label, metrics.fMeasure(label, beta=1.0)))
'''
'''
print("Class %s precision = %s" % (1, metrics.precision(1)))
print("Class %s recall = %s" % (1, metrics.recall(1)))
print("Class %s F1 Measure = %s" % (1, metrics.fMeasure()))
print("Class %s precision = %s" % (2, metrics.precision(2)))
print("Class %s recall = %s" % (2, metrics.recall(2)))
print("Class %s F1 Measure = %s" % (2, metrics.fMeasure(2)))
'''
# Weighted stats
'''
print("Weighted recall = %s" % metrics.weightedRecall)
print("Weighted precision = %s" % metrics.weightedPrecision)
print("Weighted F(1) Score = %s" % metrics.weightedFMeasure())
print("Weighted F(0.5) Score = %s" % metrics.weightedFMeasure(beta=0.5))
print("Weighted false positive rate = %s" % metrics.weightedFalsePositiveRate)
'''
sc.stop()
kafka_configuration_params = {
"topic": ["BigData"],
"connectionstring": "localhost:9092"
}
from pyspark.streaming.kafka import KafkaUtils
directKafkaStream = KafkaUtils.createDirectStream(
ssc, kafka_configuration_params["topic"],
{"metadata.broker.list": kafka_configuration_params["connectionstring"]})
from pyspark.mllib.classification import SVMModel, LogisticRegressionModel, NaiveBayesModel
LR_model = LogisticRegressionModel.load(sc, "../../notebooks/LR_model")
SVM_model = SVMModel.load(sc, "../../notebooks/SVM_model")
NB_model = NaiveBayesModel.load(sc, "../../notebooks/NB_model")
import nltk
import random
from nltk.tokenize import word_tokenize
allowed_word_types = ["JJ"]
rdd_all_words = sc.textFile("../../notebooks/all_words/part-00000")
rdd_broadcast_all_words = sc.broadcast(rdd_all_words.collect())
def convert_tweet_to_instance(tweets):
rdd_tweets = tweets.map( \
lambda tweet: [word[0] for word in nltk.pos_tag(word_tokenize(tweet)) if word[1] in allowed_word_types])
def init_spark_components(self):
print("Loading Model")
self.model = NaiveBayesModel.load(sc,
path.join(self.base_path, 'model'))
self.tf = HashingTF()
def load_model(cls, path="$SPARK_HOME/NaiveBayes"):
"""
"""
return NaiveBayesModel.load(sc, path)
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
import time as tm
from threading import Thread
import numpy as np
conf = SparkConf().setAppName("appName").setMaster("local")
conf.set("spark.executor.memory", "2g")
sc = SparkContext(conf=conf)
spark = SparkSession(sc)
#Load pretrained models
output_dir1 = '/home/emmittxu/Desktop/Stock-Sentiment-alalysis/Models/myNaiveBayesModel'
output_dir2 = '/home/emmittxu/Desktop/Stock-Sentiment-alalysis/Models/sent_stockModel'
print("Loading model.......")
model1 = NaiveBayesModel.load(sc, output_dir1)
model2 = NaiveBayesModel.load(sc, output_dir2)
print("Models successfully loaded......")
#Global variables to record the number of positive and negative sentiments
negative = 0.0
neutral = 0.0
positive = 0.0
#Do feature extraction using TF-IDF and feed feature vectors to the sentiment classifier
def vectorize_feature(training):
try:
global positive
global negative
positive = 0
def loadModel(self, sc):
global model
model = NaiveBayesModel.load(
sc, "hdfs://192.168.1.33:9000/NaiveBayes.model")
def getModel(self, path): if self.type == 'NaiveBayes': return NaiveBayesModel.load(self.sc, path) elif self.type == 'DecisionTree': return DecisionTreeModel.load(self.sc, path)
for word in lowercased:
punct_removed = ''.join([letter for letter in word if not letter in PUNCTUATION])
no_punctuation.append(punct_removed)
no_stopwords = [w for w in no_punctuation if not w in STOPWORDS]
stemmed = [STEMMER.stem(w) for w in no_stopwords]
return [w for w in stemmed if w]
def parseLine(line):
parts = line.split(',')
label = float(parts[0])
features = Vectors.dense([float(x) for x in parts[1].split(' ')])
return LabeledPoint(label, features)
data = sc.textFile('C:\Users\SigurdLap\PycharmProjects\sparkTwitter\naiveBayes.txt').map(parseLine)
