Adds needs_fitting property to ComponentBase

docs/source/release_notes.rst

@@ -11,6 +11,7 @@ Release Notes
         * Updated TextFeaturizer component to no longer require an internet connection to run :pr:`1022`
         * Fixed non-deterministic element of TextFeaturizer transformations :pr:`1022`
     * Changes
+        * Added `needs_fitting` property to ComponentBase :pr:`1044`
         * Updated references to data types to use datatype lists defined in `evalml.utils.gen_utils` :pr:`1039`
     * Documentation Changes
         * Update setup.py URL to point to the github repo :pr:`1037`

evalml/pipelines/components/component_base.py

@@ -22,8 +22,6 @@ class ComponentBaseMeta(ABCMeta):
     """Metaclass that overrides creating a new component by wrapping method with validators and setters"""
     from evalml.exceptions import ComponentNotYetFittedError
 
-    NO_FITTING_REQUIRED = ['DropColumns', 'SelectColumns']
-
     @classmethod
     def set_fit(cls, method):
         @wraps(method)
@@ -41,7 +39,7 @@ def check_for_fit(cls, method):
         @wraps(method)
         def _check_for_fit(self, X=None, y=None):
             klass = type(self).__name__
-            if not self._is_fitted and klass not in cls.NO_FITTING_REQUIRED:
+            if not self._is_fitted and self.needs_fitting:
                 raise ComponentNotYetFittedError(f'This {klass} is not fitted yet. You must fit {klass} before calling {method.__name__}.')
             elif X is None and y is None:
                 return method(self)
@@ -91,6 +89,11 @@ def name(cls):
     def model_family(cls):
         """Returns ModelFamily of this component"""
 
+    @classproperty
+    def needs_fitting(self):
+        """Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances."""
+        return True
+
     @property
     def parameters(self):
         """Returns the parameters which were used to initialize the component"""

evalml/pipelines/components/transformers/column_selectors.py

@@ -85,6 +85,7 @@ class DropColumns(ColumnSelector):
     """Drops specified columns in input data."""
     name = "Drop Columns Transformer"
     hyperparameter_ranges = {}
+    needs_fitting = False
 
     def _modify_columns(self, cols, X, y=None):
         return X.drop(columns=cols, axis=1)
@@ -106,6 +107,7 @@ class SelectColumns(ColumnSelector):
     """Selects specified columns in input data."""
     name = "Select Columns Transformer"
     hyperparameter_ranges = {}
+    needs_fitting = False
 
     def _modify_columns(self, cols, X, y=None):
         return X[cols]

evalml/tests/component_tests/test_components.py

@@ -15,7 +15,6 @@
 from evalml.model_family import ModelFamily
 from evalml.pipelines.components import (
     ComponentBase,
-    ComponentBaseMeta,
     DropColumns,
     ElasticNetClassifier,
     ElasticNetRegressor,
@@ -645,10 +644,18 @@ def transform(self, X):
     transformer_subclass.transform(X)
 
 
+def test_all_transformers_needs_fitting():
+    for component_class in _all_transformers + _all_estimators:
+        if component_class.__name__ in ['DropColumns', 'SelectColumns']:
+            assert not component_class.needs_fitting
+        else:
+            assert component_class.needs_fitting
+
+
 def test_all_transformers_check_fit(X_y_binary):
     X, y = X_y_binary
     for component_class in _all_transformers:
-        if component_class.__name__ in ComponentBaseMeta.NO_FITTING_REQUIRED:
+        if not component_class.needs_fitting:
             continue
 
         component = component_class()
@@ -666,7 +673,7 @@ def test_all_transformers_check_fit(X_y_binary):
 def test_all_estimators_check_fit(X_y_binary):
     X, y = X_y_binary
     for component_class in _all_estimators:
-        if component_class.__name__ in ComponentBaseMeta.NO_FITTING_REQUIRED:
+        if not component_class.needs_fitting:
             continue
 
         component = component_class()
@@ -692,7 +699,7 @@ def test_all_estimators_check_fit(X_y_binary):
 def test_no_fitting_required_components(X_y_binary):
     X, y = X_y_binary
     for component_class in all_components:
-        if component_class.__name__ in ComponentBaseMeta.NO_FITTING_REQUIRED:
+        if not component_class.needs_fitting:
             component = component_class()
             if issubclass(component_class, Estimator):
                 component.predict(X)