Example for preprocessing.dictmapper.DictMapper and meta.outlier_classifier.OutlierClassifier

sklego/meta/outlier_classifier.py

@@ -52,6 +52,33 @@ def fit(self, X, y=None):
         ValueError
             - If the underlying model is not an outlier detection model.
             - If the underlying model does not have a `decision_function` method.
+
+        Example
+        -------
+        ```py
+        from sklearn.ensemble import IsolationForest
+        from sklego.meta.outlier_classifier import OutlierClassifier
+
+        X = [[0], [0.5], [-1], [99]]
+        y = [[0], [0], [0], [1]]
+
+        isolation_forest = IsolationForest()
+        isolation_forest.fit(X)
+        detector_preds = isolation_forest.predict(X)
+        # array[ 1  1  1 -1]
+
+        outlier_clf = OutlierClassifier(isolation_forest)
+        _ = outlier_clf.fit(X, y)
+
+        preds = outlier_clf.predict([[100], [-0.5], [0.5], [1]])
+        # array[1. 0. 0. 0.]
+
+        proba_preds = outlier_clf.predict_proba([[100], [-0.5], [0.5], [1]])
+        # [[0.34946567 0.65053433]
+        #  [0.79707913 0.20292087]
+        #  [0.80275406 0.19724594]
+        #  [0.80275406 0.19724594]]
+        ```
         """
         X, y = check_X_y(X, y, estimator=self)
         if not self._is_outlier_model():

sklego/preprocessing/dictmapper.py

@@ -43,6 +43,41 @@ def fit(self, X, y=None):
         -------
         self : DictMapper
             The fitted transformer.
+
+        Example
+        -------
+        ```py
+        import pandas as pd
+        from sklego.preprocessing.dictmapper import DictMapper
+
+        X = pd.DataFrame({
+            "city": ["Amsterdam", "Leiden", "Utrecht", "Amsterdam", "Haarlem"],
+            "university": ["uva", "lei", "uu", "vu", "none"]
+        })
+
+        mapper = {
+
+            #population
+
+            "Amsterdam": 1_181_817,
+            "Leiden": 130_181,
+            "Utrecht": 367_984,
+            "Haarlem": 165_396,
+
+            #ranking
+
+            "uva": 64,
+            "lei": 214,
+            "uu": 117,
+            "vu": 105
+        }
+
+        dict_mapper = DictMapper(mapper, 0)
+        _ = dict_mapper.fit(X)
+
+        dict_mapper.n_features_in_
+        # 2
+        ```
         """
         X = check_array(
             X,
@@ -72,6 +107,47 @@ def transform(self, X):
         ------
         ValueError
             If the number of columns from `X` differs from the number of columns when fitting.
+
+        Example
+        -------
+        ```py
+        import pandas as pd
+        from sklego.preprocessing.dictmapper import DictMapper
+
+        X = pd.DataFrame({
+            "city": ["Amsterdam", "Leiden", "Utrecht", "Amsterdam", "Haarlem"],
+            "university": ["uva", "lei", "uu", "vu", "none"]
+        })
+
+        mapper = {
+
+            #population
+
+            "Amsterdam": 1_181_817,
+            "Leiden": 130_181,
+            "Utrecht": 367_984,
+            "Haarlem": 165_396,
+
+            #ranking
+
+            "uva": 64,
+            "lei": 214,
+            "uu": 117,
+            "vu": 105
+        }
+
+        dict_mapper = DictMapper(mapper, 0)
+        _ = dict_mapper.fit(X)
+
+        X_trans = dict_mapper.transform(X)
+        X_trans
+        # array([[1181817,      64],
+        #        [ 130181,     214],
+        #        [ 367984,     117],
+        #        [1181817,     105],
+        #        [ 165396,       0]])
+
+        ```
         """
         check_is_fitted(self, ["n_features_in_"])
         X = check_array(

sklego/preprocessing/outlier_remover.py

@@ -34,7 +34,7 @@ class OutlierRemover(TrainOnlyTransformerMixin, BaseEstimator):
 
     isolation_forest = IsolationForest()
     isolation_forest.fit(X)
-    detector_preds = isolator_forest.predict(X)
+    detector_preds = isolation_forest.predict(X)
 
     outlier_remover = OutlierRemover(isolation_forest, refit=True)
     outlier_remover.fit(X)