Plot for time series regression (#1483)

* Adding graph_prediction_vs_target_over_time function to model_understanding.

* Adding PR 1483 to release notes.

* graph_prediction_vs_target_over_time only supports time series regression pipelines

* Skip test_graph_prediction_vs_target_over_time_value_error if plotly not installed.

* Adding graph_prediction_vs_target_over_time to api ref.

* Adding graph_prediction_vs_target_over_time to model_understanding __init__

* Changing y-axis label in graph_prediction_vs_target_over_time.

* Using actual instead of target in function name. Adding get_prediction_vs_actual_over_time_data

* Adding PR 1483 to future releases section of the release notes.

* Changing typo in error_msg for graph_prediction_vs_actual_over_time

docs/source/api_reference.rst

@@ -268,6 +268,8 @@ Graph Utils
     binary_objective_vs_threshold
     graph_binary_objective_vs_threshold
     graph_prediction_vs_actual
+    get_prediction_vs_actual_over_time_data
+    graph_prediction_vs_actual_over_time
 
 .. currentmodule:: evalml.model_understanding.prediction_explanations
 

docs/source/release_notes.rst

@@ -3,6 +3,7 @@ Release Notes
 
 **Future Releases**
     * Enhancements
+        * Added ``graph_prediction_vs_actual_over_time`` and ``get_prediction_vs_actual_over_time_data`` to the model understanding module for time series problems :pr:`1483`
     * Fixes
         * Fix Windows CI jobs: install ``numba`` via conda, required for ``shap`` :pr:`1490`
     * Changes

evalml/model_understanding/__init__.py

@@ -12,6 +12,8 @@
     graph_binary_objective_vs_threshold,
     partial_dependence,
     graph_partial_dependence,
-    graph_prediction_vs_actual
+    graph_prediction_vs_actual,
+    get_prediction_vs_actual_over_time_data,
+    graph_prediction_vs_actual_over_time,
 )
 from .prediction_explanations import explain_prediction, explain_predictions_best_worst, explain_predictions

evalml/model_understanding/graphs.py

@@ -555,3 +555,50 @@ def graph_prediction_vs_actual(y_true, y_pred, outlier_threshold=None):
                                 marker=_go.scatter.Marker(color=color),
                                 name=name))
     return _go.Figure(layout=layout, data=data)
+
+
+def get_prediction_vs_actual_over_time_data(pipeline, X, y, dates):
+    """Get the data needed for the prediction_vs_actual_over_time plot.
+
+    Arguments:
+        pipeline (PipelineBase): Fitted time series regression pipeline.
+        X (pd.DataFrame): Features used to generate new predictions.
+        y (pd.Series): Target values to compare predictions against.
+
+    Returns:
+       pd.DataFrame
+    """
+    return pd.DataFrame({"dates": dates,
+                         "target": y,
+                         "prediction": pipeline.predict(X, y)})
+
+
+def graph_prediction_vs_actual_over_time(pipeline, X, y, dates):
+    """Plot the target values and predictions against time on the x-axis.
+
+    Arguments:
+        pipeline (PipelineBase): Fitted time series regression pipeline.
+        X (pd.DataFrame): Features used to generate new predictions.
+        y (pd.Series): Target values to compare predictions against.
+
+    Returns:
+        plotly.Figure showing the prediction vs actual over time.
+    """
+    _go = import_or_raise("plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects")
+
+    if pipeline.problem_type != ProblemTypes.TIME_SERIES_REGRESSION:
+        raise ValueError("graph_prediction_vs_actual_over_time only supports time series regression pipelines! "
+                         f"Received {str(pipeline.problem_type)}.")
+
+    data = get_prediction_vs_actual_over_time_data(pipeline, X, y, dates)
+
+    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)

evalml/tests/model_understanding_tests/test_graphs.py

@@ -21,6 +21,7 @@
     graph_permutation_importance,
     graph_precision_recall_curve,
     graph_prediction_vs_actual,
+    graph_prediction_vs_actual_over_time,
     graph_roc_curve,
     normalize_confusion_matrix,
     partial_dependence,
@@ -954,3 +955,44 @@ def test_graph_prediction_vs_actual():
     assert len(fig_dict['data'][2]['x']) == 2
     assert len(fig_dict['data'][2]['y']) == 2
     assert fig_dict['data'][2]['name'] == ">= outlier_threshold"
+
+
+def test_graph_prediction_vs_actual_over_time():
+    go = pytest.importorskip('plotly.graph_objects', reason='Skipping plotting test because plotly not installed')
+
+    class MockPipeline:
+        problem_type = ProblemTypes.TIME_SERIES_REGRESSION
+
+        def predict(self, X, y):
+            return y + 10
+
+    y = np.arange(61)
+    dates = pd.date_range("2020-03-01", "2020-04-30")
+    pipeline = MockPipeline()
+
+    # For this test it doesn't matter what the features are
+    fig = graph_prediction_vs_actual_over_time(pipeline, X=pd.DataFrame(), y=y, dates=dates)
+
+    assert isinstance(fig, go.Figure)
+    fig_dict = fig.to_dict()
+    assert fig_dict['layout']['title']['text'] == 'Prediction vs Target over time'
+    assert fig_dict['layout']['xaxis']['title']['text'] == 'Time'
+    assert fig_dict['layout']['yaxis']['title']['text'] == 'Target Values and Predictions'
+    assert len(fig_dict['data']) == 2
+    assert fig_dict['data'][0]['line']['color'] == '#1f77b4'
+    assert len(fig_dict['data'][0]['x']) == 61
+    assert len(fig_dict['data'][0]['y']) == 61
+    assert fig_dict['data'][1]['line']['color'] == '#d62728'
+    assert len(fig_dict['data'][1]['x']) == 61
+    assert len(fig_dict['data'][1]['y']) == 61
+
+
+def test_graph_prediction_vs_actual_over_time_value_error():
+    pytest.importorskip('plotly.graph_objects', reason='Skipping plotting test because plotly not installed')
+
+    class NotTSPipeline:
+        problem_type = ProblemTypes.REGRESSION
+
+    error_msg = "graph_prediction_vs_actual_over_time only supports time series regression pipelines! Received regression."
+    with pytest.raises(ValueError, match=error_msg):
+        graph_prediction_vs_actual_over_time(NotTSPipeline(), None, None, None)