def test_plot_individual_conditional_expectation():
"""
Tests ICE plotting.
Tests
:func:`fatf.vis.feature_influence.plot_individual_conditional_expectation`
function.
"""
feature_name = 'some feature'
class_index = 1
class_name = 'middle'
figure, axis = fvfi.plot_individual_conditional_expectation(
FAKE_ICE_ARRAY, FAKE_LINESPACE, class_index, feature_name, class_name)
assert isinstance(figure, plt.Figure)
p_title, p_x_label, p_x_range, p_y_label, p_y_range = futv.get_plot_data(
axis)
# ...check title
assert p_title == 'Individual Conditional Expectation'
# ...check x range
assert np.array_equal(p_x_range, [FAKE_LINESPACE[0], FAKE_LINESPACE[-1]])
# ...check x label
assert p_x_label == feature_name
# ...check y range
assert np.array_equal(p_y_range, [-0.05, 1.05])
# ...check y label
assert p_y_label == '{} class probability'.format(class_name)
# Test the line
assert len(axis.collections) == 1
l_data, l_colour, l_alpha, l_label, l_width = futv.get_line_data(
axis.collections[0], is_collection=True)
assert len(l_data) == FAKE_ICE_ARRAY.shape[0]
for i, line_array in enumerate(l_data):
line_data = np.stack(
[FAKE_LINESPACE, FAKE_ICE_ARRAY[i, :, class_index]], axis=1)
assert np.array_equal(line_array, line_data)
assert np.isclose(l_colour,
np.array([[0.412, 0.412, 0.412, 0.5]]),
atol=1e-2).all() # dimgray mapping apparently
assert l_alpha == 0.5
assert l_label == 'ICE'
assert l_width == 1.75
# Validate plot legend
legend = [
i for i in axis.get_children()
if isinstance(i, matplotlib.legend.Legend)
]
assert len(legend) == 1
legend_texts = legend[0].get_texts()
assert len(legend_texts) == 1
assert legend_texts[0].get_text() == 'ICE'
def test_plot_partial_dependence():
"""
Tests :func:`fatf.vis.feature_influence.plot_partial_dependence` function.
"""
feature_name = 'some feature'
class_index = 1
class_name = 'middle'
figure, axis = fvfi.plot_partial_dependence(FAKE_PD_ARRAY, FAKE_LINESPACE,
class_index, feature_name,
class_name)
assert isinstance(figure, plt.Figure)
p_title, p_x_label, p_x_range, p_y_label, p_y_range = futv.get_plot_data(
axis)
# ...check title
assert p_title == 'Partial Dependence'
# ...check x range
assert np.array_equal(p_x_range, [FAKE_LINESPACE[0], FAKE_LINESPACE[-1]])
# ...check x label
assert p_x_label == feature_name
# ...check y range
assert np.array_equal(p_y_range, [-0.05, 1.05])
# ...check y label
assert p_y_label == '{} class probability'.format(class_name)
# Test the line
assert len(axis.lines) == 1
l_data, l_colour, l_alpha, l_label, l_width = futv.get_line_data(
axis.lines[0])
line_data = np.stack([FAKE_LINESPACE, FAKE_PD_ARRAY[:, class_index]],
axis=1)
assert np.array_equal(l_data, line_data)
assert l_colour == 'lightsalmon'
assert l_alpha == 0.6
assert l_label == 'PD'
assert l_width == 7
# Validate plot legend
legend = [
i for i in axis.get_children()
if isinstance(i, matplotlib.legend.Legend)
]
assert len(legend) == 1
legend_texts = legend[0].get_texts()
assert len(legend_texts) == 1
assert legend_texts[0].get_text() == 'PD'
def test_get_line_data():
"""
Tests importing :mod:`fatf.utils.testing.vis.get_line_data` function.
