Fixed bug where PerColumnImputer cannot handle dropped columns

docs/source/changelog.rst

@@ -14,6 +14,7 @@ Changelog
         * Define getter method for component `parameters` :pr:`847`
     * Fixes
         * Fixed bug where SimpleImputer cannot handle dropped columns :pr:`846`
+        * Fixed bug where PerColumnImputer cannot handle dropped columns :pr:`855`
         * Enforce requirement that builtin components save all inputted values in their parameters dict :pr:`847`
         * Don't list base classes in `all_components` output :pr:`847`
     * Changes

evalml/pipelines/components/transformers/imputers/per_column_imputer.py

@@ -1,6 +1,7 @@
-from sklearn.impute import SimpleImputer as SkImputer
-
 from evalml.pipelines.components.transformers import Transformer
+from evalml.pipelines.components.transformers.imputers.simple_imputer import (
+    SimpleImputer
+)
 
 
 class PerColumnImputer(Transformer):
@@ -50,7 +51,7 @@ def fit(self, X, y=None):
             strategy_dict = self.impute_strategies.get(column, dict())
             strategy = strategy_dict.get('impute_strategy', self.default_impute_strategy)
             fill_value = strategy_dict.get('fill_value', None)
-            self.imputers[column] = SkImputer(strategy=strategy, fill_value=fill_value)
+            self.imputers[column] = SimpleImputer(impute_strategy=strategy, fill_value=fill_value)
 
         for column, imputer in self.imputers.items():
             imputer.fit(X[[column]])
@@ -67,8 +68,14 @@ def transform(self, X, y=None):
             pd.DataFrame: Transformed X
         """
         X_t = X.copy()
+        cols_to_drop = []
         for column, imputer in self.imputers.items():
-            X_t[column] = imputer.transform(X[[column]]).astype(X.dtypes[column])
+            transformed = imputer.transform(X[[column]])
+            if transformed.empty:
+                cols_to_drop += [column]
+            else:
+                X_t[column] = transformed
+        X_t = X_t.drop(cols_to_drop, axis=1)
         return X_t
 
     def fit_transform(self, X, y=None):

evalml/tests/component_tests/test_per_column_imputer.py

@@ -129,3 +129,49 @@ def test_non_numeric_valid(non_numeric_df):
 
     X_t = transformer.fit_transform(X)
     assert_frame_equal(X_expected, X_t, check_dtype=False)
+
+
+def test_fit_transform_drop_all_nan_columns():
+    X = pd.DataFrame({"all_nan": [np.nan, np.nan, np.nan],
+                      "some_nan": [np.nan, 1, 0],
+                      "another_col": [0, 1, 2]})
+    strategies = {'all_nan': {"impute_strategy": "most_frequent"},
+                  'some_nan': {"impute_strategy": "most_frequent"},
+                  'another_col': {"impute_strategy": "most_frequent"}}
+    transformer = PerColumnImputer(impute_strategies=strategies)
+    X_expected_arr = pd.DataFrame({"some_nan": [0, 1, 0], "another_col": [0, 1, 2]})
+    X_t = transformer.fit_transform(X)
+    assert_frame_equal(X_expected_arr, X_t, check_dtype=False)
+    assert_frame_equal(X, pd.DataFrame({"all_nan": [np.nan, np.nan, np.nan],
+                                        "some_nan": [np.nan, 1, 0],
+                                        "another_col": [0, 1, 2]}))
+
+
+def test_transform_drop_all_nan_columns():
+    X = pd.DataFrame({"all_nan": [np.nan, np.nan, np.nan],
+                      "some_nan": [np.nan, 1, 0],
+                      "another_col": [0, 1, 2]})
+    strategies = {'all_nan': {"impute_strategy": "most_frequent"},
+                  'some_nan': {"impute_strategy": "most_frequent"},
+                  'another_col': {"impute_strategy": "most_frequent"}}
+    transformer = PerColumnImputer(impute_strategies=strategies)
+    transformer.fit(X)
+    X_expected_arr = pd.DataFrame({"some_nan": [0, 1, 0], "another_col": [0, 1, 2]})
+    assert_frame_equal(X_expected_arr, transformer.transform(X), check_dtype=False)
+    assert_frame_equal(X, pd.DataFrame({"all_nan": [np.nan, np.nan, np.nan],
+                                        "some_nan": [np.nan, 1, 0],
+                                        "another_col": [0, 1, 2]}))
+
+
+def test_transform_drop_all_nan_columns_empty():
+    X = pd.DataFrame([[np.nan, np.nan, np.nan]])
+    strategies = {'0': {"impute_strategy": "most_frequent"}, }
+    transformer = PerColumnImputer(impute_strategies=strategies)
+    assert transformer.fit_transform(X).empty
+    assert_frame_equal(X, pd.DataFrame([[np.nan, np.nan, np.nan]]))
+
+    strategies = {'0': {"impute_strategy": "most_frequent"}}
+    transformer = PerColumnImputer(impute_strategies=strategies)
+    transformer.fit(X)
+    assert transformer.transform(X).empty
+    assert_frame_equal(X, pd.DataFrame([[np.nan, np.nan, np.nan]]))