def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
self.model = RandomForestClassifier(n_estimators=5, max_depth=2)
self.model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
self.model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
self.dashboard = ExplainerDashboard(self.explainer,
[
ShapDependenceTab(self.explainer, title="Test Tab!"),
ShapDependenceTab,
"importances"
], title="Test Title!")
self.pkl_dir = Path.cwd() / "tests" / "cli_assets"
self.explainer.dump(self.pkl_dir / "explainer.joblib")
self.explainer.to_yaml(self.pkl_dir / "explainer.yaml")
self.dashboard.to_yaml(self.pkl_dir / "dashboard.yaml",
explainerfile=str(self.pkl_dir / "explainer.joblib"))
def predict(model_path: Path, test_df_path: Path, predict_path: Path) -> None:
"""Recieves trained model and test dataset and persists predictions.
Args:
model_path (Path): Path to where trained model is.
test_df_path (Path): Location of test dataframe.
predict_path (Path): Path to store predictions.
"""
# Read test dataset.
test_df = pd.read_csv(test_df_path)
x_test, y_test = test_df.drop("target", axis=1), test_df.loc[:, "target"]
mask = (x_test.dtypes == "int64").tolist()
x_test = x_test.loc[:, mask]
# Read trained model and predict
with open(model_path, "rb") as fd:
model = pickle.load(fd)
preds = model.predict_proba(x_test)
explainer = ClassifierExplainer(
model,
x_test,
y_test,
descriptions=None, # defaults to None
)
explainer.dump(predict_path / "explainer.joblib")
# Persist predictions...
with open(predict_path / "preds.pkl", "wb") as fd:
pickle.dump(preds, fd, pickle.HIGHEST_PROTOCOL)
def generate_assets():
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=5, max_depth=2)
model.fit(X_train, y_train)
explainer = ClassifierExplainer(model,
X_test,
y_test,
cats=[{
'Gender':
['Sex_female', 'Sex_male', 'Sex_nan']
}, 'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
dashboard = ExplainerDashboard(explainer, [
ShapDependenceComposite(explainer, title="Test Tab!"),
ShapDependenceComposite, "importances"
],
title="Test Title!")
pkl_dir = Path.cwd() / "tests" / "test_assets"
explainer.to_yaml(pkl_dir / "explainer.yaml")
dashboard.to_yaml(pkl_dir / "dashboard.yaml",
explainerfile=str(pkl_dir / "explainer.joblib"),
dump_explainer=True)
return None
class DashboardTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
self.model = RandomForestClassifier(n_estimators=5, max_depth=2)
self.model.fit(X_train, y_train)
self.explainer = ClassifierExplainer(
self.model, X_test, y_test,
cats=[{'Gender': ['Sex_female', 'Sex_male', 'Sex_nan']},
'Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
self.dashboard = ExplainerDashboard(self.explainer,
[
ShapDependenceTab(self.explainer, title="Test Tab!"),
ShapDependenceTab,
"importances"
], title="Test Title!")
