def get_base_data(config):
df = pd.read_csv(os.path.join(config["clean"]["baseDir"],
config["vectorize"]["cleanHash"],
"result.csv"),
low_memory=False)
udf = pd.read_csv(os.path.join(config["clean"]["baseDir"],
config["vectorize"]["cleanHash"],
"useable.csv"),
low_memory=False)
with open(os.path.join(config["vectorize"]["outputDir"], "info.json"),
"r") as f:
info = json.load(f)
stopWords = util.getStopWords(config)
return [{
"all":
df.id.count(),
"annot":
df[~df.notAnnot].id.count(),
"payloadMinLength":
config["clean"]["payloadMinLength"],
"duplicates":
df[~df.notAnnot][df.duplicate].id.count(),
"useable":
udf[udf.useable].id.count(),
"labelsets":
udf.labels.nunique(),
"labelsetsOnce":
udf.groupby(df.labels).labels.count().value_counts().get(1),
"labelCardinality":
sum(udf.nol) / len(udf),
"labelDensity":
sum(udf.nol) / (len(udf) * len(util.getLabels(config)[1:])),
"special":
udf[udf.special].id.count(),
"allFeatures":
info["allFeatures_bow"],
"noTrain":
info["noTrain"],
"noTest":
info["noTest"],
"noTrain_train":
info["noTrain_train"],
"noTrain_val":
info["noTrain_val"],
"noStopWords":
len(stopWords),
"wc_mean":
udf.wc.mean(),
"wc_median":
udf.wc.median(),
"wc_first_quartile":
udf.wc.quantile(.25)
}]
def unigram_noStop(md):
"""
arguments:
md is a util.MovieData object
returns:
a dictionary containing a mapping from unigram features from the reviews
to their values on this util.MovieData object, with stop words removed
"""
unigramCount = unigram_feats(md)
for sword in util.getStopWords():
del unigramCount[sword]
return unigramCount
def bigram_feats_noStop(md):
c = Counter()
for rev in util.MovieData.reviewers:
if hasattr(md,rev):
# count occurrences of asciified, lowercase, non-numeric unigrams
# after removing punctuation
stopWords = util.getStopWords()
wordList = util.punct_patt.sub("",
util.asciify(md.__dict__[rev].strip().lower())).split()
wordList = [x for x in wordList if util.non_numeric(x) and util.notStopWord(x, stopWords)]
bigrams = zip(wordList, wordList[1:])
c.update(token for token in bigrams)
return c
from nltk.tokenize import RegexpTokenizer
from nltk.stem.porter import PorterStemmer
from gensim import corpora, models
import gensim
import codecs
import util
import dataImporter
tokenizer = RegexpTokenizer(r'\w+')
# create English stop words list
en_stop = util.getStopWords('stopwords.txt')
userinput = str(raw_input('Enter your list: '))
channel_list = []
for input in userinput.split(','):
channel_list.append(input)
for channel in channel_list:
dataImporter.writeChannelMessagesToFile(channel+'.txt', channel)
doc_set = []
for channel in channel_list:
doc = 'doc_' + channel
doc = codecs.open(channel+'.txt', encoding='utf-8').read()
doc_set.append(doc)
# list for tokenized documents in loop
texts = []
# loop through document list
for doc in doc_set:
