Example #1
0
def to_libfm(examples, libfm_filename):
    libfm_file = open(libfm_filename, 'w')
    for example in examples:
        x_i = model.represent(example)[:-1]
        y_i = model.label(example)
        user_id = example['user']
        user_id_feature = "%d:1" % (len(x_i)+user_id)
        libfm_file.write("%d %s %s\n" % (y_i, sp_vector(x_i), user_id_feature))
    libfm_file.close()
Example #2
0
def to_libfm(examples, libfm_filename):
    libfm_file = open(libfm_filename, 'w')
    for example in examples:
        x_i = model.represent(example)[:-1]
        y_i = model.label(example)
        user_id = example['user']
        user_id_feature = "%d:1" % (len(x_i) + user_id)
        libfm_file.write("%d %s %s\n" % (y_i, sp_vector(x_i), user_id_feature))
    libfm_file.close()
def get_test_examples():
    global test_X
    global test_y
    if not test_X:
        print "Loading test examples"
        _log_time()
        test_examples = music.load_examples("data/test_40k_10k.pkl")
        _print_time_diff()
        print "Obtaining X and y values"
        _log_time()
        test_X = [model.represent(example) for example in test_examples]
        _print_time_diff()
        _log_time()
        test_y = [model.label(example) for example in test_examples]
        _print_time_diff()
    return test_X, test_y
    print "Readying testing data"
    _log_time()
    music.ready_testing_data()
    _print_time_diff()
    _draw_separator()


print "Start loading examples"
_log_time()
examples = music.load_examples("data/train.pkl")
_print_time_diff()
_draw_separator()

print "Obtaining all x and y values"
_log_time()
all_X = [model.represent(example) for example in examples]
_print_time_diff()
_log_time()
all_y = [model.label(example) for example in examples]
_print_time_diff()
_draw_separator()


def print_consolidated_scores(scores):
    _draw_separator(".", 5)
    print "%0.6f (+/- %0.6f)" % (scores.mean(), scores.std() / 2)
    _draw_separator("`", 5)


def _get_mean_score(scores):
    return "%0.6f" % (scores.mean())
Example #5
0
    scores = cross_val_score(knr, X, y, scoring='neg_mean_squared_error', cv=3)
    return scores


if __name__ == "__main__":
    import music

    train_examples = music.load_examples('data/train.pkl')
    # poly = PolynomialNetworkRegressor(degreex=3, n_components=2, tol=1e-3, warm_start=True, random_state=0)
    fm = pylibfm.FM(num_iter=10,
                    verbose=True,
                    task="regression",
                    initial_learning_rate=0.001,
                    learning_rate_schedule="optimal")
    v = DictVectorizer()
    X = np.asarray([model.represent(example) for example in train_examples])
    y = np.asarray([model.label(example) for example in train_examples])
    # fm.fit(sparse.csr_matrix(X), y)
    # svr_rbf.fit(X, y)
    # pca = PCA(n_components=100)
    # pca.fit(X)
    # X_fit = pca.transform(X)
    # print "pca done"
    # xs = [x[0] for x in X_fit]
    # ys = [x[1] for x in X_fit]
    # plt.scatter(xs, ys)
    # plt.show()
    # print v.fit_transform(X)
    # print X_fitM
    y_np = np.asarray(y)
    plt.hist(y_np, bins=np.arange(y_np.min(), y_np.max() + 1))