Feature names and importances (#621)

* feature names and importances

* None check

* StackingClassifier has no feature_importances_

* StackingClassifier has no feature_names_in_

flaml/automl.py

@@ -856,6 +856,20 @@ class AutoML(BaseEstimator):
     def n_features_in_(self):
         return self._trained_estimator.n_features_in_
 
+    @property
+    def feature_names_in_(self):
+        attr = getattr(self, "_trained_estimator", None)
+        attr = attr and getattr(attr, "feature_names_in_", None)
+        if attr is not None:
+            return attr
+        return getattr(self, "_feature_names_in_", None)
+
+    @property
+    def feature_importances_(self):
+        attr = getattr(self, "_trained_estimator", None)
+        attr = attr and getattr(attr, "feature_importances_", None)
+        return attr
+
     @property
     def time_to_find_best_model(self) -> float:
         """Time taken to find best model in seconds."""
@@ -1118,16 +1132,22 @@ class AutoML(BaseEstimator):
                 X, y, self._state.task
             )
             self._label_transformer = self._transformer.label_transformer
-            if hasattr(self._label_transformer, "label_list"):
-                self._state.fit_kwargs.update(
-                    {"label_list": self._label_transformer.label_list}
-                )
-            elif self._state.task == TOKENCLASSIFICATION:
-                if "label_list" not in self._state.fit_kwargs:
+            if self._state.task == TOKENCLASSIFICATION:
+                if hasattr(self._label_transformer, "label_list"):
+                    self._state.fit_kwargs.update(
+                        {"label_list": self._label_transformer.label_list}
+                    )
+                elif "label_list" not in self._state.fit_kwargs:
                     for each_fit_kwargs in self._state.fit_kwargs_by_estimator.values():
                         assert (
                             "label_list" in each_fit_kwargs
-                        ), "For the token-classification task, you must either (1) pass token labels; or (2) pass id labels and the label list. Please refer to the documentation for more details: https://microsoft.github.io/FLAML/docs/Examples/AutoML-NLP#a-simple-token-classification-example"
+                        ), "For the token-classification task, you must either (1) pass token labels; or (2) pass id labels and the label list. "
+                        "Please refer to the documentation for more details: https://microsoft.github.io/FLAML/docs/Examples/AutoML-NLP#a-simple-token-classification-example"
+            self._feature_names_in_ = (
+                self._X_train_all.columns.to_list()
+                if hasattr(self._X_train_all, "columns")
+                else None
+            )
 
         self._sample_weight_full = self._state.fit_kwargs.get(
             "sample_weight"

flaml/model.py

@@ -134,6 +134,43 @@ class BaseEstimator:
         """Trained model after fit() is called, or None before fit() is called."""
         return self._model
 
+    @property
+    def feature_names_in_(self):
+        """
+        if self._model has attribute feature_names_in_, return it.
+        otherwise, if self._model has attribute feature_name_, return it.
+        otherwise, if self._model has attribute feature_names, return it.
+        otherwise, if self._model has method get_booster, return the feature names.
+        otherwise, return None.
+        """
+        if hasattr(self._model, "feature_names_in_"):  # for sklearn, xgboost>=1.6
+            return self._model.feature_names_in_
+        if hasattr(self._model, "feature_name_"):  # for lightgbm
+            return self._model.feature_name_
+        if hasattr(self._model, "feature_names"):  # for XGBoostEstimator
+            return self._model.feature_names
+        if hasattr(self._model, "get_booster"):
+            # get feature names for xgboost<1.6
+            # https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.feature_names
+            booster = self._model.get_booster()
+            return booster.feature_names
+        return None
+
+    @property
+    def feature_importances_(self):
+        """
+        if self._model has attribute feature_importances_, return it.
+        otherwise, if self._model has attribute coef_, return it.
+        otherwise, return None.
+        """
+        if hasattr(self._model, "feature_importances_"):
+            # for sklearn, lightgbm, catboost, xgboost
+            return self._model.feature_importances_
+        elif hasattr(self._model, "coef_"):  # for linear models
+            return self._model.coef_
+        else:
+            return None
+
     def _preprocess(self, X):
         return X
 

test/automl/test_classification.py

@@ -115,6 +115,8 @@ class TestClassification(unittest.TestCase):
             "ensemble": True,
         }
         automl.fit(X, y, **automl_settings)
+        print(automl.feature_names_in_)
+        print(automl.feature_importances_)
         del automl
 
