Exemple #1
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def top_recommendations_lda():
    params = get_best_params("lda")
    info = getMeta()
    phi = params["phi"]
    kappa = params["kappa"]

    reviews = get_test_reviews()
    rating_values = np.asarray([0,1.0,2.0,3.0,4.0,5.0])
    precision = 0.0
    num_users = 0
    for user in xrange(info["users"]):
        movie_ratings = []
        for movie in xrange(info["movies"]):
            topic = np.argmax(phi[movie,:])
            rating = np.dot(kappa[:,user,topic]/np.sum(kappa[:,user,topic]), rating_values)
            movie_ratings.append((movie, rating))
        movie_ratings = sorted(movie_ratings, key=lambda x: x[1])
        top_movies_for_user = set(movie for movie, rating in movie_ratings[-1000:])

        user_precision = 0.0
        movies = reviews[user, :].nonzero()[0]
        for movie in movies:
            if movie in top_movies_for_user:
                user_precision += 1
        if len(movies) > 0:
            num_users += 1
            precision += (user_precision / len(movies))
    return precision / num_users
Exemple #2
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def top_recommendations_poisson():
    params = get_best_params("poisson")
    info = getMeta()
    beta = params["beta"]
    theta = params["theta"]

    reviews = get_test_reviews()

    precision = 0.0
    num_users = 0
    for user in xrange(info["users"]):
        movie_ratings = []
        for movie in xrange(info["movies"]):
            rating = np.dot(theta[user, :], beta[movie, :])
            movie_ratings.append((movie, rating))

        movie_ratings = sorted(movie_ratings, key=lambda x: x[1])
        top_movies_for_user = set(movie for movie, rating in movie_ratings[-1000:])

        user_precision = 0.0
        movies = reviews[user, :].nonzero()[0]

        for movie in movies:
            if movie in top_movies_for_user:
                user_precision += 1
        if len(movies) > 0:
            num_users += 1
            precision += (user_precision / len(movies))

    return precision / num_users
Exemple #3
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def top_recommendations_poisson():
    params = get_best_params("poisson")
    info = getMeta()
    beta = params["beta"]
    theta = params["theta"]

    reviews = get_test_reviews()

    precision = 0.0
    num_users = 0
    for user in xrange(info["users"]):
        movie_ratings = []
        for movie in xrange(info["movies"]):
            rating = np.dot(theta[user, :], beta[movie, :])
            movie_ratings.append((movie, rating))

        movie_ratings = sorted(movie_ratings, key=lambda x: x[1])
        top_movies_for_user = set(movie
                                  for movie, rating in movie_ratings[-1000:])

        user_precision = 0.0
        movies = reviews[user, :].nonzero()[0]

        for movie in movies:
            if movie in top_movies_for_user:
                user_precision += 1
        if len(movies) > 0:
            num_users += 1
            precision += (user_precision / len(movies))

    return precision / num_users
Exemple #4
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def top_recommendations_lda():
    params = get_best_params("lda")
    info = getMeta()
    phi = params["phi"]
    kappa = params["kappa"]

    reviews = get_test_reviews()
    rating_values = np.asarray([0, 1.0, 2.0, 3.0, 4.0, 5.0])
    precision = 0.0
    num_users = 0
    for user in xrange(info["users"]):
        movie_ratings = []
        for movie in xrange(info["movies"]):
            topic = np.argmax(phi[movie, :])
            rating = np.dot(
                kappa[:, user, topic] / np.sum(kappa[:, user, topic]),
                rating_values)
            movie_ratings.append((movie, rating))
        movie_ratings = sorted(movie_ratings, key=lambda x: x[1])
        top_movies_for_user = set(movie
                                  for movie, rating in movie_ratings[-1000:])

        user_precision = 0.0
        movies = reviews[user, :].nonzero()[0]
        for movie in movies:
            if movie in top_movies_for_user:
                user_precision += 1
        if len(movies) > 0:
            num_users += 1
            precision += (user_precision / len(movies))
    return precision / num_users
Exemple #5
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def test_poisson():
    params = get_best_params("poisson")
    beta = params["beta"]
    theta = params["theta"]

    reviews = get_test_reviews()
    rmse = 0.0
    rmses = []
    count = 0
    for user, movie in izip(*reviews.nonzero()):
        true_rating = reviews[user, movie]
        mean_rating = np.dot(theta[user, :], beta[movie, :])
        mean_rating = max(1, min(5, mean_rating + 1))
        rmse += (true_rating - mean_rating) ** 2
        rmses.append((true_rating - mean_rating) ** 2)
        count += 1

    return math.sqrt(rmse / count)
Exemple #6
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def test_lda():
    params = get_best_params("lda")
    info = getMeta()
    phi = params["phi"]
    kappa = params["kappa"]

    reviews = get_test_reviews()
    rmse = 0.0
    count = 0

    rating_values = np.asarray([0,1.0,2.0,3.0,4.0,5.0])
    for user, movie in izip(*reviews.nonzero()):
        topic = np.argmax(phi[movie,:])
        estimated_rating = np.dot(kappa[:,user,topic]/np.sum(kappa[:,user,topic]), rating_values)
        true_rating = reviews[user, movie]
        rmse += (true_rating - estimated_rating) ** 2
        count += 1
    return math.sqrt(rmse / count)
Exemple #7
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def test_poisson():
    params = get_best_params("poisson")
    beta = params["beta"]
    theta = params["theta"]

    reviews = get_test_reviews()
    rmse = 0.0
    rmses = []
    count = 0
    for user, movie in izip(*reviews.nonzero()):
        true_rating = reviews[user, movie]
        mean_rating = np.dot(theta[user, :], beta[movie, :])
        mean_rating = max(1, min(5, mean_rating + 1))
        rmse += (true_rating - mean_rating)**2
        rmses.append((true_rating - mean_rating)**2)
        count += 1

    return math.sqrt(rmse / count)
Exemple #8
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def test_lda():
    params = get_best_params("lda")
    info = getMeta()
    phi = params["phi"]
    kappa = params["kappa"]

    reviews = get_test_reviews()
    rmse = 0.0
    count = 0

    rating_values = np.asarray([0, 1.0, 2.0, 3.0, 4.0, 5.0])
    for user, movie in izip(*reviews.nonzero()):
        topic = np.argmax(phi[movie, :])
        estimated_rating = np.dot(
            kappa[:, user, topic] / np.sum(kappa[:, user, topic]),
            rating_values)
        true_rating = reviews[user, movie]
        rmse += (true_rating - estimated_rating)**2
        count += 1
    return math.sqrt(rmse / count)