# Split data approximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4], seed=0)
# Prøve split i 5 deler, cross validation
# Train a naive Bayes model.
model = NaiveBayes.train(training, 1.0)
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p: (model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
# Save and load model
model.save(sc, "target/tmp/myNaiveBayesModel")
sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel")
analysis_type = 'hashtag_analysis'
send_df_to_dashboard(hashtag_counts_df, analysis_type)
except:
e = sys.exc_info()[0]
print("There is an error: %s" % e)
conf = SparkConf()
conf.setAppName("TwitterStreamApp")
sc = SparkContext(conf=conf)
sc.setLogLevel("ERROR")
ssc = StreamingContext(sc, 3)
ssc.checkpoint("checkpoint_models")
htf = HashingTF(50000)
NB_output_dir = '/spark/NaiveBayes'
NB_load_model = NaiveBayesModel.load(sc, NB_output_dir)
# Sentiment Analysis #
## 01 read tweets from stream ##
dataStream = ssc.socketTextStream("localhost", 9009)
## 02 split the text into words #
words = dataStream.map(lambda x: x.split(" "))
## 03 transformed the words into features ##
features = words.map(lambda x: htf.transform(x))
## 04 predict the sentiment ##
prediction = features.map(lambda x: classify(x))
## 05 label the sentiments ##
label_sentiments = prediction.map(lambda x: ('positive', 1)
if x == 1 else ('negative', 1))
## 06 aggregate the results using sentiment as key ##
tf_val = 1048576
# LOADING AND COMPUTING TF's TRAINING MODEL
print('Loading TRAINING_TF_MODEL...', end="")
tf_training = sc.pickleFile(os.getcwd() + "/Desktop/MODEL/TF/TF_MODEL_" +
str(tf_val))
print('done!')
print('Computing TF-IDF MODEL...', end="")
idf_training = IDF(minDocFreq=5).fit(tf_training)
print('done!')
print('Loading Naive Bayes Model...', end="")
NBM = NaiveBayesModel.load(
sc,
os.getcwd() + "/Desktop/MODEL/NBM/NaiveBayesModel_" + str(tf_val))
print('done!')
print('READY TO PROCESS DATA...')
kafkaParams = {'metadata.broker.list"': kafka_brokers}
# CREATE DIRECT KAFKA STREAM WITH BROKERS AND TOPICS
streamData = KafkaUtils.createDirectStream(
ssc, [kafka_topic], {"metadata.broker.list": kafka_brokers})
######### FROM NOW ON, EACH ACTION OR TRANSFORMATION IS DONE ON A SINGLE INCOMING BATCH OF TWEETS #########
# PRE-PROCESSING TWEETS DATA (TESTING)
obj1 = TweetPreProcessing()
def parseLine(line):
parts = line.split(',')
label = float(parts[0])
features = Vectors.dense([float(x) for x in parts[1].split(' ')])
return LabeledPoint(label, features)
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="Jay")
# $example on$
data = sc.textFile('data/mllib/naive_bayes_data.txt').map(parseLine)
# Split data aproximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4], seed=0)
# Train a naive Bayes model.
model = NaiveBayes.train(training, 1.0)
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p: (model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
# Save and load model
model.save(sc, "jay/myNaiveBayesModel")
sameModel = NaiveBayesModel.load(sc, "jay/myNaiveBayesModel")
# $example off$
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="PythonNaiveBayesExample")
# $example on$
data = sc.textFile("data/mllib/sample_naive_bayes_data.txt").map(parseLine)
