From 3486e343da9f9b80529bc285bff799c1f37aab01 Mon Sep 17 00:00:00 2001
From: baby123 <10339928+baby12@user.noreply.gitee.com>
Date: Thu, 8 Dec 2022 14:13:29 +0000
Subject: [PATCH 1/3] =?UTF-8?q?=E4=BB=A3=E7=A0=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: baby123 <10339928+baby12@user.noreply.gitee.com>
---
 NL2SQL.rar | Bin 0 -> 1087846 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 NL2SQL.rar

diff --git a/NL2SQL.rar b/NL2SQL.rar
new file mode 100644
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-- 
Gitee


From f287e032077c1a510ae054e8297231258cf17194 Mon Sep 17 00:00:00 2001
From: Mint_7 <1102588009@qq.com>
Date: Thu, 8 Dec 2022 22:59:21 +0800
Subject: [PATCH 2/3] =?UTF-8?q?2022/12/8:=E5=8F=82=E8=B5=9B=E4=BB=A3?=
 =?UTF-8?q?=E7=A0=81=E6=8F=90=E4=BA=A4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 code/.gitignore                       |  15 +
 code/README.md                        |  91 +++
 code/config.py                        |  26 +
 code/config/chisp-config.yml          |  43 ++
 code/config/leaderboard.json          |  72 +++
 code/evaluation.py                    | 866 +++++++++++++++++++++++++
 code/evaluation.sh                    |  19 +
 code/models/__init__.py               |   0
 code/models/agg_predictor.py          | 163 +++++
 code/models/andor_predictor.py        |  94 +++
 code/models/col_predictor.py          | 200 ++++++
 code/models/desasc_limit_predictor.py | 105 +++
 code/models/having_predictor.py       | 103 +++
 code/models/keyword_predictor.py      | 160 +++++
 code/models/multisql_predictor.py     | 113 ++++
 code/models/net_utils.py              |  48 ++
 code/models/op_predictor.py           | 176 +++++
 code/models/root_teminal_predictor.py | 103 +++
 code/models/supermodel.py             | 893 ++++++++++++++++++++++++++
 code/preprocess_data.py               | 789 +++++++++++++++++++++++
 code/requirements.txt                 |   1 +
 code/test.py                          | 102 +++
 code/test_gen.sh                      |  27 +
 code/train.py                         | 273 ++++++++
 code/train_all.sh                     |  49 ++
 code/utils/__init__.py                |   0
 code/utils/process_sql.py             | 564 ++++++++++++++++
 code/utils/util.py                    | 523 +++++++++++++++
 code/utils/word_embedding.py          | 261 ++++++++
 29 files changed, 5879 insertions(+)
 create mode 100644 code/.gitignore
 create mode 100644 code/README.md
 create mode 100644 code/config.py
 create mode 100644 code/config/chisp-config.yml
 create mode 100644 code/config/leaderboard.json
 create mode 100644 code/evaluation.py
 create mode 100644 code/evaluation.sh
 create mode 100644 code/models/__init__.py
 create mode 100644 code/models/agg_predictor.py
 create mode 100644 code/models/andor_predictor.py
 create mode 100644 code/models/col_predictor.py
 create mode 100644 code/models/desasc_limit_predictor.py
 create mode 100644 code/models/having_predictor.py
 create mode 100644 code/models/keyword_predictor.py
 create mode 100644 code/models/multisql_predictor.py
 create mode 100644 code/models/net_utils.py
 create mode 100644 code/models/op_predictor.py
 create mode 100644 code/models/root_teminal_predictor.py
 create mode 100644 code/models/supermodel.py
 create mode 100644 code/preprocess_data.py
 create mode 100644 code/requirements.txt
 create mode 100644 code/test.py
 create mode 100644 code/test_gen.sh
 create mode 100644 code/train.py
 create mode 100644 code/train_all.sh
 create mode 100644 code/utils/__init__.py
 create mode 100644 code/utils/process_sql.py
 create mode 100644 code/utils/util.py
 create mode 100644 code/utils/word_embedding.py

diff --git a/code/.gitignore b/code/.gitignore
new file mode 100644
index 0000000..3a5c32b
--- /dev/null
+++ b/code/.gitignore
@@ -0,0 +1,15 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# log files
+*.log
+
+# data folders
+data/*
+database/*
+embedding/*
+
+# pycharm folders
+.idea/
diff --git a/code/README.md b/code/README.md
new file mode 100644
index 0000000..06494fb
--- /dev/null
+++ b/code/README.md
@@ -0,0 +1,91 @@
+# CSpider: A Large-Scale Chinese Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
+
+CSpider is a large Chinese dataset for complex and cross-domain semantic parsing and text-to-SQL task (natural language interfaces for relational databases). It is released with our EMNLP 2019 paper: [A Pilot Study for Chinese SQL Semantic Parsing](https://arxiv.org/abs/1909.13293). This repo contains all code for evaluation, preprocessing, and all baselines used in our paper. Please refer to [the task site](https://taolusi.github.io/CSpider-explorer/) for more general introduction and the leaderboard.
+
+### Changelog
+- `10/2019` We start a Chinese text-to-SQL task with the full dataset translated from [Spider](https://yale-lily.github.io/spider). The submission tutorial and our dataset can be found at our [task site](https://taolusi.github.io/CSpider-explorer/). Please follow it to get your results on the unreleased test data. Thank [Tao Yu](https://taoyds.github.io/) for sharing the test set with us.
+- `9/2019` The dataset used in our EMNLP 2019 paper is redivided based on the training and deveploment sets from Spider. The dataset can be downloaded from [here](https://drive.google.com/drive/folders/1SVAdUQqZ2UjjcSCSxhVXRPcXxIMu1r_C?usp=sharing). This dataset is just released to reproduce the results in our paper. To join the CSpider leaderboard and better compare with the original English results, please refer to our [task site](https://taolusi.github.io/CSpider-explorer/) for full dataset.
+
+### Citation
+When you use the CSpider dataset, we would appreciate it if you cite the following:
+```
+@inproceedings{min2019pilot,
+  title={A Pilot Study for Chinese SQL Semantic Parsing},
+  author={Min, Qingkai and Shi, Yuefeng and Zhang, Yue},
+  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
+  pages={3643--3649},
+  year={2019}
+}
+```
+Our dataset is based on [Spider](https://github.com/taoyds/spider/), please cite it too.
+
+### Baseline models
+
+#### Environment Setup
+
+1. The code uses Python 2.7 and [Pytorch 0.2.0](https://pytorch.org/get-started/previous-versions/) GPU, and will update python and Pytorch soon.
+2. Install Pytorch via conda: `conda install pytorch=0.2.0 -c pytorch`
+3. Install Python dependency: `pip install -r requirements.txt`
+
+#### Prepare Data, Embeddings, and Pretrained Models
+1. Download the data, embedding and database:
+  - To use the full dataset(recommended), download train/dev data from [Google Drive](https://drive.google.com/drive/folders/1TxCUq1ydPuBdDdHF3MkHT-8zixluQuLa?usp=sharing) or [BaiduNetDisk](https://pan.baidu.com/s/1aDYht6eSIyBgceCjeuSmpw)(code: cgh1) and evaluate on the unreleased test data based on the submission tutorial on our [task site](https://taolusi.github.io/CSpider-explorer/). Specifically,
+    - Put the downloaded `train.json` and `dev.json` under `chisp/data/char/` directory. To use word-based methods, please do the word segmentation first and put the json files under `chisp/data/word/` directory.
+    - Put the downloaded `char_emb.txt` under `chisp/embedding/` directory. This is generated from the Tencent multilingual embeddings for the cross-lingual word embeddings schema. To use monolingual embedding schema, step 2 is necessary.
+    - Put the downloaded `database` directory under `chisp/` directory.
+    - Put the downloaded `train_gold.sql` and `dev_glod.sql` under `chisp/data/` directory.
+  - To use the dataset redivided based on the original train and dev data in our paper, download the train/dev/test data from [here](https://drive.google.com/drive/folders/1SVAdUQqZ2UjjcSCSxhVXRPcXxIMu1r_C?usp=sharing). This dataset is released just to reproduce the results in our paper and results based on this dataset cannot join the leaderboard. Specifically,
+    - Put the downloaded `data`, `database` and `embedding` directory under `chisp/` directory. And you can run all the experiments(step 2 is necessary) shown in our paper.
+    - `models` directory contains all the pretrained models.
+2. (optional) Download the pretrained [Glove](https://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip), and put it as `chisp/embedding/glove.%dB.%dd.txt`
+3. Generate training files for each module: `python preprocess_data.py -s char|word`
+
+#### Folder/File Description
+- ``data/`` contains:
+    - ``char/`` for character-based raw train/dev/test data, corresponding processed dataset and saved models can be found at ``char/generated_datasets``.
+    - ``word/`` for word-based raw train/dev/test data, corresponding processed dataset and saved models can be found at ``word/generated_datasets``.
+- ``train.py`` is the main file for training. Use ``train_all.sh`` to train all the modules (see below).
+- ``test.py`` is the main file for testing. It uses ``supermodel.py`` to call the trained modules and generate SQL queries. In practice, use ``test_gen.sh`` to generate SQL queries.
+- ``evaluation.py`` is for evaluation. It uses ``process_sql.py``. In practice, use ``evaluation.sh`` to evaluate the generated SQL queries.
+
+
+#### Training
+Run ``train_all.sh`` to train all the modules.
+It looks like:
+```
+python train.py \
+    --data_root       path/to/char/or/word/based/generated_data \
+    --save_dir        path/to/save/trained/module \
+    --train_component <module_name> \
+    --emb_path        path/to/embeddings
+    --col_emb_path    path/to/corresponding/embeddings/for/column
+```
+
+#### Testing
+Run ``test_gen.sh`` to generate SQL queries.
+``test_gen.sh`` looks like:
+```
+python test.py \
+    --test_data_path  path/to/char/or/word/based/raw/dev/or/test/data \
+    --models          path/to/trained/module \
+    --output_path     path/to/print/generated/SQL \
+    --emb_path        path/to/embeddings
+    --col_emb_path    path/to/corresponding/embeddings/for/column
+```
+
+#### Evaluation
+Run ``evaluation.sh`` to evaluate generated SQL queries.
+``evaluation.sh`` looks like:
+```
+python evaluation.py \
+    --gold            path/to/gold/dev/or/test/queries \
+    --pred            path/to/predicted/dev/or/test/queries \
+    --etype           evaluation/metric \
+    --db              path/to/database \
+    --table           path/to/tables \
+```
+``evalution.py`` is from the general evaluation process in [the Spider github page](https://github.com/taoyds/spider).
+
+#### Acknowledgement
+
+The implementation is based on [SyntaxSQLNet](https://github.com/taoyds/syntaxSQL). Please cite it too if you use this code.
diff --git a/code/config.py b/code/config.py
new file mode 100644
index 0000000..d7c0dea
--- /dev/null
+++ b/code/config.py
@@ -0,0 +1,26 @@
+class _Config:
+    def __init__(self):
+        self.emb_size = 200
+        self.col_emb_size = 300
+        self.hidden_size = 400
+        self.batch_size = 10
+        self.epoch = 600
+        self.dropout = 0.5
+        self.num_layers = 2
+        self.learning_rate = 1e-4
+        self.toy = False
+        self.train_emb = False
+        self.history_type = 'full'  # full, part or no
+        self.nogpu = False
+        self.table_type = 'std' # choices=['std','no'], help='standard, hierarchical, or no table info'
+
+    def _char_init(self):
+        self.data_root = "./data/char/generated_datasets"
+        self.sep_emb = "./embedding/char/separate_emb.txt"
+        self.comb_emb = "./embedding/char/combine_emb.txt"
+    def _word_init(self):
+        self.data_root = "./data/word/generated_datasets"
+        self.sep_emb = "./embedding/word/separate_emb.txt"
+        self.comb_emb = "./embedding/word/combine_emb.txt"
+
+global_config = _Config()
diff --git a/code/config/chisp-config.yml b/code/config/chisp-config.yml
new file mode 100644
index 0000000..acf8b00
--- /dev/null
+++ b/code/config/chisp-config.yml
@@ -0,0 +1,43 @@
+# Allows at most 5 submissions per user per period, where period is 24 hours by default.
+max_submissions_per_period: 2
+
+# UUID of the worksheet where prediction and evaluation bundles are created for submissions.
