test changes and code clarity

evalml/model_understanding/visualizations.py

@@ -446,6 +446,7 @@ def graph_prediction_vs_actual_over_time(
         dates,
     )
 
+    fig = None
     if is_multiseries(pipeline.problem_type):
         id_list = (
             [single_series] if single_series is not None else data["series_id"].unique()
@@ -481,8 +482,6 @@ def graph_prediction_vs_actual_over_time(
             fig.update_yaxes(title_text=y.name)
         if single_series is not None:
             fig.update_layout(
-                height=600,
-                width=1000,
                 title_text=f"Graph for Series {single_series}",
             )
         else:
@@ -491,32 +490,32 @@ def graph_prediction_vs_actual_over_time(
                 width=1500,
                 title_text="Graph for Multiseries",
             )
-        return fig
-
-    data = [
-        _go.Scatter(
-            x=data["dates"],
-            y=data["target"],
-            mode="lines+markers",
-            name="Target",
-            line=dict(color="#1f77b4"),
-        ),
-        _go.Scatter(
-            x=data["dates"],
-            y=data["prediction"],
-            mode="lines+markers",
-            name="Prediction",
-            line=dict(color="#d62728"),
-        ),
-    ]
-    # Let plotly pick the best date format.
-    layout = _go.Layout(
-        title={"text": "Prediction vs Target over time"},
-        xaxis={"title": "Time"},
-        yaxis={"title": "Target Values and Predictions"},
-    )
+    else:
+        data = [
+            _go.Scatter(
+                x=data["dates"],
+                y=data["target"],
+                mode="lines+markers",
+                name="Target",
+                line=dict(color="#1f77b4"),
+            ),
+            _go.Scatter(
+                x=data["dates"],
+                y=data["prediction"],
+                mode="lines+markers",
+                name="Prediction",
+                line=dict(color="#d62728"),
+            ),
+        ]
+        # Let plotly pick the best date format.
+        layout = _go.Layout(
+            title={"text": "Prediction vs Target over time"},
+            xaxis={"title": "Time"},
+            yaxis={"title": "Target Values and Predictions"},
+        )
 
-    return _go.Figure(data=data, layout=layout)
+        fig = _go.Figure(data=data, layout=layout)
+    return fig
 
 
 def get_linear_coefficients(estimator, features=None):

evalml/tests/model_understanding_tests/test_visualizations.py

@@ -472,57 +472,13 @@ class NotTSPipeline:
         )
 
 
-def test_graph_prediction_vs_actual_over_time_multiseries_single(
-    multiseries_ts_data_stacked,
-    go,
-    component_graph_multiseries,
-    pipeline_parameters_multiseries,
-):
-    X, y = multiseries_ts_data_stacked
-    X_train, _, y_train, _ = split_multiseries_data(
-        X,
-        y,
-        "series_id",
-        "date",
-    )
-    pipeline = MultiseriesRegressionPipeline(
-        component_graph_multiseries,
-        pipeline_parameters_multiseries,
-    )
-    pipeline.fit(X_train, y_train)
-    fig = graph_prediction_vs_actual_over_time(
-        pipeline,
-        X,
-        y,
-        X_train,
-        y_train,
-        X["date"],
-        "1",
-    )
-    assert isinstance(fig, go.Figure)
-    fig_dict = fig.to_dict()
-
-    assert fig_dict["layout"]["title"]["text"] == "Graph for Series 1"
-    assert fig_dict["layout"]["xaxis"]["title"]["text"] == "Time"
-    assert fig_dict["layout"]["yaxis"]["title"]["text"] == "target"
-    assert len(fig_dict["data"]) == 2
-
-    assert len(fig_dict["data"][0]["x"]) == len(X["date"].unique())
-    assert len(fig_dict["data"][0]["y"]) == len(X["date"].unique())
-    assert not np.isnan(fig_dict["data"][0]["y"]).all()
-    assert fig_dict["data"][0]["name"] == "Series 1: Target"
-
-    assert len(fig_dict["data"][1]["x"]) == len(X["date"].unique())
-    assert len(fig_dict["data"][1]["y"]) == len(X["date"].unique())
-    assert not np.isnan(fig_dict["data"][1]["y"]).all()
-    assert fig_dict["data"][1]["name"] == "Series 1: Prediction"
-
-
+@pytest.mark.parametrize("single_series", ["0", None])
 def test_graph_prediction_vs_actual_over_time_multiseries(
     multiseries_ts_data_stacked,
     go,
     component_graph_multiseries,
     pipeline_parameters_multiseries,
+    single_series,
 ):
     X, y = multiseries_ts_data_stacked
     X_train, _, y_train, _ = split_multiseries_data(
@@ -543,17 +499,23 @@ def test_graph_prediction_vs_actual_over_time_multiseries(
         X_train,
         y_train,
         X["date"],
+        single_series=single_series,
     )
     assert isinstance(fig, go.Figure)
 
     fig_dict = fig.to_dict()
-    assert fig_dict["layout"]["title"]["text"] == "Graph for Multiseries"
+
+    if single_series is not None:
+        assert fig_dict["layout"]["title"]["text"] == "Graph for Series 0"
+        assert len(fig_dict["data"]) == 2
+    else:
+        assert fig_dict["layout"]["title"]["text"] == "Graph for Multiseries"
+        # there's 5 series, and each series has two lines (one each for target/prediction)
+        assert len(fig_dict["data"]) == 10
+
     assert fig_dict["layout"]["xaxis"]["title"]["text"] == "Time"
     assert fig_dict["layout"]["yaxis"]["title"]["text"] == "target"
 
-    # there's 5 series, and each series has two lines (one each for target/prediction)
-    assert len(fig_dict["data"]) == 10
-
     curr_series = 0
     for i in range(len(fig_dict["data"])):
         assert len(fig_dict["data"][i]["x"]) == len(X["date"].unique())

evalml/utils/__init__.py

@@ -41,3 +41,4 @@
     _determine_downcast_type,
     _get_incompatible_nullable_types,
 )
+from evalml.utils.lazy_loader import LazyLoader