def Explanation_Dashboard(global_explanation,
model=None,
x_train=pd.DataFrame(),
x_test=pd.DataFrame(),
y_train=pd.DataFrame(),
y_test=pd.DataFrame(),
explantion_data=None,
design=None):
if globals_file.imported_model_files:
model, x_train, x_test, y_train, y_test = get_best_pipeline(design)
if model == None:
return "Exported Pipeline is missing"
if x_train.empty:
return "Training features is missing"
if x_test.empty:
return "Testing features is missing"
if y_train.empty:
return "Training Target is missing"
if y_test.empty:
return "Testing Target is missing"
if explantion_data == 'Training':
ExplanationDashboard(global_explanation,
model,
dataset=x_train,
true_y=y_train)
else:
ExplanationDashboard(global_explanation,
model,
dataset=x_test,
true_y=y_test)
def test_explanation_dashboard_many_columns(self):
X, y = make_classification(n_features=2000)
# Split data into train and test
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
classes = np.unique(y_train).tolist()
feature_names = ["col" + str(i) for i in list(range(X_train.shape[1]))]
X_train = pd.DataFrame(X_train, columns=feature_names)
X_test = pd.DataFrame(X_test, columns=feature_names)
knn = sklearn.neighbors.KNeighborsClassifier()
knn.fit(X_train, y_train)
model_task = ModelTask.Classification
explainer = MimicExplainer(knn,
X_train,
LGBMExplainableModel,
model_task=model_task)
global_explanation = explainer.explain_global(X_test)
ExplanationDashboard(explanation=global_explanation,
model=knn,
dataset=X_test,
true_y=y_test,
classes=classes)
def Explanation_Dashboard(global_explanation,
model,
x_train,
x_test,
y_train,
y_test,
explantion_data=None):
if explantion_data == 'Training':
ExplanationDashboard(global_explanation,
model,
dataset=x_train,
true_y=y_train)
else:
ExplanationDashboard(global_explanation,
model,
dataset=x_test,
true_y=y_test)
def test_local_explanation(self, mimic_explainer):
# Validate visualizing ExplanationDashboard with a local explanation
x_train, x_test, y_train, y_test, feature_names, target_names = create_cancer_data()
# Fit an SVM model
model = create_sklearn_svm_classifier(x_train, y_train)
explainer = mimic_explainer(model, x_train, LGBMExplainableModel,
features=feature_names, classes=target_names)
explanation = explainer.explain_local(x_test)
ExplanationDashboard(explanation, model, dataset=x_test, true_y=y_test)
def test_raw_timestamp_explanation(self, mimic_explainer):
df = retrieve_dataset(
'insurance_claims.csv',
na_values='?',
parse_dates=['policy_bind_date', 'incident_date'])
label = 'fraud_reported'
df_y = df[label]
df_X = df.drop(columns=label)
x_train, x_test, y_train, y_test = train_test_split(df_X,
df_y,
test_size=0.2,
random_state=7)
str_cols = df_X.select_dtypes(
exclude=[np.number, np.datetime64]).columns.tolist()
dt_cols = df_X.select_dtypes(include=[np.datetime64]).columns.tolist()
numeric_cols = df_X.select_dtypes(include=[np.number]).columns.tolist()
transforms_list = []
for str_col in str_cols:
transforms_list.append(
(str_col,
Pipeline(steps=[('imputer',
SimpleImputer(strategy='most_frequent')
), ('ohe', OneHotEncoder(sparse=False))]),
[str_col]))
for numeric_col in numeric_cols:
transforms_list.append(
(numeric_col,
Pipeline(steps=[('imputer', SimpleImputer(
strategy='mean')), ('scaler', StandardScaler())]),
[numeric_col]))
for dt_col in dt_cols:
transforms_list.append(
(dt_col, Pipeline(steps=[('scaler', StandardScaler())]),
[dt_col]))
transformations = ColumnTransformer(transforms_list)
x_train_transformed = transformations.fit_transform(x_train)
model = create_lightgbm_classifier(x_train_transformed, y_train)
model_task = ModelTask.Classification
features = df_X.columns.tolist()
explainer = mimic_explainer(model,
x_train,
LGBMExplainableModel,
transformations=transformations,
features=features,
model_task=model_task)
explanation = explainer.explain_global(x_train)
dashboard_pipeline = Pipeline(steps=[('preprocess',
transformations), ('model',
model)])
ExplanationDashboard(explanation,
dashboard_pipeline,
dataset=x_train,
true_y=y_train)
x_train, x_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=0)
clf = svm.SVC(gamma=0.001, C=100., probability=True)
model = clf.fit(x_train, y_train)
explainer = TabularExplainer(model,
x_train,
features=feature_names,
classes=classes)
global_explanation = explainer.explain_global(x_test)
instance_num = 0
local_explanation = explainer.explain_local(x_test[instance_num, :])
prediction_value = clf.predict(x_test)[instance_num]
sorted_local_importance_values = local_explanation.get_ranked_local_values()[
prediction_value]
sorted_local_importance_names = local_explanation.get_ranked_local_names()[
prediction_value]
ExplanationDashboard(global_explanation, model, dataset=x_test, true_y=y_test)
ModelPerformanceDashboard(model, dataset=x_test, true_y=y_test)
input("Press Enter to continue...")