#!/usr/bin/env python

# coding: utf-8

# In[1]:

import csv

from pandas import DataFrame

import pandas as pd

column = ['Title','Body']

dfBA = DataFrame(columns=column)

dfT = DataFrame(columns=[0, 1, 2, 3, 4])

# In[2]:

def create_data_frame(data):

dfT = dfT.append(DataFrame(data=t))

# In[3]:

def parser(path):

create_data_frame(terms)

# In[4]:

path = 'data/vzn/TrainingData.csv'

parser(path)

import pandas as pd

df = pd.concat([dfBA, dfT], axis=1)

# In[5]:

dfBA

# In[6]:

dfT

# In[7]:

from nltk.corpus import stopwords

stopWordList=stopwords.words('english')

stopWordList.remove('no')

stopWordList.remove('not')

import unicodedata

import spacy

def removeAscendingChar(data):

data=unicodedata.normalize('NFKD', data).encode('ascii', 'ignore').decode('utf-8', 'ignore')

return data

def removeCharDigit(text):

return text

from nltk.stem.wordnet import WordNetLemmatizer

from nltk.tokenize import ToktokTokenizer

lemma=WordNetLemmatizer()

token=ToktokTokenizer()

def lemitizeWords(text):

return text

def stopWordsRemove(text):

return text

def PreProcessing(text):

return text

# In[ ]:

totalText=''

for x in df['Body']:

ps = PreProcessing(x)

totalText = totalText + " " + ps

# In[ ]:

from wordcloud import WordCloud

import matplotlib.pyplot as plt

wc=WordCloud(max_font_size=60).generate(totalText)

plt.figure(figsize=(16,12))

plt.imshow(wc, interpolation="bilinear")

# In[ ]:

import nltk

freqdist = nltk.FreqDist(token.tokenize(totalText))

plt.figure(figsize=(16,5))

freqdist.plot(50)

# In[ ]:

totalText=''

for x in df['Title']:

ps=PreProcessing(x)

totalText=totalText+" "+ps

# In[ ]:

from wordcloud import WordCloud

import matplotlib.pyplot as plt

wc=WordCloud(max_font_size=60).generate(totalText)

plt.figure(figsize=(16,12))

plt.imshow(wc, interpolation="bilinear")

# In[ ]:

import nltk

freqdist = nltk.FreqDist(token.tokenize(totalText))

freqdist

plt.figure(figsize=(16,5))

freqdist.plot(50)

# In[ ]:

x = df.iloc[:, 0:2].values

y = df.iloc[:, 2:-1]

# In[ ]:

okList=[]

for cl in dfT.columns:

for n in df[cl]:

okList.append(n)

okList=list(set(okList))

del(okList[okList.index('0')])

# In[ ]:

print(okList)

# In[ ]:

newDF=DataFrame(columns=okList)

# In[ ]:

for x in range(dfT.count()[0]):

someDict={}

for d in okList:

rowdata=list(dfT.iloc[x])

if d in rowdata:

someDict[d]=1

else:

someDict[d]=0

newDF=newDF.append(someDict,ignore_index=True)

# In[ ]:

newDF

# In[ ]:

dfBA.index=range(dfBA.count()[0])

df=dfBA.join(newDF)

# In[ ]:

df

# ### Binary relevance

# In[168]:

x=df.iloc[:,0:2].values

y=df.iloc[:,2:-1].values

# using binary relevance

from sklearn.feature_extraction.text import CountVectorizer

from pandas import DataFrame

x1=df.Title

x2=df.Body

from pandas import DataFrame

cv=CountVectorizer().fit(x1)

header=DataFrame(cv.transform(x1).todense(),columns=cv.get_feature_names())

cvArticle=CountVectorizer().fit(x2)

article=DataFrame(cvArticle.transform(x2).todense(),columns=cvArticle.get_feature_names())

import pandas as pd

x=pd.concat([header,article],axis=1)

# In[169]:

from sklearn.feature_extraction.text import TfidfTransformer

tfidfhead=TfidfTransformer().fit(header)

head=DataFrame(tfidfhead.transform(header).todense())

tfidfart=TfidfTransformer().fit(article)

art=DataFrame(tfidfart.transform(article).todense())

import pandas as pd

x=pd.concat([head,art],axis=1)

# In[170]:

# using binary relevance

from skmultilearn.problem_transform import BinaryRelevance

from sklearn.naive_bayes import GaussianNB

from sklearn.model_selection import train_test_split

# initialize binary relevance multi-label classifier

# with a gaussian naive bayes base classifier

xtrain,xtest,ytrain,ytest=train_test_split(x,y)

classifier = BinaryRelevance(GaussianNB())

# train

classifier.fit(xtrain.astype(float), ytrain.astype(float))

predictions = classifier.predict(xtest.astype(float))

predictions.toarray()

from sklearn.metrics import accuracy_score

accuracy_score(ytest.astype(float),predictions)

# ### Classifier chains

# In[171]:

# using classifier chains

from skmultilearn.problem_transform import ClassifierChain

from sklearn.naive_bayes import GaussianNB

# initialize classifier chains multi-label classifier

# with a gaussian naive bayes base classifier

classifier = ClassifierChain(GaussianNB())

# train

classifier.fit(xtrain.astype(float), ytrain.astype(float))

# predict

predictions = classifier.predict(xtest.astype(float))

accuracy_score(ytest.astype(float),predictions)

# ### Label powerset

# In[172]:

# using Label Powerset

from skmultilearn.problem_transform import LabelPowerset

from sklearn.naive_bayes import GaussianNB

# initialize Label Powerset multi-label classifier

# with a gaussian naive bayes base classifier

classifier = LabelPowerset(GaussianNB())

# train

classifier.fit(xtrain.astype(float), ytrain.astype(float))

# predict

predictions = classifier.predict(xtest.astype(float))

accuracy_score(ytest.astype(float),predictions)

# ### MLkNN

# In[173]:

from skmultilearn.adapt import MLkNN

classifier = MLkNN(k=7)

# train

classifier.fit(xtrain.astype(float), ytrain.astype(float))

# predict

predictions = classifier.predict(xtest.astype(float))

accuracy_score(ytest.astype(float),predictions)

# In[ ]: