fix var len layer

deeptables/models/layers.py

@@ -931,20 +931,11 @@ def __init__(self, pooling_strategy='max', dropout_rate=0.,  **kwargs):
         super(VarLenColumnEmbedding, self).__init__(**kwargs)
 
     def build(self, input_shape):
-        import keras
         super(VarLenColumnEmbedding, self).build(input_shape)
-
-        height = input_shape[1]
-        if self.pooling_strategy == "mean":
-            self._pooling_layer = keras.layers.AveragePooling2D(pool_size=(height, 1))
-        else:
-            self._pooling_layer = keras.layers.MaxPooling2D(pool_size=(height, 1))
-
         if self.dropout_rate > 0:
             self._dropout = SpatialDropout1D(self.dropout_rate)
         else:
             self._dropout = None
-
         self.built = True
 
     def call(self, inputs):
@@ -957,14 +948,8 @@ def call(self, inputs):
         else:
             dropout_output = embedding_output
 
-        # 3. expand dim for polling
-        inputs_4d = tf.expand_dims(dropout_output, 3)  # add channels dim
-
-        # 4. polling
-        tensor_pooling = self._pooling_layer(inputs_4d)
-
-        # 5. format output
-        return tf.squeeze(tensor_pooling, 3)
+        # 3. format output
+        return dropout_output
 
     def compute_mask(self, inputs, mask):
         return None

deeptables/tests/models/var_len_categorical_test.py

@@ -5,33 +5,33 @@
 from hypernets.tabular import get_tool_box
 
 
-# class TestVarLenCategoricalFeature:
-#
-#     def setup_class(cls):
-#         cls.df = dsutils.load_movielens().drop(['timestamp', "title"], axis=1)
-#
-#     def test_var_categorical_feature(self):
-#         X = self.df.copy()
-#         y = X.pop('rating').values.astype('float32')
-#
-#         conf = deeptable.ModelConfig(nets=['dnn_nets'],
-#                                      task=consts.TASK_REGRESSION,
-#                                      categorical_columns=["movie_id", "user_id", "gender", "occupation", "zip", "title",
-#                                                           "age"],
-#                                      metrics=['mse'],
-#                                      fixed_embedding_dim=True,
-#                                      embeddings_output_dim=4,
-#                                      apply_gbm_features=False,
-#                                      apply_class_weight=True,
-#                                      earlystopping_patience=5,
-#                                      var_len_categorical_columns=[('genres', "|", "max")]
-#                                      )
-#
-#         dt = deeptable.DeepTable(config=conf)
-#
-#         X_train, X_validation, y_train, y_validation = get_tool_box(X).train_test_split(X, y, test_size=0.2)
-#
-#         model, history = dt.fit(X_train, y_train, validation_data=(X_validation, y_validation),
-#                                 epochs=10, batch_size=32)
-#
-#         assert 'genres' in model.model.input_names
+class TestVarLenCategoricalFeature:
+
+    def setup_class(cls):
+        cls.df = dsutils.load_movielens().drop(['timestamp', "title"], axis=1)
+
+    def test_var_categorical_feature(self):
+        X = self.df.copy()
+        y = X.pop('rating').values.astype('float32')
+
+        conf = deeptable.ModelConfig(nets=['dnn_nets'],
+                                     task=consts.TASK_REGRESSION,
+                                     categorical_columns=["movie_id", "user_id", "gender", "occupation", "zip", "title",
+                                                          "age"],
+                                     metrics=['mse'],
+                                     fixed_embedding_dim=True,
+                                     embeddings_output_dim=4,
+                                     apply_gbm_features=False,
+                                     apply_class_weight=True,
+                                     earlystopping_patience=5,
+                                     var_len_categorical_columns=[('genres', "|", "max")]
+                                     )
+
+        dt = deeptable.DeepTable(config=conf)
+
+        X_train, X_validation, y_train, y_validation = get_tool_box(X).train_test_split(X, y, test_size=0.2)
+
+        model, history = dt.fit(X_train, y_train, validation_data=(X_validation, y_validation),
+                                epochs=10, batch_size=32)
+        names = [_.name for _ in model.model.inputs]
+        assert 'genres' in names

deeptables/tests/models/zdask_var_len_categorical_test.py

@@ -1,16 +1,16 @@
 # -*- encoding: utf-8 -*-
-# from hypernets.tests.tabular.tb_dask import is_dask_installed, if_dask_ready, setup_dask
-# from .var_len_categorical_test import TestVarLenCategoricalFeature
-#
-# if is_dask_installed:
-#     import dask.dataframe as dd
-#
+from hypernets.tests.tabular.tb_dask import is_dask_installed, if_dask_ready, setup_dask
+from .var_len_categorical_test import TestVarLenCategoricalFeature
 
-# @if_dask_ready
-# class TestVarLenCategoricalFeatureByDask(TestVarLenCategoricalFeature):
-#
-#     def setup_class(self):
-#         TestVarLenCategoricalFeature.setup_class(self)
-#
-#         setup_dask(self)
-#         self.df = dd.from_pandas(self.df, npartitions=2)
+if is_dask_installed:
+    import dask.dataframe as dd
+
+
+@if_dask_ready
+class TestVarLenCategoricalFeatureByDask(TestVarLenCategoricalFeature):
+
+    def setup_class(self):
+        TestVarLenCategoricalFeature.setup_class(self)
+
+        setup_dask(self)
+        self.df = dd.from_pandas(self.df, npartitions=2)