Tathagatd96

2257

From: [email protected] (Michael Collier)
Subject: Converting images to HP LaserJet III?
Nntp-Posting-Host: hampton

comp.graphics

[1 1 3 3 3 3 3 2 2 2]

Tathagatd96

#SVM Implementation

text_clf=Pipeline([('vect',CountVectorizer()),('tfidf',TfidfTransformer()),('clf',SGDClassifier(loss='hinge',alpha=1e-3,n_iter=5,random_state=42))])
text_clf.fit(twenty_train.data,twenty_train.target)
predicted=text_clf.predict(doc_test)

print "SVM Accuracy:"
print(np.mean(predicted==twenty_test.target))

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

#Building a pipeline

text_clf=Pipeline([('vect',CountVectorizer()),('tfidf',TfidfTransformer()),('clf',MultinomialNB())])

text_clf=text_clf.fit(twenty_train.data,twenty_train.target)

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

#tf-idf
tfidf_transformer=TfidfTransformer()

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

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])

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

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

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