Rename MultiClassificationObjective to `MulticlassClassificationObj…

…ective` (#715)

* Rename MultiClassificationObjective to MulticlassClassificationObjective, to align with our pipeline naming scheme

* Remove hyphen from multiclass in two places

* Changelog

* Lint.

docs/source/changelog.rst

@@ -41,6 +41,7 @@ Changelog
         * Standardize `import_or_raise` error messages :pr:`683`
         * Renamed `pipeline.feature_importance_graph` to `pipeline.graph_feature_importances` :pr:`700`
         * Moved ROC and confusion matrix methods to `evalml.pipelines.plot_utils` :pr:`704`
+        * Renamed `MultiClassificationObjective` to `MulticlassClassificationObjective`, to align with pipeline naming scheme :pr:`715`
     * Documentation Changes
         * Fixed some sphinx warnings :pr:`593`
         * Fixed docstring for AutoClassificationSearch with correct command :pr:`599`

evalml/objectives/__init__.py

@@ -36,5 +36,5 @@
 )
 from .utils import get_objective, get_objectives
 from .binary_classification_objective import BinaryClassificationObjective
-from .multiclass_classification_objective import MultiClassificationObjective
+from .multiclass_classification_objective import MulticlassClassificationObjective
 from .regression_objective import RegressionObjective

evalml/objectives/multiclass_classification_objective.py

@@ -3,9 +3,9 @@
 from evalml.problem_types import ProblemTypes
 
 
-class MultiClassificationObjective(ObjectiveBase):
+class MulticlassClassificationObjective(ObjectiveBase):
     """
-    Base class for all multi-class classification objectives.
+    Base class for all multiclass classification objectives.
 
     problem_type (ProblemTypes): Specifies the type of problem this objective is defined for (multiclass classification).
     """

evalml/objectives/standard_metrics.py

@@ -3,7 +3,9 @@
 from sklearn.preprocessing import label_binarize
 
 from .binary_classification_objective import BinaryClassificationObjective
-from .multiclass_classification_objective import MultiClassificationObjective
+from .multiclass_classification_objective import (
+    MulticlassClassificationObjective
+)
 from .regression_objective import RegressionObjective
 
 from evalml.exceptions import DimensionMismatchError
@@ -23,7 +25,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.accuracy_score(y_true, y_predicted)
 
 
-class AccuracyMulticlass(MultiClassificationObjective):
+class AccuracyMulticlass(MulticlassClassificationObjective):
     """Accuracy score for multiclass classification"""
     name = "Accuracy Multiclass"
     greater_is_better = True
@@ -47,7 +49,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.balanced_accuracy_score(y_true, y_predicted)
 
 
-class BalancedAccuracyMulticlass(MultiClassificationObjective):
+class BalancedAccuracyMulticlass(MulticlassClassificationObjective):
     """Balanced accuracy score for multiclass classification"""
     name = "Balanced Accuracy Multiclass"
     greater_is_better = True
@@ -67,7 +69,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.f1_score(y_true, y_predicted, zero_division=0.0)
 
 
-class F1Micro(MultiClassificationObjective):
+class F1Micro(MulticlassClassificationObjective):
     """F1 score for multiclass classification using micro averaging"""
     name = "F1 Micro"
     greater_is_better = True
@@ -77,7 +79,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.f1_score(y_true, y_predicted, average='micro', zero_division=0.0)
 
 
-class F1Macro(MultiClassificationObjective):
+class F1Macro(MulticlassClassificationObjective):
     """F1 score for multiclass classification using macro averaging"""
     name = "F1 Macro"
     greater_is_better = True
@@ -87,7 +89,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.f1_score(y_true, y_predicted, average='macro', zero_division=0.0)
 
 
-class F1Weighted(MultiClassificationObjective):
+class F1Weighted(MulticlassClassificationObjective):
     """F1 score for multiclass classification using weighted averaging"""
     name = "F1 Weighted"
     greater_is_better = True
@@ -107,7 +109,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.precision_score(y_true, y_predicted, zero_division=0.0)
 
