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MAINT Use class_of_interest in DecisionBoundaryDisplay #772

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MAINT Use class_of_interest in DecisionBoundaryDisplay #772

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MAINT Use class_of_interest in DecisionBoundaryDisplay
Mar 20, 2024
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Mar 20, 2024
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62 changes: 27 additions & 35 deletions python_scripts/trees_sol_01.py
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Original file line number Diff line number Diff line change
Expand Up @@ -150,46 +150,38 @@

# %% tags=["solution"]
import numpy as np
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I think that we can simplify the code by using some groupby that will avoid using the loc here:

from matplotlib import cm

_, axs = plt.subplots(ncols=3, nrows=1, sharey=True, figsize=(12, 5))
plt.suptitle("Predicted probabilities for decision tree model", y=1.05)
plt.subplots_adjust(bottom=0.45)

for idx, (class_of_interest, data_class) in enumerate(
    data_test.groupby(by=target_test)
):
    axs[idx].set_title(f"Class {class_of_interest}")
    disp = DecisionBoundaryDisplay.from_estimator(
        tree,
        data_test,
        response_method="predict_proba",
        class_of_interest=class_of_interest,
        ax=axs[idx],
        vmin=0,
        vmax=1,
    )
    data_class.plot.scatter(
        x="Culmen Length (mm)",
        y="Culmen Depth (mm)",
        ax=axs[idx],
        marker="o",
        s=100,
        c="w",
        edgecolor="k",
    )

ax = plt.axes([0.15, 0.14, 0.7, 0.05])
plt.colorbar(
    cm.ScalarMappable(cmap="viridis"), cax=ax, orientation="horizontal"
)
_ = plt.title("Probability")

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I feel that we have less matplotlib boilerplate code in this case since we rely on pandas directly.

from matplotlib import cm

xx = np.linspace(30, 60, 100)
yy = np.linspace(10, 23, 100)
xx, yy = np.meshgrid(xx, yy)
Xfull = pd.DataFrame(
{"Culmen Length (mm)": xx.ravel(), "Culmen Depth (mm)": yy.ravel()}
)

probas = tree.predict_proba(Xfull)
n_classes = len(np.unique(tree.classes_))

classes = np.unique(tree.classes_)
_, axs = plt.subplots(ncols=3, nrows=1, sharey=True, figsize=(12, 5))
plt.suptitle("Predicted probabilities for decision tree model", y=0.8)

for class_of_interest in range(n_classes):
axs[class_of_interest].set_title(
f"Class {tree.classes_[class_of_interest]}"
)
imshow_handle = axs[class_of_interest].imshow(
probas[:, class_of_interest].reshape((100, 100)),
extent=(30, 60, 10, 23),
vmin=0.0,
vmax=1.0,
origin="lower",
cmap="viridis",
plt.suptitle("Predicted probabilities for decision tree model", y=1.05)
plt.subplots_adjust(bottom=0.45)

for idx, class_of_interest in enumerate(classes):
axs[idx].set_title(f"Class {class_of_interest}")
disp = DecisionBoundaryDisplay.from_estimator(
tree,
data_test,
response_method="predict_proba",
class_of_interest=class_of_interest,
ax=axs[idx],
vmin=0,
vmax=1,
)
axs[class_of_interest].set_xlabel("Culmen Length (mm)")
if class_of_interest == 0:
axs[class_of_interest].set_ylabel("Culmen Depth (mm)")
idx = target_test == tree.classes_[class_of_interest]
axs[class_of_interest].scatter(
data_test["Culmen Length (mm)"].loc[idx],
data_test["Culmen Depth (mm)"].loc[idx],
axs[idx].scatter(
data_test["Culmen Length (mm)"].loc[target_test == class_of_interest],
data_test["Culmen Depth (mm)"].loc[target_test == class_of_interest],
marker="o",
c="w",
edgecolor="k",
)
axs[idx].set_xlabel("Culmen Length (mm)")
axs[idx].set_ylabel("Culmen Depth (mm)" if idx == 0 else None)

ax = plt.axes([0.15, 0.04, 0.7, 0.05])
plt.colorbar(imshow_handle, cax=ax, orientation="horizontal")
ax = plt.axes([0.15, 0.14, 0.7, 0.05])
plt.colorbar(
cm.ScalarMappable(cmap="viridis"), cax=ax, orientation="horizontal"
)
_ = plt.title("Probability")

# %% [markdown] tags=["solution"]
Expand All @@ -201,9 +193,9 @@
# the certainty.
# ```
#
# In future versions of scikit-learn `DecisionBoundaryDisplay` will support a
# `class_of_interest` parameter that will allow in particular for a
# visualization of `predict_proba` in multi-class settings.
# Since scikit-learn v1.4, `DecisionBoundaryDisplay` supports a
# `class_of_interest` parameter that allows in particular for a visualization of
# `predict_proba` in multi-class settings.
#
# We also plan to make it possible to visualize the `predict_proba` values for
# the class with the maximum predicted probability (without having to pass a
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