若您對上述圖表的實現細節感興趣，以下是用於生成瀑布圖、蜂群圖、交互圖和依賴圖的範例代碼。👇
import shap
import matplotlib.pyplot as plt
import numpy as np
# --- 0. Setup & Global Settings ---
plt.rcParams['font.family'] = 'Arial'
plt.rcParams['font.size'] = '24'
plt.rcParams['axes.unicode_minus'] = False
# Assumption: 'best_model' is your trained XGBoost/RF model
# Assumption: 'X_train' and 'X_test' are pandas DataFrames
# 1. Calculate SHAP values as Numpy Arrays (for Beeswarm, Dependence, Interaction)
explainer_tree = shap.TreeExplainer(best_model)
shap_values_numpy = explainer_tree.shap_values(X_train)
# 2. Calculate SHAP values as Explanation Object (Specifically for Waterfall)
explainer_obj = shap.Explainer(best_model, X_test)
shap_values_obj = explainer_obj(X_test)
##################################################################
# #
# (a) Waterfall Plot #
# Visualizes contribution for a single sample #
# #
##################################################################
class_idx = 4 # Target class
sample_idx = 3 # Specific sample to explain
plt.figure()
shap.plots.waterfall(
shap_values_obj[sample_idx, :, class_idx],
max_display=9,
show=False
)
# Customizing style
ax = plt.gca()
ax.set_xlabel(ax.get_xlabel(), fontsize=36)
ax.set_ylabel(ax.get_ylabel(), fontsize=36)
ax.spines['bottom'].set_linewidth(3)
plt.show()
##################################################################
# #
# (b) Beeswarm Plot #
# Global summary of feature importance #
# #
##################################################################
class_idx = 5
plt.figure(figsize=(10, 8))
shap.summary_plot(
shap_values_numpy[..., class_idx],
X_train,
feature_names=X_train.columns,
plot_type="dot",
show=False,
cmap='Greys' # or 'plasma'
)
# Customize Color Bar
cbar = plt.gcf().axes[-1]
cbar.set_ylabel('Parameter Value', fontsize=24)
cbar.tick_params(labelsize=20)
plt.show()
##################################################################
# #
# (c) Interaction Plot #
# Visualizes interaction effects between features #
# #
##################################################################
# Note: Calculation can be expensive
shap_interaction_values = explainer_tree.shap_interaction_values(X_test)
class_idx = 4
plt.figure()
shap.summary_plot(
shap_interaction_values[..., class_idx],
X_test,
show=False,
max_display=6,
cmap='Greys'
)
# Clean up subplots
axes = plt.gcf().axes
for ax in axes:
ax.spines['bottom'].set_linewidth(2)
ax.tick_params(axis="x", labelsize=18, width=2)
ax.set_title(ax.get_title(), fontsize=14)
plt.subplots_adjust(wspace=0.3, hspace=0.4)
plt.show()
##################################################################
# #
# (d) Dependence Plot #
# Feature relationship colored by interaction #
# #
##################################################################
Feature_X = 'P06' # Main feature
Feature_Y = 'P07' # Interaction feature
class_idx = 4
shap.dependence_plot(
Feature_X,
shap_values_numpy[..., class_idx],
X_train,
interaction_index=Feature_Y,
dot_size=100,
show=False
)
# Customize Axes
ax = plt.gca()
ax.tick_params(axis='both', which='major', labelsize=36, width=2)
ax.set_ylabel(f'SHAP value ({Feature_X})', fontsize=36)
ax.spines['bottom'].set_linewidth(3)
ax.spines['left'].set_linewidth(3)
plt.show()
##################################################################
# #
# (e) Advanced Composite Plot (Beeswarm + Bar) #
# Combines Beeswarm (Bottom Axis) & Importance (Top Axis) #
# #
##################################################################
class_idx = 5
fig, ax1 = plt.subplots(figsize=(10, 8))
# 1. Main Beeswarm Plot (on ax1)
shap.summary_plot(
shap_values_numpy[..., class_idx],
X_train,
feature_names=X_train.columns,
plot_type="dot",
show=False,
color_bar=True,
cmap='Greys' # or 'plasma'
)
# Customize Color Bar
cbar = plt.gcf().axes[-1]
cbar.set_ylabel('Parameter Value', fontsize=24)
cbar.tick_params(labelsize=20)
# Adjust layout to make room for the top axis
plt.gca().set_position([0.2, 0.2, 0.65, 0.65])
# 2. Feature Importance Bar Plot (on Top Axis ax2)
# Create a twin axis sharing the y-axis
ax2 = ax1.twiny()
shap.summary_plot(
shap_values_numpy[..., class_idx],
X_train,
plot_type="bar",
show=False
)
# Align position with the main plot
plt.gca().set_position([0.2, 0.2, 0.65, 0.65])
# Style the bars (Transparent & Light Color)
bars = ax2.patches
for bar in bars:
bar.set_color('#CCE5FB') # Light blue background bars
bar.set_alpha(0.4) # Transparency
# Customize Axes Labels
ax1.set_xlabel(f'Shapley Value Contribution (F{class_idx})', fontsize=24, labelpad=5)
ax1.set_ylabel('Parameters', fontsize=24)
ax2.set_xlabel('Mean Shapley Value (Parameter Importance)', fontsize=24, labelpad=10)
# Move ax2 (Bar plot axis) to the top
ax2.xaxis.set_label_position('top')
ax2.xaxis.tick_top()
# Ensure ax1 (dots) is drawn ON TOP OF ax2 (bars)
ax1.set_zorder(ax1.get_zorder() + 1)
ax1.patch.set_visible(False) # Make ax1 background transparent
plt.show()
