Esempio n. 1
0
def test_chainmapper():
    # the chain needs at lest one mapper
    assert_raises(ValueError, ChainMapper, [])
    # a typical first mapper is to flatten
    cm = ChainMapper([FlattenMapper()])

    # few container checks
    assert_equal(len(cm), 1)
    assert_true(isinstance(cm[0], FlattenMapper))

    # now training
    # come up with data
    samples_shape = (2, 2, 4)
    data_shape = (4,) + samples_shape
    data = np.arange(np.prod(data_shape)).reshape(data_shape)
    pristinedata = data.copy()
    target = [
        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
        [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
        [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
        [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
    ]
    target = np.array(target)

    # if it is not trained it knows nothing
    cm.train(data)

    # a new mapper should appear when doing feature selection
    cm.append(FeatureSliceMapper(range(1, 16)))
    assert_equal(cm.forward1(data[0]).shape, (15,))
    assert_equal(len(cm), 2)
    # multiple slicing
    cm.append(FeatureSliceMapper([9, 14]))
    assert_equal(cm.forward1(data[0]).shape, (2,))
    assert_equal(len(cm), 3)

    # check reproduction
    cm_clone = eval(repr(cm))
    assert_equal(repr(cm_clone), repr(cm))

    # what happens if we retrain the whole beast an same data as before
    cm.train(data)
    assert_equal(cm.forward1(data[0]).shape, (2,))
    assert_equal(len(cm), 3)

    # let's map something
    mdata = cm.forward(data)
    assert_array_equal(mdata, target[:, [10, 15]])
    # and back
    rdata = cm.reverse(mdata)
    # original shape
    assert_equal(rdata.shape, data.shape)
    # content as far it could be restored
    assert_array_equal(rdata[rdata > 0], data[rdata > 0])
    assert_equal(np.sum(rdata > 0), 8)
Esempio n. 2
0
def test_chainmapper():
    # the chain needs at lest one mapper
    assert_raises(ValueError, ChainMapper, [])
    # a typical first mapper is to flatten
    cm = ChainMapper([FlattenMapper()])

    # few container checks
    assert_equal(len(cm), 1)
    assert_true(isinstance(cm[0], FlattenMapper))

    # now training
    # come up with data
    samples_shape = (2, 2, 4)
    data_shape = (4, ) + samples_shape
    data = np.arange(np.prod(data_shape)).reshape(data_shape)
    pristinedata = data.copy()
    target = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
              [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
              [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]]
    target = np.array(target)

    # if it is not trained it knows nothing
    cm.train(data)

    # a new mapper should appear when doing feature selection
    cm.append(FeatureSliceMapper(range(1, 16)))
    assert_equal(cm.forward1(data[0]).shape, (15, ))
    assert_equal(len(cm), 2)
    # multiple slicing
    cm.append(FeatureSliceMapper([9, 14]))
    assert_equal(cm.forward1(data[0]).shape, (2, ))
    assert_equal(len(cm), 3)

    # check reproduction
    cm_clone = eval(repr(cm))
    assert_equal(repr(cm_clone), repr(cm))

    # what happens if we retrain the whole beast an same data as before
    cm.train(data)
    assert_equal(cm.forward1(data[0]).shape, (2, ))
    assert_equal(len(cm), 3)

    # let's map something
    mdata = cm.forward(data)
    assert_array_equal(mdata, target[:, [10, 15]])
    # and back
    rdata = cm.reverse(mdata)
    # original shape
    assert_equal(rdata.shape, data.shape)
    # content as far it could be restored
    assert_array_equal(rdata[rdata > 0], data[rdata > 0])
    assert_equal(np.sum(rdata > 0), 8)
Esempio n. 3
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    def testChainMapper(self):
        data = N.array([N.arange(24).reshape(3,4,2) + (i * 100)
                            for i in range(10)])

        startpoints = [ 2, 4, 3, 5 ]
        m = ChainMapper([BoxcarMapper(startpoints, 2),
                         DenseArrayMapper(mask=N.ones((2, 3, 4, 2)))])
        mp = m.forward(data)
        # 4 startpoint, with each two samples of shape (3,4,2)
        self.failUnless(mp.shape == (4, 48))

        self.failUnless(m.reverse(N.arange(48)).shape == (2, 3, 4, 2))

        # should behave a DenseArrayMapper alone
        self.failUnless((N.array([n for n in m.getNeighbor(24, radius=1.1)])
                         == N.array((0, 24, 25, 26, 32))).all())
Esempio n. 4
0
def test_chainmapper():
    # the chain needs at lest one mapper
    assert_raises(ValueError, ChainMapper, [])
    # a typical first mapper is to flatten
    cm = ChainMapper([FlattenMapper()])

    # few container checks
    assert_equal(len(cm), 1)
    assert_true(isinstance(cm[0], FlattenMapper))

    # now training
    # come up with data
    samples_shape = (2, 2, 4)
    data_shape = (4,) + samples_shape
    data = np.arange(np.prod(data_shape)).reshape(data_shape)
    pristinedata = data.copy()
    target = [
        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
        [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
        [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
        [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63],
    ]
    target = np.array(target)

    # if it is not trained it knows nothing
    cm.train(data)

    # a new mapper should appear when doing feature selection
    cm.append(StaticFeatureSelection(range(1, 16)))
    assert_equal(cm.forward1(data[0]).shape, (15,))
    assert_equal(len(cm), 2)
    # multiple slicing
    cm.append(StaticFeatureSelection([9, 14]))
    assert_equal(cm.forward1(data[0]).shape, (2,))
    assert_equal(len(cm), 3)

    # check reproduction
    if __debug__:
        # debug mode needs special test as it enhances the repr output
        # with module info and id() appendix for objects
        import mvpa

        cm_clone = eval(repr(cm))
        assert_equal("#".join(repr(cm_clone).split("#")[:-1]), "#".join(repr(cm).split("#")[:-1]))
    else:
        cm_clone = eval(repr(cm))
        assert_equal(repr(cm_clone), repr(cm))

    # what happens if we retrain the whole beast an same data as before
    cm.train(data)
    assert_equal(cm.forward1(data[0]).shape, (2,))
    assert_equal(len(cm), 3)

    # let's map something
    mdata = cm.forward(data)
    assert_array_equal(mdata, target[:, [10, 15]])
    # and back
    rdata = cm.reverse(mdata)
    # original shape
    assert_equal(rdata.shape, data.shape)
    # content as far it could be restored
    assert_array_equal(rdata[rdata > 0], data[rdata > 0])
    assert_equal(np.sum(rdata > 0), 8)

    # Lets construct a dataset with mapper assigned and see
    # if sub-selecting a feature adjusts trailing StaticFeatureSelection
    # appropriately
    ds_subsel = Dataset.from_wizard(data, mapper=cm)[:, 1]
    tail_sfs = ds_subsel.a.mapper[-1]
    assert_equal(repr(tail_sfs), "StaticFeatureSelection(slicearg=array([14]))")