ReddRadar
Agent skill
bio-machine-learning-prediction-explanation
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-machine-learning-prediction-explanation
SKILL.md
name: bio-machine-learning-prediction-explanation description: Explains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions. Use when interpreting biomarker classifiers or understanding model predictions. tool_type: python primary_tool: shap measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Model Interpretation for Omics Classifiers
SHAP TreeExplainer (v0.47+ API)
import shap
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
explainer = shap.TreeExplainer(model)
# CORRECT (v0.47+): Call explainer directly, NOT .shap_values()
shap_values = explainer(X_test)
# shap_values is an Explanation object
# .values has shape (n_samples, n_features) for binary
# .base_values has expected value
print(f'SHAP values shape: {shap_values.values.shape}')
Summary Plot (Global Feature Importance)
import shap
import matplotlib.pyplot as plt
# Beeswarm plot: shows impact direction and magnitude
shap.plots.beeswarm(shap_values, max_display=20, show=False)
plt.tight_layout()
plt.savefig('shap_summary.png', dpi=150, bbox_inches='tight')
plt.close()
# Bar plot: mean absolute SHAP values
shap.plots.bar(shap_values, max_display=20, show=False)
plt.savefig('shap_bar.png', dpi=150, bbox_inches='tight')
Force Plot (Individual Prediction)
# Explain single prediction
sample_idx = 0
shap.plots.force(shap_values[sample_idx], matplotlib=True, show=False)
plt.savefig('shap_force_single.png', dpi=150, bbox_inches='tight')
# Waterfall plot (cleaner alternative)
shap.plots.waterfall(shap_values[sample_idx], max_display=15, show=False)
plt.savefig('shap_waterfall.png', dpi=150, bbox_inches='tight')
SHAP for XGBoost
from xgboost import XGBClassifier
import shap
xgb = XGBClassifier(n_estimators=100, random_state=42, eval_metric='logloss')
xgb.fit(X_train, y_train)
explainer = shap.TreeExplainer(xgb)
shap_values = explainer(X_test)
# For XGBoost, shap_values contains log-odds contributions
shap.plots.beeswarm(shap_values, max_display=20)
LIME (Local Interpretable Model-agnostic Explanations)
from lime.lime_tabular import LimeTabularExplainer
import numpy as np
explainer = LimeTabularExplainer(
X_train.values,
feature_names=X_train.columns.tolist(),
class_names=['control', 'disease'],
mode='classification'
)
# Explain single instance
sample_idx = 0
exp = explainer.explain_instance(
X_test.iloc[sample_idx].values,
model.predict_proba,
num_features=20
)
exp.save_to_file('lime_explanation.html')
# Or get as list: exp.as_list()
Extract Top Features from SHAP
import pandas as pd
import numpy as np
# Mean absolute SHAP value per feature
mean_shap = np.abs(shap_values.values).mean(axis=0)
feature_importance = pd.DataFrame({
'feature': X_test.columns,
'mean_shap': mean_shap
}).sort_values('mean_shap', ascending=False)
top_features = feature_importance.head(20)
top_features.to_csv('shap_top_features.csv', index=False)
Dependence Plot (Feature Interactions)
# Shows how SHAP value varies with feature value
# Automatically colors by interacting feature
shap.plots.scatter(shap_values[:, 'GENE1'], color=shap_values, show=False)
plt.savefig('shap_dependence.png', dpi=150, bbox_inches='tight')
Multi-class SHAP
explainer = shap.TreeExplainer(model)
shap_values = explainer(X_test)
# For multi-class, shap_values.values has shape (n_samples, n_features, n_classes)
# Access class-specific values:
class_idx = 1
shap.plots.beeswarm(shap_values[:, :, class_idx], max_display=20)
Related Skills
- machine-learning/omics-classifiers - Train models to interpret
- machine-learning/biomarker-discovery - Compare with selection-based importance
- data-visualization/heatmaps-clustering - Visualize SHAP values as heatmap
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
FreedomIntelligence/OpenClaw-Medical-Skills
vcf-annotator
Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.
FreedomIntelligence/OpenClaw-Medical-Skills
chemist-analyst
Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.
FreedomIntelligence/OpenClaw-Medical-Skills
bio-alignment-io
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
FreedomIntelligence/OpenClaw-Medical-Skills
sleep-analyzer
分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。
FreedomIntelligence/OpenClaw-Medical-Skills
metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
FreedomIntelligence/OpenClaw-Medical-Skills
bio-hi-c-analysis-matrix-operations
Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.
Didn't find tool you were looking for?