import nltk

import string

import itertools

import math

from sklearn.ensemble import RandomForestClassifier

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import BaggingClassifier

from sklearn.naive_bayes import GaussianNB

from sklearn.neighbors import KNeighborsClassifier

from sklearn.ensemble import GradientBoostingClassifier

import sklearn

from sklearn import cross_validation

from sklearn.feature_extraction import DictVectorizer

import logging

import numpy

from sklearn.externals import joblib

from concurrent.futures import ThreadPoolExecutor

from concurrent.futures import ProcessPoolExecutor

import concurrent.futures

logging.basicConfig(level=logging.DEBUG)

def file_as_posts(filename):

curr_post += l

def post_as_tokens(post):

tokenizer = nltk.tokenize.WhitespaceTokenizer()

for t in tokenizer.tokenize(post):

yield t.replace("//MARK\\\\", "")

def post_as_labeled_tokens(post):

yield (token.replace("//MARK\\\\", ""), marked)

def post_as_tagged_labeled_tokens(post):

yield from ((x[0][0], x[0][1], x[1]) for x in zip(tags, (t[1] for t in labeled_tokens)))

def post_as_tagged_tokens(post):

tokens = list(post_as_tokens(post))

tags = filter(lambda x: x[1] not in string.punctuation, nltk.pos_tag(tokens))

yield from tags

def post_as_ngrams(post, gram_size):

yield list(last_n_tokens)

def post_as_labeled_ngrams(post, gram_size):

yield (last_n_tokens, marked[center])

def type_of_string(s):

return "none"

def numeric_fingerprint(s):

return any(c.isdigit() for c in s)

def extract_suffix(s):

return s[-2:]

import re

model_sig_pattern = re.compile('[A-Z]+[A-Z0-9]*-[0-9][A-Z0-9]*')

def is_model_sig(s):

return model_sig_pattern.match(s) != None

numeric_plural_or_poss_pattern = re.compile("[A-Za-z0-9]+'?s")

def is_numeric_plural(s):

return numeric_plural_or_poss_pattern.match(s) != None

companies = {"ableplanet", "akg", "ar", "audeze", "altec", "audiotechnica", "beats", "behringer", "beyerdynamic", "bk", "bose", "b&w", "califone", "cortex", "creative", "cta", "denon", "etymotic", "everglide", "focal", "fostex", "futuresonics", "ge", "gemini", "genius", "grado", "hifiman", "icetech", "ifrogz", "inland", "isymphony", "jvc", "jwin", "klipsch", "koss", "labtec", "logitech", "ltb", "m-audio", "marshall", "maxell", "monoprice", "monster", "nady", "numark", "otto", "panasonic", "paradigm", "phiaton", "philips", "pioneer", "polk", "psb", "psyko", "rca", "roland", "samson", "sennheiser", "sherwood", "shure", "skullcandy", "sol", "sony", "soul", "stanton", "stax", "targus", "tascam", "tdk", "ultrasone", "velodyne", "vestax", "vic", "v-moda", "yamaha", "zagg", "alo", "audioquest", "burson", "fiio", "grace", "oppo", "schitt", "centrance", "woo"}

def is_company(s):

return res

def ends_sentence(s):

s = s.strip()

return s.endswith(".") or s.endswith("!") or s.endswith(";")

keywords = {"amp", "amps", "amplifier", "dac", "dacs", "cable", "cables", "headphones", "headphone", "HP"}

def is_keyword(s):

if s in keywords:

return s[0:2]

return "n"

words = set(nltk.corpus.words.words())

def is_english(s):

return s in words

def has_demarc(s):

return False

def features_for_ngrams(ngrams, previous):

return vec

def ngrams_as_training_data(ngrams):

yield vec, ngram[1]

def ngrams_as_data(ngrams):

for ngram in ngrams:

vec = features_for_ngrams(ngram, "FILL THIS IN")

yield vec

def post_to_vector(post, gram_size):

return list(ngrams_as_training_data(post_as_labeled_ngrams(post, gram_size)))

def file_as_training_vectors(filename, gram_size):

return

def first_of_each(item):

return tuple(toR)

def file_as_vectors(filename, gram_size):

yield from zip(data, map(first_of_each, grams))

class Corpus:

print(" ".join(gram), "|")

# with trigrams and CV search:

#INFO:root:Recall (finding true positives): 0.515901060071

#INFO:root:Precision (avoid false positives): 0.696897374702

import sys

if __name__ == "__main__":

c = Corpus(["lcd_impressions.txt", "he400_impressions.txt", "asgard2_impressions.txt"], 3)

if sys.argv[1] == "extract":

c.extract()

elif sys.argv[1] == "train":

c.load_training_data()

c.train()

elif sys.argv[1] == "search":

c.load_training_data()

c.parameter_search()

elif sys.argv[1] == "test":

c.load_model()

c.identify_entities("he400_nolabels.txt")

# this file has 1017980 samples (trigrams)

#c.extract()

#c.load_training_data()

#c.train()

#c.load_model()

#c.identify_entities("lcd_eval.txt")

#print(max(map(lambda x: x[0], enumerate(file_as_posts("lcd_impressions.txt")))))