Add widedeep_v12_2/4/5/6.py

widedeep_v12_2.py

@@ -0,0 +1,234 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+# vim:fenc=utf-8
+#
+# Copyright © 2025 StrayWarrior <i@straywarrior.com>
+#
+# Distributed under terms of the MIT license.
+
+
+raw_input = open("data_fields_v3.config").readlines()
+input_fields = dict(
+    map(lambda x: (x[0], x[1]),
+        map(lambda x: x.strip().split(' '), raw_input)))
+dense_features = open("features_top300.config").readlines()
+dense_features = [name.strip().lower() for name in dense_features]
+top_dense_features = open('features_top100.config').readlines()
+top_dense_features = [name.strip().lower() for name in top_dense_features]
+
+sparse_features = [
+    "cid", "adid", "adverid",
+    "region", "city", "brand",
+    "vid", "cate1", "cate2",
+]
+tag_features = [
+    "user_vid_return_tags_2h", "user_vid_return_tags_1d", "user_vid_return_tags_3d",
+    "user_vid_return_tags_7d", "user_vid_return_tags_14d"
+]
+seq_features = [
+    "user_cid_click_list", "user_cid_conver_list"
+]
+
+input_type_map = {
+    'BIGINT': 'INT64',
+    'DOUBLE': 'DOUBLE',
+    'STRING': 'STRING'
+}
+
+print("""train_config {
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.0010
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.0006
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.002
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  num_steps: 200000
+  sync_replicas: true
+  save_checkpoints_steps: 1100
+  log_step_count_steps: 100
+  save_summary_steps: 100
+}
+eval_config {
+  metrics_set {
+    auc {
+    }
+  }
+  eval_online: true
+  eval_interval_secs: 120
+}
+data_config {
+  batch_size: 512
+  num_epochs: 1
+  shuffle: false
+""")
+
+for name in input_fields:
+    input_type = input_type_map[input_fields[name]]
+    default_spec = ''
+    if name in dense_features:
+        default_spec = '\n    default_val: "0"'
+    print(f"""  input_fields {{
+    input_name: "{name}"
+    input_type: {input_type}{default_spec}
+  }}""")
+    # default_val: "0"
+
+print("""  label_fields: "has_conversion"
+  prefetch_size: 32
+  input_type: OdpsInputV2
+}
+""")
+
+for name in dense_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: RawFeature
+  boundaries: [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0]
+  embedding_dim: 6
+}}""")
+
+for name in sparse_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: IdFeature
+  hash_bucket_size: 1000000
+  embedding_dim: 6
+}}""")
+
+for name in tag_features + seq_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: TagFeature
+  hash_bucket_size: 1000000
+  embedding_dim: 6
+  separator: ','
+}}""")
+
+def wide_and_deep():
+    print("""
+model_config {
+  model_class: "WideAndDeep"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features + sparse_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in dense_features + sparse_features + tag_features + seq_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  wide_and_deep {
+    wide_output_dim: 8
+
+    dnn {
+      hidden_units: [256, 128, 64]
+    }
+    final_dnn {
+      hidden_units: [64, 32]
+    }
+    l2_regularization: 1e-5
+  }
+  embedding_regularization: 1e-6
+}""")
+
+def deep_fm():
+    print("""
+model_config {
+  model_class: "DeepFM"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features + sparse_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in top_dense_features + sparse_features + tag_features + seq_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  deepfm {
+    wide_output_dim: 8
+
+    dnn {
+      hidden_units: [256, 128, 64]
+    }
+
+    final_dnn {
+      hidden_units: [64, 32]
+    }
+    l2_regularization: 1e-5
+  }
+  embedding_regularization: 1e-6
+}""")
+
+def fm():
+    print("""
+model_config {
+  model_class: "FM"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in dense_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  fm {
+  }
+  embedding_regularization: 1e-5
+}""")
+
+def config_export():
+    print("""
+export_config {
+  exporter_type: "final"
+}
+""")
+
+
+deep_fm()
+config_export()

