Made get_prediction_vs_actual_data public

docs/source/api_reference.rst

@@ -267,6 +267,7 @@ Utility Methods
     binary_objective_vs_threshold
     get_prediction_vs_actual_over_time_data
     partial_dependence
+    get_prediction_vs_actual_data
 
 
 Graph Utility Methods

evalml/model_understanding/__init__.py

@@ -12,6 +12,7 @@
     graph_binary_objective_vs_threshold,
     partial_dependence,
     graph_partial_dependence,
+    get_prediction_vs_actual_data,
     graph_prediction_vs_actual,
     get_prediction_vs_actual_over_time_data,
     graph_prediction_vs_actual_over_time,

evalml/model_understanding/graphs.py

@@ -507,8 +507,26 @@ def _calculate_axis_range(arr):
     return [min_value - margins, max_value + margins]
 
 
-def _get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold):
-    """Helper method to help calculate the y_true and y_pred dataframe, with a column for outliers"""
+def get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold=None):
+    """Combines y_true and y_pred into a single dataframe and adds a column for outliers. Used in `graph_prediction_vs_actual()`.
+
+    Arguments:
+        y_true (pd.Series): The real target values of the data
+        y_pred (pd.Series): The predicted values outputted by the regression model.
+        outlier_threshold (int, float): A positive threshold for what is considered an outlier value. This value is compared to the absolute difference
+                                 between each value of y_true and y_pred. Values within this threshold will be blue, otherwise they will be yellow.
+                                 Defaults to None
+
+    Returns:
+        pd.DataFrame with the following columns:
+                * `prediction`: Predicted values from regression model.
+                * `actual`: Real target values.
+                * `outlier`: Colors indicating which values are in the threshold for what is considered an outlier value.
+
+    """
+    if outlier_threshold and outlier_threshold <= 0:
+        raise ValueError(f"Threshold must be positive! Provided threshold is {outlier_threshold}")
+
     y_true = _convert_to_woodwork_structure(y_true)
     y_true = _convert_woodwork_types_wrapper(y_true.to_series())
     y_pred = _convert_to_woodwork_structure(y_pred)
@@ -547,7 +565,7 @@ def graph_prediction_vs_actual(y_true, y_pred, outlier_threshold=None):
     if outlier_threshold and outlier_threshold <= 0:
         raise ValueError(f"Threshold must be positive! Provided threshold is {outlier_threshold}")
 
-    df = _get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold)
+    df = get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold)
     data = []
 
     x_axis = _calculate_axis_range(df['prediction'])

evalml/tests/model_understanding_tests/test_graphs.py

@@ -20,6 +20,7 @@
     confusion_matrix,
     decision_tree_data_from_estimator,
     decision_tree_data_from_pipeline,
+    get_prediction_vs_actual_data,
     graph_binary_objective_vs_threshold,
     graph_confusion_matrix,
     graph_partial_dependence,
@@ -931,6 +932,31 @@ def test_jupyter_graph_check(import_check, jupyter_check, X_y_binary, X_y_regres
         import_check.assert_called_with('ipywidgets', warning=True)
 
 
+def test_get_prediction_vs_actual_data():
+    y_true = [1, 2, 3000, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+    y_pred = [5, 4, 2, 8, 6, 6, 5, 1, 7, 2, 1, 3000]
+
+    with pytest.raises(ValueError, match="Threshold must be positive!"):
+        get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold=-1)
+
+    outlier_loc = [2, 11]
+    results = get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold=2000)
+    assert isinstance(results, pd.DataFrame)
+    assert np.array_equal(results['prediction'], y_pred)
+    assert np.array_equal(results['actual'], y_true)
+    for i, value in enumerate(results['outlier']):
+        if i in outlier_loc:
+            assert value == "#ffff00"
+        else:
+            assert value == '#0000ff'
+
+    results = get_prediction_vs_actual_data(y_true, y_pred)
+    assert isinstance(results, pd.DataFrame)
+    assert np.array_equal(results['prediction'], y_pred)
+    assert np.array_equal(results['actual'], y_true)
+    assert (results['outlier'] == '#0000ff').all()
+
+
 def test_graph_prediction_vs_actual_default():
     go = pytest.importorskip('plotly.graph_objects', reason='Skipping plotting test because plotly not installed')
     y_true = [1, 2, 3000, 4, 5, 6, 7, 8, 9, 10, 11, 12]