"""
# Test collection
true_data = [[[0, 1], [0, 1]], [[4, 3], [0, 5]]]
true_label = 'my label'
true_colour = 'green'
true_alpha = 0.5
true_width = 7
plot_figure, plot_axis = plt.subplots(1, 1)
line_collection = matplotlib.collections.LineCollection(
true_data,
label=true_label,
color=true_colour,
alpha=true_alpha,
linewidth=true_width)
plot_axis.add_collection(line_collection)
assert len(plot_axis.collections) == 1
data, colour, alpha, label, width = futv.get_line_data(
plot_axis.collections[0], is_collection=True)
assert np.array_equal(true_data, data)
assert np.allclose([[0.0, 0.5, 0.0, 0.5]], colour, atol=1e-2)
assert true_alpha == alpha
assert true_label == label
assert true_width == width
# Test a line
true_data_x = [0, 1, 2, 3, 4]
true_data_y = [5, 10, 15, 10, 5]
plot_figure, plot_axis = plt.subplots(1, 1)
plot_axis.plot(true_data_x,
true_data_y,
color=true_colour,
linewidth=true_width,
alpha=true_alpha,
label=true_label)
assert len(plot_axis.lines) == 1
data, colour, alpha, label, width = futv.get_line_data(plot_axis.lines[0],
is_collection=False)
assert data.shape == (5, 2)
assert np.array_equal(true_data_x, data[:, 0])
assert np.array_equal(true_data_y, data[:, 1])
assert true_colour == colour
assert true_alpha == alpha
assert true_label == label
assert true_width == width
data, colour, alpha, label, width = futv.get_line_data(plot_axis.lines[0])
assert data.shape == (5, 2)
assert np.array_equal(true_data_x, data[:, 0])
assert np.array_equal(true_data_y, data[:, 1])
assert true_colour == colour
assert true_alpha == alpha
assert true_label == label
assert true_width == width
def test_ice_pd_overlay():
"""
Tests overlaying PD plot on top of an ICE plot.
"""
f_name = 'some feature'
c_index = 1
c_name = 'middle'
figure, axis = fvfi.plot_individual_conditional_expectation(
FAKE_ICE_ARRAY, FAKE_LINESPACE, c_index, f_name, c_name)
assert isinstance(figure, plt.Figure)
assert isinstance(axis, plt.Axes)
none, axis = fvfi.plot_partial_dependence(FAKE_PD_ARRAY, FAKE_LINESPACE,
c_index, f_name, c_name, axis)
assert none is None
assert isinstance(axis, plt.Axes)
# Inspect the canvas
p_title, p_x_label, p_x_range, p_y_label, p_y_range = futv.get_plot_data(
axis)
# ...check title
assert p_title == ('Individual Conditional Expectation &\nPartial '
'Dependence')
# ...check x range
assert np.array_equal(p_x_range, [FAKE_LINESPACE[0], FAKE_LINESPACE[-1]])
# ...check x label
assert p_x_label == f_name
# ...check y range
assert np.array_equal(p_y_range, [-0.05, 1.05])
# ...check y label
assert p_y_label == '{} class probability'.format(c_name)
# Check ICE
assert len(axis.collections) == 1
l_data, l_colour, l_alpha, l_label, l_width = futv.get_line_data(
axis.collections[0], is_collection=True)
assert len(l_data) == FAKE_ICE_ARRAY.shape[0]
for i, line_array in enumerate(l_data):
line_data = np.stack([FAKE_LINESPACE, FAKE_ICE_ARRAY[i, :, c_index]],
axis=1)
assert np.array_equal(line_array, line_data)
assert np.isclose(l_colour,
np.array([[0.412, 0.412, 0.412, 0.5]]),
atol=1e-2).all() # dimgray mapping apparently
assert l_alpha == 0.5
assert l_label == 'ICE'
assert l_width == 1.75
# Check PD
assert len(axis.lines) == 1
l_data, l_colour, l_alpha, l_label, l_width = futv.get_line_data(
axis.lines[0])
line_data = np.stack([FAKE_LINESPACE, FAKE_PD_ARRAY[:, c_index]], axis=1)
assert np.array_equal(l_data, line_data)
assert l_colour == 'lightsalmon'
assert l_alpha == 0.6
assert l_label == 'PD'
assert l_width == 7
# Validate plot legend
legend = [
i for i in axis.get_children()
if isinstance(i, matplotlib.legend.Legend)
]
assert len(legend) == 1
legend_texts = legend[0].get_texts()
assert len(legend_texts) == 2
assert legend_texts[0].get_text() == 'PD'
assert legend_texts[1].get_text() == 'ICE'