MemphisMeng
/ contentFiltering.py
Created
August 12, 2020 19:16
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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__: |
MemphisMeng
/ collaborative filtering.py
Last active
August 12, 2020 16:34
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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) |
MemphisMeng
/ simulate.py
Created
August 4, 2020 18:23
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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}, |
MemphisMeng
/ overSampling.py
Created
August 3, 2020 20:35
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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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| # "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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