+log_worksheet_uuid: '0xd2d37ae7db5b40d09aa52850ed34ee1e'    
+
+# Configure the tag that participants use to submit to the competition.
+# In this example, any bundle with the tag `some-competition-submit` would be
+# considered as an official submission.
+submission_tag: cspider-test-submit
+
+# Configure how to mimic the submitted prediction bundles. When evaluating a submission, 
+# `new` bundle will replace `old` bundle.
+# For a machine learning competition, `old` bundle might be the dev set and `new` bundle
+# might be the hidden test set.
+predict:
+  mimic:
+  - {new: '0x064a87a9db764d7a814726c77b86ff73', old: '0xd2e9047706aa44a38df48bf16b4385f6'}
+
+# Configure how to evaluate the new prediction bundles.
+# In this example, evaluate.py is script that takes in the paths of the test labels and 
+# predicted labels and outputs the evaluation results.
+evaluate:
+  # Essentially
+  #     cl run evaluate.py:0x089063eb85b64b239b342405b5ebab57 \
+  #            test.json:0x5538cba32e524fad8b005cd19abb9f95 \
+  #            predictions.json:{predict}/predictions.json --- \
+  #            python evaluate.py test.json predictions.json
+  # where {predict} gets filled in with the uuid of the mimicked bundle above.
+  dependencies:
+  - {child_path: evaluation.py, parent_uuid: '0xed9c9d64b6e74056a98a5a592d9286c9'}
+  - {child_path: dev_gold.txt, parent_uuid: '0x7c4006535b2d40288931afd71cc4e8e5'}
+  - {child_path: predicted_sql.txt, parent_uuid: '0x7c4006535b2d40288931afd71cc4e8e5'}
+  - {child_path: tables.json, parent_path: data, parent_uuid: '0xd2e9047706aa44a38df48bf16b4385f6'}
+  - {child_path: database, parent_uuid: '0x794231a420384b6aa5086407ac21286b'}
+  command: python evaluation.py --gold dev_gold.txt --pred predicted_sql.txt --etype match --db database --table data/tables.json
+
+# Define how to extract the scores from the evaluation bundle.
+# In this example, result.json is a JSON file outputted from the evaluation step
+# with F1 and exact match metrics (e.g. {"f1": 91, "exact_match": 92}).
+score_specs:
+- {key: '/result.json:dev_f1', name: dev_f1}
+- {key: '/result.json:test_f1', name: test_f1}
diff --git a/code/config/leaderboard.json b/code/config/leaderboard.json
new file mode 100644
index 0000000..c620cbf
--- /dev/null
+++ b/code/config/leaderboard.json
@@ -0,0 +1,72 @@
+{
+    "config": {
+        "allow_multiple_models": false,
+        "allow_orphans": true,
+        "count_failed_submissions": true,
+        "evaluate": {
+            "command": "python evaluation.py --gold dev_gold.txt --pred predicted_sql.txt --etype match --db database --table data/tables.json",
+            "dependencies": [
+                {
+                    "child_path": "evaluation.py",
+                    "parent_path": "",
+                    "parent_uuid": "0xed9c9d64b6e74056a98a5a592d9286c9"
+                },
+                {
+                    "child_path": "dev_gold.txt",
+                    "parent_path": "",
+                    "parent_uuid": "0x7c4006535b2d40288931afd71cc4e8e5"
+                },
+                {
+                    "child_path": "predicted_sql.txt",
+                    "parent_path": "",
+                    "parent_uuid": "0x7c4006535b2d40288931afd71cc4e8e5"
+                },
+                {
+                    "child_path": "tables.json",
+                    "parent_path": "data",
+                    "parent_uuid": "0xd2e9047706aa44a38df48bf16b4385f6"
+                },
+                {
+                    "child_path": "database",
+                    "parent_path": "",
+                    "parent_uuid": "0x794231a420384b6aa5086407ac21286b"
+                }
+            ],
+            "metadata": {},
+            "tag": "competition-evaluate"
+        },
+        "host": "https://worksheets.codalab.org",
+        "log_worksheet_uuid": "0xd2d37ae7db5b40d09aa52850ed34ee1e",
+        "make_predictions_public": false,
+        "max_leaderboard_size": 10000,
+        "max_submissions_per_period": 2,
+        "max_submissions_total": 10000,
+        "metadata": {},
+        "predict": {
+            "depth": 10,
+            "metadata": {},
+            "mimic": [
+                {
+                    "new": "0x064a87a9db764d7a814726c77b86ff73",
+                    "old": "0xd2e9047706aa44a38df48bf16b4385f6"
+                }
+            ],
+            "tag": "competition-predict"
+        },
+        "quota_period_seconds": 86400,
+        "refresh_period_seconds": 60,
+        "score_specs": [
+            {
+                "key": "/result.json:dev_f1",
+                "name": "dev_f1"
+            },
+            {
+                "key": "/result.json:test_f1",
+                "name": "test_f1"
+            }
+        ],
+        "submission_tag": "cspider-test-submit"
+    },
+    "leaderboard": [],
+    "updated": 1615514134.3646703
+}
\ No newline at end of file
diff --git a/code/evaluation.py b/code/evaluation.py
new file mode 100644
index 0000000..9a3292c
--- /dev/null
+++ b/code/evaluation.py
@@ -0,0 +1,866 @@
+################################
+# val: number(float)/string(str)/sql(dict)
+# col_unit: (agg_id, col_id, isDistinct(bool))
+# val_unit: (unit_op, col_unit1, col_unit2)
+# table_unit: (table_type, col_unit/sql)
+# cond_unit: (not_op, op_id, val_unit, val1, val2)
+# condition: [cond_unit1, 'and'/'or', cond_unit2, ...]
+# sql {
+#   'select': (isDistinct(bool), [(agg_id, val_unit), (agg_id, val_unit), ...])
+#   'from': {'table_units': [table_unit1, table_unit2, ...], 'conds': condition}
+#   'where': condition
+#   'groupBy': [col_unit1, col_unit2, ...]
+#   'orderBy': ('asc'/'desc', [val_unit1, val_unit2, ...])
+#   'having': condition
+#   'limit': None/limit value
+#   'intersect': None/sql
+#   'except': None/sql
+#   'union': None/sql
+# }
+################################
+
+import os, sys
+import json
+import sqlite3
+import traceback
+import argparse
+
+from utils.process_sql import tokenize, get_schema, get_tables_with_alias, Schema, get_sql
+
+# Flag to disable value evaluation
+DISABLE_VALUE = True
+# Flag to disable distinct in select evaluation
+DISABLE_DISTINCT = True
+
+
+CLAUSE_KEYWORDS = ('select', 'from', 'where', 'group', 'order', 'limit', 'intersect', 'union', 'except')
+JOIN_KEYWORDS = ('join', 'on', 'as')
+
+WHERE_OPS = ('not', 'between', '=', '>', '<', '>=', '<=', '!=', 'in', 'like', 'is', 'exists')
+UNIT_OPS = ('none', '-', '+', "*", '/')
+AGG_OPS = ('none', 'max', 'min', 'count', 'sum', 'avg')
+TABLE_TYPE = {
+    'sql': "sql",
+    'table_unit': "table_unit",
+}
+
+COND_OPS = ('and', 'or')
+SQL_OPS = ('intersect', 'union', 'except')
+ORDER_OPS = ('desc', 'asc')
+
+
+HARDNESS = {
+    "component1": ('where', 'group', 'order', 'limit', 'join', 'or', 'like'),
+    "component2": ('except', 'union', 'intersect')
+}
+
+
+def condition_has_or(conds):
+    return 'or' in conds[1::2]
+
+
+def condition_has_like(conds):
+    return WHERE_OPS.index('like') in [cond_unit[1] for cond_unit in conds[::2]]
+
+
+def condition_has_sql(conds):
+    for cond_unit in conds[::2]:
+        val1, val2 = cond_unit[3], cond_unit[4]
+        if val1 is not None and type(val1) is dict:
+            return True
+        if val2 is not None and type(val2) is dict:
+            return True
+    return False
+
+
+def val_has_op(val_unit):
+    return val_unit[0] != UNIT_OPS.index('none')
+
+
+def has_agg(unit):
+    return unit[0] != AGG_OPS.index('none')
+
+
+def accuracy(count, total):
+    if count == total:
+        return 1
+    return 0
+
+
+def recall(count, total):
+    if count == total:
+        return 1
+    return 0
+
+
+def F1(acc, rec):
+    if (acc + rec) == 0:
+        return 0
+    return (2. * acc * rec) / (acc + rec)
+
+
+def get_scores(count, pred_total, label_total):
+    if pred_total != label_total:
+        return 0,0,0
+    elif count == pred_total:
+        return 1,1,1
+    return 0,0,0
+
+
+def eval_sel(pred, label):
+    pred_sel = pred['select'][1]
+    label_sel = label['select'][1]
+    label_wo_agg = [unit[1] for unit in label_sel]
+    pred_total = len(pred_sel)
+    label_total = len(label_sel)
+    cnt = 0
+    cnt_wo_agg = 0
+
+    for unit in pred_sel:
+        if unit in label_sel:
+            cnt += 1
+            label_sel.remove(unit)
+        if unit[1] in label_wo_agg:
+            cnt_wo_agg += 1
+            label_wo_agg.remove(unit[1])
+
+    return label_total, pred_total, cnt, cnt_wo_agg
+
+
+def eval_where(pred, label):
+    pred_conds = [unit for unit in pred['where'][::2]]
+    label_conds = [unit for unit in label['where'][::2]]
+    label_wo_agg = [unit[2] for unit in label_conds]
+    pred_total = len(pred_conds)
+    label_total = len(label_conds)
+    cnt = 0
+    cnt_wo_agg = 0
+
+    for unit in pred_conds:
+        if unit in label_conds:
+            cnt += 1
+            label_conds.remove(unit)
+        if unit[2] in label_wo_agg:
+            cnt_wo_agg += 1
+            label_wo_agg.remove(unit[2])
+
+    return label_total, pred_total, cnt, cnt_wo_agg
+
+
+def eval_group(pred, label):
+    pred_cols = [unit[1] for unit in pred['groupBy']]
+    label_cols = [unit[1] for unit in label['groupBy']]
+    pred_total = len(pred_cols)
+    label_total = len(label_cols)
+    cnt = 0
+    pred_cols = [pred.split(".")[1] if "." in pred else pred for pred in pred_cols]
+    label_cols = [label.split(".")[1] if "." in label else label for label in label_cols]
+    for col in pred_cols:
+        if col in label_cols:
+            cnt += 1
+            label_cols.remove(col)
+    return label_total, pred_total, cnt
+
+
+def eval_having(pred, label):
+    pred_total = label_total = cnt = 0
+    if len(pred['groupBy']) > 0:
+        pred_total = 1
+    if len(label['groupBy']) > 0:
+        label_total = 1
+
+    pred_cols = [unit[1] for unit in pred['groupBy']]
+    label_cols = [unit[1] for unit in label['groupBy']]
+    if pred_total == label_total == 1 \
+            and pred_cols == label_cols \
+            and pred['having'] == label['having']:
+        cnt = 1
+
+    return label_total, pred_total, cnt
+
+
+def eval_order(pred, label):
+    pred_total = label_total = cnt = 0
+    if len(pred['orderBy']) > 0:
+        pred_total = 1
+    if len(label['orderBy']) > 0:
+        label_total = 1
+    if len(label['orderBy']) > 0 and pred['orderBy'] == label['orderBy'] and \
+            ((pred['limit'] is None and label['limit'] is None) or (pred['limit'] is not None and label['limit'] is not None)):
+        cnt = 1
+    return label_total, pred_total, cnt
+
+
+def eval_and_or(pred, label):
+    pred_ao = pred['where'][1::2]
+    label_ao = label['where'][1::2]
+    pred_ao = set(pred_ao)
+    label_ao = set(label_ao)
+
+    if pred_ao == label_ao:
+        return 1,1,1
+    return len(pred_ao),len(label_ao),0
+
+
+def get_nestedSQL(sql):
+    nested = []
+    for cond_unit in sql['from']['conds'][::2] + sql['where'][::2] + sql['having'][::2]:
+        if type(cond_unit[3]) is dict:
+            nested.append(cond_unit[3])
+        if type(cond_unit[4]) is dict:
+            nested.append(cond_unit[4])
+    if sql['intersect'] is not None:
+        nested.append(sql['intersect'])
+    if sql['except'] is not None:
+        nested.append(sql['except'])
+    if sql['union'] is not None:
+        nested.append(sql['union'])
+    return nested
+
+
+def eval_nested(pred, label):
+    label_total = 0
+    pred_total = 0
+    cnt = 0
+    if pred is not None:
+        pred_total += 1
+    if label is not None:
+        label_total += 1
+    if pred is not None and label is not None:
+        cnt += Evaluator().eval_exact_match(pred, label)
+    return label_total, pred_total, cnt
+
+
+def eval_IUEN(pred, label):
+    lt1, pt1, cnt1 = eval_nested(pred['intersect'], label['intersect'])
+    lt2, pt2, cnt2 = eval_nested(pred['except'], label['except'])
+    lt3, pt3, cnt3 = eval_nested(pred['union'], label['union'])
