Wine Recommendations using Nearest Neighbors

24.04.2019 — 3 min read

Wine Recommendations

import pandas as pd
import numpy as np
import string
from nltk.probability import FreqDist
from nltk.tokenize import RegexpTokenizer
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.neighbors import NearestNeighbors
%matplotlib inline

def toWordList(text):
    text_array = []
    word_list= []
    tokenizer = RegexpTokenizer(r'\w+')
    for idx in range(len(text)):
        text_array.append(np.array(tokenizer.tokenize(text[idx].lower())))
    for words in text_array:
        for word in words:
            word_list.append(word)
    return np.array(word_list)

def text_process(text):
    nopunc = [char for char in text if char not in string.punctuation]
    nopunc = ''.join(nopunc)
    return [word for word in nopunc.split() if word.lower() not in vectorizer.get_stop_words()]

def get_recommended_wines(search_term, model, vectorizer):
    distances, indices = nbrs.kneighbors(vectorizer.transform([search_term]), 5)
    distances = distances.flatten()
    indices = indices.flatten()
    descriptions = reviewsDf.iloc[indices]['description']
    titles = reviewsDf.iloc[indices]['title']
    return distances, np.array(descriptions), np.array(titles)

def showResults(closest):
    for i in range(len(closest[0])):
        print(f'Score: {closest[0][i]}')
        print(f'Title: {closest[2][i]}')
        print(f'Description: {closest[1][i]}')
        print('\n')

Data Prep

reviewsDf = pd.read_csv('./data/winemag-data-130k-v2.csv')

reviewsDf = reviewsDf[['description','title']]

descriptions = reviewsDf.description.tolist()

all_words = toWordList(descriptions)

wordFreq = FreqDist(all_words)

We want to remove the 20 most common words since they are not useful.

plt.figure(figsize=(10, 5))
wordFreq.plot(20,cumulative=False) # Top 50 used words
plt.show()

stop_words = [word[0] for word in wordFreq.most_common(20)]

Create NB Model

vectorizer = CountVectorizer(stop_words = stop_words, analyzer=text_process)
tfidf_matrix = vectorizer.fit_transform(descriptions)

nbrs = NearestNeighbors(n_neighbors=5, metric='cosine').fit(tfidf_matrix)

Let's query for "white and sweet wine with a vanilla flavor"

closest = get_recommended_wines('white and sweet wine with a vanilla flavor', nbrs, vectorizer)

showResults(closest)

Score: 0.5256583509747431 Title: The White Knight 2011 Riesling (Lake County) Description: This is a sweet wine with flavors of white sugar, orange, honey and vanilla, all brightened by crisp acidity.

Score: 0.5256583509747431 Title: Rexford 2010 Regan Vineyard Pinot Gris (Santa Cruz Mountains) Description: This seems heavy and sweet for a Pinot Gris, with flavors of white sugar, orange and vanilla.

Score: 0.5285954792089682 Title: Lander-Jenkins 2010 Spirit Hawk Chardonnay (California) Description: Very sweet in white sugared orange and vanilla flavors, like a honey-nut candy bar. Will satisfy Chard lovers with a sweet tooth

Score: 0.5669872981077806 Title: Bougetz 2013 Sauvignon Blanc (Napa Valley) Description: Barrel-fermented and blended with a splash of Sémillon, this is a creamy, rounded and concentrated white, intense in vanilla and apricot flavor.

Not let's query for "white and sweet wine with a vanilla flavor"

closest = get_recommended_wines('red and dry with cherry flavor', nbrs, vectorizer)

showResults(closest)

Score: 0.4429139854688444 Title: Gantenbein 2011 Pinot Noir (Switzerland) Description: This wine is cherry red with soft brown tinges, offering a nose of cherry with notes of summer farmstand. The predominant flavor is of tart cherry, interlaced with hints of red raspberry and bell pepper.

Score: 0.4787139648573131 Title: Cramele Recas 2014 Dreambird Merlot (Viile Timisului) Description: This easy-drinking red wine has aromas of cherry, black plum and eucalyptus. Flavors of red cherry, cherry turnover and vanilla remain on the palate through the soft finish.

Score: 0.4787139648573131 Title: Camille Giroud 2008 Charmes Chambertin (Charmes-Chambertin) Description: A solid structure and layers of dense fruit characterize this wine. Rich acidity, red cherry flavor and a dry, dark tannic core are surrounded by juicy red berry fruits and the freshest finish.

Score: 0.4836022205056778 Title: Merriam 2008 SNED Red (Sonoma County) Description: A rustic red blend, with tobacco, herb, cherry and red currant flavors that finish dry and spicy. Useful as a bistro-style wine.

Score: 0.4896896369201712 Title: Kovács Nimród 2012 Monopole Rhapsody Red (Eger) Description: This Hungarian red blend is garnet in color, with aromas of red cherry and black berry and flavors of red cherry and pomegranate. The tannins are soft with a medium-length finish.