self.pkl_dir = Path.cwd() / "tests" / "cli_assets"
self.explainer.dump(self.pkl_dir / "explainer.joblib")
self.explainer.to_yaml(self.pkl_dir / "explainer.yaml")
self.dashboard.to_yaml(self.pkl_dir / "dashboard.yaml",
explainerfile=str(self.pkl_dir / "explainer.joblib"))
def test_yaml(self):
yaml = self.dashboard.to_yaml()
self.assertIsInstance(yaml, str)
def test_yaml_dict(self):
yaml_dict = self.dashboard.to_yaml(return_dict=True)
self.assertIsInstance(yaml_dict, dict)
self.assertIn("dashboard", yaml_dict)
def test_load_config_joblib(self):
db = ExplainerDashboard.from_config(
self.pkl_dir / "explainer.joblib",
self.pkl_dir / "dashboard.yaml")
self.assertIsInstance(db, ExplainerDashboard)
def test_load_config_yaml(self):
db = ExplainerDashboard.from_config(
self.pkl_dir / "dashboard.yaml")
self.assertIsInstance(db, ExplainerDashboard)
def test_load_config_explainer(self):
db = ExplainerDashboard.from_config(
self.explainer, self.pkl_dir / "dashboard.yaml")
self.assertIsInstance(db, ExplainerDashboard)
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = CatBoostClassifier(iterations=100, verbose=0).fit(X_train, y_train)
explainer = ClassifierExplainer(
model, X_test, y_test,
cats=['Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
X_cats, y_cats = explainer.X_merged, explainer.y.astype("int")
model = CatBoostClassifier(iterations=5, verbose=0).fit(X_cats, y_cats, cat_features=[8, 9])
self.explainer = ClassifierExplainer(model, X_cats, y_cats,
cats=['Sex'],
labels=['Not survived', 'Survived'],
idxs=X_test.index)
def build_explainer(explainer_config):
if isinstance(explainer_config,
(Path, str)) and str(explainer_config).endswith(".yaml"):
config = yaml.safe_load(open(str(explainer_config), "r"))
elif isinstance(explainer_config, dict):
config = explainer_config
assert 'explainer' in config, \
"Please pass a proper explainer.yaml config file that starts with `explainer:`!"
config = explainer_config['explainer']
print(f"explainerdashboard ===> Loading model from {config['modelfile']}")
model = pickle.load(open(config['modelfile'], "rb"))
print(f"explainerdashboard ===> Loading data from {config['datafile']}")
if str(config['datafile']).endswith('.csv'):
df = pd.read_csv(config['datafile'])
elif str(config['datafile']).endswith('.parquet'):
df = pd.read_parquet(config['datafile'])
else:
raise ValueError("datafile should either be a .csv or .parquet!")
print(
f"explainerdashboard ===> Using column {config['data_target']} to generate X, y "
)
target_col = config['data_target']
X = df.drop(target_col, axis=1)
y = df[target_col]
if config['data_index'] is not None:
print(
f"explainerdashboard ===> Generating index from column {config['data_index']}"
)
assert config['data_index'] in X.columns, \
(f"Cannot find data_index column ({config['data_index']})"
f" in datafile ({config['datafile']})!"
"Please set it to the proper index column name, or set it to null")
X = X.set_index(config['data_index'])
params = config['params']
if config['explainer_type'] == "classifier":
print(f"explainerdashboard ===> Generating ClassifierExplainer...")
explainer = ClassifierExplainer(model, X, y, **params)
elif config['explainer_type'] == "regression":
print(f"explainerdashboard ===> Generating RegressionExplainer...")
explainer = ClassifierExplainer(model, X, y, **params)
