A "Best of the Best Practices" (BOBP) guide to developing in Python.
- "Build tools for others that you want to be built for you." - Kenneth Reitz
- "Simplicity is alway better than functionality." - Pieter Hintjens
{ | |
// Color | |
"color_scheme": "Packages/Theme - Nil/Big Duo.tmTheme", | |
"theme": "Nil.sublime-theme", | |
// Font | |
"font_face": "Ubuntu Mono", | |
"font_options": ["subpixel_antialias"], | |
"font_size": 15.0, | |
// Caret | |
"caret_style": "phase", |
#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
import string | |
from text.blob import Blobber | |
from text.taggers import PerceptronTagger, PatternTagger, NLTKTagger | |
def accuracy(test_set, tagger): | |
n_correct = 0 | |
total = 0 | |
tb = Blobber(pos_tagger=tagger) |
import math | |
from text.blob import TextBlob as tb | |
def tf(word, blob): | |
return blob.words.count(word) / len(blob.words) | |
def n_containing(word, bloblist): | |
return sum(1 for blob in bloblist if word in blob) | |
def idf(word, bloblist): |
from textblob.classifiers import NaiveBayesClassifier | |
train = [ | |
('amor', "spanish"), | |
("perro", "spanish"), | |
("playa", "spanish"), | |
("sal", "spanish"), | |
("oceano", "spanish"), | |
("love", "english"), | |
("dog", "english"), |
import random | |
from nltk.corpus import movie_reviews | |
from textblob.classifiers import NaiveBayesClassifier | |
random.seed(1) | |
train = [ | |
('I love this sandwich.', 'pos'), | |
('This is an amazing place!', 'pos'), | |
('I feel very good about these beers.', 'pos'), | |
('This is my best work.', 'pos'), |
from textblob.classifiers import NaiveBayesClassifier | |
from textblob import TextBlob | |
train = [ | |
('I love this sandwich.', 'pos'), | |
('This is an amazing place!', 'pos'), | |
('I feel very good about these beers.', 'pos'), | |
('This is my best work.', 'pos'), | |
("What an awesome view", 'pos'), | |
('I do not like this restaurant', 'neg'), |
from text.classifiers import NaiveBayesClassifier | |
train = [ | |
('I love this sandwich.', 'pos'), | |
('This is an amazing place!', 'pos'), | |
('I feel very good about these beers.', 'pos'), | |
('This is my best work.', 'pos'), | |
("What an awesome view", 'pos'), | |
('I do not like this restaurant', 'neg'), | |
('I am tired of this stuff.', 'neg'), |
Works well with sloria's cookiecutter-pypackage template.
my_project/__init__.py
git checkout dev
python setup.py test
tox