 
-class PrecisionMicro(MultiClassificationObjective):
+class PrecisionMicro(MulticlassClassificationObjective):
     """Precision score for multiclass classification using micro averaging"""
     name = "Precision Micro"
     greater_is_better = True
@@ -117,7 +119,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.precision_score(y_true, y_predicted, average='micro', zero_division=0.0)
 
 
-class PrecisionMacro(MultiClassificationObjective):
+class PrecisionMacro(MulticlassClassificationObjective):
     """Precision score for multiclass classification using macro averaging"""
     name = "Precision Macro"
     greater_is_better = True
@@ -127,7 +129,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.precision_score(y_true, y_predicted, average='macro', zero_division=0.0)
 
 
-class PrecisionWeighted(MultiClassificationObjective):
+class PrecisionWeighted(MulticlassClassificationObjective):
     """Precision score for multiclass classification using weighted averaging"""
     name = "Precision Weighted"
     greater_is_better = True
@@ -147,7 +149,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.recall_score(y_true, y_predicted, zero_division=0.0)
 
 
-class RecallMicro(MultiClassificationObjective):
+class RecallMicro(MulticlassClassificationObjective):
     """Recall score for multiclass classification using micro averaging"""
     name = "Recall Micro"
     greater_is_better = True
@@ -157,7 +159,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.recall_score(y_true, y_predicted, average='micro', zero_division=0.0)
 
 
-class RecallMacro(MultiClassificationObjective):
+class RecallMacro(MulticlassClassificationObjective):
     """Recall score for multiclass classification using macro averaging"""
     name = "Recall Macro"
     greater_is_better = True
@@ -167,7 +169,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.recall_score(y_true, y_predicted, average='macro', zero_division=0.0)
 
 
-class RecallWeighted(MultiClassificationObjective):
+class RecallWeighted(MulticlassClassificationObjective):
     """Recall score for multiclass classification using weighted averaging"""
     name = "Recall Weighted"
     greater_is_better = True
@@ -187,7 +189,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.roc_auc_score(y_true, y_predicted)
 
 
-class AUCMicro(MultiClassificationObjective):
+class AUCMicro(MulticlassClassificationObjective):
     """AUC score for multiclass classification using micro averaging"""
     name = "AUC Micro"
     greater_is_better = True
@@ -198,7 +200,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.roc_auc_score(y_true, y_predicted, average='micro')
 
 
-class AUCMacro(MultiClassificationObjective):
+class AUCMacro(MulticlassClassificationObjective):
     """AUC score for multiclass classification using macro averaging"""
     name = "AUC Macro"
     greater_is_better = True
@@ -209,7 +211,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.roc_auc_score(y_true, y_predicted, average='macro')
 
 
-class AUCWeighted(MultiClassificationObjective):
+class AUCWeighted(MulticlassClassificationObjective):
     """AUC Score for multiclass classification using weighted averaging"""
     name = "AUC Weighted"
     greater_is_better = True
@@ -230,7 +232,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.log_loss(y_true, y_predicted)
 
 
-class LogLossMulticlass(MultiClassificationObjective):
+class LogLossMulticlass(MulticlassClassificationObjective):
     """Log Loss for multiclass classification"""
     name = "Log Loss Multiclass"
     greater_is_better = False
@@ -250,7 +252,7 @@ def objective_function(self, y_predicted, y_true, X=None):
         return metrics.matthews_corrcoef(y_true, y_predicted)
 
 
-class MCCMulticlass(MultiClassificationObjective):
+class MCCMulticlass(MulticlassClassificationObjective):
     """Matthews correlation coefficient for multiclass classification"""
     name = "MCC Multiclass"
     greater_is_better = True

evalml/pipelines/classification/random_forest_multiclass.py

@@ -3,5 +3,5 @@
 
 class RFMulticlassClassificationPipeline(MulticlassClassificationPipeline):
     """Random Forest Pipeline for multiclass classification"""
-    custom_name = "Random Forest Multi-class Classification Pipeline"
+    custom_name = "Random Forest Multiclass Classification Pipeline"
     component_graph = ['One Hot Encoder', 'Simple Imputer', 'RF Classifier Select From Model', 'Random Forest Classifier']