return explainer
# Have to run on http://127.0.0.1:8050/ to work, to figure out if can work with streamlit or not.
# Import libraries here
from sklearn.ensemble import RandomForestClassifier
from explainerdashboard import ClassifierExplainer, ExplainerDashboard
from explainerdashboard.datasets import titanic_survive, feature_descriptions
X_train, y_train, X_test, y_test = titanic_survive()
model = RandomForestClassifier(n_estimators=50,
max_depth=10).fit(X_train, y_train)
explainer = ClassifierExplainer(model,
X_test,
y_test,
cats=['Sex', 'Deck', 'Embarked'],
descriptions=feature_descriptions,
labels=['Not survived', 'Survived'])
ExplainerDashboard(explainer).run()
class CatBoostClassifierTests(unittest.TestCase):
def setUp(self):
X_train, y_train, X_test, y_test = titanic_survive()
train_names, test_names = titanic_names()
model = CatBoostClassifier(iterations=100,
verbose=0).fit(X_train, y_train)
explainer = ClassifierExplainer(model,
X_test,
y_test,
cats=['Deck', 'Embarked'],
labels=['Not survived', 'Survived'])
X_cats, y_cats = explainer.X_cats, explainer.y
model = CatBoostClassifier(iterations=5,
verbose=0).fit(X_cats,
y_cats,
cat_features=[8, 9])
self.explainer = ClassifierExplainer(
model,
X_cats,
y_cats,
cats=['Sex'],
labels=['Not survived', 'Survived'])
def test_explainer_len(self):
self.assertEqual(len(self.explainer), len(titanic_survive()[2]))
def test_int_idx(self):
self.assertEqual(self.explainer.get_int_idx(titanic_names()[1][0]), 0)
def test_random_index(self):
self.assertIsInstance(self.explainer.random_index(), int)
self.assertIsInstance(self.explainer.random_index(return_str=True),
str)
def test_ordered_cats(self):
self.assertEqual(self.explainer.ordered_cats("Sex"),
['Sex_female', 'Sex_male'])
self.assertEqual(
self.explainer.ordered_cats("Deck", topx=2, sort='alphabet'),
['Deck_A', 'Deck_B'])
self.assertIsInstance(self.explainer.ordered_cats("Deck", sort='freq'),
list)
self.assertIsInstance(
self.explainer.ordered_cats("Deck", topx=3, sort='freq'), list)
self.assertIsInstance(self.explainer.ordered_cats("Deck", sort='shap'),
list)
self.assertIsInstance(
self.explainer.ordered_cats("Deck", topx=3, sort='shap'), list)
def test_preds(self):
self.assertIsInstance(self.explainer.preds, np.ndarray)
def test_row_from_input(self):
input_row = self.explainer.get_row_from_input(
self.explainer.X.iloc[[0]].values.tolist())
self.assertIsInstance(input_row, pd.DataFrame)
input_row = self.explainer.get_row_from_input(
self.explainer.X_cats.iloc[[0]].values.tolist())
self.assertIsInstance(input_row, pd.DataFrame)
input_row = self.explainer.get_row_from_input(
self.explainer.X_cats[self.explainer.columns_ranked_by_shap(
cats=True)].iloc[[0]].values.tolist(),
ranked_by_shap=True)
self.assertIsInstance(input_row, pd.DataFrame)
input_row = self.explainer.get_row_from_input(
self.explainer.X[self.explainer.columns_ranked_by_shap(
cats=False)].iloc[[0]].values.tolist(),
ranked_by_shap=True)
self.assertIsInstance(input_row, pd.DataFrame)
def test_pred_percentiles(self):
self.assertIsInstance(self.explainer.pred_percentiles, np.ndarray)
def test_columns_ranked_by_shap(self):
self.assertIsInstance(self.explainer.columns_ranked_by_shap(), list)
self.assertIsInstance(self.explainer.columns_ranked_by_shap(cats=True),
list)
def test_equivalent_col(self):
self.assertEqual(self.explainer.equivalent_col("Sex_female"), "Sex")
self.assertEqual(self.explainer.equivalent_col("Sex"), "Sex_female")
self.assertIsNone(self.explainer.equivalent_col("random"))
def test_get_col(self):
self.assertIsInstance(self.explainer.get_col("Sex"), pd.Series)
self.assertEqual(self.explainer.get_col("Sex").dtype, "object")
self.assertIsInstance(self.explainer.get_col("Deck"), pd.Series)
self.assertEqual(self.explainer.get_col("Deck").dtype, "object")
self.assertIsInstance(self.explainer.get_col("Age"), pd.Series)
self.assertEqual(self.explainer.get_col("Age").dtype, np.float)
def test_permutation_importances(self):
self.assertIsInstance(self.explainer.permutation_importances,
pd.DataFrame)
self.assertIsInstance(self.explainer.permutation_importances_cats,
pd.DataFrame)
def test_X_cats(self):
self.assertIsInstance(self.explainer.X_cats, pd.DataFrame)
def test_columns_cats(self):
self.assertIsInstance(self.explainer.columns_cats, list)
def test_metrics(self):
self.assertIsInstance(self.explainer.metrics(), dict)
def test_mean_abs_shap_df(self):
self.assertIsInstance(self.explainer.mean_abs_shap_df(), pd.DataFrame)
def test_top_interactions(self):
self.assertIsInstance(self.explainer.shap_top_interactions("Age"),
list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Age", topx=4), list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Age", cats=True), list)
self.assertIsInstance(
