Tathagatd96

sklearn.datasets.load_files("C://Users/Tathagat Dasgupta/Desktop/ML Project/20news-18828")

categories=['alt.atheism','soc.religion.christian','comp.graphics','sci.med']
print "hello"

twenty_train=fetch_20newsgroups(subset='train',categories=categories,shuffle=True,random_state=42)

#twenty_train.target_names=['alt.atheism','comp.graphics','sci.med','soc.religion.christian']

print len(twenty_train.data)

Tathagatd96

#tf-idf
tfidf_transformer=TfidfTransformer()

X_train_tfidf=tfidf_transformer.fit_transform(X_train_counts)
print(X_train_tfidf.shape)

Tathagatd96

#Classifier Training


clf=MultinomialNB().fit(X_train_tfidf,twenty_train.target)

docs_new=['God is love','OpenGL on the GPU is fast']
X_new_counts=count_vect.transform(docs_new)
X_new_tfidf=tfidf_transformer.transform(X_new_counts)

predicted=clf.predict(X_new_tfidf)

Tathagatd96

#Performance on test set

twenty_test=fetch_20newsgroups(subset='test',categories=categories,shuffle=True,random_state=42)
doc_test=twenty_test.data
predicted=text_clf.predict(doc_test)
print "Classifier Accuracy:"
print(np.mean(predicted==twenty_test.target))

Tathagatd96

from sklearn.datasets import fetch_20newsgroups
import sklearn.datasets
from sklearn.feature_extraction.text import CountVectorizer,CharNGramAnalyzer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.linear_model import SGDClassifier

from sklearn.svm.sparse import LinearSVC
import numpy as np

Tathagatd96

sklearn.datasets.load_files("C://Users/Tathagat Dasgupta/Desktop/ML Project/20news-18828")

categories=['alt.atheism','soc.religion.christian','comp.graphics','sci.med']
print "hello"

twenty_train=fetch_20newsgroups(subset='train',categories=categories,shuffle=True,random_state=42)

Tathagatd96

print len(twenty_train.data)

print("\n".join(twenty_train.data[0].split("\n")[:3]))

print(twenty_train.target_names[twenty_train.target[0]])
    
#Preprocessing

#Tokenizing text

Tathagatd96

from sklearn.datasets import fetch_20newsgroups
import sklearn.datasets
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.linear_model import SGDClassifier
import numpy as np

Tathagatd96

sklearn.datasets.load_files("C://Users/Tathagat Dasgupta/Desktop/ML Project/20news-18828")

categories=['alt.atheism','soc.religion.christian','comp.graphics','sci.med']
print "hello"

twenty_train=fetch_20newsgroups(subset='train',categories=categories,shuffle=True,random_state=42)

Tathagatd96

print len(twenty_train.data)

print("\n".join(twenty_train.data[0].split("\n")[:3]))

print(twenty_train.target_names[twenty_train.target[0]])

print(twenty_train.target[:10])

for t in twenty_train.target[:10]:
    print(twenty_train.target_names[t])