widedeep_v12_4.py

@@ -0,0 +1,253 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+# vim:fenc=utf-8
+#
+# Copyright © 2025 StrayWarrior <i@straywarrior.com>
+#
+# Distributed under terms of the MIT license.
+
+"""
+删除容易导致偏差的viewall特征
+"""
+
+
+raw_input = open("data_fields_v3.config").readlines()
+input_fields = dict(
+    map(lambda x: (x[0], x[1]),
+        map(lambda x: x.strip().split(' '), raw_input)))
+
+def read_features(filename, excludes=None):
+    features = open(filename).readlines()
+    features = [name.strip().lower() for name in features]
+    if excludes:
+        for x in excludes:
+            if x in features:
+                features.remove(x)
+    return features
+
+exclude_features = ['viewall',]
+
+dense_features = read_features("features_top300.config", exclude_features)
+top_dense_features = read_features('features_top100.config', exclude_features)
+
+sparse_features = [
+    "cid", "adid", "adverid",
+    "region", "city", "brand",
+    "vid", "cate1", "cate2",
+    "apptype", "hour", "hour_quarter", "root_source_scene", "root_source_channel", "is_first_layer", "title_split",
+    "profession"
+]
+tag_features = [
+    "user_vid_return_tags_2h", "user_vid_return_tags_1d", "user_vid_return_tags_3d",
+    "user_vid_return_tags_7d", "user_vid_return_tags_14d"
+]
+seq_features = [
+    "user_cid_click_list", "user_cid_conver_list"
+]
+
+input_type_map = {
+    'BIGINT': 'INT64',
+    'DOUBLE': 'DOUBLE',
+    'STRING': 'STRING'
+}
+
+print("""train_config {
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.0010
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.0006
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.002
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  num_steps: 200000
+  sync_replicas: true
+  save_checkpoints_steps: 1100
+  log_step_count_steps: 100
+  save_summary_steps: 100
+}
+eval_config {
+  metrics_set {
+    auc {
+    }
+  }
+  eval_online: true
+  eval_interval_secs: 120
+}
+data_config {
+  batch_size: 512
+  num_epochs: 1
+""")
+
+for name in input_fields:
+    input_type = input_type_map[input_fields[name]]
+    default_spec = ''
+    if name in dense_features:
+        default_spec = '\n    default_val: "0"'
+    print(f"""  input_fields {{
+    input_name: "{name}"
+    input_type: {input_type}{default_spec}
+  }}""")
+    # default_val: "0"
+
+print("""  label_fields: "has_conversion"
+  prefetch_size: 32
+  input_type: OdpsInputV2
+}
+""")
+
+for name in dense_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: RawFeature
+  boundaries: [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0]
+  embedding_dim: 6
+}}""")
+
+for name in sparse_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: IdFeature
+  hash_bucket_size: 1000000
+  embedding_dim: 6
+}}""")
+
+for name in tag_features + seq_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: TagFeature
+  hash_bucket_size: 1000000
+  embedding_dim: 6
+  separator: ','
+}}""")
+
+
+def wide_and_deep():
+    print("""
+model_config {
+  model_class: "WideAndDeep"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features + sparse_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in dense_features + sparse_features + tag_features + seq_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  wide_and_deep {
+    wide_output_dim: 8
+
+    dnn {
+      hidden_units: [256, 128, 64]
+    }
+    final_dnn {
+      hidden_units: [64, 32]
+    }
+    l2_regularization: 1e-5
+  }
+  embedding_regularization: 1e-6
+}""")
+
+
+def deep_fm():
+    print("""
+model_config {
+  model_class: "DeepFM"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features + sparse_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in top_dense_features + sparse_features + tag_features + seq_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  deepfm {
+    wide_output_dim: 8
+
+    dnn {
+      hidden_units: [256, 128, 64]
+    }
+
+    final_dnn {
+      hidden_units: [64, 32]
+    }
+    l2_regularization: 1e-5
+  }
+  embedding_regularization: 1e-6
+}""")
+
+
+def fm():
+    print("""
+model_config {
+  model_class: "FM"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in dense_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  fm {
+  }
+  embedding_regularization: 1e-5
+}""")
+
+
+def config_export():
+    print("""
+export_config {
+  exporter_type: "final"
+}
+""")
+
+
+deep_fm()
+config_export()