# Split data approximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4], seed=0)
# Train a naive Bayes model.
model = NaiveBayes.train(training, 1.0)
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p: (model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
print("model accuracy {}".format(accuracy))
# Save and load model
output_dir = "target/tmp/myNaiveBayesModel"
shutil.rmtree(output_dir, ignore_errors=True)
model.save(sc, output_dir)
sameModel = NaiveBayesModel.load(sc, output_dir)
predictionAndLabel = test.map(lambda p: (sameModel.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
print("sameModel accuracy {}".format(accuracy))
# $example off$
def classify(self, transformer):
votes = []
for c in self._classifiers:
v = c.predict(transformer)
votes.append(v)
return mode(votes)
conf = SparkConf()
conf.setAppName("TA")
sc = SparkContext(conf=conf)
tre = StreamingContext(sc, 10)
htf = HashingTF(50000)
NB_directory = 'hdfs://master:9000/user/hadoop/NaiveBayes'
NB_model = NaiveBayesModel.load(sc, NB_directory)
LR_directory = 'hdfs://master:9000/user/hadoop/LogisticRegression'
LR_model = LogisticRegressionModel.load(sc, LR_directory)
DT_output_dir = 'hdfs://master:9000/user/hadoop/DT'
DT_model = DecisionTreeModel.load(sc, DT_output_dir)
voted_classifier = VoteClassifier(NB_model, LR_model, DT_model)
def sentiment(test_sample):
sample_data_test = test_sample.split(" ")
cli = htf.transform(sample_data_test)
return voted_classifier.classify(cli)
def salary_pre(request):
sc=SparkContext('local','test')
spark = SparkSession.builder.getOrCreate()
hive_con=HiveContext(sc)
nd_idf=IDFModel.load('hdfs://localhost:9000/ndidf')
agg_idf=IDFModel.load('hdfs://localhost:9000/aggidf')
model=NaiveBayesModel.load(sc,'hdfs://localhost:9000/nymodel')
# hive_con.sql('use zp')
# testdata=hive_con.sql('select education,mon_wa,name,work_area,work_desp,work_exp,work_lable from `qtzp` where id=789')
# testdataRDD = testdata.rdd.map(lambda i: Row(**{
# 'education': new_edu_trans(i.education),
# 'salary': mon_wa_trans(i.mon_wa),
# 'name': i.name,
# 'city': i.work_area,
# 'work_desp': i.work_desp,
# 'work_exp': i.work_exp,
# 'work_lable': i.work_lable
# }))
# dataDF=testdataRDD.map(lambda i:Row(**{
# 'salary': int(i.salary),
# 'agg': [i.education] + [i.city] + [i.work_lable] + [i.work_exp],
# 'name_and_desp': desp_text_division(i.name + ',' + i.work_desp)
# })).toDF()
# dataDF.show()
city=request.POST.get('city')
edu=request.POST.get('education')
introduce=request.POST.get('introduce')
position=request.POST.get('job')
exp=request.POST.get('exp')
dataRDD=sc.parallelize([[edu,city,position,exp,introduce]])
# schema=StructType([StructField('education',StringType(),True),StructField('work_area',StringType(),True),StructField('work_lable',
# StringType(),True),StructField('work_exp',StringType(),True),StructField('work_desp',StringType(),True)])
# rowRDD=dataRDD.map(lambda i:Row(i[0],i[1],i[2],i[3],i[4]))
# dataDF=spark.createDataFrame(rowRDD,schema)
# dataDF.show()
dataDF=dataRDD.map(lambda i:Row(**{
'education':i[0],
'work_area':i[1],
'work_lable':i[2],
'work_exp':i[3],
'work_desp':i[4]
})).map(lambda i:Row(**{
'education':str(new_edu_trans(i.education)),
'city':[i.work_area],
'work_desp':i.work_desp,
'work_lable':[i.work_lable],
'work_exp':[i.work_exp]
})).map(lambda i:Row(**{
'agg':[i.education] + i.city + i.work_lable + i.work_exp,
'name_and_desp':desp_text_division(i.work_desp)
})).toDF()
dataDF.show()
ndtf = HashingTF(inputCol='name_and_desp', outputCol='ndFeatures', numFeatures=10240)
aggtf = HashingTF(inputCol='agg', outputCol='Features_agg', numFeatures=256)