         automl = AutoML()
@@ -246,6 +248,8 @@ class TestClassification(unittest.TestCase):
         )
         automl = AutoML()
         automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
+        print(automl.feature_names_in_)
+        print(automl.feature_importances_)
         subprocess.check_call(
             [sys.executable, "-m", "pip", "install", "-U", "xgboost", "--user"]
         )

test/automl/test_notebook_example.py

@@ -86,6 +86,8 @@ def test_automl(budget=5, dataset_format="dataframe", hpo_method=None):
     print(automl.resource_attr)
     print(automl.max_resource)
     print(automl.min_resource)
+    print(automl.feature_names_in_)
+    print(automl.feature_importances_)
     if budget < performance_check_budget:
         automl.fit(X_train=X_train, y_train=y_train, ensemble=True, **settings)
 

test/test_model.py

@@ -69,17 +69,29 @@ def test_prep():
     lr = LRL2Classifier()
     lr.fit(X, y)
     lr.predict(X)
+    print(lr.feature_names_in_)
+    print(lr.feature_importances_)
     lgbm = LGBMEstimator(n_estimators=4)
     lgbm.fit(X, y)
+    print(lgbm.feature_names_in_)
+    print(lgbm.feature_importances_)
     cat = CatBoostEstimator(n_estimators=4)
     cat.fit(X, y)
+    print(cat.feature_names_in_)
+    print(cat.feature_importances_)
     knn = KNeighborsEstimator(task="regression")
     knn.fit(X, y)
+    print(knn.feature_names_in_)
+    print(knn.feature_importances_)
     xgb = XGBoostEstimator(n_estimators=4, max_leaves=4)
     xgb.fit(X, y)
     xgb.predict(X)
+    print(xgb.feature_names_in_)
+    print(xgb.feature_importances_)
     rf = RandomForestEstimator(task="regression", n_estimators=4, criterion="gini")
     rf.fit(X, y)
+    print(rf.feature_names_in_)
+    print(rf.feature_importances_)
 
     prophet = Prophet()
     try:
@@ -115,3 +127,9 @@ def test_prep():
     lgbm.predict(X[:2])
     lgbm.fit(X, y, period=2)
     lgbm.predict(X[:2])
+    print(lgbm.feature_names_in_)
+    print(lgbm.feature_importances_)
+
+
+if __name__ == "__main__":
+    test_prep()

website/docs/Examples/AutoML-for-LightGBM.md

@@ -14,7 +14,7 @@ settings = {
     "time_budget": 60,  # total running time in seconds
     "metric": 'r2',  # primary metrics for regression can be chosen from: ['mae','mse','r2']
     "estimator_list": ['lgbm'],  # list of ML learners; we tune lightgbm in this example
-    "task": 'regression',  # task type  
+    "task": 'regression',  # task type
     "log_file_name": 'houses_experiment.log',  # flaml log file
     "seed": 7654321,    # random seed
 }
@@ -89,7 +89,7 @@ print(automl.model.estimator)
 
 ```python
 import matplotlib.pyplot as plt
-plt.barh(automl.model.estimator.feature_name_, automl.model.estimator.feature_importances_)
+plt.barh(automl.feature_names_in_, automl.feature_importances_)
 ```
 ![png](../Use-Cases/images/feature_importance.png)
 

website/docs/Examples/AutoML-for-XGBoost.md

@@ -14,7 +14,7 @@ settings = {
     "time_budget": 60,  # total running time in seconds
     "metric": 'r2',  # primary metrics for regression can be chosen from: ['mae','mse','r2']
     "estimator_list": ['xgboost'],  # list of ML learners; we tune XGBoost in this example
-    "task": 'regression',  # task type  
+    "task": 'regression',  # task type
     "log_file_name": 'houses_experiment.log',  # flaml log file
     "seed": 7654321,    # random seed
 }
@@ -119,7 +119,7 @@ print(automl.model.estimator)
 ```python
 import matplotlib.pyplot as plt
 
-plt.barh(X_train.columns, automl.model.estimator.feature_importances_)
+plt.barh(automl.feature_names_in_, automl.feature_importances_)
 ```
 ![png](images/xgb_feature_importance.png)