path: root/utils.py

import re
import funcy
import csv
import numpy as np
import datetime
import json
from collections import defaultdict
import glob
from scipy.spatial.distance import cosine


with open('data/styles.json') as f:
    styles = json.load(f)



with open('data/beer_info.json', 'r') as f:
    beer_names = json.load(f)


attr_to_styles = defaultdict(list)
style_to_attrs = defaultdict(list)
with open('data/sparser_beer_data.csv') as f:
    reader = csv.DictReader(f)
    for line in reader:
        style = line.pop('Style')
        for k, v in line.items():
            if v == '1':
                attr_to_styles[k].append(style)
                style_to_attrs[style].append(k)


# In[7]:

with open('data/final_data_small.json') as f:
    final_data = json.load(f)




beer_by_style = defaultdict(list)
for beer, data in final_data.items():
    try:
        real_style = beer_names[beer][1][0]
        if beer_names[beer][3] > 7:
            beer_by_style[real_style].append(data['embed'])
        
    except:
        pass


style_centers = {}
for style, datas in beer_by_style.items():
    style_centers[style.strip()] = np.mean(datas, axis=0)



style_name_to_num = {y[0]: x for x,y in styles.items()}



attr_centers = {}
for attr, rel_styles in attr_to_styles.items():
    centers = [style_centers[x] for x in rel_styles]
    attr_mean = np.mean(centers, axis=0)
    attr_centers[attr] = attr_mean



embeddings =  [(x[0], x[1]['embed']) for x in final_data.items()]



small_embeddings = []
small_styles = []
bad = 0
for e in embeddings:
    newkey =  e[0]
    if newkey in beer_names:
        info= beer_names[newkey]
        
        if info[3] > 25:
            small_embeddings.append(e)
            small_styles.append(info[1][0])
    else:
        bad += 1

def get_closest(beer_id):
    one_embed = final_data[beer_id]['embed']
    
    beer_ids = []
    for thing in sorted(small_embeddings, key = lambda x: cosine(one_embed, x[1]), reverse=False)[1:11]:

        if thing[0] in beer_names:
            beer_ids.append(thing[0])
            print(thing[0])
            print('=' * 50)
    return beer_ids
            
def get_closest_to_point(one_embed, style_limit=[]):
#     one_embed = final_data[beer_id]['embed']

    if style_limit:
        possible_beers = []
        for style, e in zip(small_styles, small_embeddings):
            if style == style_limit:
                possible_beers.append(e)
    else:
        possible_beers = small_embeddings
    print(len(possible_beers))

    to_return = []
    for thing in sorted(possible_beers, key = lambda x: cosine(one_embed, x[1]), reverse=False)[:5]:

        
#         print(b, a)
        if thing[0] in beer_names:
            bn = beer_names[thing[0]]

            to_return.append((thing[0], bn[0], bn[1][0]))
    return to_return



def translate_to_attr(beer_id, to_attr, amt):
    embedding = final_data[beer_id]['embed']
    if amt < 0:
        back = True
        amt = abs(amt)
        
    else:
        back = False

    relevant_styles = attr_to_styles[to_attr]
    small_rel_centers = {x: y for x, y in style_centers.items() if x in relevant_styles}

    sorted_centers = sorted(small_rel_centers, key=lambda x: cosine(small_rel_centers[x], embedding))
    closest_center = sorted_centers[0]
    closest_center_vector = style_centers[closest_center]
    
    vector_between = closest_center_vector - embedding

    print('Moving Towards/From: {}'.format(closest_center))
    print('=' *10)
    print('\n')
    dist_between = np.linalg.norm(closest_center_vector-embedding)
    amt_dict = {
        1: 4,
        2: 2,
        3: 1
    }
    x = amt_dict[amt]
    print('Moving {}%'.format(1/float(x) * 100))
    if back:
        new_point = embedding - (vector_between/x)
    else:
        new_point = embedding + (vector_between/x)
    return get_closest_to_point(new_point)


def translate_to_style(beer_id, style):
    embedding = final_data[beer_id]['embed']
    closest_center_vector = style_centers[style]
    
    vector_between = closest_center_vector - embedding
#     print(vector_between)
    print('Moving Towards/From: {}'.format(style))
    print('=' *10)
    print('\n')
    dist_between = np.linalg.norm(closest_center_vector-embedding)

    TRANSLATION_CONSTANT = 4  # 4 is slight
    
    new_point = embedding + (vector_between/TRANSLATION_CONSTANT)
    return get_closest_to_point(new_point, style_limit=style)

def get_drinks_like(beer_id):
    data = final_data[beer_id]
    return data['alc']

