def nearCrits(crit, critset, filmset, fracTrain = 0.7, learnRate = 0.024):
import random
from nltk import SklearnClassifier
from sklearn.linear_model import LogisticRegression
labeledset = makeLabeledset(crit,filmset,critset)
random.shuffle(labeledset)
trainset = labeledset[:int(len(labeledset)*fracTrain)]
clf = SklearnClassifier(LogisticRegression(C=learnRate))
clf.train(trainset)
critdist = []
baseline = clf.prob_classify({}).prob('fresh')
for crit in critset:
quickdic = {}
quickdic[crit] = 'fresh'
dist = clf.prob_classify(quickdic).prob('fresh') - baseline
critdist.append( (crit,dist) )
critdist.sort(key=lambda x: x[1], reverse=True)
for i in range(-10,10):
print (critdist[i])
def suggestions(labeledset, featureset, num = 20):
from nltk import SklearnClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.naive_bayes import MultinomialNB
clf = SklearnClassifier(LogisticRegression(C=0.024))
clf.train(labeledset)
filmsSeen = []
for film in labeledset:
filmsSeen.append( film[0]['title'] )
suggestions = []
for film in featureset:
if film['title'] in filmsSeen: continue
suggestions.append( (film['title'], clf.prob_classify(film).prob('fresh') ))
suggestions.sort(key=lambda x: x[1], reverse=True)
return suggestions[:num]
