Merge 11b70bd into 5f12932

autokeras/auto_model.py

@@ -194,22 +194,19 @@ def predict(self, x, batch_size=32, **kwargs):
         """Predict the output for a given testing data.
 
         # Arguments
-            x: tf.data.Dataset or numpy.ndarray. Testing data.
+            x: Any allowed types according to the input node. Testing data.
             batch_size: Int. Defaults to 32.
             **kwargs: Any arguments supported by keras.Model.predict.
 
         # Returns
             A list of numpy.ndarray objects or a single numpy.ndarray.
             The predicted results.
         """
-        best_model = self.tuner.get_best_models(1)[0]
-        best_trial = self.tuner.get_best_trials(1)[0]
-        best_hp = best_trial.hyperparameters
-
-        self.tuner.load_trial(best_trial)
-        x = self._process_xy(x, predict=True)
-        x = self.hypermodel.preprocess(best_hp, x)
-        x = x.batch(batch_size)
+        best_model, x = self._prepare_best_model_and_data(
+            x=x,
+            y=None,
+            batch_size=batch_size,
+            predict=True)
         y = best_model.predict(x, **kwargs)
         y = self._postprocess(y)
         if isinstance(y, list) and len(y) == 1:
@@ -225,6 +222,39 @@ def _postprocess(self, y):
             new_y.append(temp_y)
         return new_y
 
+    def evaluate(self, x, y=None, batch_size=32, **kwargs):
+        """Evaluate the best model for the given data.
+
+        # Arguments
+            x: Any allowed types according to the input node. Testing data.
+            y: Any allowed types according to the head. Testing targets.
+                Defaults to None.
+            batch_size: Int. Defaults to 32.
+            **kwargs: Any arguments supported by keras.Model.evaluate.
+
+        # Returns
+            Scalar test loss (if the model has a single output and no metrics) or
+            list of scalars (if the model has multiple outputs and/or metrics).
+            The attribute model.metrics_names will give you the display labels for
+            the scalar outputs.
+        """
+        best_model, data = self._prepare_best_model_and_data(
+            x=x,
+            y=y,
+            batch_size=batch_size)
+        return best_model.evaluate(data, **kwargs)
+
+    def _prepare_best_model_and_data(self, x, y, batch_size, predict=False):
+        best_model = self.tuner.get_best_models(1)[0]
+        best_trial = self.tuner.get_best_trials(1)[0]
+        best_hp = best_trial.hyperparameters
+
+        self.tuner.load_trial(best_trial)
+        x = self._process_xy(x, y, predict=predict)
+        x = self.hypermodel.preprocess(best_hp, x)
+        x = x.batch(batch_size)
+        return best_model, x
+
 
 class GraphAutoModel(AutoModel):
     """A HyperModel defined by a graph of HyperBlocks.

tests/test_auto_model.py

@@ -12,7 +12,7 @@ def tmp_dir(tmpdir_factory):
     return tmpdir_factory.mktemp('test_auto_model')
 
 
-def test_graph_auto_model_basic(tmp_dir):
+def test_basics(tmp_dir):
     x_train = np.random.rand(100, 32)
     y_train = np.random.rand(100, 1)
 
@@ -21,16 +21,32 @@ def test_graph_auto_model_basic(tmp_dir):
     output_node = ak.DenseBlock()(output_node)
     output_node = ak.RegressionHead()(output_node)
 
-    graph = ak.GraphAutoModel(input_node,
-                              output_node,
-                              directory=tmp_dir,
-                              max_trials=1)
-    graph.fit(x_train, y_train, epochs=1, validation_data=(x_train, y_train))
-    result = graph.predict(x_train)
-
+    auto_model = ak.GraphAutoModel(input_node,
+                                   output_node,
+                                   directory=tmp_dir,
+                                   max_trials=1)
+    auto_model.fit(x_train, y_train, epochs=1, validation_data=(x_train, y_train))
+    result = auto_model.predict(x_train)
     assert result.shape == (100, 1)
 
 
+def test_evaluate(tmp_dir):
+    x_train = np.random.rand(100, 32)
+    y_train = np.random.rand(100, 1)
+
+    input_node = ak.Input()
+    output_node = input_node
+    output_node = ak.DenseBlock()(output_node)
+    output_node = ak.RegressionHead()(output_node)
+
+    auto_model = ak.GraphAutoModel(input_node,
+                                   output_node,
+                                   directory=tmp_dir,
+                                   max_trials=1)
+    auto_model.fit(x_train, y_train, epochs=1, validation_data=(x_train, y_train))
+    auto_model.evaluate(x_train, y_train)
+
+
 def test_merge(tmp_dir):
     x_train = np.random.rand(100, 33)
     y_train = np.random.rand(100, 1)