data = ndtf.transform(dataDF)
data = aggtf.transform(data)
idfdata = nd_idf.transform(data)
idfdata = agg_idf.transform(idfdata)
RDD = idfdata.rdd
# featuresRDD = RDD.map(lambda i: (i.salary, i.ndfeatures.toArray().tolist() + i.features_agg.toArray().tolist())) #test
featuresRDD = RDD.map(lambda i: i.ndfeatures.toArray().tolist() + i.features_agg.toArray().tolist()) #应用
# featuresRDD = featuresRDD.map(lambda i: features_trans(i)) #test
featuresRDD=featuresRDD.map(lambda i:DenseVector(i)) #应用
# result=featuresRDD.map(lambda i: model.predict(i.features)).collect() #test
result=featuresRDD.map(lambda i:model.predict(i)).collect()
# result=result[0]
spark.stop()
sc.stop()
city_mean=models.CSR.objects.using('db2').filter(city__contains=city)
city_mean=city_mean[0].salary
salary=result_trans(result[0])
pos_mean=models.ITS.objects.using('db2').get(name=position)
pos_mean=pos_mean.salary
data_lst=[city_mean,pos_mean,salary]
data_lst=json.dumps(data_lst)
return render(request,'salary.html',{'result':result,'position':position,'city':city,'edu':edu,'exp':exp,'data':data_lst})
def calculateSentiment(self,sc,query):
model = NaiveBayesModel.load(sc,"finalproject/model/NaiveBayesModel")
query = query
print (query)
twitDG = TwitterDataGenerator()
twitDG.getData(query)
inputFile = sc.textFile("finalproject/tweets.csv").distinct()
input_id = inputFile.zipWithIndex().map(lambda l:(l[1],l[0]))
preprocessedData = self.preProcess(inputFile)
inputFileProcessed = self.processInputFile(inputFile)
print("#################################################################################################")
print(preprocessedData.take(5))
print("--------------------------------------------------------------------------------------------------")
print(inputFileProcessed.take(5))
print("input file processed ",inputFileProcessed.count())
print("preprocessed count",preprocessedData.count())
hashingTF = HashingTF()
tfData = preprocessedData.map(lambda tup: hashingTF.transform(tup))
idfData = IDF().fit(tfData)
tfidfData = idfData.transform(tfData)
output = tfidfData.map(lambda rec: model.predict(rec))
i_I=inputFileProcessed.map(lambda l: l[0]).zipWithIndex().map(lambda l:(l[1],l[0]))
print("input file count",inputFile.count())
print ("output file count",output.count())
o_I=output.zipWithIndex().map(lambda l:(l[1],l[0]))
i_o =i_I.join(o_I).map(lambda l:l[1])
print(i_o.take(i_o.count()))
print(i_o.count())
outputJson = {}
tweetList = []
tweet = {}
positiveCount =0
negativeCount =0
for i in i_o.take(i_o.count()):
print(i)
#print data,data1
if i[1] == 0.0:
negativeCount = negativeCount+1
text = "This is a negative Tweet"
elif i[1] == 1.0:
positiveCount = positiveCount + 1
text = "This is a positive Tweet"
#data = text
#replace(u"\u2022", "*").encode("utf-8")
if len(i[0]) > 4:
tweet = {}
tweet['value'] = i[0].encode("ascii","ignore")
tweet['sentiment'] = text
tweetList.append(tweet)
print i[0].encode("ascii","ignore")
print text
print "-------------------------------------"
#print unicode(str(data),"utf-8")
print (positiveCount)
print (negativeCount)
outputJson["tweets"] = json.dumps(tweetList)
outputJson["positiveTweetCount"] = positiveCount
outputJson["negativeTweetCount"] = negativeCount
wordflatMap = preprocessedData.flatMap(lambda xs: [x for x in xs]).map(lambda x:x.encode("ascii","ignore")).map(lambda x: (x, 1)).reduceByKey(add)
wordFlatMap_reversed = wordflatMap.map(lambda l:(l[1],l[0])).filter(lambda l: (l[1]!="rt" and l[1]!=query))
wordFlatMap_sorted = wordFlatMap_reversed.sortByKey(False)
print (wordFlatMap_sorted.take(10))
outputFrequencyList = {}