# print(styles)

num_to_style = {0: 'European Export / Dortmunder',
 1: 'German Bock',
 2: 'Low Alcohol Beer',
 3: 'American Black Ale',
 4: 'German Helles',
 5: 'New England IPA',
 6: 'American Amber / Red Lager',
 7: 'Irish Dry Stout',
 8: 'Leipzig Gose',
 9: 'German Maibock',
 10: 'Scottish Gruit / Ancient Herbed Ale',
 11: 'Belgian Strong Pale Ale',
 12: 'Robust Porter ',
 13: 'English Dark Mild Ale',
 14: 'Belgian Lambic',
 15: 'Belgian IPA',
 16: 'American Pale Ale (APA)',
 17: 'American Imperial Porter',
 18: 'American IPA',
 19: 'Belgian Gueuze',
 20: 'American Wheatwine Ale',
 21: 'California Common / Steam Beer',
 22: 'Smoke Porter',
 23: 'English Pale Mild Ale',
 24: 'Rye Beer',
 25: 'Russian Kvass',
 26: 'German Altbier',
 27: 'American Malt Liquor',
 28: 'Foreign / Export Stout',
 29: 'Japanese Rice Lager',
 30: 'German Pilsner',
 31: 'German Weizenbock',
 32: 'Belgian Witbier',
 33: 'English Old Ale',
 34: 'American Imperial Red Ale',
 35: 'Belgian Quadrupel (Quad)',
 36: 'American Stout',
 37: 'Belgian Faro',
 38: 'Pumpkin Beer',
 39: 'American Porter',
 40: 'Vienna Lager',
 41: 'Belgian Dark Ale',
 42: 'American Brut IPA',
 43: 'British Barleywine',
 44: 'German Kölsch',
 45: 'American Barleywine',
 46: 'German Kellerbier / Zwickelbier',
 47: 'Scotch Ale / Wee Heavy',
 48: 'European Strong Lager',
 49: 'German Kristalweizen',
 50: 'Baltic Porter',
 51: 'Chile Beer',
 52: 'American Cream Ale',
 53: '[ India Pale Ales ]',
 54: 'American Imperial Pilsner',
 55: 'American Imperial IPA',
 56: 'English Porter',
 57: 'English Sweet / Milk Stout',
 58: 'American Lager',
 59: 'American Imperial Stout',
 60: 'Belgian Blonde Ale ',
 61: 'English India Pale Ale (IPA)',
 62: 'German Eisbock',
 63: 'Belgian Pale Ale',
 64: 'American Light Lager',
 65: 'Russian Imperial Stout',
 66: 'German Hefeweizen',
 67: 'German Märzen / Oktoberfest',
 68: 'Flanders Red Ale',
 69: 'English Stout',
 70: 'Belgian Dubbel',
 71: 'American Blonde Ale',
 72: 'American Brown Ale',
 73: 'Finnish Sahti',
 74: 'English Oatmeal Stout',
 75: 'Fruit and Field Beer',
 76: 'Belgian Tripel',
 77: 'Belgian Strong Dark Ale',
 78: 'American Dark Wheat Ale',
 79: 'Smoke Beer',
 80: 'English Extra Special / Strong Bitter (ESB)',
 81: 'European Pale Lager',
 82: 'American Amber / Red Ale',
 83: 'Flanders Oud Bruin',
 84: 'American Strong Ale',
 85: 'English Brown Ale',
 86: 'European Dark Lager',
 87: 'French Bière de Garde',
 88: 'American Pale Wheat Ale',
 89: 'Munich Dunkel Lager',
 90: 'German Doppelbock',
 91: 'German Rauchbier',
 92: 'German Roggenbier',
 93: 'Scottish Ale',
 94: 'German Dunkelweizen',
 95: 'English Bitter',
 96: 'English Strong Ale',
 97: 'Winter Warmer',
 98: 'Herb and Spice Beer',
 99: 'American Adjunct Lager',
 100: 'Belgian Fruit Lambic',
 101: 'Berliner Weisse',
 102: 'Irish Red Ale',
 103: 'Bière de Champagne / Bière Brut',
 104: 'English Pale Ale',
 105: 'American Brett',
 106: 'Belgian Saison',
 107: 'Japanese Happoshu',
 108: 'Bohemian Pilsener',
 109: 'German Schwarzbier',
 110: 'Braggot',
 111: 'American Wild Ale'}

def normalize(ret):
    s = sum(ret.values())
    for k, v in ret.items():
        ret[k] = v/s
    return ret


def get_style_preds(beer_id):
    data = final_data[beer_id]
    local_styles = data['style']

    top_5 = sorted(local_styles)[-5:]
    print(top_5)
    ret = {}
    for idx, score in enumerate(local_styles):
        # print(idx)
        if score in top_5:
            # print(style_keys[idx])
            # print(idx), styles[str(idx)][0]
            ret[num_to_style[idx]] = score

    ret = normalize(ret)

    return [{'name': x, 'score': y} for x, y in ret.items()]