widedeep_v12_5.py

@@ -0,0 +1,254 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+# vim:fenc=utf-8
+#
+# Copyright © 2025 StrayWarrior <i@straywarrior.com>
+#
+# Distributed under terms of the MIT license.
+
+"""
+删除容易导致偏差的viewall特征
+"""
+
+
+raw_input = open("data_fields_v3.config").readlines()
+input_fields = dict(
+    map(lambda x: (x[0], x[1]),
+        map(lambda x: x.strip().split(' '), raw_input)))
+
+def read_features(filename, excludes=None):
+    features = open(filename).readlines()
+    features = [name.strip().lower() for name in features]
+    if excludes:
+        for x in excludes:
+            if x in features:
+                features.remove(x)
+    return features
+
+exclude_features = ['viewall', "e1_tags_14d_maxscore","e2_tags_14d_avgscore","e2_tags_14d_maxscore","e1_tags_14d_avgscore","e2_tags_7d_maxscore","e2_tags_7d_avgscore","e2_tags_3d_avgscore","e1_tags_3d_maxscore","e1_tags_7d_maxscore","e2_tags_3d_maxscore","e1_tags_3d_avgscore","e1_tags_7d_avgscore"
+]
+
+dense_features = read_features("features_top300.config", exclude_features)
+top_dense_features = read_features('features_top100.config', exclude_features)
+
+sparse_features = [
+    "cid", "adid", "adverid",
+    "region", "city", "brand",
+    "vid", "cate1", "cate2",
+    "apptype", "hour", "hour_quarter", "root_source_scene", "root_source_channel", "is_first_layer", "title_split",
+    "profession"
+]
+tag_features = [
+    "user_vid_return_tags_2h", "user_vid_return_tags_1d", "user_vid_return_tags_3d",
+    "user_vid_return_tags_7d", "user_vid_return_tags_14d"
+]
+seq_features = [
+    "user_cid_click_list", "user_cid_conver_list"
+]
+
+input_type_map = {
+    'BIGINT': 'INT64',
+    'DOUBLE': 'DOUBLE',
+    'STRING': 'STRING'
+}
+
+print("""train_config {
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.0010
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.0006
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.002
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  num_steps: 200000
+  sync_replicas: true
+  save_checkpoints_steps: 1100
+  log_step_count_steps: 100
+  save_summary_steps: 100
+}
+eval_config {
+  metrics_set {
+    auc {
+    }
+  }
+  eval_online: true
+  eval_interval_secs: 120
+}
+data_config {
+  batch_size: 512
+  num_epochs: 1
+""")
+
+for name in input_fields:
+    input_type = input_type_map[input_fields[name]]
+    default_spec = ''
+    if name in dense_features:
+        default_spec = '\n    default_val: "0"'
+    print(f"""  input_fields {{
+    input_name: "{name}"
+    input_type: {input_type}{default_spec}
+  }}""")
+    # default_val: "0"
+
+print("""  label_fields: "has_conversion"
+  prefetch_size: 32
+  input_type: OdpsInputV2
+}
+""")
+
+for name in dense_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: RawFeature
+  boundaries: [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0]
+  embedding_dim: 6
+}}""")
+
+for name in sparse_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: IdFeature
+  hash_bucket_size: 1000000
+  embedding_dim: 6
+}}""")
+
+for name in tag_features + seq_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: TagFeature
+  hash_bucket_size: 1000000
+  embedding_dim: 6
+  separator: ','
+}}""")
+
+
+def wide_and_deep():
+    print("""
+model_config {
+  model_class: "WideAndDeep"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features + sparse_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in dense_features + sparse_features + tag_features + seq_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  wide_and_deep {
+    wide_output_dim: 8
+
+    dnn {
+      hidden_units: [256, 128, 64]
+    }
+    final_dnn {
+      hidden_units: [64, 32]
+    }
+    l2_regularization: 1e-5
+  }
+  embedding_regularization: 1e-6
+}""")
+
+
+def deep_fm():
+    print("""
+model_config {
+  model_class: "DeepFM"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features + sparse_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in top_dense_features + sparse_features + tag_features + seq_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  deepfm {
+    wide_output_dim: 8
+
+    dnn {
+      hidden_units: [256, 128, 64]
+    }
+
+    final_dnn {
+      hidden_units: [64, 32]
+    }
+    l2_regularization: 1e-5
+  }
+  embedding_regularization: 1e-6
+}""")
+
+
+def fm():
+    print("""
+model_config {
+  model_class: "FM"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in dense_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  fm {
+  }
+  embedding_regularization: 1e-5
+}""")
+
+
+def config_export():
+    print("""
+export_config {
+  exporter_type: "final"
+}
+""")
+
+
+deep_fm()
+config_export()