+    label_total = lt1 + lt2 + lt3
+    pred_total = pt1 + pt2 + pt3
+    cnt = cnt1 + cnt2 + cnt3
+    return label_total, pred_total, cnt
+
+
+def get_keywords(sql):
+    res = set()
+    if len(sql['where']) > 0:
+        res.add('where')
+    if len(sql['groupBy']) > 0:
+        res.add('group')
+    if len(sql['having']) > 0:
+        res.add('having')
+    if len(sql['orderBy']) > 0:
+        res.add(sql['orderBy'][0])
+        res.add('order')
+    if sql['limit'] is not None:
+        res.add('limit')
+    if sql['except'] is not None:
+        res.add('except')
+    if sql['union'] is not None:
+        res.add('union')
+    if sql['intersect'] is not None:
+        res.add('intersect')
+
+    # or keyword
+    ao = sql['from']['conds'][1::2] + sql['where'][1::2] + sql['having'][1::2]
+    if len([token for token in ao if token == 'or']) > 0:
+        res.add('or')
+
+    cond_units = sql['from']['conds'][::2] + sql['where'][::2] + sql['having'][::2]
+    # not keyword
+    if len([cond_unit for cond_unit in cond_units if cond_unit[0]]) > 0:
+        res.add('not')
+
+    # in keyword
+    if len([cond_unit for cond_unit in cond_units if cond_unit[1] == WHERE_OPS.index('in')]) > 0:
+        res.add('in')
+
+    # like keyword
+    if len([cond_unit for cond_unit in cond_units if cond_unit[1] == WHERE_OPS.index('like')]) > 0:
+        res.add('like')
+
+    return res
+
+
+def eval_keywords(pred, label):
+    pred_keywords = get_keywords(pred)
+    label_keywords = get_keywords(label)
+    pred_total = len(pred_keywords)
+    label_total = len(label_keywords)
+    cnt = 0
+
+    for k in pred_keywords:
+        if k in label_keywords:
+            cnt += 1
+    return label_total, pred_total, cnt
+
+
+def count_agg(units):
+    return len([unit for unit in units if has_agg(unit)])
+
+
+def count_component1(sql):
+    count = 0
+    if len(sql['where']) > 0:
+        count += 1
+    if len(sql['groupBy']) > 0:
+        count += 1
+    if len(sql['orderBy']) > 0:
+        count += 1
+    if sql['limit'] is not None:
+        count += 1
+    if len(sql['from']['table_units']) > 0:  # JOIN
+        count += len(sql['from']['table_units']) - 1
+
+    ao = sql['from']['conds'][1::2] + sql['where'][1::2] + sql['having'][1::2]
+    count += len([token for token in ao if token == 'or'])
+    cond_units = sql['from']['conds'][::2] + sql['where'][::2] + sql['having'][::2]
+    count += len([cond_unit for cond_unit in cond_units if cond_unit[1] == WHERE_OPS.index('like')])
+
+    return count
+
+
+def count_component2(sql):
+    nested = get_nestedSQL(sql)
+    return len(nested)
+
+
+def count_others(sql):
+    count = 0
+    # number of aggregation
+    agg_count = count_agg(sql['select'][1])
+    agg_count += count_agg(sql['where'][::2])
+    agg_count += count_agg(sql['groupBy'])
+    if len(sql['orderBy']) > 0:
+        agg_count += count_agg([unit[1] for unit in sql['orderBy'][1] if unit[1]] +
+                            [unit[2] for unit in sql['orderBy'][1] if unit[2]])
+    agg_count += count_agg(sql['having'])
+    if agg_count > 1:
+        count += 1
+
+    # number of select columns
+    if len(sql['select'][1]) > 1:
+        count += 1
+
+    # number of where conditions
+    if len(sql['where']) > 1:
+        count += 1
+
+    # number of group by clauses
+    if len(sql['groupBy']) > 1:
+        count += 1
+
+    return count
+
+
+class Evaluator:
+    """A simple evaluator"""
+    def __init__(self):
+        self.partial_scores = None
+
+    def eval_hardness(self, sql):
+        count_comp1_ = count_component1(sql)
+        count_comp2_ = count_component2(sql)
+        count_others_ = count_others(sql)
+
+        if count_comp1_ <= 1 and count_others_ == 0 and count_comp2_ == 0:
+            return "easy"
+        elif (count_others_ <= 2 and count_comp1_ <= 1 and count_comp2_ == 0) or \
+                (count_comp1_ <= 2 and count_others_ < 2 and count_comp2_ == 0):
+            return "medium"
+        elif (count_others_ > 2 and count_comp1_ <= 2 and count_comp2_ == 0) or \
+                (2 < count_comp1_ <= 3 and count_others_ <= 2 and count_comp2_ == 0) or \
+                (count_comp1_ <= 1 and count_others_ == 0 and count_comp2_ <= 1):
+            return "hard"
+        else:
+            return "extra"
+
+    def eval_exact_match(self, pred, label):
+        partial_scores = self.eval_partial_match(pred, label)
+        self.partial_scores = partial_scores
+
+        for _, score in partial_scores.items():
+            if score['f1'] != 1:
+                return 0
+        if len(label['from']['table_units']) > 0:
+            label_tables = sorted(label['from']['table_units'])
+            pred_tables = sorted(pred['from']['table_units'])
+            return label_tables == pred_tables
+        return 1
+
+    def eval_partial_match(self, pred, label):
+        res = {}
+
+        label_total, pred_total, cnt, cnt_wo_agg = eval_sel(pred, label)
+        acc, rec, f1 = get_scores(cnt, pred_total, label_total)
+        res['select'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+        acc, rec, f1 = get_scores(cnt_wo_agg, pred_total, label_total)
+        res['select(no AGG)'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+
+        label_total, pred_total, cnt, cnt_wo_agg = eval_where(pred, label)
+        acc, rec, f1 = get_scores(cnt, pred_total, label_total)
+        res['where'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+        acc, rec, f1 = get_scores(cnt_wo_agg, pred_total, label_total)
+        res['where(no OP)'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+
+        label_total, pred_total, cnt = eval_group(pred, label)
+        acc, rec, f1 = get_scores(cnt, pred_total, label_total)
+        res['group(no Having)'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+
+        label_total, pred_total, cnt = eval_having(pred, label)
+        acc, rec, f1 = get_scores(cnt, pred_total, label_total)
+        res['group'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+
+        label_total, pred_total, cnt = eval_order(pred, label)
+        acc, rec, f1 = get_scores(cnt, pred_total, label_total)
+        res['order'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+
+        label_total, pred_total, cnt = eval_and_or(pred, label)
+        acc, rec, f1 = get_scores(cnt, pred_total, label_total)
+        res['and/or'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+
+        label_total, pred_total, cnt = eval_IUEN(pred, label)
+        acc, rec, f1 = get_scores(cnt, pred_total, label_total)
+        res['IUEN'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+
+        label_total, pred_total, cnt = eval_keywords(pred, label)
+        acc, rec, f1 = get_scores(cnt, pred_total, label_total)
+        res['keywords'] = {'acc': acc, 'rec': rec, 'f1': f1,'label_total':label_total,'pred_total':pred_total}
+
+        return res
+
+
+def isValidSQL(sql, db):
+    conn = sqlite3.connect(db)
+    cursor = conn.cursor()
+    try:
+        cursor.execute(sql)
+    except:
+        return False
+    return True
+
+
+def print_scores(scores, etype):
+    levels = ['easy', 'medium', 'hard', 'extra', 'all']
+    partial_types = ['select', 'select(no AGG)', 'where', 'where(no OP)', 'group(no Having)',
+                     'group', 'order', 'and/or', 'IUEN', 'keywords']
+
+    print("{:20} {:20} {:20} {:20} {:20} {:20}".format("", *levels))
+    counts = [scores[level]['count'] for level in levels]
+    print("{:20} {:<20d} {:<20d} {:<20d} {:<20d} {:<20d}".format("count", *counts))
+
+    if etype in ["all", "exec"]:
+        print('=====================   EXECUTION ACCURACY     =====================')
+        this_scores = [scores[level]['exec'] for level in levels]
+        print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format("execution", *this_scores))
+
+    if etype in ["all", "match"]:
+        print('\n====================== EXACT MATCHING ACCURACY =====================')
+        exact_scores = [scores[level]['exact'] for level in levels]
+        print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format("exact match", *exact_scores))
+        print('\n---------------------PARTIAL MATCHING ACCURACY----------------------')
+        for type_ in partial_types:
+            this_scores = [scores[level]['partial'][type_]['acc'] for level in levels]
+            print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format(type_, *this_scores))
+
+        print('---------------------- PARTIAL MATCHING RECALL ----------------------')
+        for type_ in partial_types:
+            this_scores = [scores[level]['partial'][type_]['rec'] for level in levels]
+            print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format(type_, *this_scores))
+
+        print('---------------------- PARTIAL MATCHING F1 --------------------------')
+        for type_ in partial_types:
+            this_scores = [scores[level]['partial'][type_]['f1'] for level in levels]
+            print("{:20} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f} {:<20.3f}".format(type_, *this_scores))
+
+
+def evaluate(gold, predict, db_dir, etype, kmaps):
+    with open(gold) as f:
+        glist = [l.strip().split('\t') for l in f.readlines() if len(l.strip()) > 0]
+
+    with open(predict) as f:
+        plist = [l.strip().split('\t') for l in f.readlines() if len(l.strip()) > 0]
+    # plist = [("select max(Share),min(Share) from performance where Type != 'terminal'", "orchestra")]
+    # glist = [("SELECT max(SHARE) ,  min(SHARE) FROM performance WHERE TYPE != 'Live final'", "orchestra")]
+    evaluator = Evaluator()
+
+    levels = ['easy', 'medium', 'hard', 'extra', 'all']
+    partial_types = ['select', 'select(no AGG)', 'where', 'where(no OP)', 'group(no Having)',
+                     'group', 'order', 'and/or', 'IUEN', 'keywords']
+    entries = []
+    scores = {}
+
+    for level in levels:
+        scores[level] = {'count': 0, 'partial': {}, 'exact': 0.}
+        scores[level]['exec'] = 0
+        for type_ in partial_types:
+            scores[level]['partial'][type_] = {'acc': 0., 'rec': 0., 'f1': 0.,'acc_count':0,'rec_count':0}
+
+    eval_err_num = 0
+    for p, g in zip(plist, glist):
+        p_str = p[0]
+        g_str, db = g
+        db_name = db
+        db = os.path.join(db_dir, db, db + ".sqlite")
+        schema = Schema(get_schema(db))
+        g_sql = get_sql(schema, g_str)
+        hardness = evaluator.eval_hardness(g_sql)
+        scores[hardness]['count'] += 1
+        scores['all']['count'] += 1
+
+        try:
+            p_sql = get_sql(schema, p_str)
+        except:
+            # If p_sql is not valid, then we will use an empty sql to evaluate with the correct sql
+            p_sql = {
+            "except": None,
+            "from": {
+                "conds": [],
+                "table_units": []
+            },
+            "groupBy": [],
+            "having": [],
+            "intersect": None,
+            "limit": None,
+            "orderBy": [],
+            "select": [
+                False,
+                []
+            ],
+            "union": None,
+            "where": []
+            }
+            eval_err_num += 1
+            print("eval_err_num:{}".format(eval_err_num))
+
+        # rebuild sql for value evaluation
+        kmap = kmaps[db_name]
+        g_valid_col_units = build_valid_col_units(g_sql['from']['table_units'], schema)
+        g_sql = rebuild_sql_val(g_sql)
+        g_sql = rebuild_sql_col(g_valid_col_units, g_sql, kmap)
+        p_valid_col_units = build_valid_col_units(p_sql['from']['table_units'], schema)
+        p_sql = rebuild_sql_val(p_sql)
+        p_sql = rebuild_sql_col(p_valid_col_units, p_sql, kmap)
+
+        if etype in ["all", "exec"]:
+            exec_score = eval_exec_match(db, p_str, g_str, p_sql, g_sql)
+            if exec_score:
+                scores[hardness]['exec'] += 1
+
+        if etype in ["all", "match"]:
