le.py 文件源码

def test(degree):
    error_rate_of_artist = []
    weight_of_artist = []
    f_of_artist = []
    F = 0.0
    for j in range(0, 50):
        p = artists_play_inday[j]
        apcount = [0] * 184
        apdate = range(0, 184)
        for i in p:
            apcount[i[1]] = i[0]

        x = np.asarray(apdate[:122])
        x_test = np.asarray(apdate[122:])
        X = x[:, np.newaxis]
        y = np.asarray(apcount[:122])
        y_test_true = np.asarray(apcount[122:])

        X_test = x_test[:, np.newaxis]

        model = make_pipeline(PolynomialFeatures(degree), Ridge())
        model.fit(X, y)
        y_test_pred = model.predict(X_test)

        error_rate_pow2_sum = 0.0
        weight = 0.0
        for idx in range(0, len(x_test)):
            y_true = y_test_true[idx]
            if y_true == 0:
                y_true = 1 # deal with divide by zero

            error_rate_pow2_sum += (float((int(math.ceil(y_test_pred[idx])) - y_true)) / float(y_true) )**2
            weight += y_test_true[idx]

        error_rate_j = math.sqrt(error_rate_pow2_sum / float(len(x_test)))
        error_rate_of_artist.append(error_rate_j)
        weight_j = math.sqrt(weight)
        weight_of_artist.append(weight_j)
        f_j = (1 - error_rate_j) * weight_j
        f_of_artist.append(f_j)
        F += f_j

    print 'degree', degree
    print 'error_rate_of_artist', error_rate_of_artist
    print 'weight_of_artist', weight_of_artist
    print 'f_of_artist', f_of_artist
    print 'F', F