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da8e446 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from sklearn.inspection import permutation_importance
import matplotlib.pyplot as plt
import shap
def explain_model(model, X, y=None, feature_names=None):
"""
Explain model predictions using various techniques
"""
st.subheader("🔍 Model Explainability")
if feature_names is None:
feature_names = X.columns if hasattr(X, 'columns') else [f"Feature {i}" for i in range(X.shape[1])]
# Create tabs for different explanation methods
tab1, tab2, tab3 = st.tabs(["Feature Importance", "SHAP Values", "Partial Dependence"])
with tab1:
st.markdown("### 📊 Feature Importance")
# Method selection
method = st.radio(
"Importance method",
["Built-in", "Permutation"],
horizontal=True
)
if method == "Built-in":
if hasattr(model, 'feature_importances_'):
importance = model.feature_importances_
importance_df = pd.DataFrame({
'feature': feature_names,
'importance': importance
}).sort_values('importance', ascending=False)
fig = px.bar(importance_df.head(20), x='importance', y='feature',
orientation='h', title="Feature Importance (Built-in)")
st.plotly_chart(fig, use_container_width=True)
else:
st.warning("Model doesn't have built-in feature importance")
else: # Permutation importance
if y is not None:
with st.spinner("Calculating permutation importance..."):
perm_importance = permutation_importance(model, X, y, n_repeats=10)
importance_df = pd.DataFrame({
'feature': feature_names,
'importance': perm_importance.importances_mean,
'std': perm_importance.importances_std
}).sort_values('importance', ascending=False)
fig = go.Figure()
fig.add_trace(go.Bar(
x=importance_df['importance'].head(20),
y=importance_df['feature'].head(20),
orientation='h',
error_x=dict(
type='data',
array=importance_df['std'].head(20),
visible=True
)
))
fig.update_layout(title="Permutation Importance (with error bars)",
xaxis_title="Importance")
st.plotly_chart(fig, use_container_width=True)
else:
st.warning("Need target values for permutation importance")
with tab2:
st.markdown("### 📈 SHAP Values")
if hasattr(model, 'predict'):
with st.spinner("Calculating SHAP values (this may take a moment)..."):
try:
# Create explainer based on model type
if str(type(model)).find('sklearn') != -1:
explainer = shap.Explainer(model, X[:100]) # Use subset for speed
else:
explainer = shap.TreeExplainer(model) if hasattr(model, 'feature_importances_') else shap.Explainer(model, X[:100])
# Calculate SHAP values
shap_values = explainer(X[:100]) # Limit to 100 samples for performance
# Summary plot
st.markdown("#### SHAP Summary Plot")
fig, ax = plt.subplots()
shap.summary_plot(shap_values, X[:100], feature_names=feature_names, show=False)
st.pyplot(fig)
plt.close()
# Waterfall plot for a single prediction
st.markdown("#### Single Prediction Explanation")
sample_idx = st.slider("Select sample index", 0, min(99, len(X)-1), 0)
fig, ax = plt.subplots()
shap.waterfall_plot(shap_values[sample_idx], show=False)
st.pyplot(fig)
plt.close()
except Exception as e:
st.error(f"Error calculating SHAP values: {str(e)}")
st.info("Try using a smaller sample or a different model type")
else:
st.warning("Model doesn't support prediction")
with tab3:
st.markdown("### 📉 Partial Dependence Plots")
if hasattr(model, 'predict') and len(feature_names) > 0:
from sklearn.inspection import partial_dependence
selected_feature = st.selectbox("Select feature for PDP", feature_names)
if selected_feature:
feature_idx = list(feature_names).index(selected_feature)
# Calculate partial dependence
pdp = partial_dependence(model, X, [feature_idx], grid_resolution=50)
# Create plot
fig = go.Figure()
fig.add_trace(go.Scatter(
x=pdp['values'][0],
y=pdp['average'][0],
mode='lines+markers',
name='Partial Dependence'
))
fig.update_layout(
title=f"Partial Dependence Plot for {selected_feature}",
xaxis_title=selected_feature,
yaxis_title="Prediction"
)
st.plotly_chart(fig, use_container_width=True)
# Individual conditional expectation (ICE) plots
if st.checkbox("Show ICE plots"):
ice_data = []
for i in range(min(10, X.shape[0])): # Show up to 10 lines
ice = partial_dependence(model, X.iloc[i:i+1], [feature_idx], grid_resolution=20)
ice_data.append(ice['average'][0])
fig = go.Figure()
for i, ice in enumerate(ice_data):
fig.add_trace(go.Scatter(
x=pdp['values'][0],
y=ice,
mode='lines',
name=f'Sample {i}',
line=dict(width=1, color='lightgray')
))
# Add average line
fig.add_trace(go.Scatter(
x=pdp['values'][0],
y=pdp['average'][0],
mode='lines',
name='Average',
line=dict(width=3, color='red')
))
fig.update_layout(
title=f"ICE Plots for {selected_feature}",
xaxis_title=selected_feature,
yaxis_title="Prediction"
)
st.plotly_chart(fig, use_container_width=True)
else:
st.warning("Need more features for partial dependence plots") |