美购精排模型

mlp/train.py

@@ -75,25 +75,39 @@ def getTrainColumns(train_columns,data_vocab):
     inputs = {}
     # 离散特征
     for feature in train_columns:
-        # if data_vocab.get(feature):
-        #     cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
-        #     col = tf.feature_column.embedding_column(cat_col, 10)
-        #     columns.append(col)
-        #     dataColumns.append(feature)
-        #     inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
-        if feature.startswith("userRatedHistory") or feature.count("__") > 0 or feature in embedding_columns:
-            cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature, vocabulary_list=data_vocab[feature])
-            col = tf.feature_column.embedding_column(cat_col, 10)
+        if data_vocab.get(feature):
+            if feature.count("__")>0 or feature.count("Bucket") > 0:
+                cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
+                col = tf.feature_column.embedding_column(cat_col, 5)
                 columns.append(col)
                 dataColumns.append(feature)
                 inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
-
-        elif feature in one_hot_columns or feature.count("Bucket") > 0:
+            elif feature in one_hot_columns:
                 cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature, vocabulary_list=data_vocab[feature])
                 col = tf.feature_column.indicator_column(cat_col)
                 columns.append(col)
                 dataColumns.append(feature)
                 inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
+            else:
+                cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
+                col = tf.feature_column.embedding_column(cat_col, 10)
+                columns.append(col)
+                dataColumns.append(feature)
+                inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
+#         if feature.startswith("userRatedHistory") or feature.count("__") > 0 or feature in embedding_columns:
+#             cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature, vocabulary_list=data_vocab[feature])
+# #             col = tf.feature_column.embedding_column(cat_col, 10)
+#             col = tf.feature_column.indicator_column(cat_col)
+#             columns.append(col)
+#             dataColumns.append(feature)
+#             inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
+
+#         elif feature in one_hot_columns or feature.count("Bucket") > 0:
+#             cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature, vocabulary_list=data_vocab[feature])
+#             col = tf.feature_column.indicator_column(cat_col)
+#             columns.append(col)
+#             dataColumns.append(feature)
+#             inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
 
         elif feature in ITEM_NUMBER_COLUMNS:
             col = tf.feature_column.numeric_column(feature)
@@ -108,7 +122,9 @@ def getTrainColumns(train_columns,data_vocab):
 def train(columns,inputs,train_dataset):
     deep = tf.keras.layers.DenseFeatures(columns)(inputs)
     deep = tf.keras.layers.Dense(64, activation='relu')(deep)
+    deep = tf.keras.layers.Dropout(0.2)(deep)
     deep = tf.keras.layers.Dense(64, activation='relu')(deep)
+    deep = tf.keras.layers.Dropout(0.2)(deep)
     output_layer = tf.keras.layers.Dense(1, activation='sigmoid')(deep)
     model = tf.keras.Model(inputs, output_layer)
 
@@ -119,13 +135,13 @@ def train(columns,inputs,train_dataset):
         metrics=['accuracy', tf.keras.metrics.AUC(curve='ROC'), tf.keras.metrics.AUC(curve='PR')])
 
     # train the model
-    print("train start...")
     model.fit(train_dataset, epochs=5)
-    print("train end...")
     print("train save...")
 
     model.save(model_file, include_optimizer=False, save_format='tf')
 
+    return model
+
 
 def evaluate(model,test_dataset):
     # evaluate the model
@@ -185,10 +201,11 @@ if __name__ == '__main__':
     test_data = getDataSet(df_test,shuffleSize=testSize)
 
 
+    print("train start...")
     timestmp3 = int(round(time.time()))
     model = train(trainColumns,inputs,train_data)
     timestmp4 = int(round(time.time()))
-    print("读取数据耗时h:{}".format((timestmp4 - timestmp3)/60/60))
+    print("train end...耗时h:{}".format((timestmp4 - timestmp3)/60/60))
 
     evaluate(model,test_data)
 

spark/featureEng.py

@@ -32,13 +32,22 @@ import utils.connUtils as connUtils
     特征工程
 """
 
