# Guessing gender
import configparser
import sys
from TwitterAPI import TwitterAPI

def get_twitter(config_file):
    """ Read the config_file and construct an instance of TwitterAPI.
    Args:
      config_file ... A config file in ConfigParser format with Twitter credentials
    Returns:
      An instance of TwitterAPI.
    """
    config = configparser.ConfigParser()
    config.read(config_file)
    twitter = TwitterAPI(
                   config.get('twitter', 'consumer_key'),
                   config.get('twitter', 'consumer_secret'),
                   config.get('twitter', 'access_token'),
                   config.get('twitter', 'access_token_secret'))
    return twitter

twitter = get_twitter('twitter.cfg')
tweets = []
n_tweets=1000
for r in twitter.request('statuses/filter', {'track': 'i'}):
    tweets.append(r)
    if len(tweets) % 100 == 0:
        print('%d tweets' % len(tweets))
    if len(tweets) >= n_tweets:
        break
print('fetched %d tweets' % len(tweets))

# not all tweets are returned
# https://dev.twitter.com/streaming/overview/messages-types#limit_notices
[t for t in tweets if 'user' not in t][:6]

# restrict to actual tweets
# (remove "deleted" tweets)
tweets = [t for t in tweets if 'user' in t]
print('fetched %d tweets' % len(tweets))

# Print 10 names.
names = [t['user']['name'] for t in tweets]
names[:10]

# Fetch census name data from:
# http://www2.census.gov/topics/genealogy/1990surnames/
import requests
from pprint import pprint
males_url = 'http://www2.census.gov/topics/genealogy/' + \
            '1990surnames/dist.male.first'
females_url = 'http://www2.census.gov/topics/genealogy/' + \
              '1990surnames/dist.female.first'
males = requests.get(males_url).text.split('\n')
females = requests.get(females_url).text.split('\n')
print('males:')
pprint(males[:10])
print('females:')
pprint(females[:10])

# Get names. 
male_names = set([m.split()[0].lower() for m in males if m])
female_names = set([f.split()[0].lower() for f in females if f])
print('%d male and %d female names' % (len(male_names), len(female_names)))
print('males:\n' + '\n'.join(list(male_names)[:10]))
print('\nfemales:\n' + '\n'.join(list(female_names)[:10]))

from collections import Counter

def print_genders(tweets):
    counts = Counter([t['gender'] for t in tweets])
    print('%.2f of accounts are labeled with gender' % 
          ((counts['male'] + counts['female']) / sum(counts.values())))
    print('gender counts:\n', counts)
    for t in tweets[:20]:
        print(t['gender'], t['user']['name'])
    
print_genders(tweets)

# What about ambiguous names?
def print_ambiguous_names(male_names, female_names):
    ambiguous = [n for n in male_names if n in female_names]  # names on both lists
    print('found %d ambiguous names:\n'% len(ambiguous))
    print('\n'.join(ambiguous[:20]))
    
print_ambiguous_names(male_names, female_names)

# Keep names that are more frequent in one gender than the other.
def get_percents(name_list):
    # parse raw data to extract, e.g., the percent of males names John.
    return dict([(n.split()[0].lower(), float(n.split()[1]))
                  for n in name_list if n])

males_pct = get_percents(males)
females_pct = get_percents(females)

# Assign a name as male if it is more common among males than femals.
male_names = set([m for m in male_names if m not in female_names or
              males_pct[m] > females_pct[m]])
female_names = set([f for f in female_names if f not in male_names or
              females_pct[f] > males_pct[f]])

print_ambiguous_names(male_names, female_names)
print('%d male and %d female names' % (len(male_names), len(female_names)))

# Relabel twitter users (compare with above)
gender_by_name(tweets, male_names, female_names)
print_genders(tweets)

# Who are the unknowns?
# "Filtered" data can have big impact on analysis.
unknown_names = Counter(t['user']['name']
                        for t in tweets if t['gender'] == 'unknown')
unknown_names.most_common(20)

# How do the profiles of male Twitter users differ from
# those of female users?

male_profiles = [t['user']['description'] for t in tweets
                if t['gender'] == 'male']

female_profiles = [t['user']['description'] for t in tweets
                if t['gender'] == 'female']
#male_profiles = [t['text'] for t in tweets
#                if t['gender'] == 'male']

#female_profiles = [t['text'] for t in tweets
#                if t['gender'] == 'female']

import re
def tokenize(s):
    return re.sub('\W+', ' ', s).lower().split() if s else []

male_words = Counter()
female_words = Counter()

for p in male_profiles:
    male_words.update(Counter(tokenize(p)))
                      
for p in female_profiles:
    female_words.update(Counter(tokenize(p)))

print('Most Common Male Terms:')
pprint(male_words.most_common(10))
    
print('\nMost Common Female Terms:')
pprint(female_words.most_common(10))

print(len(male_words))
print(len(female_words))

# Compute difference
diff_counts = dict([(w, female_words[w] - male_words[w])
                    for w in
                    set(female_words.keys()) | set(male_words.keys())])

sorted_diffs = sorted(diff_counts.items(), key=lambda x: x[1])

print('Top Male Terms (diff):')
pprint(sorted_diffs[:10])

print('\nTop Female Terms (diff):')
pprint(sorted_diffs[-10:])

def counts_to_probs(gender_words):
    """ Compute probability of each term according to the frequency
    in a gender. """
    total = sum(gender_words.values())
    return dict([(word, count / total)
                 for word, count in gender_words.items()])

male_probs = counts_to_probs(male_words)
female_probs = counts_to_probs(female_words)

print('p(w|male)')
pprint(sorted(male_probs.items(), key=lambda x: -x[1])[:10])

print('\np(w|female)')
pprint(sorted(female_probs.items(), key=lambda x: -x[1])[:10])

def odds_ratios(male_probs, female_probs):
    return dict([(w, female_probs[w] / male_probs[w])
                 for w in
                 set(male_probs) | set(female_probs)])

ors = odds_ratios(male_probs, female_probs)

print(len(male_probs))
print(len(female_probs))
print(female_probs['rock'])
'rock' in male_probs

# Additive smoothing. Add count of 1 for all words.
all_words = set(male_words) | set(female_words)
male_words.update(all_words)  
female_words.update(all_words)

male_probs = counts_to_probs(male_words)
female_probs = counts_to_probs(female_words)
print('\n'.join(str(x) for x in 
                sorted(male_probs.items(), key=lambda x: -x[1])[:10]))

# Even though word doesn't appear, has non-zerp probability.
print(male_probs['rock'])

ors = odds_ratios(male_probs, female_probs)

sorted_ors = sorted(ors.items(), key=lambda x: -x[1])

print('Top Female Terms (OR):')
pprint(sorted_ors[:20])

print('\nTop Male Terms (OR):')
pprint(sorted_ors[-20:])