Python DPlib Examples

Example #1

File: TOKENIZE.py Project: HACP/RHETORICS

import DPlib, sys



filename = '/home/hugo/DEEPLEARNING/SENTIMENT_ANALYSIS/data/training/raw_tweets_1452787775.7.json'

DATA = DPlib.getDATA(filename)

for item in DATA:
    print item + ',0'

Example #2

File: CLASS.py Project: HACP/NLP_CLASS_STRATEGIC

import DPlib

# text to be analized
text = "Soledad_OBrien Soledad- As an HR influencer, we look forward to your thoughts on our social recruiting White Paper: http://t.co/C9lvr6zL17"

# loading NAME class
NAMES, vNAMES = DPlib.getNames()

print ""
# print original text
print text

# print analized text
print DPlib.textClass(text, NAMES)

Example #3

File: kNN.py Project: HACP/MODELS_NN

import DPlib
from sklearn import neighbors, metrics, svm
client = 'AlienVault'
data_path = '/home/hugo/DATA/' 
data_file = 'BRENNT_' + client + '_Data.csv'
test_file = 'BRENNT_' + client + '_Test.csv'
aux_path = client + '/'
cat_list = [2,5,6,23,24,25,26,27]
stats_file = client + '.stats'
name_list = client + '.names'

DPlib.getLabels(data_path, data_file, cat_list, aux_path, stats_file)

DATA, LABEL = DPlib.getAllModData(data_path, data_file, aux_path, name_list, stats_file)
tDATA, tLABEL = DPlib.getAllModData(data_path, test_file, aux_path, name_list, stats_file)

clfkNNu = neighbors.KNeighborsClassifier(3, 'uniform', p=5)
clfkNNd = neighbors.KNeighborsClassifier(3, 'distance', p=5)
clfkNNc = neighbors.NearestCentroid()

clfkNNu.fit(DATA, LABEL)
clfkNNd.fit(DATA, LABEL)
clfkNNc.fit(DATA, LABEL)

pLABELkNNu = clfkNNu.predict(tDATA)
pLABELkNNd = clfkNNd.predict(tDATA)
pLABELkNNc = clfkNNc.predict(tDATA)

V = [pLABELkNNu, pLABELkNNd, pLABELkNNc]

Example #4

File: BG_SUBTRACTION.py Project: HACP/BACKGROUND_CONTENT

import numpy as np
import pylab as P

#name = 'CCMHOCKEY'
#name = 'CARTERS'
#name = 'SYNCFROG'
#name = '99DESIGNS'
#name = 'BAUERHOCKEY'
#name = 'HIREVUE'
#name = 'ADVANCEAUTO'
#name = 'GETRESPONSE'
name = 'SALESLOFT'
#name = 'SALESWISE'

dBG = DPlib.getBG()

dKT, lKT, sdKT, aKT = DPlib.getKT(name)

dDIFF, sdDIFF = DPlib.getDIFF(dBG, dKT, sdKT)

X, Y = PNlib.plotTOPICS(name, sdDIFF, dKT)

TOP = DPlib.getTOP(X, Y, sdDIFF)

AM, lAM = DPlib.generateAM(TOP, lKT)

DPlib.generateDATA(name, TOP, dDIFF, dKT, aKT, lAM, AM)

"""
def getKT(name):

Example #5

File: kNN.py Project: HACP/MODELS_NN

import DPlib
from sklearn import neighbors, metrics, svm

client = 'AlienVault'
data_path = '/home/hugo/DATA/'
data_file = 'BRENNT_' + client + '_Data.csv'
test_file = 'BRENNT_' + client + '_Test.csv'
aux_path = client + '/'
cat_list = [2, 5, 6, 23, 24, 25, 26, 27]
stats_file = client + '.stats'
name_list = client + '.names'

DPlib.getLabels(data_path, data_file, cat_list, aux_path, stats_file)

DATA, LABEL = DPlib.getAllModData(data_path, data_file, aux_path, name_list,
                                  stats_file)
tDATA, tLABEL = DPlib.getAllModData(data_path, test_file, aux_path, name_list,
                                    stats_file)

clfkNNu = neighbors.KNeighborsClassifier(3, 'uniform', p=5)
clfkNNd = neighbors.KNeighborsClassifier(3, 'distance', p=5)
clfkNNc = neighbors.NearestCentroid()

clfkNNu.fit(DATA, LABEL)
clfkNNd.fit(DATA, LABEL)
clfkNNc.fit(DATA, LABEL)

pLABELkNNu = clfkNNu.predict(tDATA)
pLABELkNNd = clfkNNd.predict(tDATA)
pLABELkNNc = clfkNNc.predict(tDATA)

Example #6

File: CLASS.py Project: HACP/NLP_CLASS_STRATEGIC

import DPlib

#text to be analized
text = 'Soledad_OBrien Soledad- As an HR influencer, we look forward to your thoughts on our social recruiting White Paper: http://t.co/C9lvr6zL17'

#loading NAME class
NAMES, vNAMES = DPlib.getNames()

print ''
#print original text
print text

#print analized text
print DPlib.textClass(text, NAMES)