+            exact_score = evaluator.eval_exact_match(p_sql, g_sql)
+            partial_scores = evaluator.partial_scores
+            if exact_score == 0:
+                print("{} pred: {}".format(hardness,p_str))
+                print("{} gold: {}".format(hardness,g_str))
+                print("")
+            scores[hardness]['exact'] += exact_score
+            scores['all']['exact'] += exact_score
+            for type_ in partial_types:
+                if partial_scores[type_]['pred_total'] > 0:
+                    scores[hardness]['partial'][type_]['acc'] += partial_scores[type_]['acc']
+                    scores[hardness]['partial'][type_]['acc_count'] += 1
+                if partial_scores[type_]['label_total'] > 0:
+                    scores[hardness]['partial'][type_]['rec'] += partial_scores[type_]['rec']
+                    scores[hardness]['partial'][type_]['rec_count'] += 1
+                scores[hardness]['partial'][type_]['f1'] += partial_scores[type_]['f1']
+                if partial_scores[type_]['pred_total'] > 0:
+                    scores['all']['partial'][type_]['acc'] += partial_scores[type_]['acc']
+                    scores['all']['partial'][type_]['acc_count'] += 1
+                if partial_scores[type_]['label_total'] > 0:
+                    scores['all']['partial'][type_]['rec'] += partial_scores[type_]['rec']
+                    scores['all']['partial'][type_]['rec_count'] += 1
+                scores['all']['partial'][type_]['f1'] += partial_scores[type_]['f1']
+
+            entries.append({
+                'predictSQL': p_str,
+                'goldSQL': g_str,
+                'hardness': hardness,
+                'exact': exact_score,
+                'partial': partial_scores
+            })
+
+    for level in levels:
+        if scores[level]['count'] == 0:
+            continue
+        if etype in ["all", "exec"]:
+            scores[level]['exec'] /= scores[level]['count']
+
+        if etype in ["all", "match"]:
+            scores[level]['exact'] /= scores[level]['count']
+            for type_ in partial_types:
+                if scores[level]['partial'][type_]['acc_count'] == 0:
+                    scores[level]['partial'][type_]['acc'] = 0
+                else:
+                    scores[level]['partial'][type_]['acc'] = scores[level]['partial'][type_]['acc'] / \
+                                                             scores[level]['partial'][type_]['acc_count'] * 1.0
+                if scores[level]['partial'][type_]['rec_count'] == 0:
+                    scores[level]['partial'][type_]['rec'] = 0
+                else:
+                    scores[level]['partial'][type_]['rec'] = scores[level]['partial'][type_]['rec'] / \
+                                                             scores[level]['partial'][type_]['rec_count'] * 1.0
+                if scores[level]['partial'][type_]['acc'] == 0 and scores[level]['partial'][type_]['rec'] == 0:
+                    scores[level]['partial'][type_]['f1'] = 1
+                else:
+                    scores[level]['partial'][type_]['f1'] = \
+                        2.0 * scores[level]['partial'][type_]['acc'] * scores[level]['partial'][type_]['rec'] / (
+                        scores[level]['partial'][type_]['rec'] + scores[level]['partial'][type_]['acc'])
+
+    print_scores(scores, etype)
+
+
+def eval_exec_match(db, p_str, g_str, pred, gold):
+    """
+    return 1 if the values between prediction and gold are matching
+    in the corresponding index. Currently not support multiple col_unit(pairs).
+    """
+    conn = sqlite3.connect(db)
+    cursor = conn.cursor()
+    try:
+        cursor.execute(p_str)
+        p_res = cursor.fetchall()
+    except:
+        return False
+
+    cursor.execute(g_str)
+    q_res = cursor.fetchall()
+
+    def res_map(res, val_units):
+        rmap = {}
+        for idx, val_unit in enumerate(val_units):
+            key = tuple(val_unit[1]) if not val_unit[2] else (val_unit[0], tuple(val_unit[1]), tuple(val_unit[2]))
+            rmap[key] = [r[idx] for r in res]
+        return rmap
+
+    p_val_units = [unit[1] for unit in pred['select'][1]]
+    q_val_units = [unit[1] for unit in gold['select'][1]]
+    return res_map(p_res, p_val_units) == res_map(q_res, q_val_units)
+
+
+# Rebuild SQL functions for value evaluation
+def rebuild_cond_unit_val(cond_unit):
+    if cond_unit is None or not DISABLE_VALUE:
+        return cond_unit
+
+    not_op, op_id, val_unit, val1, val2 = cond_unit
+    if type(val1) is not dict:
+        val1 = None
+    else:
+        val1 = rebuild_sql_val(val1)
+    if type(val2) is not dict:
+        val2 = None
+    else:
+        val2 = rebuild_sql_val(val2)
+    return not_op, op_id, val_unit, val1, val2
+
+
+def rebuild_condition_val(condition):
+    if condition is None or not DISABLE_VALUE:
+        return condition
+
+    res = []
+    for idx, it in enumerate(condition):
+        if idx % 2 == 0:
+            res.append(rebuild_cond_unit_val(it))
+        else:
+            res.append(it)
+    return res
+
+
+def rebuild_sql_val(sql):
+    if sql is None or not DISABLE_VALUE:
+        return sql
+
+    sql['from']['conds'] = rebuild_condition_val(sql['from']['conds'])
+    sql['having'] = rebuild_condition_val(sql['having'])
+    sql['where'] = rebuild_condition_val(sql['where'])
+    sql['intersect'] = rebuild_sql_val(sql['intersect'])
+    sql['except'] = rebuild_sql_val(sql['except'])
+    sql['union'] = rebuild_sql_val(sql['union'])
+
+    return sql
+
+
+# Rebuild SQL functions for foreign key evaluation
+def build_valid_col_units(table_units, schema):
+    col_ids = [table_unit[1] for table_unit in table_units if table_unit[0] == TABLE_TYPE['table_unit']]
+    prefixs = [col_id[:-2] for col_id in col_ids]
+    valid_col_units= []
+    for value in schema.idMap.values():
+        if '.' in value and value[:value.index('.')] in prefixs:
+            valid_col_units.append(value)
+    return valid_col_units
+
+
+def rebuild_col_unit_col(valid_col_units, col_unit, kmap):
+    if col_unit is None:
+        return col_unit
+
+    agg_id, col_id, distinct = col_unit
+    if col_id in kmap and col_id in valid_col_units:
+        col_id = kmap[col_id]
+    if DISABLE_DISTINCT:
+        distinct = None
+    return agg_id, col_id, distinct
+
+
+def rebuild_val_unit_col(valid_col_units, val_unit, kmap):
+    if val_unit is None:
+        return val_unit
+
+    unit_op, col_unit1, col_unit2 = val_unit
+    col_unit1 = rebuild_col_unit_col(valid_col_units, col_unit1, kmap)
+    col_unit2 = rebuild_col_unit_col(valid_col_units, col_unit2, kmap)
+    return unit_op, col_unit1, col_unit2
+
+
+def rebuild_table_unit_col(valid_col_units, table_unit, kmap):
+    if table_unit is None:
+        return table_unit
+
+    table_type, col_unit_or_sql = table_unit
+    if isinstance(col_unit_or_sql, tuple):
+        col_unit_or_sql = rebuild_col_unit_col(valid_col_units, col_unit_or_sql, kmap)
+    return table_type, col_unit_or_sql
+
+
+def rebuild_cond_unit_col(valid_col_units, cond_unit, kmap):
+    if cond_unit is None:
+        return cond_unit
+
+    not_op, op_id, val_unit, val1, val2 = cond_unit
+    val_unit = rebuild_val_unit_col(valid_col_units, val_unit, kmap)
+    return not_op, op_id, val_unit, val1, val2
+
+
+def rebuild_condition_col(valid_col_units, condition, kmap):
+    for idx in range(len(condition)):
+        if idx % 2 == 0:
+            condition[idx] = rebuild_cond_unit_col(valid_col_units, condition[idx], kmap)
+    return condition
+
+
+def rebuild_select_col(valid_col_units, sel, kmap):
+    if sel is None:
+        return sel
+    distinct, _list = sel
+    new_list = []
+    for it in _list:
+        agg_id, val_unit = it
+        new_list.append((agg_id, rebuild_val_unit_col(valid_col_units, val_unit, kmap)))
+    if DISABLE_DISTINCT:
+        distinct = None
+    return distinct, new_list
+
+
+def rebuild_from_col(valid_col_units, from_, kmap):
+    if from_ is None:
+        return from_
+
+    from_['table_units'] = [rebuild_table_unit_col(valid_col_units, table_unit, kmap) for table_unit in from_['table_units']]
+    from_['conds'] = rebuild_condition_col(valid_col_units, from_['conds'], kmap)
+    return from_
+
+
+def rebuild_group_by_col(valid_col_units, group_by, kmap):
+    if group_by is None:
+        return group_by
+
+    return [rebuild_col_unit_col(valid_col_units, col_unit, kmap) for col_unit in group_by]
+
+
+def rebuild_order_by_col(valid_col_units, order_by, kmap):
+    if order_by is None or len(order_by) == 0:
+        return order_by
+
+    direction, val_units = order_by
+    new_val_units = [rebuild_val_unit_col(valid_col_units, val_unit, kmap) for val_unit in val_units]
+    return direction, new_val_units
+
+
+def rebuild_sql_col(valid_col_units, sql, kmap):
+    if sql is None:
+        return sql
+
+    sql['select'] = rebuild_select_col(valid_col_units, sql['select'], kmap)
+    sql['from'] = rebuild_from_col(valid_col_units, sql['from'], kmap)
+    sql['where'] = rebuild_condition_col(valid_col_units, sql['where'], kmap)
+    sql['groupBy'] = rebuild_group_by_col(valid_col_units, sql['groupBy'], kmap)
+    sql['orderBy'] = rebuild_order_by_col(valid_col_units, sql['orderBy'], kmap)
+    sql['having'] = rebuild_condition_col(valid_col_units, sql['having'], kmap)
+    sql['intersect'] = rebuild_sql_col(valid_col_units, sql['intersect'], kmap)
+    sql['except'] = rebuild_sql_col(valid_col_units, sql['except'], kmap)
+    sql['union'] = rebuild_sql_col(valid_col_units, sql['union'], kmap)
+
+    return sql
+
+
+def build_foreign_key_map(entry):
+    cols_orig = entry["column_names_original"]
+    tables_orig = entry["table_names_original"]
+
+    # rebuild cols corresponding to idmap in Schema
+    cols = []
+    for col_orig in cols_orig:
+        if col_orig[0] >= 0:
+            t = tables_orig[col_orig[0]]
+            c = col_orig[1]
+            cols.append("__" + t.lower() + "." + c.lower() + "__")
+        else:
+            cols.append("__all__")
+
+    def keyset_in_list(k1, k2, k_list):
+        for k_set in k_list:
+            if k1 in k_set or k2 in k_set:
+                return k_set
+        new_k_set = set()
+        k_list.append(new_k_set)
+        return new_k_set
+
+    foreign_key_list = []
+    foreign_keys = entry["foreign_keys"]
+    for fkey in foreign_keys:
+        key1, key2 = fkey
+        key_set = keyset_in_list(key1, key2, foreign_key_list)
+        key_set.add(key1)
+        key_set.add(key2)
+
+    foreign_key_map = {}
+    for key_set in foreign_key_list:
+        sorted_list = sorted(list(key_set))
+        midx = sorted_list[0]
+        for idx in sorted_list:
+            foreign_key_map[cols[idx]] = cols[midx]
+
+    return foreign_key_map
+
+
+def build_foreign_key_map_from_json(table):
+    with open(table) as f:
+        data = json.load(f)
+    tables = {}
+    for entry in data:
+        tables[entry['db_id']] = build_foreign_key_map(entry)
+    return tables
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--gold', dest='gold', type=str)
+    parser.add_argument('--pred', dest='pred', type=str)
+    parser.add_argument('--db', dest='db', type=str)
+    parser.add_argument('--table', dest='table', type=str)
+    parser.add_argument('--etype', dest='etype', type=str)
+    args = parser.parse_args()
+
+    gold = args.gold
+    pred = args.pred
+    db_dir = args.db
+    table = args.table
+    etype = args.etype
+
+    assert etype in ["all", "exec", "match"], "Unknown evaluation method"
+
+    kmaps = build_foreign_key_map_from_json(table)
+
+    evaluate(gold, pred, db_dir, etype, kmaps)
\ No newline at end of file
diff --git a/code/evaluation.sh b/code/evaluation.sh
new file mode 100644
index 0000000..b62bcbb
--- /dev/null
+++ b/code/evaluation.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+schema="char"  # char or word
+#schema="word"