self.explainer.shap_top_interactions("Sex", cats=True), list)
def test_permutation_importances_df(self):
self.assertIsInstance(self.explainer.permutation_importances_df(),
pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(topx=3), pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(cats=True), pd.DataFrame)
self.assertIsInstance(
self.explainer.permutation_importances_df(cutoff=0.01),
pd.DataFrame)
def test_contrib_df(self):
self.assertIsInstance(self.explainer.contrib_df(0), pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, cats=False),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, topx=3),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, sort='high-to-low'),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, sort='low-to-high'),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_df(0, sort='importance'),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_df(X_row=self.explainer.X.iloc[[0]]),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_df(X_row=self.explainer.X_cats.iloc[[0]]),
pd.DataFrame)
def test_contrib_summary_df(self):
self.assertIsInstance(self.explainer.contrib_summary_df(0),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, cats=False),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, topx=3),
pd.DataFrame)
self.assertIsInstance(self.explainer.contrib_summary_df(0, round=3),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(0, sort='low-to-high'),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(0, sort='high-to-low'),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(0, sort='importance'),
pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(
X_row=self.explainer.X.iloc[[0]]), pd.DataFrame)
self.assertIsInstance(
self.explainer.contrib_summary_df(
X_row=self.explainer.X_cats.iloc[[0]]), pd.DataFrame)
def test_shap_base_value(self):
self.assertIsInstance(self.explainer.shap_base_value,
(np.floating, float))
def test_shap_values_shape(self):
self.assertTrue(
self.explainer.shap_values.shape == (len(self.explainer),
len(self.explainer.columns)))
def test_shap_values(self):
self.assertIsInstance(self.explainer.shap_values, np.ndarray)
self.assertIsInstance(self.explainer.shap_values_cats, np.ndarray)
def test_mean_abs_shap(self):
self.assertIsInstance(self.explainer.mean_abs_shap, pd.DataFrame)
self.assertIsInstance(self.explainer.mean_abs_shap_cats, pd.DataFrame)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def test_prediction_result_df(self):
df = self.explainer.prediction_result_df(0)
self.assertIsInstance(df, pd.DataFrame)
def test_pdp_df(self):
self.assertIsInstance(self.explainer.pdp_df("Age"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Sex"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Deck"), pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Age", index=0),
pd.DataFrame)
self.assertIsInstance(self.explainer.pdp_df("Sex_male", index=0),
pd.DataFrame)
self.assertIsInstance(
self.explainer.pdp_df("Age", X_row=self.explainer.X.iloc[[0]]),
pd.DataFrame)
self.assertIsInstance(
self.explainer.pdp_df("Age",
X_row=self.explainer.X_cats.iloc[[0]]),
pd.DataFrame)
def test_plot_importances(self):
fig = self.explainer.plot_importances()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_importances(kind='permutation')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_importances(topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_importances(cats=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_contributions(self):
fig = self.explainer.plot_shap_contributions(0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, cats=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, cutoff=0.05)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, sort='high-to-low')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, sort='low-to-high')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(0, sort='importance')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(
X_row=self.explainer.X.iloc[[0]], sort='importance')
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_contributions(
X_row=self.explainer.X_cats.iloc[[0]], sort='importance')
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_summary(self):
fig = self.explainer.plot_shap_summary()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_summary(topx=3)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_summary(cats=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_shap_dependence(self):
