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_plot_data():
"""
Tests importing :mod:`fatf.utils.testing.vis.get_plot_data` function.
"""
true_title = 'my title'
true_x_label = 'x label'
true_x_range = [-0.42, 12.34]
true_y_label = 'y label'
true_y_range = [-7, 7]
plot_figure, plot_axis = plt.subplots(1, 1)
plot_axis.set_title(true_title)
plot_axis.set_xlim(true_x_range)
plot_axis.set_xlabel(true_x_label)
plot_axis.set_ylim(true_y_range)
plot_axis.set_ylabel(true_y_label)
title, x_label, x_range, y_label, y_range = futv.get_plot_data(plot_axis)
assert true_title == title
assert true_x_label == x_label
assert true_y_label == y_label
assert np.array_equal(true_y_range, y_range)
assert np.array_equal(true_x_range, x_range)
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'
def test_prepare_a_canvas():
"""
Tests :func:`fatf.vis.feature_influence._prepare_a_canvas`.
This test checks for the plot title, x range, x label, y range and y label.
"""
title = 'plot title'
title_custom = 'custom plot title'
class_index = 0
x_range = [-5, 3]
y_range = [-0.05, 1.05]
class_name_n = None
class_name_s = 'class name'
feature_name_n = None
feature_name_s = 'feature name'
# Plotting from scratch
axis = None
#
figure, plot = fvfi._prepare_a_canvas(title, axis, class_index,
class_name_s, feature_name_n,
x_range)
assert isinstance(figure, plt.Figure)
p_title, p_x_label, p_x_range, p_y_label, p_y_range = futv.get_plot_data(
plot)
# ...check title
assert p_title == title
# ...check x range
assert np.array_equal(p_x_range, x_range)
# ...check x label
assert p_x_label == "Selected Feature's Linespace"
# ...check y range
assert np.array_equal(p_y_range, y_range)
# ...check y label
assert p_y_label == '{} class probability'.format(class_name_s)
#
figure, plot = fvfi._prepare_a_canvas(title, axis, class_index,
class_name_n, feature_name_s,
x_range)
assert isinstance(figure, plt.Figure)
p_title, p_x_label, p_x_range, p_y_label, p_y_range = futv.get_plot_data(
plot)
# ...check title
assert p_title == title
# ...check x range
assert np.array_equal(p_x_range, x_range)
# ...check x label
assert p_x_label == feature_name_s
# ...check y range
assert np.array_equal(p_y_range, y_range)
# ...check y label
assert p_y_label == '{} (class index) class probability'.format(
class_index)
# Plotting on an existing axis
fig, axis = plt.subplots(1, 1)
#
axis.set_xlim(np.array([-3, 3]))
msg = ('The x-axis range of the plot given in the plot_axis parameter '
'differs from the x-axis range of this plot.')
with pytest.raises(ValueError) as exin:
fvfi._prepare_a_canvas(title, axis, class_index, class_name_n,
feature_name_n, x_range)
assert str(exin.value) == msg
#
axis.set_xlim(np.array(x_range))
axis.set_ylim(np.array([0, 1]))
msg = ('The y-axis range of the plot given in the plot_axis parameter '
'differs from the y-axis range of this plot.')
with pytest.raises(ValueError) as exin:
fvfi._prepare_a_canvas(title, axis, class_index, class_name_n,
feature_name_n, x_range)
assert str(exin.value) == msg
#
axis.set_ylim(np.array(y_range))
axis.set_title(title_custom)
#
# Do not extend plot title; new feature name and new class name.
figure, plot = fvfi._prepare_a_canvas('', axis, class_index, class_name_s,
feature_name_s, x_range)
assert figure is None
p_title, p_x_label, p_x_range, p_y_label, p_y_range = futv.get_plot_data(
plot)
# ...check title
assert p_title == title_custom
# ...check x range
assert np.array_equal(p_x_range, x_range)
# ...check x label
assert p_x_label == feature_name_s
# ...check y range
assert np.array_equal(p_y_range, y_range)
# ...check y label
assert p_y_label == '{} class probability'.format(class_name_s)
#
# Do not extend plot title; no new feature name & existing feature name and
# no new class name and existing class name.
figure, plot = fvfi._prepare_a_canvas('', plot, class_index, class_name_n,
feature_name_n, x_range)
assert figure is None
p_title, p_x_label, p_x_range, p_y_label, p_y_range = futv.get_plot_data(
plot)
# ...check title
assert p_title == title_custom
# ...check x range
assert np.array_equal(p_x_range, x_range)
# ...check x label
assert p_x_label == feature_name_s
# ...check y range
assert np.array_equal(p_y_range, y_range)
# ...check y label
assert p_y_label == '{} class probability'.format(class_name_s)
#
# Do not extend plot title; no new feature name & no existing feature name
# and no new class name and no existing class name.
axis.set_ylabel(None)
axis.set_xlabel(None)
figure, plot = fvfi._prepare_a_canvas('extension', axis, class_index,
class_name_n, feature_name_n,
x_range)
assert figure is None
p_title, p_x_label, p_x_range, p_y_label, p_y_range = futv.get_plot_data(
plot)
# ...check title
assert p_title == '{} &\nextension'.format(title_custom)
# ...check x range
assert np.array_equal(p_x_range, x_range)
# ...check x label
assert p_x_label == "Selected Feature's Linespace"
# ...check y range
assert np.array_equal(p_y_range, y_range)
# ...check y label
assert p_y_label == '{} (class index) class probability'.format(
class_index)