mostFrequentWordList = []
wordCount = {}
words =[]
counts = []
for i in wordFlatMap_sorted.take(10):
wordCount = {}
wordCount['word'] = i[1]
wordCount['count'] = i[0]
mostFrequentWordList.append(wordCount)
outputJson["frequency"] = json.dumps(mostFrequentWordList)
return outputJson
tokenhtml = tokenize(a)
print(tokenhtml)
for i in range(0, len(tokenhtml)):
body = ''
body += tokenhtml[i] + ' '
html_dict.append({"label": "0", "text": body})
sc = SparkContext()
htmldata = sc.parallelize(html_dict)
labels = htmldata.map(lambda doc: doc["label"], preservesPartitioning=True)
tf = HashingTF().transform(
htmldata.map(lambda doc: doc["text"], preservesPartitioning=True))
idf = IDF().fit(tf)
tfidf = idf.transform(tf)
end_tfidf = datetime.now()
tfidf_time = format(end_tfidf - start_tfidf)
dataset = labels.zip(tfidf).map(lambda x: LabeledPoint(x[0], x[1]))
sameModel = NaiveBayesModel.load(
sc, "/Users/apple/Dropbox/2016Spring/COSC526/MacHW1/mymodel")
start_predict = datetime.now()
predictionAndLabel = dataset.map(lambda p: (sameModel.predict(p.features)))
predict_time = format(end_predict - start_predict)
accuracy = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == v).count() / dataset.count()
print(tfidf_time)
print(accuracy)
import sys
os.environ['SPARK_HOME'] = 'spark/spark'
sys.path.append('spark/spark/python/')
try:
from pyspark import SparkContext
from pyspark import SparkConf
print("Successfully imported Spark Modules")
except ImportError as e:
print("Can not import Spark Modules", e)
sys.exit(1)
config = SparkConf().setMaster('local[*]').setAppName('SparkService')
sc = SparkContext(conf=config)
sc.setLogLevel("ERROR")
from pyspark.mllib.feature import HashingTF
from pyspark.mllib.classification import NaiveBayesModel
hashingTF = HashingTF()
sameModel = NaiveBayesModel.load(sc, "spark/nbm")
print(sameModel.predict(hashingTF.transform("This is good place".split(" "))))
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)
label = float(parts[0])
features = Vectors.dense([float(x) for x in parts[1].split(' ')])
return LabeledPoint(label, features)
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="PythonNaiveBayesExample")
# $example on$
data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)
# Split data aproximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4], seed=0)
# Train a naive Bayes model.
model = NaiveBayes.train(training, 1.0)
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p:
(model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == v).count() / test.count()
# Save and load model
model.save(sc, "target/tmp/myNaiveBayesModel")
sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel")
# $example off$
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)
from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
from pyspark.mllib.linalg import Vectors
from pyspark.mllib.regression import LabeledPoint
def parseLine(line):
parts = line.split(',')
label = float(parts[0])
features = Vectors.dense([float(x) for x in parts[1].split(' ')])
return LabeledPoint(label, features)
data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)
# Split data aproximately into training (60%) and test (40%)
training, test = data.randomSplit([0.6, 0.4], seed=0)
# Train a naive Bayes model.
model = NaiveBayes.train(training, 1.0)
# Make prediction and test accuracy.
predictionAndLabel = test.map(lambda p: (model.predict(p.features), p.label))
accuracy = 1.0 * predictionAndLabel.filter(
lambda (x, v): x == v).count() / test.count()
# Save and load model
model.save(sc, "myModelPath")
sameModel = NaiveBayesModel.load(sc, "myModelPath")