+
+embedding="multi"  # multi for multi-lingual or mono for monolingual
+#embedding="mono"
+
+SAVE_PATH="data/${schema}/generated_datasets/saved_models_multi_2022-12-05-06:04:27"
+
+# evaluation
+python evaluation.py \
+    --gold "data/dev_gold.sql" \
+    --pred "${SAVE_PATH}/dev_result.txt" \
+    --etype "match" \
+    --db "database" \
+    --table "data/tables.json" \
+   > "result_${schema}_${embedding}.log" \
+   2>&1 &
diff --git a/code/models/__init__.py b/code/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/code/models/agg_predictor.py b/code/models/agg_predictor.py
new file mode 100644
index 0000000..f52510b
--- /dev/null
+++ b/code/models/agg_predictor.py
@@ -0,0 +1,163 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class AggPredictor(nn.Module):
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(AggPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.col_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.q_num_att = nn.Linear(N_h, N_h)
+        self.hs_num_att = nn.Linear(N_h, N_h)
+        self.agg_num_out_q = nn.Linear(N_h, N_h)
+        self.agg_num_out_hs = nn.Linear(N_h, N_h)
+        self.agg_num_out_c = nn.Linear(N_h, N_h)
+        self.agg_num_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 4)) #for 0-3 agg num
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.agg_out_q = nn.Linear(N_h, N_h)
+        self.agg_out_hs = nn.Linear(N_h, N_h)
+        self.agg_out_c = nn.Linear(N_h, N_h)
+        self.agg_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 5)) #for 1-5 aggregators
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len, col_emb_var, col_len, col_name_len, gt_col):
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        max_col_len = max(col_len)
+        B = len(q_len)
+
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+        col_enc, _ = col_name_encode(col_emb_var, col_name_len, col_len, self.col_lstm)
+
+        col_emb = []
+        for b in range(B):
+            col_emb.append(col_enc[b, gt_col[b]])
+        col_emb = torch.stack(col_emb)
+
+        # Predict agg number
+        att_val_qc_num = torch.bmm(col_emb.unsqueeze(1), self.q_num_att(q_enc).transpose(1, 2)).view(B, -1)
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc_num[idx, num:] = -100
+        att_prob_qc_num = self.softmax(att_val_qc_num)
+        q_weighted_num = (q_enc * att_prob_qc_num.unsqueeze(2)).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_val_hc_num = torch.bmm(col_emb.unsqueeze(1), self.hs_num_att(hs_enc).transpose(1, 2)).view(B, -1)
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc_num[idx, num:] = -100
+        att_prob_hc_num = self.softmax(att_val_hc_num)
+        hs_weighted_num = (hs_enc * att_prob_hc_num.unsqueeze(2)).sum(1)
+        # agg_num_score: (B, 4)
+        agg_num_score = self.agg_num_out(self.agg_num_out_q(q_weighted_num) + int(self.use_hs)* self.agg_num_out_hs(hs_weighted_num) + self.agg_num_out_c(col_emb))
+
+        # Predict aggregators
+        att_val_qc = torch.bmm(col_emb.unsqueeze(1), self.q_att(q_enc).transpose(1, 2)).view(B, -1)
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc[idx, num:] = -100
+        att_prob_qc = self.softmax(att_val_qc)
+        q_weighted = (q_enc * att_prob_qc.unsqueeze(2)).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_val_hc = torch.bmm(col_emb.unsqueeze(1), self.hs_att(hs_enc).transpose(1, 2)).view(B, -1)
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc[idx, num:] = -100
+        att_prob_hc = self.softmax(att_val_hc)
+        hs_weighted = (hs_enc * att_prob_hc.unsqueeze(2)).sum(1)
+        # agg_score: (B, 5)
+        agg_score = self.agg_out(self.agg_out_q(q_weighted) + int(self.use_hs)* self.agg_out_hs(hs_weighted) + self.agg_out_c(col_emb))
+
+        score = (agg_num_score, agg_score)
+
+        return score
+
+
+    def loss(self, score, truth):
+        loss = 0
+        B = len(truth)
+        agg_num_score, agg_score = score
+        #loss for the column number
+        truth_num = [len(t) for t in truth] # double check truth format and for test cases
+        data = torch.from_numpy(np.array(truth_num))
+        truth_num_var = Variable(data.cuda())
+        loss += self.CE(agg_num_score, truth_num_var)
+        #loss for the key words
+        T = len(agg_score[0])
+        truth_prob = np.zeros((B, T), dtype=np.float32)
+        for b in range(B):
+            truth_prob[b][truth[b]] = 1
+        data = torch.from_numpy(truth_prob)
+        truth_var = Variable(data.cuda())
+        #loss += self.mlsml(agg_score, truth_var)
+        #loss += self.bce_logit(agg_score, truth_var) # double check no sigmoid
+        pred_prob = self.sigm(agg_score)
+        bce_loss = -torch.mean( 3*(truth_var * \
+                torch.log(pred_prob+1e-10)) + \
+                (1-truth_var) * torch.log(1-pred_prob+1e-10) )
+        loss += bce_loss
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        num_err, err, tot_err = 0, 0, 0
+        B = len(truth)
+        pred = []
+        agg_num_score, agg_score = [x.data.cpu().numpy() for x in score]
+        for b in range(B):
+            cur_pred = {}
+            agg_num = np.argmax(agg_num_score[b]) #double check
+            cur_pred['agg_num'] = agg_num
+            cur_pred['agg'] = np.argsort(-agg_score[b])[:agg_num]
+            pred.append(cur_pred)
+
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            agg_num, agg = p['agg_num'], p['agg']
+            flag = True
+            if agg_num != len(t): # double check truth format and for test cases
+                num_err += 1
+                flag = False
+            if flag and set(agg) != set(t):
+                err += 1
+                flag = False
+            if not flag:
+                tot_err += 1
+
+        return np.array((num_err, err, tot_err))
diff --git a/code/models/andor_predictor.py b/code/models/andor_predictor.py
new file mode 100644
index 0000000..49c6240
--- /dev/null
+++ b/code/models/andor_predictor.py
@@ -0,0 +1,94 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class AndOrPredictor(nn.Module):
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(AndOrPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.ao_out_q = nn.Linear(N_h, N_h)
+        self.ao_out_hs = nn.Linear(N_h, N_h)
+        self.ao_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 2)) #for and/or
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len):
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        B = len(q_len)
+
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+
+        att_np_q = np.ones((B, max_q_len))
+        att_val_q = torch.from_numpy(att_np_q).float()
+        att_val_q = Variable(att_val_q.cuda())
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_q[idx, num:] = -100
+        att_prob_q = self.softmax(att_val_q)
+        q_weighted = (q_enc * att_prob_q.unsqueeze(2)).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_np_h = np.ones((B, max_hs_len))
+        att_val_h = torch.from_numpy(att_np_h).float()
+        att_val_h = Variable(att_val_h.cuda())
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_h[idx, num:] = -100
+        att_prob_h = self.softmax(att_val_h)
+        hs_weighted = (hs_enc * att_prob_h.unsqueeze(2)).sum(1)
+        # ao_score: (B, 2)
+        ao_score = self.ao_out(self.ao_out_q(q_weighted) + int(self.use_hs)* self.ao_out_hs(hs_weighted))
+
+        return ao_score
+
+
+    def loss(self, score, truth):
+        loss = 0
+        data = torch.from_numpy(np.array(truth))
+        truth_var = Variable(data.cuda())
+        loss = self.CE(score, truth_var)
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        err = 0
+        B = len(score)
+        pred = []
+        for b in range(B):
+            pred.append(np.argmax(score[b].data.cpu().numpy()))
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            if p != t:
+                err += 1
+
+        return err
diff --git a/code/models/col_predictor.py b/code/models/col_predictor.py
new file mode 100644
index 0000000..d3ada97
--- /dev/null
+++ b/code/models/col_predictor.py
@@ -0,0 +1,200 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class ColPredictor(nn.Module):
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(ColPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.col_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.q_num_att = nn.Linear(N_h, N_h)
+        self.hs_num_att = nn.Linear(N_h, N_h)
+        self.col_num_out_q = nn.Linear(N_h, N_h)
+        self.col_num_out_hs = nn.Linear(N_h, N_h)
+        self.col_num_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 6)) # num of cols: 1-3
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.col_out_q = nn.Linear(N_h, N_h)
+        self.col_out_c = nn.Linear(N_h, N_h)
+        self.col_out_hs = nn.Linear(N_h, N_h)
+        self.col_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 1))
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len, col_emb_var, col_len, col_name_len):
+
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        max_col_len = max(col_len)
+        B = len(q_len)
+
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+        col_enc, _ = col_name_encode(col_emb_var, col_name_len, col_len, self.col_lstm)
+
+        # Predict column number: 1-3
+        # att_val_qc_num: (B, max_col_len, max_q_len)
+        att_val_qc_num = torch.bmm(col_enc, self.q_num_att(q_enc).transpose(1, 2))
+        for idx, num in enumerate(col_len):
+            if num < max_col_len:
+                att_val_qc_num[idx, num:, :] = -100
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc_num[idx, :, num:] = -100
+        att_prob_qc_num = self.softmax(att_val_qc_num.view((-1, max_q_len))).view(B, -1, max_q_len)
+        # q_weighted_num: (B, hid_dim)
+        q_weighted_num = (q_enc.unsqueeze(1) * att_prob_qc_num.unsqueeze(3)).sum(2).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        # att_val_hc_num: (B, max_col_len, max_hs_len)
+        att_val_hc_num = torch.bmm(col_enc, self.hs_num_att(hs_enc).transpose(1, 2))
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc_num[idx, :, num:] = -100
+        for idx, num in enumerate(col_len):
+            if num < max_col_len:
+                att_val_hc_num[idx, num:, :] = -100
+        att_prob_hc_num = self.softmax(att_val_hc_num.view((-1, max_hs_len))).view(B, -1, max_hs_len)
+        hs_weighted_num = (hs_enc.unsqueeze(1) * att_prob_hc_num.unsqueeze(3)).sum(2).sum(1)
+        # self.col_num_out: (B, 3)
+        col_num_score = self.col_num_out(self.col_num_out_q(q_weighted_num) + int(self.use_hs)* self.col_num_out_hs(hs_weighted_num))
+
+        # Predict columns.
+        att_val_qc = torch.bmm(col_enc, self.q_att(q_enc).transpose(1, 2))
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc[idx, :, num:] = -100
+        att_prob_qc = self.softmax(att_val_qc.view((-1, max_q_len))).view(B, -1, max_q_len)
+        # q_weighted: (B, max_col_len, hid_dim)
+        q_weighted = (q_enc.unsqueeze(1) * att_prob_qc.unsqueeze(3)).sum(2)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_val_hc = torch.bmm(col_enc, self.hs_att(hs_enc).transpose(1, 2))
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc[idx, :, num:] = -100
+        att_prob_hc = self.softmax(att_val_hc.view((-1, max_hs_len))).view(B, -1, max_hs_len)
+        hs_weighted = (hs_enc.unsqueeze(1) * att_prob_hc.unsqueeze(3)).sum(2)
+        # Compute prediction scores
+        # self.col_out.squeeze(): (B, max_col_len)
+        col_score = self.col_out(self.col_out_q(q_weighted) + int(self.use_hs)* self.col_out_hs(hs_weighted) + self.col_out_c(col_enc)).view(B,-1)
+
+        for idx, num in enumerate(col_len):
+            if num < max_col_len:
+                col_score[idx, num:] = -100
+
+        score = (col_num_score, col_score)
+
+        return score
+
+    def loss(self, score, truth):
+        #here suppose truth looks like [[[1, 4], 3], [], ...]
