from explainerdashboard import RegressionExplainer, ExplainerDashboard
from explainerdashboard.custom import *
from joblib import load
import pandas as pd
from sklearn.model_selection import train_test_split
# Load model & data
dec_tree = load('dec_tree_v3.joblib')
df_data = pd.read_csv('data_v3_enc.csv')
# Prepare & split data
X = df_data.drop(['Duration', 'Timestamp'], axis=1)
y = df_data.Duration
Xt, X_small, yt, y_small = train_test_split(X, y, test_size=0.01, random_state=0)
exp = RegressionExplainer(dec_tree, X_small, y_small, cats=['Day_of_week', 'Hour', 'Vehicle', 'Position'])
# Build
db = ExplainerDashboard(exp, [ShapDependenceComposite, WhatIfComposite], hide_whatifpdp=True)
# Save
exp.dump("explainer.joblib")
db.to_yaml("dashboard.yaml")
descriptions=feature_descriptions,
labels=['Not survived', 'Survived'])
_ = ExplainerDashboard(clas_explainer)
clas_explainer.dump(pkl_dir / "clas_explainer.joblib")
# regression
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
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")