-ITEM_MULTI_COLUMN_EXTRA_MAP = {"first_demands": 10,
-                       "second_demands": 30,
-                       "first_solutions": 2,
-                       "second_solutions": 14,
+ITEM_MULTI_COLUMN_EXTRA_MAP = {"first_demands": 1,
+                       "second_demands": 5,
+                       "first_solutions": 1,
+                       "second_solutions": 5,
                        "first_positions": 1,
-                       "second_positions": 20,
-                       "tags_v3": 30,
+                       "second_positions": 5,
+                       "tags_v3": 10,
+                       }
+
+USER_MULTI_COLUMN_EXTRA_MAP = {"first_demands": 1,
+                       "second_demands": 3,
+                       "first_solutions": 1,
+                       "second_solutions": 3,
+                       "first_positions": 1,
+                       "second_positions": 3,
+                       "tags_v3": 5,
                        }
 
 ITEM_NUMBER_COLUMNS = ["lowest_price","smart_rank2","case_count","ordered_user_ids_count"]
@@ -125,23 +134,21 @@ def addUserFeatures(samples):
         .filter(F.col("userRatingCount") > 1)
 
     # user偏好
-    for c in ["first_demands","second_demands","first_solutions","second_solutions","first_positions","second_positions"]:
-        new_col = "user" + "__"+c
-        samples = samples\
-            .withColumn(new_col, extractTagsUdf(F.collect_list(when(F.col('label') == 1, F.col(c)).otherwise(F.lit(None))).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1)))) \
-            .withColumn(new_col+"__1", F.when(F.col(new_col)[0].isNotNull(),F.col(new_col)[0]).otherwise("-1")) \
-            .withColumn(new_col+"__2", F.when(F.col(new_col)[1].isNotNull(),F.col(new_col)[1]).otherwise("-1")) \
-            .withColumn(new_col+"__3", F.when(F.col(new_col)[2].isNotNull(),F.col(new_col)[2]).otherwise("-1")) \
-            .drop(new_col)
-        # .drop(c).drop(new_col)
-
-    # tags
-    c = "tags_v3"
+    for c,v in USER_MULTI_COLUMN_EXTRA_MAP.items():
         new_col = "user" + "__" + c
         samples = samples.withColumn(new_col, extractTagsUdf(F.collect_list(when(F.col('label') == 1, F.col(c)).otherwise(F.lit(None))).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1))))
-    for i in range(1,10):
-        samples = samples.withColumn(new_col+"__"+str(i), F.when(F.col(new_col)[i-1].isNotNull(),F.col(new_col)[i-1]).otherwise("-1"))
+        for i in range(1, v+1):
+            samples = samples.withColumn(new_col + "__" + str(i),F.when(F.col(new_col)[i - 1].isNotNull(), F.col(new_col)[i - 1]).otherwise("-1"))
         samples = samples.drop(new_col)
+        # .drop(c).drop(new_col)
+
+    # tags
+    # c = "tags_v3"
+    # new_col = "user" + "__" + c
+    # samples = samples.withColumn(new_col, extractTagsUdf(F.collect_list(when(F.col('label') == 1, F.col(c)).otherwise(F.lit(None))).over(sql.Window.partitionBy('userid').orderBy('timestamp').rowsBetween(-100, -1))))
+    # for i in range(1,10):
+    #     samples = samples.withColumn(new_col+"__"+str(i), F.when(F.col(new_col)[i-1].isNotNull(),F.col(new_col)[i-1]).otherwise("-1"))
+    # samples = samples.drop(new_col)
 
     pipelineStage = []
 
@@ -151,7 +158,7 @@ def addUserFeatures(samples):
 
     # bucketing
     for c in ["userRatingCount","userRatingAvg","userRatingStddev"]:
-        pipelineStage.append(QuantileDiscretizer(numBuckets=10, inputCol=c, outputCol=c + "Bucket"))
+        pipelineStage.append(QuantileDiscretizer(numBuckets=20, inputCol=c, outputCol=c + "Bucket"))
 
     featurePipeline = Pipeline(stages=pipelineStage)
     samples = featurePipeline.fit(samples).transform(samples)