def test_DecisionMatrixPlotter_wheatmap_default_axis(
decision_matrix, fig_test, fig_ref
):
dm = decision_matrix(
seed=42,
min_alternatives=3,
max_alternatives=3,
min_criteria=3,
max_criteria=3,
)
plotter = plot.DecisionMatrixPlotter(dm=dm)
test_ax = fig_test.subplots()
with mock.patch("matplotlib.pyplot.gca", return_value=test_ax):
plotter.wheatmap()
# EXPECTED
labels = [
f"{c} {o.to_string()}" for c, o in zip(dm.criteria, dm.objectives)
]
weights = dm.weights.to_frame().T
exp_ax = fig_ref.subplots()
sns.heatmap(weights, ax=exp_ax, annot=True, cmap=plt.cm.get_cmap())
exp_ax.set_xticklabels(labels)
exp_ax.set_xlabel("Criteria")
size = fig_ref.get_size_inches() / [1, 5]
fig_ref.set_size_inches(size)
def test_DecisionMatrixPlotter_wheatmap(decision_matrix, fig_test, fig_ref):
dm = decision_matrix(
seed=42,
min_alternatives=3,
max_alternatives=3,
min_criteria=3,
max_criteria=3,
)
plotter = plot.DecisionMatrixPlotter(dm=dm)
test_ax = fig_test.subplots()
plotter.wheatmap(ax=test_ax)
# EXPECTED
labels = [
f"{c} {o.to_string()}" for c, o in zip(dm.criteria, dm.objectives)
]
weights = dm.weights.to_frame().T
exp_ax = fig_ref.subplots()
sns.heatmap(weights, ax=exp_ax, annot=True, cmap=plt.cm.get_cmap())
exp_ax.set_xticklabels(labels)
exp_ax.set_xlabel("Criteria")
def test_DecisionMatrixPlotter_box(decision_matrix, orient, fig_test, fig_ref):
dm = decision_matrix(
seed=42,
min_alternatives=3,
max_alternatives=3,
min_criteria=3,
max_criteria=3,
)
plotter = plot.DecisionMatrixPlotter(dm=dm)
test_ax = fig_test.subplots()
plotter.box(ax=test_ax, orient=orient)
# EXPECTED
labels = [
f"{c} {o.to_string()}" for c, o in zip(dm.criteria, dm.objectives)
]
exp_ax = fig_ref.subplots()
sns.boxplot(data=dm.matrix, ax=exp_ax, orient=orient)
if orient == "v":
exp_ax.set_xticklabels(labels)
exp_ax.set_xlabel("Criteria")
elif orient == "h":
exp_ax.set_yticklabels(labels)
exp_ax.set_ylabel("Criteria")
def test_DecisionMatrixPlotter_wbar(decision_matrix, fig_test, fig_ref):
dm = decision_matrix(
seed=42,
min_alternatives=3,
max_alternatives=3,
min_criteria=3,
max_criteria=3,
)
plotter = plot.DecisionMatrixPlotter(dm=dm)
test_ax = fig_test.subplots()
plotter.wbar(ax=test_ax)
# EXPECTED
labels = [
f"{c} {o.to_string()}" for c, o in zip(dm.criteria, dm.objectives)
]
weights = dm.weights.to_frame().T
exp_ax = fig_ref.subplots()
weights.plot.bar(ax=exp_ax)
exp_ax.set_xlabel("Alternatives")
exp_ax.legend(labels)
def test_DecisionMatrixPlotter_wogive(decision_matrix, fig_test, fig_ref):
dm = decision_matrix(
seed=42,
min_alternatives=3,
max_alternatives=3,
min_criteria=3,
max_criteria=3,
)
plotter = plot.DecisionMatrixPlotter(dm=dm)
test_ax = fig_test.subplots()
plotter.wogive(ax=test_ax)
# EXPECTED
weights = dm.weights.to_frame()
exp_ax = fig_ref.subplots()
sns.ecdfplot(data=weights, ax=exp_ax)
def test_DecisionMatrixPlotter_ogive(decision_matrix, fig_test, fig_ref):
dm = decision_matrix(
seed=42,
min_alternatives=3,
max_alternatives=3,
min_criteria=3,
max_criteria=3,
)
plotter = plot.DecisionMatrixPlotter(dm=dm)
test_ax = fig_test.subplots()
plotter.ogive(ax=test_ax)
# EXPECTED
labels = [
f"{c} {o.to_string()}" for c, o in zip(dm.criteria, dm.objectives)
]
exp_ax = fig_ref.subplots()
sns.ecdfplot(data=dm.matrix, ax=exp_ax)
exp_ax.legend(labels)
