add 'auto' value for importance_type param in plotting

python-package/lightgbm/plotting.py

@@ -32,7 +32,7 @@ def plot_importance(
     title: Optional[str] = 'Feature importance',
     xlabel: Optional[str] = 'Feature importance',
     ylabel: Optional[str] = 'Features',
-    importance_type: str = 'split',
+    importance_type: str = 'auto',
     max_num_features: Optional[int] = None,
     ignore_zero: bool = True,
     figsize: Optional[Tuple[float, float]] = None,
@@ -65,8 +65,9 @@ def plot_importance(
     ylabel : str or None, optional (default="Features")
         Y-axis title label.
         If None, title is disabled.
-    importance_type : str, optional (default="split")
+    importance_type : str, optional (default="auto")
         How the importance is calculated.
+        If "auto", if ``booster`` parameter is LGBMModel, ``booster.importance_type`` attribute is used; otherwise "split".
         If "split", result contains numbers of times the feature is used in a model.
         If "gain", result contains total gains of splits which use the feature.
     max_num_features : int or None, optional (default=None)
@@ -97,7 +98,12 @@ def plot_importance(
 
     if isinstance(booster, LGBMModel):
         booster = booster.booster_
-    elif not isinstance(booster, Booster):
+        if importance_type == "auto":
+            importance_type = booster.importance_type
+    elif isinstance(booster, Booster):
+        if importance_type == "auto":
+            importance_type = "split"
+    else:
         raise TypeError('booster must be Booster or LGBMModel.')
 
     importance = booster.feature_importance(importance_type=importance_type)

tests/python_package_test/test_plotting.py

@@ -57,8 +57,7 @@ def test_plot_importance(params, breast_cancer_split, train_data):
     for patch in ax1.patches:
         assert patch.get_facecolor() == (1., 0, 0, 1.)  # red
 
-    ax2 = lgb.plot_importance(gbm0, color=['r', 'y', 'g', 'b'],
-                              title=None, xlabel=None, ylabel=None)
+    ax2 = lgb.plot_importance(gbm0, color=['r', 'y', 'g', 'b'], title=None, xlabel=None, ylabel=None)
     assert isinstance(ax2, matplotlib.axes.Axes)
     assert ax2.get_title() == ''
     assert ax2.get_xlabel() == ''
@@ -69,6 +68,24 @@ def test_plot_importance(params, breast_cancer_split, train_data):
     assert ax2.patches[2].get_facecolor() == (0, .5, 0, 1.)  # g
     assert ax2.patches[3].get_facecolor() == (0, 0, 1., 1.)  # b
 
+    gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True, importance_type="gain")
+    gbm2.fit(X_train, y_train)
+
+    def get_bounds_of_first_patch(axes):
+        return axes.patches[0].get_extents().bounds
+
+    first_bar1 = get_bounds_of_first_patch(lgb.plot_importance(gbm1))
+    first_bar2 = get_bounds_of_first_patch(lgb.plot_importance(gbm1, importance_type="split"))
+    first_bar3 = get_bounds_of_first_patch(lgb.plot_importance(gbm1, importance_type="gain"))
+    first_bar4 = get_bounds_of_first_patch(lgb.plot_importance(gbm2))
+    first_bar5 = get_bounds_of_first_patch(lgb.plot_importance(gbm2, importance_type="split"))
+    first_bar6 = get_bounds_of_first_patch(lgb.plot_importance(gbm2, importance_type="gain"))
+
+    assert first_bar1 == first_bar2
+    assert first_bar1 == first_bar5
+    assert first_bar3 == first_bar4
+    assert first_bar3 == first_bar6
+
 
 @pytest.mark.skipif(not MATPLOTLIB_INSTALLED, reason='matplotlib is not installed')
 def test_plot_split_value_histogram(params, breast_cancer_split, train_data):