def analyse(factory):
data = iter_corpus()
predictor = factory()
predictor.fit(data)
p1 = ExtractText()
X1 = p1.transform(data)
p2 = EncodingText(predictor.vocabulary)
p2.fit(X1)
X = p2.transform(X1)
y = [a.rating for a in data]
(v1, v2, score, words) = predictor.classifier.analyse(X, predictor.vocabulary)
labels = ["neg", "pos", "mix", "other"]
counter = []
for i in range(len(set(y))):
counter.append([0.0] * len(predictor.vocabulary))
for i in range(np.size(X, 0)):
x = X[i]
label = y[i]
for w in x:
counter[label - 1][w] += 1.0
counter = np.array(counter)
cl = []
for i in range(len(predictor.vocabulary)):
cl_max = max(counter[:, i])
for j in range(len(set(y))):
if counter[j, i] == cl_max:
cl.append(j)
visualise(v1, predictor.vocabulary, cl)
visualise(v2, predictor.vocabulary, cl)
for i in range(len(score)):
print "sentiment - " + str(labels[i])
for j in range(len(score[i])):
print words[i][j] + " : " + str(score[i][j])
def analyse(factory):
data=iter_corpus()
predictor=factory()
predictor.fit(data)
p1=ExtractText(True)
X1=p1.transform(data)
p2=EncodingText(predictor.vocabulary)
p2.fit(X1)
X=p2.transform(X1)
y=[a.rating for a in data]
(v1,v2,score,words)=predictor.classifier.analyse(X,predictor.vocabulary)
labels=['neg','pos','mix','other']
counter=[]
for i in range(len(set(y))):
counter.append([0.0]*len(predictor.vocabulary))
for i in range(np.size(X,0)):
x=X[i]
label=y[i]
for w in x:
counter[label-1][w]+=1.0
counter=np.array(counter)
cl=[]
for i in range(len(predictor.vocabulary)):
cl_max=max(counter[:,i])
for j in range(len(set(y))):
if counter[j,i]==cl_max:
cl.append(j)
visualise(v1,predictor.vocabulary,cl)
visualise(v2,predictor.vocabulary,cl)
for i in range(len(score)):
print 'sentiment - '+str(labels[i])
for j in range(len(score[i])):
print words[i][j]+' : '+str(score[i][j])
value = float(value)
except ValueError:
pass
new[key] = value
return new
if __name__ == "__main__":
import argparse
import json
import csv
import sys
from corpus import iter_corpus, iter_test_corpus
from predictor import PhraseSentimentPredictor
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("filename")
config = parser.parse_args()
config = json.load(open(config.filename))
start=time.time()
predictor = PhraseSentimentPredictor(**config)
predictor.fit(list(iter_corpus()))
print "fitting takes "+str(time.time()-start)
test = list(iter_test_corpus())
#prediction = predictor.predict(test)
score = predictor.score(test,'test')
print("test score {}%".format(score * 100))
print 'programme finished!'
"""
Created on Mon Sep 7 15:36:55 2015
@author: VAIO
"""
from collections import defaultdict
from sklearn.pipeline import make_pipeline, make_union
from corpus import iter_corpus, iter_test_corpus
from transformations import (ExtractText, ExtractAuthor,ExtractDate,EncodingText)
import csv
from settings import DATA_PATH
def target(phrases):
return [datapoint.rating for datapoint in phrases]
phrases = list(iter_corpus())
vocabulary=[]
with open(DATA_PATH + '/vocabulary',encoding='utf-8') as f:
rd=csv.reader(f)
for line in rd:
vocabulary.append(line[0])
pipeline1 = [ExtractText()]
pipeline1.append(EncodingText(vocabulary))
pipeline=make_pipeline(*pipeline1)
y = target(phrases)
self.last = new
if __name__ == "__main__":
import argparse
import json
from evaluation import analyse
from predictor import PhraseSentimentPredictor
# get vocabulary
from corpus import iter_corpus
import csv,os
from transformations import ExtractText
if not os.path.exists('./data/vocabulary'):
datapoints=list(iter_corpus())
vocabulary=set()
et=ExtractText()
X=et.transform(datapoints)
for datap in X:
for w in datap.split():
vocabulary.add(w)
vocabulary=list(vocabulary)
vocabulary.sort()
with open('./data/vocabulary','wb') as f:
wr=csv.writer(f)
for voc in vocabulary:
wr.writerow([voc])
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("filename")
self.last = new
if __name__ == "__main__":
import argparse
import json
from evaluation import analyse
from predictor import PhraseSentimentPredictor
# get vocabulary
from corpus import iter_corpus
import csv, os
from transformations import ExtractText
if not os.path.exists('./data/vocabulary'):
datapoints = list(iter_corpus())
vocabulary = set()
et = ExtractText()
X = et.transform(datapoints)
for datap in X:
for w in datap.split():
vocabulary.add(w.lower())
vocabulary = list(vocabulary)
vocabulary.sort()
with open('./data/vocabulary', 'wb') as f:
wr = csv.writer(f)
for voc in vocabulary:
wr.writerow([voc])
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("filename")
