Added Additional Regression Objectives

docs/source/api_reference.rst

@@ -130,6 +130,12 @@ Regression
     :nosignatures:
 
     R2
+    MAE
+    MSE
+    MSLE
+    MedianAE
+    MaxError
+    ExpVariance
 
 .. currentmodule:: evalml.problem_types
 
@@ -155,6 +161,3 @@ Tuners
     :nosignatures:
 
     SKOptTuner
-
-
-

evalml/__init__.py

@@ -5,24 +5,24 @@
 # hack to prevent warnings from skopt
 # must import sklearn first
 import sklearn
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore", DeprecationWarning)
-    import skopt
 
 import evalml.demos
+import evalml.model_types
 import evalml.objectives
 import evalml.pipelines
 # import evalml.models
 import evalml.preprocessing
 import evalml.problem_types
-import evalml.pipelines
-import evalml.model_types
 import evalml.utils
-
-from evalml.pipelines import list_model_types, save_pipeline, load_pipeline
 from evalml.models import AutoClassifier, AutoRegressor
 from evalml.pipelines import list_model_types, load_pipeline, save_pipeline
 
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore", DeprecationWarning)
+    import skopt
+
+
+
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 
 

evalml/objectives/standard_metrics.py

@@ -239,6 +239,78 @@ def score(self, y_predicted, y_true):
         return metrics.r2_score(y_true, y_predicted)
 
 
+class MAE(ObjectiveBase):
+    """Mean absolute error for regression"""
+    needs_fitting = False
+    greater_is_better = False
+    need_proba = False
+    name = "MAE"
+    problem_types = [ProblemTypes.REGRESSION]
+
+    def score(self, y_predicted, y_true):
+        return metrics.mean_absolute_error(y_true, y_predicted)
+
+
+class MSE(ObjectiveBase):
+    """Mean squared error for regression"""
+    needs_fitting = False
+    greater_is_better = False
+    need_proba = False
+    name = "MSE"
+    problem_types = [ProblemTypes.REGRESSION]
+
+    def score(self, y_predicted, y_true):
+        return metrics.mean_squared_error(y_true, y_predicted)
+
+
+class MSLE(ObjectiveBase):
+    """Mean squared log error for regression"""
+    needs_fitting = False
+    greater_is_better = False
+    need_proba = False
+    name = "MSLE"
+    problem_types = [ProblemTypes.REGRESSION]
+
+    def score(self, y_predicted, y_true):
+        return metrics.mean_squared_log_error(y_true, y_predicted)
+
+
+class MedianAE(ObjectiveBase):
+    """Median absolute error for regression"""
+    needs_fitting = False
+    greater_is_better = False
+    need_proba = False
+    name = "MedianAE"
+    problem_types = [ProblemTypes.REGRESSION]
+
+    def score(self, y_predicted, y_true):
+        return metrics.median_absolute_error(y_true, y_predicted)
+
+
+class MaxError(ObjectiveBase):
+    """Maximum residual error for regression"""
+    needs_fitting = False
+    greater_is_better = False
+    need_proba = False
+    name = "MaxError"
+    problem_types = [ProblemTypes.REGRESSION]
+
+    def score(self, y_predicted, y_true):
+        return metrics.max_error(y_true, y_predicted)
+
+
+class ExpVariance(ObjectiveBase):
+    """Explained variance score for regression"""
+    needs_fitting = False
+    greater_is_better = True
+    need_proba = False
+    name = "ExpVariance"
+    problem_types = [ProblemTypes.REGRESSION]
+
+    def score(self, y_predicted, y_true):
+        return metrics.explained_variance_score(y_true, y_predicted)
+
+
 def _handle_predictions(y_true, y_pred):
     if len(np.unique(y_true)) > 2:
         classes = np.unique(y_true)

evalml/objectives/utils.py

@@ -23,6 +23,12 @@
     "log_loss": standard_metrics.LogLoss(),
     "mcc": standard_metrics.MCC(),
     "r2": standard_metrics.R2(),
+    "mae": standard_metrics.MAE(),
+    "mse": standard_metrics.MSE(),
+    "msle": standard_metrics.MSLE(),
+    "median_ae": standard_metrics.MedianAE(),
+    "max_error": standard_metrics.MaxError(),
+    "exp_var": standard_metrics.ExpVariance()
 }
 
 

evalml/pipelines/__init__.py

@@ -11,4 +11,4 @@
     list_model_types,
     load_pipeline,
     save_pipeline
-)
+)

evalml/tests/objective_tests/test_objectives.py

@@ -8,6 +8,7 @@
     get_objectives
 )
 from evalml.pipelines import LogisticRegressionPipeline
+from evalml.problem_types import ProblemTypes
 
 
 def test_get_objective():
@@ -16,9 +17,9 @@ def test_get_objective():
 
 
 def test_get_objectives_types():
-    assert len(get_objectives('multiclass')) == 14
-    assert len(get_objectives('binary')) == 6
-    assert len(get_objectives('regression')) == 1
+    assert len(get_objectives(ProblemTypes.MULTICLASS)) == 14
+    assert len(get_objectives(ProblemTypes.BINARY)) == 6
+    assert len(get_objectives(ProblemTypes.REGRESSION)) == 7
 
 
 def test_binary_average(X_y):

evalml/utils/__init__.py

@@ -1,3 +1,3 @@
 # flake8:noqa
+from .convert_time import convert_to_seconds
 from .logging_utils import Logger
-from .convert_time import convert_to_seconds