+        loss = 0
+        B = len(truth)
+        col_num_score, col_score = score
+        #loss for the column number
+        truth_num = [len(t) - 1 for t in truth] # double check truth format and for test cases
+        data = torch.from_numpy(np.array(truth_num))
+        truth_num_var = Variable(data.cuda())
+        loss += self.CE(col_num_score, truth_num_var)
+        #loss for the key words
+        T = len(col_score[0])
+        # print("T {}".format(T))
+        truth_prob = np.zeros((B, T), dtype=np.float32)
+        for b in range(B):
+            gold_l = []
+            for t in truth[b]:
+                if isinstance(t, list):
+                    gold_l.extend(t)
+                else:
+                    gold_l.append(t)
+            truth_prob[b][gold_l] = 1
+        data = torch.from_numpy(truth_prob)
+        # print("data {}".format(data))
+        # print("data {}".format(data.cuda()))
+        truth_var = Variable(data.cuda())
+        #loss += self.mlsml(col_score, truth_var)
+        #loss += self.bce_logit(col_score, truth_var) # double check no sigmoid
+        pred_prob = self.sigm(col_score)
+        bce_loss = -torch.mean( 3*(truth_var * \
+                torch.log(pred_prob+1e-10)) + \
+                (1-truth_var) * torch.log(1-pred_prob+1e-10) )
+        loss += bce_loss
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        num_err, err, tot_err = 0, 0, 0
+        B = len(truth)
+        pred = []
+        col_num_score, col_score = [x.data.cpu().numpy() for x in score]
+        for b in range(B):
+            cur_pred = {}
+            col_num = np.argmax(col_num_score[b]) + 1 #double check
+            cur_pred['col_num'] = col_num
+            cur_pred['col'] = np.argsort(-col_score[b])[:col_num]
+            pred.append(cur_pred)
+
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            col_num, col = p['col_num'], p['col']
+            flag = True
+            if col_num != len(t): # double check truth format and for test cases
+                num_err += 1
+                flag = False
+            #to eval col predicts, if the gold sql has JOIN and foreign key col, then both fks are acceptable
+            fk_list = []
+            regular = []
+            for l in t:
+                if isinstance(l, list):
+                    fk_list.append(l)
+                else:
+                    regular.append(l)
+
+            if flag: #double check
+                for c in col:
+                    for fk in fk_list:
+                        if c in fk:
+                            fk_list.remove(fk)
+                    for r in regular:
+                        if c == r:
+                            regular.remove(r)
+
+                if len(fk_list) != 0 or len(regular) != 0:
+                    err += 1
+                    flag = False
+
+            if not flag:
+                tot_err += 1
+
+        return np.array((num_err, err, tot_err))
diff --git a/code/models/desasc_limit_predictor.py b/code/models/desasc_limit_predictor.py
new file mode 100644
index 0000000..1bec67c
--- /dev/null
+++ b/code/models/desasc_limit_predictor.py
@@ -0,0 +1,105 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class DesAscLimitPredictor(nn.Module):
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(DesAscLimitPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.col_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.dat_out_q = nn.Linear(N_h, N_h)
+        self.dat_out_hs = nn.Linear(N_h, N_h)
+        self.dat_out_c = nn.Linear(N_h, N_h)
+        self.dat_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 4)) #for 4 desc/asc limit/none combinations
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len, col_emb_var, col_len, col_name_len, gt_col):
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        max_col_len = max(col_len)
+        B = len(q_len)
+
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+        col_enc, _ = col_name_encode(col_emb_var, col_name_len, col_len, self.col_lstm)
+
+        # get target/predicted column's embedding
+        # col_emb: (B, hid_dim)
+        col_emb = []
+        for b in range(B):
+            col_emb.append(col_enc[b, gt_col[b]])
+        col_emb = torch.stack(col_emb) # [B, dim]
+        # self.q_att(q_enc).transpose(1, 2): [B, dim, max_q_len]
+        att_val_qc = torch.bmm(col_emb.unsqueeze(1), self.q_att(q_enc).transpose(1, 2)).view(B, -1)
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc[idx, num:] = -100
+        att_prob_qc = self.softmax(att_val_qc)
+        q_weighted = (q_enc * att_prob_qc.unsqueeze(2)).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_val_hc = torch.bmm(col_emb.unsqueeze(1), self.hs_att(hs_enc).transpose(1, 2)).view(B, -1)
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc[idx, num:] = -100
+        att_prob_hc = self.softmax(att_val_hc)
+        hs_weighted = (hs_enc * att_prob_hc.unsqueeze(2)).sum(1)
+        # dat_score: (B, 4)
+        dat_score = self.dat_out(self.dat_out_q(q_weighted) + int(self.use_hs)* self.dat_out_hs(hs_weighted) + self.dat_out_c(col_emb))
+
+        return dat_score
+
+
+    def loss(self, score, truth):
+        loss = 0
+        data = torch.from_numpy(np.array(truth))
+        truth_var = Variable(data.cuda())
+        loss = self.CE(score, truth_var)
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        err = 0
+        B = len(score)
+        pred = []
+        for b in range(B):
+            pred.append(np.argmax(score[b].data.cpu().numpy()))
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            if p != t:
+                err += 1
+
+        return err
diff --git a/code/models/having_predictor.py b/code/models/having_predictor.py
new file mode 100644
index 0000000..b6355c6
--- /dev/null
+++ b/code/models/having_predictor.py
@@ -0,0 +1,103 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class HavingPredictor(nn.Module):
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(HavingPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.col_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.hv_out_q = nn.Linear(N_h, N_h)
+        self.hv_out_hs = nn.Linear(N_h, N_h)
+        self.hv_out_c = nn.Linear(N_h, N_h)
+        self.hv_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 2)) #for having/none
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len, col_emb_var, col_len, col_name_len, gt_col):
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        max_col_len = max(col_len)
+        B = len(q_len)
+
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+        col_enc, _ = col_name_encode(col_emb_var, col_name_len, col_len, self.col_lstm)
+
+        # get target/predicted column's embedding
+        # col_emb: (B, hid_dim)
+        col_emb = []
+        for b in range(B):
+            col_emb.append(col_enc[b, gt_col[b]])
+        col_emb = torch.stack(col_emb)
+        att_val_qc = torch.bmm(col_emb.unsqueeze(1), self.q_att(q_enc).transpose(1, 2)).view(B,-1)
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc[idx, num:] = -100
+        att_prob_qc = self.softmax(att_val_qc)
+        q_weighted = (q_enc * att_prob_qc.unsqueeze(2)).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_val_hc = torch.bmm(col_emb.unsqueeze(1), self.hs_att(hs_enc).transpose(1, 2)).view(B,-1)
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc[idx, num:] = -100
+        att_prob_hc = self.softmax(att_val_hc)
+        hs_weighted = (hs_enc * att_prob_hc.unsqueeze(2)).sum(1)
+        # hv_score: (B, 2)
+        hv_score = self.hv_out(self.hv_out_q(q_weighted) + int(self.use_hs)* self.hv_out_hs(hs_weighted) + self.hv_out_c(col_emb))
+
+        return hv_score
+
+
+    def loss(self, score, truth):
+        loss = 0
+        data = torch.from_numpy(np.array(truth))
+        truth_var = Variable(data.cuda())
+        loss = self.CE(score, truth_var)
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        err = 0
+        B = len(score)
+        pred = []
+        for b in range(B):
+            pred.append(np.argmax(score[b].data.cpu().numpy()))
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            if p != t:
+                err += 1
+
+        return err
diff --git a/code/models/keyword_predictor.py b/code/models/keyword_predictor.py
new file mode 100644
index 0000000..daf6547
--- /dev/null
+++ b/code/models/keyword_predictor.py
@@ -0,0 +1,160 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class KeyWordPredictor(nn.Module):
+    '''Predict if the next token is (SQL key words):
+        WHERE, GROUP BY, ORDER BY. excluding SELECT (it is a must)'''
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(KeyWordPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.kw_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.q_num_att = nn.Linear(N_h, N_h)
+        self.hs_num_att = nn.Linear(N_h, N_h)
+        self.kw_num_out_q = nn.Linear(N_h, N_h)
+        self.kw_num_out_hs = nn.Linear(N_h, N_h)
+        self.kw_num_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 4)) # num of key words: 0-3
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.kw_out_q = nn.Linear(N_h, N_h)
+        self.kw_out_hs = nn.Linear(N_h, N_h)
+        self.kw_out_kw = nn.Linear(N_h, N_h)
+        self.kw_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 1))
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len, kw_emb_var, kw_len):
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        B = len(q_len)
+
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+        kw_enc, _ = run_lstm(self.kw_lstm, kw_emb_var, kw_len)
+
+        # Predict key words number: 0-3
+        att_val_qkw_num = torch.bmm(kw_enc, self.q_num_att(q_enc).transpose(1, 2))
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qkw_num[idx, :, num:] = -100
+        att_prob_qkw_num = self.softmax(att_val_qkw_num.view((-1, max_q_len))).view(B, -1, max_q_len)
+        # q_weighted: (B, hid_dim)
+        q_weighted_num = (q_enc.unsqueeze(1) * att_prob_qkw_num.unsqueeze(3)).sum(2).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by key words attentions
+        att_val_hskw_num = torch.bmm(kw_enc, self.hs_num_att(hs_enc).transpose(1, 2))
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hskw_num[idx, :, num:] = -100
+        att_prob_hskw_num = self.softmax(att_val_hskw_num.view((-1, max_hs_len))).view(B, -1, max_hs_len)
+        hs_weighted_num = (hs_enc.unsqueeze(1) * att_prob_hskw_num.unsqueeze(3)).sum(2).sum(1)
+        # Compute prediction scores
+        # self.kw_num_out: (B, 4)
+        kw_num_score = self.kw_num_out(self.kw_num_out_q(q_weighted_num) + int(self.use_hs)* self.kw_num_out_hs(hs_weighted_num))
+
+        # Predict key words: WHERE, GROUP BY, ORDER BY.
+        att_val_qkw = torch.bmm(kw_enc, self.q_att(q_enc).transpose(1, 2))
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qkw[idx, :, num:] = -100
+        att_prob_qkw = self.softmax(att_val_qkw.view((-1, max_q_len))).view(B, -1, max_q_len)
+        # q_weighted: (B, 3, hid_dim)
+        q_weighted = (q_enc.unsqueeze(1) * att_prob_qkw.unsqueeze(3)).sum(2)
+
+        # Same as the above, compute SQL history embedding weighted by key words attentions
+        att_val_hskw = torch.bmm(kw_enc, self.hs_att(hs_enc).transpose(1, 2))
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hskw[idx, :, num:] = -100
+        att_prob_hskw = self.softmax(att_val_hskw.view((-1, max_hs_len))).view(B, -1, max_hs_len)
+        hs_weighted = (hs_enc.unsqueeze(1) * att_prob_hskw.unsqueeze(3)).sum(2)
+        # Compute prediction scores
+        # self.kw_out.squeeze(): (B, 3)
+        kw_score = self.kw_out(self.kw_out_q(q_weighted) + int(self.use_hs)* self.kw_out_hs(hs_weighted) + self.kw_out_kw(kw_enc)).view(B,-1)
+
+        score = (kw_num_score, kw_score)
+
+        return score
+
+    def loss(self, score, truth):
+        loss = 0
+        B = len(truth)
+        kw_num_score, kw_score = score
+        #loss for the key word number
+        truth_num = [len(t) for t in truth] # double check to exclude select
+        data = torch.from_numpy(np.array(truth_num))
+        truth_num_var = Variable(data.cuda())
+        loss += self.CE(kw_num_score, truth_num_var)
+        #loss for the key words
+        T = len(kw_score[0])
+        truth_prob = np.zeros((B, T), dtype=np.float32)
+        for b in range(B):
+            truth_prob[b][truth[b]] = 1
+        data = torch.from_numpy(truth_prob)
+        truth_var = Variable(data.cuda())
+        #loss += self.mlsml(kw_score, truth_var)
+        #loss += self.bce_logit(kw_score, truth_var) # double check no sigmoid for kw
+        pred_prob = self.sigm(kw_score)
+        bce_loss = -torch.mean( 3*(truth_var * \
+                torch.log(pred_prob+1e-10)) + \
+                (1-truth_var) * torch.log(1-pred_prob+1e-10) )
+        loss += bce_loss
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        num_err, err, tot_err = 0, 0, 0
+        B = len(truth)
+        pred = []
+        kw_num_score, kw_score = [x.data.cpu().numpy() for x in score]
+        for b in range(B):
+            cur_pred = {}
+            kw_num = np.argmax(kw_num_score[b])
+            cur_pred['kw_num'] = kw_num
+            cur_pred['kw'] = np.argsort(-kw_score[b])[:kw_num]
+            pred.append(cur_pred)
+
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            kw_num, kw = p['kw_num'], p['kw']
+            flag = True
+            if kw_num != len(t): # double check to excluding select
+                num_err += 1
+                flag = False
+            if flag and set(kw) != set(t):
+                err += 1
+                flag = False
+            if not flag:
+                tot_err += 1
+
+        return np.array((num_err, err, tot_err))
diff --git a/code/models/multisql_predictor.py b/code/models/multisql_predictor.py
new file mode 100644
index 0000000..3ff4e2a
--- /dev/null
+++ b/code/models/multisql_predictor.py
@@ -0,0 +1,113 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class MultiSqlPredictor(nn.Module):
+    '''Predict if the next token is (multi SQL key words):
+        NONE, EXCEPT, INTERSECT, or UNION.'''
