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kingychiu · Jul 9, 2023 · f7bc8f6 · f7bc8f6
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diff --git a/README.md b/README.md
@@ -105,7 +105,7 @@ beartype = "^0.14.1"
 ### Tree Models with `feature_importances_` Attribute
 
 ```python
-# Import the function
+# import the package
 import target_permutation_importances as tpi
 
 # Prepare a dataset
@@ -163,7 +163,7 @@ Running 2 actual runs and 10 random runs
 ### Linear Models with `coef_` Attribute
 
 ```python
-# Import the function
+# import the package
 import target_permutation_importances as tpi
 
 # Prepare a dataset
@@ -214,7 +214,7 @@ Outputs:
 ### With `sklearn.multioutput`
 
 ```python
-# Import the function
+# import the package
 import target_permutation_importances as tpi
 
 # Prepare a dataset
@@ -328,11 +328,12 @@ Read the followings for details:
 `TargetPermutationImportancesWrapper` follows scikit-learn interfaces and support scikit-learn feature selection method such as `SelectFromModel`:
 
 ```python
-# Import the function
+# Import the package
 import target_permutation_importances as tpi
 
 # Prepare a dataset
 import pandas as pd
+import numpy as np
 from sklearn.datasets import load_breast_cancer
 
 # Models
@@ -372,9 +373,26 @@ selector = SelectFromModel(
 ).fit(Xpd, data.target)
 selected_x = selector.transform(Xpd)
 print(selected_x.shape)
+print(selector.get_feature_names_out())
 ```
 Fork above code from [Kaggle](https://www.kaggle.com/code/kingychiu/target-permutation-importances-basic-usage/notebook).
 
+Outputs:
+```
+Running 2 actual runs and 10 random runs
+100%|██████████| 2/2 [00:01<00:00,  1.80it/s]
+100%|██████████| 10/10 [00:06<00:00,  1.55it/s]
+                 feature  importance
+22       worst perimeter    0.151953
+27  worst concave points    0.124407
+20          worst radius    0.119090
+7    mean concave points    0.098747
+23            worst area    0.096943
+(569, 5)
+['mean concave points' 'worst radius' 'worst perimeter' 'worst area'
+ 'worst concave points']
+ ```
+
 ---
 
 ## Feature Selection Examples

diff --git a/docs/index.md b/docs/index.md
@@ -105,7 +105,7 @@ beartype = "^0.14.1"
 ### Tree Models with `feature_importances_` Attribute
 
 ```python
-# Import the function
+# import the package
 import target_permutation_importances as tpi
 
 # Prepare a dataset
@@ -163,7 +163,7 @@ Running 2 actual runs and 10 random runs
 ### Linear Models with `coef_` Attribute
 
 ```python
-# Import the function
+# import the package
 import target_permutation_importances as tpi
 
 # Prepare a dataset
@@ -214,7 +214,7 @@ Outputs:
 ### With `sklearn.multioutput`
 
 ```python
-# Import the function
+# import the package
 import target_permutation_importances as tpi
 
 # Prepare a dataset
@@ -328,11 +328,12 @@ Read the followings for details:
 `TargetPermutationImportancesWrapper` follows scikit-learn interfaces and support scikit-learn feature selection method such as `SelectFromModel`:
 
 ```python
-# Import the function
+# Import the package
 import target_permutation_importances as tpi
 
 # Prepare a dataset
 import pandas as pd
+import numpy as np
 from sklearn.datasets import load_breast_cancer
 
 # Models
@@ -372,9 +373,26 @@ selector = SelectFromModel(
 ).fit(Xpd, data.target)
 selected_x = selector.transform(Xpd)
 print(selected_x.shape)
+print(selector.get_feature_names_out())
 ```
 Fork above code from [Kaggle](https://www.kaggle.com/code/kingychiu/target-permutation-importances-basic-usage/notebook).
 
+Outputs:
+```
+Running 2 actual runs and 10 random runs
+100%|██████████| 2/2 [00:01<00:00,  1.80it/s]
+100%|██████████| 10/10 [00:06<00:00,  1.55it/s]
+                 feature  importance
+22       worst perimeter    0.151953
+27  worst concave points    0.124407
+20          worst radius    0.119090
+7    mean concave points    0.098747
+23            worst area    0.096943
+(569, 5)
+['mean concave points' 'worst radius' 'worst perimeter' 'worst area'
+ 'worst concave points']
+ ```
+
 ---
 
 ## Feature Selection Examples

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "target-permutation-importances"
-version = "1.0.19a1"
+version = "1.0.19"
 description = "Compute (Target) Permutation Importances of a machine learning model"
 authors = [{name = "Anthony Chiu", email = "kingychiu@gmail.com"}]
 maintainers = [{name = "Anthony Chiu", email = "kingychiu@gmail.com"}]
@@ -25,7 +25,7 @@ classifiers = [
 
 [tool.poetry]
 name = "target-permutation-importances"
-version = "1.0.19a1"
+version = "1.0.19"
 description = "Compute (Target) Permutation Importances of a machine learning model"
 authors = ["Anthony Chiu <kingychiu@gmail.com>"]
 maintainers = ["Anthony Chiu <kingychiu@gmail.com>"]

diff --git a/target_permutation_importances/functional.py b/target_permutation_importances/functional.py
@@ -357,7 +357,7 @@ def compute(
 
     Example:
         ```python
-        # Import the function
+        # import the package
         import target_permutation_importances as tpi
 
         # Prepare a dataset

diff --git a/target_permutation_importances/sklearn_wrapper.py b/target_permutation_importances/sklearn_wrapper.py
@@ -41,11 +41,12 @@ def __init__(
 
         Example:
             ```python
-            # Import the function
+            # Import the package
             import target_permutation_importances as tpi
 
             # Prepare a dataset
             import pandas as pd
+            import numpy as np
             from sklearn.datasets import load_breast_cancer
 
             # Models
@@ -85,6 +86,7 @@ def __init__(
             ).fit(Xpd, data.target)
             selected_x = selector.transform(Xpd)
             print(selected_x.shape)
+            print(selector.get_feature_names_out())
             ```
         """
         self.model_cls = model_cls