• ( ) TEST TEST TEST TEST TEST TRAIN TEST TEST TEST TEST TEST TRAIN TRAIN TEST TRAIN TRAIN TEST TRAIN TEST TRAIN TEST (※1) 1 2 3 4 5 ※1 N 1 1 TEST N-1 TRAIN 2 1 TEST N-1 TRAIN N
ADSExplainer, create a global explanation object using # the MLXGlobalExplainer provider from ads.explanations.mlx_global_explainer import MLXGlobalExplainer global_explainer = explainer.global_explanation( provider=MLXGlobalExplainer()) # A summary of the global feature permutation importance algorithm and # how to interpret the output can be displayed with global_explainer.feature_importance_summary() # Compute the global Feature Permutation Importance explanation importances = global_explainer.compute_feature_importance() # ADS supports multiple visualizations for the global Feature # Permutation Importance explanations (see "Interpretation" above) # Simple bar chart highlighting the average impact on model score # across multiple iterations of the algorithm importances.show_in_notebook() # Build the model using AutoML. 'model' is a subclass of type ADSModel. # Note that the ADSExplainer below works with any model (classifier or # regressor) that is wrapped in an ADSModel import logging from ads.automl.provider import OracleAutoMLProvider from ads.automl.driver import AutoML ml_engine = OracleAutoMLProvider(n_jobs=-1, loglevel=logging.ERROR) oracle_automl = AutoML(train, provider=ml_engine) model, baseline = oracle_automl.train() # Create the ADS explainer object, which is used to construct global # and local explanation objects. The ADSExplainer takes as input the # model to explain and the train/test dataset from ads.explanations.explainer import ADSExplainer explainer = ADSExplainer(test, model, training_data=train) Global Explainer – Feature Importance Sample Code
import MLXGlobalExplainer global_explainer = explainer.global_explanation( provider=MLXGlobalExplainer()) # A summary of the global partial feature dependence explanation # algorithm and how to interpret the output can be displayed with global_explainer.partial_dependence_summary() # Compute the 1-feature PDP on the categorical feature, "sex", # and numerical feature, "age" pdp_sex = global_explainer.compute_partial_dependence("sex") pdp_age = global_explainer.compute_partial_dependence( "age", partial_range=(0, 1)) # ADS supports PDP visualizations for both 1-feature and 2-feature # Feature Dependence explanations, and ICE visualizations for 1-feature # Feature Dependence explanations (see "Interpretation" above) # Visualize the categorical feature PDP for the True (Survived) label pdp_sex.show_in_notebook(labels=True) # Note that the ADSExplainer below works with any model (classifier or # regressor) that is wrapped in an ADSModel import logging from ads.automl.provider import OracleAutoMLProvider from ads.automl.driver import AutoML ml_engine = OracleAutoMLProvider(n_jobs=-1, loglevel=logging.ERROR) oracle_automl = AutoML(train, provider=ml_engine) model, baseline = oracle_automl.train() # Create the ADS explainer object, which is used to construct # global and local explanation objects. The ADSExplainer takes # as input the model to explain and the train/test dataset from ads.explanations.explainer import ADSExplainer explainer = ADSExplainer(test, model, training_data=train) # With ADSExplainer, create a global explanation object using # the MLXGlobalExplainer provider Global Explainer – ICE/PDP Sample Code
import MLXLocalExplainer local_explainer = explainer.local_explanation( provider=MLXLocalExplainer()) # A summary of the local explanation algorithm and how to interpret # the output can be displayed with local_explainer.summary() # Select a specific sample (instance/row) to generate a local # explanation for sample = 14 # Compute the local explanation on our sample from the test set explanation = local_explainer.explain(test.X.iloc[sample:sample+1], test.y.iloc[sample:sample+1]) # Visualize the explanation for the label True (Survived). See # the "Interpretation" section above for more information explanation.show_in_notebook(labels=True) # Build the model using AutoML. 'model' is a subclass of type ADSModel. # Note that the ADSExplainer below works with any model (classifier or # regressor) that is wrapped in an ADSModel import logging from ads.automl.provider import OracleAutoMLProvider from ads.automl.driver import AutoML ml_engine = OracleAutoMLProvider(n_jobs=-1, loglevel=logging.ERROR) oracle_automl = AutoML(train, provider=ml_engine) model, baseline = oracle_automl.train() # Create the ADS explainer object, which is used to construct # global and local explanation objects. The ADSExplainer takes # as input the model to explain and the train/test dataset from ads.explanations.explainer import ADSExplainer explainer = ADSExplainer(test, model, training_data=train) # With ADSExplainer, create a local explanation object using # the MLXLocalExplainer provider Local Explainer
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