Exemple #1
0
def test_predict():
    (I, J, K) = (5, 3, 2)
    R = numpy.array(
        [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15]],
        dtype=float)
    M = numpy.ones((I, J))
    K = 3

    U = numpy.array([[125., 126.], [126., 126.], [126., 126.], [126., 126.],
                     [126., 126.]])
    V = numpy.array([[84., 84.], [84., 84.], [84., 84.]])

    M_test = numpy.array([[0, 0, 1], [0, 1, 0], [0, 0, 0], [1, 1, 0],
                          [0, 0,
                           0]])  #R->3,5,10,11, R_pred->21084,21168,21168,21168
    MSE = (444408561. + 447872569. + 447660964. + 447618649) / 4.
    R2 = 1. - (444408561. + 447872569. + 447660964. + 447618649) / (
        4.25**2 + 2.25**2 + 2.75**2 + 3.75**2)  #mean=7.25
    Rp = 357. / (
        math.sqrt(44.75) * math.sqrt(5292.)
    )  #mean=7.25,var=44.75, mean_pred=21147,var_pred=5292, corr=(-4.25*-63 + -2.25*21 + 2.75*21 + 3.75*21)

    nmf = NMF(R, M, K)
    nmf.U = U
    nmf.V = V
    performances = nmf.predict(M_test)

    assert performances['MSE'] == MSE
    assert performances['R^2'] == R2
    assert performances['Rp'] == Rp
Exemple #2
0
                        for metric in metrics}  # averaged over repeats
for (fraction, Ms, Ms_test) in zip(fractions_unknown, all_Ms, all_Ms_test):
    print "Trying fraction %s." % fraction

    # Run the algorithm <repeats> times and store all the performances
    for metric in metrics:
        all_performances[metric].append([])
    for (repeat, M, M_test) in zip(range(0, repeats), Ms, Ms_test):
        print "Repeat %s of fraction %s." % (repeat + 1, fraction)

        nmf = NMF(R, M, K)
        nmf.initialise(init_UV, expo_prior)
        nmf.run(iterations)

        # Measure the performances
        performances = nmf.predict(M_test)
        for metric in metrics:
            # Add this metric's performance to the list of <repeat> performances for this fraction
            all_performances[metric][-1].append(performances[metric])

    # Compute the average across attempts
    for metric in metrics:
        average_performances[metric].append(
            sum(all_performances[metric][-1]) / repeats)


print "repeats=%s \nfractions_unknown = %s \nall_performances = %s \naverage_performances = %s" % \
    (repeats,fractions_unknown,all_performances,average_performances)
'''
repeats=10 
fractions_unknown = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]