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(MultiSqlPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.mkw_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.multi_out_q = nn.Linear(N_h, N_h)
+        self.multi_out_hs = nn.Linear(N_h, N_h)
+        self.multi_out_c = nn.Linear(N_h, N_h)
+        self.multi_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 1))
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+
+        if gpu:
+            self.cuda()
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len, mkw_emb_var, mkw_len):
+        # print("q_emb_shape:{} hs_emb_shape:{}".format(q_emb_var.size(), hs_emb_var.size()))
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        B = len(q_len)
+
+        # q_enc: (B, max_q_len, hid_dim)
+        # hs_enc: (B, max_hs_len, hid_dim)
+        # mkw: (B, 4, hid_dim)
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+        mkw_enc, _ = run_lstm(self.mkw_lstm, mkw_emb_var, mkw_len)
+
+        # Compute attention values between multi SQL key words and question tokens.
+        # qmkw_att(q_enc).transpose(1, 2): (B, hid_dim, max_q_len)
+        # att_val_qmkw: (B, 4, max_q_len)
+        # print("mkw_enc {} q_enc {}".format(mkw_enc.size(), self.q_att(q_enc).transpose(1, 2).size()))
+        att_val_qmkw = torch.bmm(mkw_enc, self.q_att(q_enc).transpose(1, 2))
+        # assign appended positions values -100
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qmkw[idx, :, num:] = -100
+        # att_prob_qmkw: (B, 4, max_q_len)
+        att_prob_qmkw = self.softmax(att_val_qmkw.view((-1, max_q_len))).view(B, -1, max_q_len)
+        # q_enc.unsqueeze(1): (B, 1, max_q_len, hid_dim)
+        # att_prob_qmkw.unsqueeze(3): (B, 4, max_q_len, 1)
+        # q_weighted: (B, 4, hid_dim)
+        q_weighted = (q_enc.unsqueeze(1) * att_prob_qmkw.unsqueeze(3)).sum(2)
+
+        # Same as the above, compute SQL history embedding weighted by key words attentions
+        att_val_hsmkw = torch.bmm(mkw_enc, self.hs_att(hs_enc).transpose(1, 2))
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hsmkw[idx, :, num:] = -100
+        att_prob_hsmkw = self.softmax(att_val_hsmkw.view((-1, max_hs_len))).view(B, -1, max_hs_len)
+        hs_weighted = (hs_enc.unsqueeze(1) * att_prob_hsmkw.unsqueeze(3)).sum(2)
+
+        # Compute prediction scores
+        # self.multi_out.squeeze(): (B, 4, 1) -> (B, 4)
+        mulit_score = self.multi_out(self.multi_out_q(q_weighted) + int(self.use_hs)* self.multi_out_hs(hs_weighted) + self.multi_out_c(mkw_enc)).view(B,-1)
+
+        return mulit_score
+
+
+    def loss(self, score, truth):
+        data = torch.from_numpy(np.array(truth))
+        truth_var = Variable(data.cuda())
+        loss = self.CE(score, truth_var)
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        err = 0
+        B = len(score)
+        pred = []
+        for b in range(B):
+            pred.append(np.argmax(score[b].data.cpu().numpy()))
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            if p != t:
+                err += 1
+
+        return err
diff --git a/code/models/net_utils.py b/code/models/net_utils.py
new file mode 100644
index 0000000..fc9815c
--- /dev/null
+++ b/code/models/net_utils.py
@@ -0,0 +1,48 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.autograd import Variable
+
+def run_lstm(lstm, inp, inp_len, hidden=None):
+    # Run the LSTM using packed sequence.
+    # This requires to first sort the input according to its length.
+    sort_perm = np.array(sorted(range(len(inp_len)),
+        key=lambda k:inp_len[k], reverse=True))
+    sort_inp_len = inp_len[sort_perm]
+    sort_perm_inv = np.argsort(sort_perm)
+    if inp.is_cuda:
+        sort_perm = torch.LongTensor(sort_perm).cuda()
+        sort_perm_inv = torch.LongTensor(sort_perm_inv).cuda()
+
+    lstm_inp = nn.utils.rnn.pack_padded_sequence(inp[sort_perm],
+            sort_inp_len, batch_first=True)
+    if hidden is None:
+        lstm_hidden = None
+    else:
+        lstm_hidden = (hidden[0][:, sort_perm], hidden[1][:, sort_perm])
+
+    sort_ret_s, sort_ret_h = lstm(lstm_inp, lstm_hidden)
+    ret_s = nn.utils.rnn.pad_packed_sequence(
+            sort_ret_s, batch_first=True)[0][sort_perm_inv]
+    ret_h = (sort_ret_h[0][:, sort_perm_inv], sort_ret_h[1][:, sort_perm_inv])
+    return ret_s, ret_h
+
+
+def col_name_encode(name_inp_var, name_len, col_len, enc_lstm):
+    #Encode the columns.
+    #The embedding of a column name is the last state of its LSTM output.
+    name_hidden, _ = run_lstm(enc_lstm, name_inp_var, name_len)
+    name_out = name_hidden[tuple(range(len(name_len))), name_len-1]
+    ret = torch.FloatTensor(
+            len(col_len), max(col_len), name_out.size()[1]).zero_()
+    if name_out.is_cuda:
+        ret = ret.cuda()
+
+    st = 0
+    for idx, cur_len in enumerate(col_len):
+        ret[idx, :cur_len] = name_out.data[st:st+cur_len]
+        st += cur_len
+    ret_var = Variable(ret)
+
+    return ret_var, col_len
+    
diff --git a/code/models/op_predictor.py b/code/models/op_predictor.py
new file mode 100644
index 0000000..9eb6ae7
--- /dev/null
+++ b/code/models/op_predictor.py
@@ -0,0 +1,176 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class OpPredictor(nn.Module):
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(OpPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.col_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.q_num_att = nn.Linear(N_h, N_h)
+        self.hs_num_att = nn.Linear(N_h, N_h)
+        self.op_num_out_q = nn.Linear(N_h, N_h)
+        self.op_num_out_hs = nn.Linear(N_h, N_h)
+        self.op_num_out_c = nn.Linear(N_h, N_h)
+        self.op_num_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 2)) #for 1-2 op num, could be changed
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.op_out_q = nn.Linear(N_h, N_h)
+        self.op_out_hs = nn.Linear(N_h, N_h)
+        self.op_out_c = nn.Linear(N_h, N_h)
+        self.op_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 11)) #for 11 operators
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len, col_emb_var, col_len, col_name_len, gt_col):
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        max_col_len = max(col_len)
+        B = len(q_len)
+
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+        col_enc, _ = col_name_encode(col_emb_var, col_name_len, col_len, self.col_lstm)
+
+        # get target/predicted column's embedding
+        # col_emb: (B, hid_dim)
+        col_emb = []
+        for b in range(B):
+            col_emb.append(col_enc[b, gt_col[b]])
+        col_emb = torch.stack(col_emb)
+
+        # Predict op number
+        att_val_qc_num = torch.bmm(col_emb.unsqueeze(1), self.q_num_att(q_enc).transpose(1, 2)).view(B,-1)
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc_num[idx, num:] = -100
+        att_prob_qc_num = self.softmax(att_val_qc_num)
+        q_weighted_num = (q_enc * att_prob_qc_num.unsqueeze(2)).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_val_hc_num = torch.bmm(col_emb.unsqueeze(1), self.hs_num_att(hs_enc).transpose(1, 2)).view(B,-1)
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc_num[idx, num:] = -100
+        att_prob_hc_num = self.softmax(att_val_hc_num)
+        hs_weighted_num = (hs_enc * att_prob_hc_num.unsqueeze(2)).sum(1)
+        # op_num_score: (B, 2)
+        op_num_score = self.op_num_out(self.op_num_out_q(q_weighted_num) + int(self.use_hs)* self.op_num_out_hs(hs_weighted_num) + self.op_num_out_c(col_emb))
+
+        # Compute attention values between selected column and question tokens.
