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# "fixtures" is the fixture table containing all the matches that have taken place
# "aggregate" is the set of selected features
fixtures = fixtures.merge(aggregate, left_on='Home', right_on='Squad')
fixtures = fixtures.merge(aggregate, left_on='Away', right_on='Squad', suffixes=('_Home', '_Away'))

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from sklearn import preprocessing
from imblearn.over_sampling import RandomOverSampler

le = preprocessing.LabelEncoder()
fixtures['Score'] = le.fit_transform(fixtures['Score'].astype(str))
ros = RandomOverSampler(random_state=42)
fixtures, Score_ = ros.fit_resample(fixtures.drop(columns=['Score']), fixtures['Score'])
fixtures['Score'] = le.inverse_transform(Score_)

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proba = {'Manchester City': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Bayern Munich': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Paris Saint-Germain': {'qtr': 300, 'semi': 0, 'final': 0, 'champion': 0},
        'Real Madrid': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Juventus': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Lyon': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Barcelona': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Napoli': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Chelsea': {'qtr': 0, 'semi': 0, 'final': 0, 'champion': 0},
        'Atalanta': {'qtr': 300, 'semi': 0, 'final': 0, 'champion': 0},

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algo = SVD()
trainset = data.build_full_trainset()
algo.fit(trainset)
# Than predict ratings for all pairs (u, i) that are NOT in the training set.
testset = trainset.build_anti_testset()
predictions = algo.test(testset)

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# movie profile
movie_profile = movies[['id', 'title', 'genres']]
movie_profile.rename(columns={'id': 'movieId'}, inplace=True)
genres = [item.strip() for l in all_genres for item in l ]
unique_genres = set(genres)
for genre in unique_genres:
  movie_profile[genre] = 0
  
for i in range(len(movie_profile)):
  if type(movie_profile['genres'].iloc[i]) != None.__class__:

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from collections import Counter
import en_core_web_sm

# preprocessing
    nlp = en_core_web_sm.load()
    tweet_article = nlp('|'.join(tweets.tweets))
    # make sure the entities we need are persons
    items = [x.text for x in tweet_article.ents if x.label_ == 'PERSON']
    # exclude the obvious misclassified entities
    items = [celebrity[0] for celebrity in Counter(items).most_common(20) if

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# if the request was not get, it must be POST and we can just proceed with sending a message back to user
    else:
        # get whatever message a user sent the bot
        output = request.get_json()
        for event in output['entry']:
            messaging = event['messaging']
            for message in messaging:
                if message.get('message'):
                    # Facebook Messenger ID for user so we know where to send response back to
                    recipient_id = message['sender']['id']

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response, similarity = transformer.match_query(message['message'].get('text'))

                        # no acceptable answers found in the pool
                        if similarity < 0.5:
                            response = "Please wait! Our representative is on the way to help you!"
                            bot.send_text_message(recipient_id, response)
                        else:
                            responses = response.split('|')
                            for r in responses:
                                if r != '':

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import os
from pymongo import MongoClient

# define on heroku settings tab
MONGODB_URI = os.environ['MONGODB_URI']

cluster = MongoClient(MONGODB_URI)
db = cluster['QnA']
collection = db['QnA']

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from ipywidgets import widgets, interactive, interact, interact_manual