Test changes

evalml/pipelines/multiseries_regression_pipeline.py

@@ -129,8 +129,12 @@ def predict_in_sample(
             self.input_target_name,
         )
         # Order series ID columns to be same as X_train
-        y_unstacked = y_unstacked[y_train_unstacked.columns]
-        X_unstacked = X_unstacked[X_train_unstacked.columns]
+        y_unstacked = y_unstacked[
+            y_train_unstacked.columns.intersection(y_unstacked.columns)
+        ]
+        X_unstacked = X_unstacked[
+            X_train_unstacked.columns.intersection(X_unstacked.columns)
+        ]
 
         X_train_unstacked = infer_feature_types(X_train_unstacked)
         y_train_unstacked = infer_feature_types(y_train_unstacked)

evalml/pipelines/utils.py

@@ -299,9 +299,9 @@ def _get_preprocessing_components(
     """
     if is_multiseries(problem_type):
         if include_decomposer:
-            components_functions = [_get_imputer, _get_decomposer]
+            components_functions = [_get_decomposer]
         else:
-            components_functions = [_get_imputer]
+            return []
 
     elif is_time_series(problem_type):
         components_functions = [

evalml/tests/conftest.py

@@ -1094,12 +1094,12 @@ def multiseries_ts_data_stacked():
 
 @pytest.fixture
 def multiseries_ts_data_unstacked():
-    feature_a = pd.DataFrame({f"feature_a_{i}": range(i, 100, 5) for i in range(5)})
+    feature_a = pd.DataFrame({f"feature_a|{i}": range(i, 100, 5) for i in range(5)})
     feature_b = pd.DataFrame(
-        {f"feature_b_{i}": range(99 - i, -1, -5) for i in range(5)},
+        {f"feature_b|{i}": range(99 - i, -1, -5) for i in range(5)},
     )
     X = pd.concat([feature_a, feature_b], axis=1)
-    y = pd.DataFrame({f"target_{i}": range(i, 100, 5) for i in range(5)})
+    y = pd.DataFrame({f"target|{i}": range(i, 100, 5) for i in range(5)})
 
     X["date"] = pd.date_range(start="1/1/2018", periods=20)
     return X, y

evalml/tests/pipeline_tests/test_pipeline_utils.py

@@ -1404,7 +1404,7 @@ def test_unstack_multiseries(
     X_unstacked, y_unstacked = multiseries_ts_data_unstacked
     y.name = target_name
     y_unstacked.columns = [
-        f"{target_name}_{i}" for i in range(len(y_unstacked.columns))
+        f"{target_name}|{i}" for i in range(len(y_unstacked.columns))
     ]
 
     X_unstacked_transformed, y_unstacked_transformed = unstack_multiseries(

evalml/utils/woodwork_utils.py

@@ -106,21 +106,28 @@ def _schema_is_equal(first, other):
     Returns:
         bool: Whether or not the two schemas are equal
     """
-    first_types_index = first.types.index.tolist()
-    other_types_index = other.types.index.tolist()
-    first_types_index.sort()
-    other_types_index.sort()
-    if first_types_index != other_types_index:
+    # first_types_index = first.types.index.tolist()
+    # other_types_index = other.types.index.tolist()
+    # first_types_index.sort()
+    # other_types_index.sort()
+    # if first_types_index != other_types_index:
+    if first.types.index.tolist() != other.types.index.tolist():
         return False
-    first_logical_types = first.types["Logical Type"].astype(str).tolist()
-    other_logical_types = other.types["Logical Type"].astype(str).tolist()
-    first_logical_types.sort()
-    other_logical_types.sort()
-    logical = [x if x != "Integer" else "Double" for x in first_logical_types] == [
-        x if x != "Integer" else "Double" for x in other_logical_types
+    # first_logical_types = first.types["Logical Type"].astype(str).tolist()
+    # other_logical_types = other.types["Logical Type"].astype(str).tolist()
+    # first_logical_types.sort()
+    # other_logical_types.sort()
+    # logical = [x if x != "Integer" else "Double" for x in first_logical_types] == [
+    #     x if x != "Integer" else "Double" for x in other_logical_types
+    # ]
+    logical = [
+        x if x != "Integer" else "Double"
+        for x in first.types["Logical Type"].astype(str).tolist()
+    ] == [
+        x if x != "Integer" else "Double"
+        for x in other.types["Logical Type"].astype(str).tolist()
     ]
     semantic = first.semantic_tags == other.semantic_tags
-    print("log:", logical, "sem:", semantic)
     return logical and semantic