widedeep_v12_6.py

@@ -0,0 +1,252 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+# vim:fenc=utf-8
+#
+# Copyright © 2025 StrayWarrior <i@straywarrior.com>
+#
+# Distributed under terms of the MIT license.
+
+"""
+删除容易导致偏差的viewall特征
+"""
+
+
+raw_input = open("data_fields_v3.config").readlines()
+input_fields = dict(
+    map(lambda x: (x[0], x[1]),
+        map(lambda x: x.strip().split(' '), raw_input)))
+
+def read_features(filename, excludes=None):
+    features = open(filename).readlines()
+    features = [name.strip().lower() for name in features]
+    if excludes:
+        for x in excludes:
+            if x in features:
+                features.remove(x)
+    return features
+
+exclude_features = ['viewall',]
+
+dense_features = read_features("features_top300.config", exclude_features)
+top_dense_features = read_features('features_top100.config', exclude_features)
+
+sparse_features = [
+    "cid", "adid", "adverid",
+    "region", "city", "brand",
+    "vid", "cate1", "cate2",
+    "apptype", "hour", "hour_quarter", "root_source_scene", "root_source_channel", "is_first_layer", "title_split",
+]
+tag_features = [
+    "user_vid_return_tags_2h", "user_vid_return_tags_1d", "user_vid_return_tags_3d",
+    "user_vid_return_tags_7d", "user_vid_return_tags_14d"
+]
+seq_features = [
+    "user_cid_click_list", "user_cid_conver_list"
+]
+
+input_type_map = {
+    'BIGINT': 'INT64',
+    'DOUBLE': 'DOUBLE',
+    'STRING': 'STRING'
+}
+
+print("""train_config {
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.0010
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.0006
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  optimizer_config {
+    adam_optimizer {
+      learning_rate {
+        constant_learning_rate {
+          learning_rate: 0.002
+        }
+      }
+    }
+    use_moving_average: false
+  }
+  num_steps: 200000
+  sync_replicas: true
+  save_checkpoints_steps: 1100
+  log_step_count_steps: 100
+  save_summary_steps: 100
+}
+eval_config {
+  metrics_set {
+    auc {
+    }
+  }
+  eval_online: true
+  eval_interval_secs: 120
+}
+data_config {
+  batch_size: 512
+  num_epochs: 1
+""")
+
+for name in input_fields:
+    input_type = input_type_map[input_fields[name]]
+    default_spec = ''
+    if name in dense_features:
+        default_spec = '\n    default_val: "0"'
+    print(f"""  input_fields {{
+    input_name: "{name}"
+    input_type: {input_type}{default_spec}
+  }}""")
+    # default_val: "0"
+
+print("""  label_fields: "has_conversion"
+  prefetch_size: 32
+  input_type: OdpsInputV2
+}
+""")
+
+for name in dense_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: RawFeature
+  boundaries: [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0]
+  embedding_dim: 6
+}}""")
+
+for name in sparse_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: IdFeature
+  hash_bucket_size: 1000000
+  embedding_dim: 6
+}}""")
+
+for name in tag_features + seq_features:
+    print(f"""feature_configs {{
+  input_names: "{name}"
+  feature_type: TagFeature
+  hash_bucket_size: 1000000
+  embedding_dim: 6
+  separator: ','
+}}""")
+
+
+def wide_and_deep():
+    print("""
+model_config {
+  model_class: "WideAndDeep"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features + sparse_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in dense_features + sparse_features + tag_features + seq_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  wide_and_deep {
+    wide_output_dim: 8
+
+    dnn {
+      hidden_units: [256, 128, 64]
+    }
+    final_dnn {
+      hidden_units: [64, 32]
+    }
+    l2_regularization: 1e-5
+  }
+  embedding_regularization: 1e-6
+}""")
+
+
+def deep_fm():
+    print("""
+model_config {
+  model_class: "DeepFM"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features + sparse_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in top_dense_features + sparse_features + tag_features + seq_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  deepfm {
+    wide_output_dim: 8
+
+    dnn {
+      hidden_units: [256, 128, 64]
+    }
+
+    final_dnn {
+      hidden_units: [64, 32]
+    }
+    l2_regularization: 1e-5
+  }
+  embedding_regularization: 1e-6
+}""")
+
+
+def fm():
+    print("""
+model_config {
+  model_class: "FM"
+   feature_groups: {
+    group_name: 'wide'""")
+
+    for name in dense_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: WIDE
+  }
+  feature_groups: {
+    group_name: 'deep'""")
+
+    for name in dense_features:
+        print(f"""    feature_names: '{name}'""")
+
+    print("""    wide_deep: DEEP
+  }
+  fm {
+  }
+  embedding_regularization: 1e-5
+}""")
+
+
+def config_export():
+    print("""
+export_config {
+  exporter_type: "final"
+}
+""")
+
+
+deep_fm()
+config_export()