def evaluate_classifiers(v,test_keys,classifier_dict,selection="r",mode="unigram"):
print "creating alpha results from classifiers..."
results = {}
if not(checkDir(sub="alpha_results/target/",selection=selection,mode=mode)):
createDir(sub="alpha_results/target/",selection=selection,mode=mode)
for cid in classifier_dict:
print "evaulating cid={0}".format(cid)
#if checkDir('/cresults/indiv')
outpath = "cresults/alpha_results/target/{0}/{1}/{2}.txt".format(mode,selection,cid)
v.score_tweets_bycid(cid)
# just need to change here to do combinations of classifiers
v.build_vote_dicts()
basic = v.basic_result_dict
weighted = v.weighted_result_dict
summarized = v.summarize_weighted_results()
for key in test_keys:
pos_votes = basic[key].count("positive")
neg_votes = basic[key].count("negative")
actual = v.instances[key].label
pos_score = summarized[key]["positive"]
neg_score = summarized[key]["negative"]
diff = pos_score-neg_score
beta=0
# this should be programable to optimize beta!
score_vote = "positive" if (pos_score > neg_score) else "negative"
count_vote = "positive" if pos_votes > neg_votes else "negative" # doesnt work for ties # if certain num negvotes?
line = (actual,pos_score,neg_score,diff,score_vote,pos_votes,neg_votes,count_vote)
results[key] = line
evaluate(results,fname=outpath)
v.reset()
def write_classifier_dict(keys,classifier_dict,selection,mode):
print "writing classifier dict"
if not(checkDir(mode=mode,sub='pickles/target/',selection=selection)):
createDir(mode=mode,sub="pickles/target/",selection=selection)
for cid,classifier in classifier_dict.items():
print "pickling cid={0}".format(cid)
print classifier
#if checkDir('/cresults/indiv')
outpath = "cresults/pickles/target/{0}/{1}/{2}.pkl".format(mode,selection,cid)
#try:
with open(outpath,'wb') as f:
cPickle.dump(classifier,f)