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| from pathlib import Path | |
| from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor | |
| from explainerdashboard import ( | |
| ClassifierExplainer, | |
| RegressionExplainer, | |
| ExplainerDashboard, | |
| ) | |
| from explainerdashboard.datasets import * | |
| pkl_dir = Path.cwd() / "pkls" | |
| # classifier | |
| print("Generating titanic explainers") | |
| print("Generating classifier explainer") | |
| X_train, y_train, X_test, y_test = titanic_survive() | |
| model = RandomForestClassifier(n_estimators=50, max_depth=5).fit(X_train, y_train) | |
| clas_explainer = ClassifierExplainer( | |
| model, | |
| X_test, | |
| y_test, | |
| cats=["Sex", "Deck", "Embarked"], | |
| descriptions=feature_descriptions, | |
| labels=["Not survived", "Survived"], | |
| ) | |
| _ = ExplainerDashboard(clas_explainer) | |
| clas_explainer.dump(pkl_dir / "clas_explainer.joblib") | |
| # regression | |
| print("Generating titanic fare explainer") | |
| X_train, y_train, X_test, y_test = titanic_fare() | |
| model = RandomForestRegressor(n_estimators=50, max_depth=5).fit(X_train, y_train) | |
| reg_explainer = RegressionExplainer( | |
| model, | |
| X_test, | |
| y_test, | |
| cats=["Sex", "Deck", "Embarked"], | |
| descriptions=feature_descriptions, | |
| units="$", | |
| ) | |
| _ = ExplainerDashboard(reg_explainer) | |
| reg_explainer.dump(pkl_dir / "reg_explainer.joblib") | |
| # multiclass | |
| print("Generating titanic embarked multiclass explainer") | |
| X_train, y_train, X_test, y_test = titanic_embarked() | |
| model = RandomForestClassifier(n_estimators=50, max_depth=5).fit(X_train, y_train) | |
| multi_explainer = ClassifierExplainer( | |
| model, | |
| X_test, | |
| y_test, | |
| cats=["Sex", "Deck"], | |
| descriptions=feature_descriptions, | |
| labels=["Queenstown", "Southampton", "Cherbourg"], | |
| ) | |
| _ = ExplainerDashboard(multi_explainer) | |
| multi_explainer.dump(pkl_dir / "multi_explainer.joblib") | |