fig = self.explainer.plot_shap_dependence("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex_female")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age", "Sex")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex_female", "Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Age", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Sex", highlight_index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Deck", topx=3, sort="freq")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Deck", topx=3, sort="shap")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Deck", sort="freq")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_shap_dependence("Deck", sort="shap")
self.assertIsInstance(fig, go.Figure)
def test_plot_pdp(self):
fig = self.explainer.plot_pdp("Age")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Sex")
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Sex", index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Age", index=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Age", X_row=self.explainer.X.iloc[[0]])
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pdp("Age",
X_row=self.explainer.X_cats.iloc[[0]])
self.assertIsInstance(fig, go.Figure)
def test_yaml(self):
yaml = self.explainer.to_yaml()
self.assertIsInstance(yaml, str)
def test_pos_label(self):
self.explainer.pos_label = 1
self.explainer.pos_label = "Not survived"
self.assertIsInstance(self.explainer.pos_label, int)
self.assertIsInstance(self.explainer.pos_label_str, str)
self.assertEqual(self.explainer.pos_label, 0)
self.assertEqual(self.explainer.pos_label_str, "Not survived")
def test_get_prop_for_label(self):
self.explainer.pos_label = 1
tmp = self.explainer.pred_percentiles
self.explainer.pos_label = 0
self.assertTrue(
np.alltrue(
self.explainer.get_prop_for_label("pred_percentiles", 1) ==
tmp))
def test_pred_probas(self):
self.assertIsInstance(self.explainer.pred_probas, np.ndarray)
def test_metrics(self):
self.assertIsInstance(self.explainer.metrics(), dict)
self.assertIsInstance(self.explainer.metrics(cutoff=0.9), dict)
self.assertIsInstance(self.explainer.metrics_descriptions(cutoff=0.9),
dict)
def test_precision_df(self):
self.assertIsInstance(self.explainer.precision_df(), pd.DataFrame)
self.assertIsInstance(self.explainer.precision_df(multiclass=True),
pd.DataFrame)
self.assertIsInstance(self.explainer.precision_df(quantiles=4),
pd.DataFrame)
def test_lift_curve_df(self):
self.assertIsInstance(self.explainer.lift_curve_df(), pd.DataFrame)
def test_prediction_result_markdown(self):
self.assertIsInstance(self.explainer.prediction_result_markdown(0),
str)
def test_calculate_properties(self):
self.explainer.calculate_properties()
def test_plot_precision(self):
fig = self.explainer.plot_precision()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_precision(multiclass=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_precision(quantiles=10, cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_cumulative_precision(self):
fig = self.explainer.plot_cumulative_precision()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_cumulative_precision(percentile=0.5)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_cumulative_precision(percentile=0.1,
pos_label=0)
self.assertIsInstance(fig, go.Figure)
def test_plot_confusion_matrix(self):
fig = self.explainer.plot_confusion_matrix(normalized=False,
binary=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=False,
binary=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=True,
binary=False)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_confusion_matrix(normalized=True,
binary=True)
self.assertIsInstance(fig, go.Figure)
def test_plot_lift_curve(self):
fig = self.explainer.plot_lift_curve()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_lift_curve(self):
fig = self.explainer.plot_lift_curve()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_lift_curve(cutoff=0.5)
self.assertIsInstance(fig, go.Figure)
def test_plot_classification(self):
fig = self.explainer.plot_classification()
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(percentage=True)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(cutoff=0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_classification(cutoff=1)
self.assertIsInstance(fig, go.Figure)
def test_plot_roc_auc(self):
fig = self.explainer.plot_roc_auc(0.5)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_roc_auc(0.0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_roc_auc(1.0)
self.assertIsInstance(fig, go.Figure)
def test_plot_pr_auc(self):
fig = self.explainer.plot_pr_auc(0.5)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pr_auc(0.0)
self.assertIsInstance(fig, go.Figure)
fig = self.explainer.plot_pr_auc(1.0)
self.assertIsInstance(fig, go.Figure)
def test_plot_prediction_result(self):
fig = self.explainer.plot_prediction_result(0)
self.assertIsInstance(fig, go.Figure)
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
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
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="$")