Example: Customized Partial Dependence Plots

• Plot PD curves for age and bmi for the decision tree. In this case plot_partial_dependence expects two curves.

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_diabetes
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.tree import DecisionTreeRegressor
from sklearn.inspection import plot_partial_dependence

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

tree = DecisionTreeRegressor().fit(X, y)
mlp = make_pipeline(StandardScaler(),
                    MLPRegressor(hidden_layer_sizes=(100, 100),
                                 tol=1e-2, 
                                 max_iter=2500, 
                                 random_state=0)).fit(X, y)

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

• Plot PD curves for the Perceptron. In this case line_kw is used to change the curve color.

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

• tree_disp and mlp_disp contain all the info needed to recreate the plots.

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")

Text(0.5, 1.0, 'Multi-layer Perceptron')

• Here we compare curves by plotting on top of each other. To plot the PD curves on the same axis, pass the axes to plot as a list.

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()

<matplotlib.legend.Legend at 0x7f7507e9bfa0>

• tree_disp.axes_ is a numpy array. It can be passed to mlp_disp to have the same affect of drawing the plots on top of each other.

• Also: mlp_disp.figure_ stores the figure, for resizing after calling plot. This time tree_disp.axes_ has two dimensions - the 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()

<matplotlib.legend.Legend at 0x7f7507e739a0>

• Plot the PD curve for only one feature.

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