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c85417e | 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 | 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")
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