Advanced Plotting with Partial Dependence

Introduction

This tutorial will guide you through the process of plotting partial dependence curves for multiple features. The partial dependence curves are used to analyze the effect of a feature on the predicted outcome of a machine learning model while holding all other features constant. This tutorial will demonstrate how to plot partial dependence curves for a decision tree and a multi-layer perceptron on the diabetes dataset.

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Skills Graph

Train Models on the Diabetes Dataset

In this step, we will train a decision tree and a multi-layer perceptron on the diabetes dataset.

diabetes = load_diabetes()
X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
y = diabetes.target

tree = DecisionTreeRegressor()
mlp = make_pipeline(
    StandardScaler(),
    MLPRegressor(hidden_layer_sizes=(100, 100), tol=1e-2, max_iter=500, random_state=0),
)
tree.fit(X, y)
mlp.fit(X, y)

Plotting Partial Dependence for Two Features

In this step, we will plot partial dependence curves for features "age" and "bmi" (body mass index) for the decision tree. With two features, PartialDependenceDisplay.from_estimator expects to plot two curves. Here the plot function place a grid of two plots using the space defined by ax.

fig, ax = plt.subplots(figsize=(12, 6))
ax.set_title("Decision Tree")
tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age", "bmi"], ax=ax)

The partial dependence curves can be plotted for the multi-layer perceptron. In this case, line_kw is passed to PartialDependenceDisplay.from_estimator to change the color of the curve.

fig, ax = plt.subplots(figsize=(12, 6))
ax.set_title("Multi-layer Perceptron")
mlp_disp = PartialDependenceDisplay.from_estimator(
    mlp, X, ["age", "bmi"], ax=ax, line_kw={"color": "red"}
)

Plotting Partial Dependence of the Two Models Together

In this step, we will plot the partial dependence curves of the two models on the same plot.

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))
tree_disp.plot(ax=ax1)
ax1.set_title("Decision Tree")
mlp_disp.plot(ax=ax2, line_kw={"color": "red"})
ax2.set_title("Multi-layer Perceptron")

Another way to compare the curves is to plot them on top of each other. Here, we create a figure with one row and two columns. The axes are passed into the PartialDependenceDisplay.plot function as a list, which will plot the partial dependence curves of each model on the same axes. The length of the axes list must be equal to the number of plots drawn.

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
tree_disp.plot(ax=[ax1, ax2], line_kw={"label": "Decision Tree"})
mlp_disp.plot(
    ax=[ax1, ax2], line_kw={"label": "Multi-layer Perceptron", "color": "red"}
)
ax1.legend()
ax2.legend()

tree_disp.axes_ is a numpy array container the axes used to draw the partial dependence plots. This can be passed to mlp_disp to have the same affect of drawing the plots on top of each other. Furthermore, the mlp_disp.figure_ stores the figure, which allows for resizing the figure after calling plot. In this case tree_disp.axes_ has two dimensions, thus plot will only show the y label and y ticks on the left most plot.

tree_disp.plot(line_kw={"label": "Decision Tree"})
mlp_disp.plot(
    line_kw={"label": "Multi-layer Perceptron", "color": "red"}, ax=tree_disp.axes_
)
tree_disp.figure_.set_size_inches(10, 6)
tree_disp.axes_[0, 0].legend()
tree_disp.axes_[0, 1].legend()
plt.show()

Plotting Partial Dependence for One Feature

In this step, we will plot the partial dependence curves for a single feature, "age", on the same axes. In this case, tree_disp.axes_ is passed into the second plot function.

tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age"])
mlp_disp = PartialDependenceDisplay.from_estimator(
    mlp, X, ["age"], ax=tree_disp.axes_, line_kw={"color": "red"}
)

Summary

This tutorial demonstrated how to plot partial dependence curves for multiple features using the PartialDependenceDisplay object. First, we trained a decision tree and a multi-layer perceptron on the diabetes dataset. Then, we plotted partial dependence curves for the decision tree and the multi-layer perceptron for two features. Next, we plotted the partial dependence curves of the two models on the same plot. Finally, we plotted the partial dependence curves for a single feature on the same axes.