Python Potato.transform Examples

Example #1

File: test_clustering.py Project: medic20/pyRiemann

def test_Potato_init():
    """Test Potato"""
    covset = generate_cov(20, 3)
    labels = np.array([0, 1]).repeat(10)

    # init
    pt = Potato()

    # fit no labels
    pt.fit(covset)

    # fit with labels
    assert_raises(ValueError, pt.fit, covset, y=[1])
    assert_raises(ValueError, pt.fit, covset, y=[0] * 20)
    assert_raises(ValueError, pt.fit, covset, y=[0, 2, 3] + [1] * 17)
    pt.fit(covset, labels)

    # transform
    pt.transform(covset)

    # transform
    pt.predict(covset)

    # lower threshold
    pt = Potato(threshold=1)
    pt.fit(covset)

Example #2

def test_Potato_init():
    """Test Potato"""
    covset = generate_cov(20, 3)
    labels = np.array([0, 1]).repeat(10)

    # init
    pt = Potato()

    # fit no labels
    pt.fit(covset)

    # fit with labels
    with pytest.raises(ValueError):
        pt.fit(covset, y=[1])

    with pytest.raises(ValueError):
        pt.fit(covset, y=[0] * 20)

    with pytest.raises(ValueError):
        pt.fit(covset, y=[0, 2, 3] + [1] * 17)

    pt.fit(covset, labels)

    # transform
    pt.transform(covset)
    pt.transform(covset[0][np.newaxis, ...])  # transform a single trial

    # predict
    pt.predict(covset)
    pt.predict(covset[0][np.newaxis, ...])  # predict a single trial

    # predict_proba
    pt.predict_proba(covset)
    pt.predict_proba(covset[0][np.newaxis, ...])

    # lower threshold
    pt = Potato(threshold=1)
    pt.fit(covset)

    # test positive labels
    pt = Potato(threshold=1, pos_label=2, neg_label=7)
    pt.fit(covset)
    assert_array_equal(np.unique(pt.predict(covset)), [2, 7])

    # test with custom positive label
    pt.fit(covset, y=[2] * 20)

    # different positive and neg label
    with pytest.raises(ValueError):
        Potato(pos_label=0)

Example #3

File: test_clustering.py Project: alexandrebarachant/pyRiemann

def test_Potato_init():
    """Test Potato"""
    covset = generate_cov(20, 3)
    labels = np.array([0, 1]).repeat(10)

    # init
    pt = Potato()

    # fit no labels
    pt.fit(covset)

    # fit with labels
    assert_raises(ValueError, pt.fit, covset, y=[1])
    assert_raises(ValueError, pt.fit, covset, y=[0] * 20)
    assert_raises(ValueError, pt.fit, covset, y=[0, 2, 3] + [1] * 17)
    pt.fit(covset, labels)

    # transform
    pt.transform(covset)

    # predict
    pt.predict(covset)

    # lower threshold
    pt = Potato(threshold=1)
    pt.fit(covset)

    # test positive labels
    pt = Potato(threshold=1, pos_label=2, neg_label=7)
    pt.fit(covset)
    assert_array_equal(np.unique(pt.predict(covset)), [2, 7])

    # test with custom positive label
    pt.fit(covset, y=[2]*20)

    # different positive and neg label
    assert_raises(ValueError, Potato, pos_label=0)

Example #4

def test_Potato_init():
    """Test Potato"""
    covset = generate_cov(20, 3)
    labels = np.array([0, 1]).repeat(10)

    # init
    pt = Potato()

    # fit no labels
    pt.fit(covset)

    # fit with labels
    assert_raises(ValueError, pt.fit, covset, y=[1])
    assert_raises(ValueError, pt.fit, covset, y=[0] * 20)
    assert_raises(ValueError, pt.fit, covset, y=[0, 2, 3] + [1] * 17)
    pt.fit(covset, labels)

    # transform
    pt.transform(covset)

    # predict
    pt.predict(covset)

    # lower threshold
    pt = Potato(threshold=1)
    pt.fit(covset)

    # test positive labels
    pt = Potato(threshold=1, pos_label=2, neg_label=7)
    pt.fit(covset)
    assert_array_equal(np.unique(pt.predict(covset)), [2, 7])

    # test with custom positive label
    pt.fit(covset, y=[2] * 20)

    # different positive and neg label
    assert_raises(ValueError, Potato, pos_label=0)