+        # q_enc.transpose(1, 2): (B, hid_dim, max_q_len)
+        # col_emb.unsqueeze(1): (B, 1, hid_dim)
+        # att_val_qc: (B, max_q_len)
+        # print("col_emb {} q_enc {}".format(col_emb.unsqueeze(1).size(),self.q_att(q_enc).transpose(1, 2).size()))
+        att_val_qc = torch.bmm(col_emb.unsqueeze(1), self.q_att(q_enc).transpose(1, 2)).view(B,-1)
+        # assign appended positions values -100
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc[idx, num:] = -100
+        # att_prob_qc: (B, max_q_len)
+        att_prob_qc = self.softmax(att_val_qc)
+        # q_enc: (B, max_q_len, hid_dim)
+        # att_prob_qc.unsqueeze(2): (B, max_q_len, 1)
+        # q_weighted: (B, hid_dim)
+        q_weighted = (q_enc * att_prob_qc.unsqueeze(2)).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_val_hc = torch.bmm(col_emb.unsqueeze(1), self.hs_att(hs_enc).transpose(1, 2)).view(B,-1)
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc[idx, num:] = -100
+        att_prob_hc = self.softmax(att_val_hc)
+        hs_weighted = (hs_enc * att_prob_hc.unsqueeze(2)).sum(1)
+
+        # Compute prediction scores
+        # op_score: (B, 10)
+        op_score = self.op_out(self.op_out_q(q_weighted) + int(self.use_hs)* self.op_out_hs(hs_weighted) + self.op_out_c(col_emb))
+
+        score = (op_num_score, op_score)
+
+        return score
+
+
+    def loss(self, score, truth):
+        loss = 0
+        B = len(truth)
+        op_num_score, op_score = score
+        truth = [t if len(t) <= 2 else t[:2] for t in truth]
+        # loss for the op number
+        truth_num = [len(t)-1 for t in truth] #num_score 0 maps to 1 in truth
+        data = torch.from_numpy(np.array(truth_num))
+        truth_num_var = Variable(data.cuda())
+        loss += self.CE(op_num_score, truth_num_var)
+        # loss for op
+        T = len(op_score[0])
+        truth_prob = np.zeros((B, T), dtype=np.float32)
+        for b in range(B):
+            truth_prob[b][truth[b]] = 1
+        data = torch.from_numpy(np.array(truth_prob))
+        truth_var = Variable(data.cuda())
+        #loss += self.mlsml(op_score, truth_var)
+        #loss += self.bce_logit(op_score, truth_var)
+        pred_prob = self.sigm(op_score)
+        bce_loss = -torch.mean( 3*(truth_var * \
+                torch.log(pred_prob+1e-10)) + \
+                (1-truth_var) * torch.log(1-pred_prob+1e-10) )
+        loss += bce_loss
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        num_err, err, tot_err = 0, 0, 0
+        B = len(truth)
+        pred = []
+        op_num_score, op_score = [x.data.cpu().numpy() for x in score]
+        for b in range(B):
+            cur_pred = {}
+            op_num = np.argmax(op_num_score[b]) + 1 #num_score 0 maps to 1 in truth, must have at least one op
+            cur_pred['op_num'] = op_num
+            cur_pred['op'] = np.argsort(-op_score[b])[:op_num]
+            pred.append(cur_pred)
+
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            op_num, op = p['op_num'], p['op']
+            flag = True
+            if op_num != len(t):
+                num_err += 1
+                flag = False
+            if flag and set(op) != set(t):
+                err += 1
+                flag = False
+            if not flag:
+                tot_err += 1
+
+        return np.array((num_err, err, tot_err))
diff --git a/code/models/root_teminal_predictor.py b/code/models/root_teminal_predictor.py
new file mode 100644
index 0000000..730a3e0
--- /dev/null
+++ b/code/models/root_teminal_predictor.py
@@ -0,0 +1,103 @@
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from models.net_utils import run_lstm, col_name_encode
+
+
+class RootTeminalPredictor(nn.Module):
+    def __init__(self, N_word, N_col, N_h, N_depth, dropout, gpu, use_hs):
+        super(RootTeminalPredictor, self).__init__()
+        self.N_h = N_h
+        self.gpu = gpu
+        self.use_hs = use_hs
+
+        self.q_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        if N_col:
+            N_word = N_col
+
+        self.hs_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.col_lstm = nn.LSTM(input_size=N_word, hidden_size=int(N_h/2),
+                num_layers=N_depth, batch_first=True,
+                dropout=dropout, bidirectional=True)
+
+        self.q_att = nn.Linear(N_h, N_h)
+        self.hs_att = nn.Linear(N_h, N_h)
+        self.rt_out_q = nn.Linear(N_h, N_h)
+        self.rt_out_hs = nn.Linear(N_h, N_h)
+        self.rt_out_c = nn.Linear(N_h, N_h)
+        self.rt_out = nn.Sequential(nn.Tanh(), nn.Linear(N_h, 2)) #for 2 operators
+
+        self.softmax = nn.Softmax(dim=1) #dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+
+    def forward(self, q_emb_var, q_len, hs_emb_var, hs_len, col_emb_var, col_len, col_name_len, gt_col):
+        max_q_len = max(q_len)
+        max_hs_len = max(hs_len)
+        max_col_len = max(col_len)
+        B = len(q_len)
+
+        q_enc, _ = run_lstm(self.q_lstm, q_emb_var, q_len)
+        hs_enc, _ = run_lstm(self.hs_lstm, hs_emb_var, hs_len)
+        col_enc, _ = col_name_encode(col_emb_var, col_name_len, col_len, self.col_lstm)
+
+        # get target/predicted column's embedding
+        # col_emb: (B, hid_dim)
+        col_emb = []
+        for b in range(B):
+            col_emb.append(col_enc[b, gt_col[b]])
+        col_emb = torch.stack(col_emb)
+        att_val_qc = torch.bmm(col_emb.unsqueeze(1), self.q_att(q_enc).transpose(1, 2)).view(B,-1)
+        for idx, num in enumerate(q_len):
+            if num < max_q_len:
+                att_val_qc[idx, num:] = -100
+        att_prob_qc = self.softmax(att_val_qc)
+        q_weighted = (q_enc * att_prob_qc.unsqueeze(2)).sum(1)
+
+        # Same as the above, compute SQL history embedding weighted by column attentions
+        att_val_hc = torch.bmm(col_emb.unsqueeze(1), self.hs_att(hs_enc).transpose(1, 2)).view(B,-1)
+        for idx, num in enumerate(hs_len):
+            if num < max_hs_len:
+                att_val_hc[idx, num:] = -100
+        att_prob_hc = self.softmax(att_val_hc)
+        hs_weighted = (hs_enc * att_prob_hc.unsqueeze(2)).sum(1)
+        # rt_score: (B, 2)
+        rt_score = self.rt_out(self.rt_out_q(q_weighted) + int(self.use_hs)* self.rt_out_hs(hs_weighted) + self.rt_out_c(col_emb))
+
+        return rt_score
+
+
+    def loss(self, score, truth):
+        loss = 0
+        data = torch.from_numpy(np.array(truth))
+        truth_var = Variable(data.cuda())
+        loss = self.CE(score, truth_var)
+
+        return loss
+
+
+    def check_acc(self, score, truth):
+        err = 0
+        B = len(score)
+        pred = []
+        for b in range(B):
+            pred.append(np.argmax(score[b].data.cpu().numpy()))
+        for b, (p, t) in enumerate(zip(pred, truth)):
+            if p != t:
+                err += 1
+
+        return err
diff --git a/code/models/supermodel.py b/code/models/supermodel.py
new file mode 100644
index 0000000..0142a0f
--- /dev/null
+++ b/code/models/supermodel.py
@@ -0,0 +1,893 @@
+import json
+import torch
+import datetime
+import time
+import argparse
+import numpy as np
+import torch.nn as nn
+import traceback
+from collections import defaultdict
+
+from utils.word_embedding import WordEmbedding
+from models.agg_predictor import AggPredictor
+from models.col_predictor import ColPredictor
+from models.desasc_limit_predictor import DesAscLimitPredictor
+from models.having_predictor import HavingPredictor
+from models.keyword_predictor import KeyWordPredictor
+from models.multisql_predictor import MultiSqlPredictor
+from models.root_teminal_predictor import RootTeminalPredictor
+from models.andor_predictor import AndOrPredictor
+from models.op_predictor import OpPredictor
+from preprocess_data import index_to_column_name
+
+
+SQL_OPS = ('none', 'intersect', 'union', 'except')
+KW_OPS = ('where', 'groupBy', 'orderBy')
+AGG_OPS = ('max', 'min', 'count', 'sum', 'avg')
+ROOT_TERM_OPS = ("root", "terminal")
+COND_OPS = ("and", "or")
+DEC_ASC_OPS = (("asc", True), ("asc", False), ("desc", True), ("desc", False))
+NEW_WHERE_OPS = (
+    '=',
+    '>',
+    '<',
+    '>=',
+    '<=',
+    '!=',
+    'like',
+    'not in',
+    'in',
+    'between')
+KW_WITH_COL = ("select", "where", "groupBy", "orderBy", "having")
+
+
+class Stack:
+    def __init__(self):
+        self.items = []
+
+    def isEmpty(self):
+        return self.items == []
+
+    def push(self, item):
+        self.items.append(item)
+
+    def pop(self):
+        return self.items.pop()
+
+    def peek(self):
+        return self.items[len(self.items) - 1]
+
+    def size(self):
+        return len(self.items)
+
+    def insert(self, i, x):
+        return self.items.insert(i, x)
+
+
+def to_batch_tables(tables, B, table_type):
+    # col_lens = []
+    col_seq = []
+    ts = [
+        tables["table_names"],
+        tables["column_names"],
+        tables["column_types"]]
+    tname_toks = [x.split(" ") for x in ts[0]]
+    col_type = ts[2]
+    cols = [x.split(" ") for xid, x in ts[1]]
+    tab_seq = [xid for xid, x in ts[1]]
+    cols_add = []
+    for tid, col, ct in zip(tab_seq, cols, col_type):
+        col_one = [ct]
+        if tid == -1:
+            tabn = ["all"]
+        else:
+            if table_type == "no":
+                tabn = []
+            else:
+                tabn = tname_toks[tid]
+        for t in tabn:
+            if t not in col:
+                col_one.append(t)
+        col_one.extend(col)
+        cols_add.append(col_one)
+
+    col_seq = [cols_add] * B
+
+    return col_seq
+
+
+class SuperModel(nn.Module):
+    def __init__(
+            self,
+            word_emb,
+            col_emb,
+            N_word,
+            N_col,
+            N_h,
+            N_depth,
+            dropout,
+            gpu=True,
+            trainable_emb=False,
+            table_type="std",
+            use_hs=True):
+        super(SuperModel, self).__init__()
+        self.gpu = gpu
+        self.N_h = N_h
+        self.N_depth = N_depth
+        self.dropout = dropout
+        self.trainable_emb = trainable_emb
+        self.table_type = table_type
+        self.use_hs = use_hs
+        self.SQL_TOK = [
+            '<UNK>',
+            '<END>',
+            'WHERE',
+            'AND',
+            'EQL',
+            'GT',
+            'LT',
+            '<BEG>']
+
+        # word embedding layer
+        self.embed_layer = WordEmbedding(word_emb, N_word, gpu,
+                                         self.SQL_TOK, trainable=trainable_emb)
+        self.q_embed_layer = self.embed_layer
+
+        if not col_emb:
+            N_col = None
+        else:
+            self.embed_layer = WordEmbedding(
+                col_emb, N_col, gpu, self.SQL_TOK, trainable=trainable_emb)
+
+        # initial all modules
+        self.multi_sql = MultiSqlPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.multi_sql.eval()
+
+        self.key_word = KeyWordPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.key_word.eval()
+
+        self.col = ColPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.col.eval()
+
+        self.op = OpPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.op.eval()
+
+        self.agg = AggPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.agg.eval()
+
+        self.root_teminal = RootTeminalPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.root_teminal.eval()
+
+        self.des_asc = DesAscLimitPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.des_asc.eval()
+
+        self.having = HavingPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.having.eval()
+
+        self.andor = AndOrPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=gpu,
+            use_hs=use_hs)
+        self.andor.eval()
+
+        self.softmax = nn.Softmax(dim=1)  # dim=1
+        self.CE = nn.CrossEntropyLoss()
+        self.log_softmax = nn.LogSoftmax()
+        self.mlsml = nn.MultiLabelSoftMarginLoss()
+        self.bce_logit = nn.BCEWithLogitsLoss()
+        self.sigm = nn.Sigmoid()
+        if gpu:
+            self.cuda()
+        self.path_not_found = 0
+
+    def forward(self, q_seq, history, tables):
+        # if self.part:
+        #     return self.part_forward(q_seq,history,tables)
+        # else:
+        return self.full_forward(q_seq, history, tables)
+
+    def full_forward(self, q_seq, history, tables):
+        B = len(q_seq)
+        # print("q_seq:{}".format(q_seq))
+        # print("Batch size:{}".format(B))
+        q_emb_var, q_len = self.q_embed_layer.gen_x_q_batch(q_seq)
+        col_seq = to_batch_tables(tables, B, self.table_type)
+
+        col_emb_var, col_name_len, col_len = self.embed_layer.gen_col_batch(
+            col_seq)
+
+        mkw_emb_var = self.embed_layer.gen_word_list_embedding(
+            ["none", "except", "intersect", "union"], (B))
+        mkw_len = np.full(q_len.shape, 4, dtype=np.int64)
+        kw_emb_var = self.embed_layer.gen_word_list_embedding(
+            ["where", "group by", "order by"], (B))
+        kw_len = np.full(q_len.shape, 3, dtype=np.int64)
+
+        stack = Stack()
+        stack.push(("root", None))
+        history = [["root"]] * B
+        andor_cond = ""
+        has_limit = False
+        # sql = {}
+        current_sql = {}
+        sql_stack = []
+        idx_stack = []
+        kw_stack = []
+        kw = ""
+        nested_label = ""
+        has_having = False
+
+        timeout = time.time() + 2  # set timer to prevent infinite recursion in SQL generation
+        failed = False
+        while not stack.isEmpty():
+            if time.time() > timeout:
+                failed = True
+                break
+            vet = stack.pop()
+            # print(vet)
+            hs_emb_var, hs_len = self.embed_layer.gen_x_history_batch(history)
+            if len(idx_stack) > 0 and stack.size() < idx_stack[-1]:
+                # print("pop!!!!!!!!!!!!!!!!!!!!!!")
+                idx_stack.pop()
+                current_sql = sql_stack.pop()
+                kw = kw_stack.pop()
+                # current_sql = current_sql["sql"]
+            # history.append(vet)
+            # print("hs_emb:{} hs_len:{}".format(hs_emb_var.size(),hs_len.size()))
+            if isinstance(vet, tuple) and vet[0] == "root":
+                if history[0][-1] != "root":
+                    history[0].append("root")
+                    hs_emb_var, hs_len = self.embed_layer.gen_x_history_batch(
+                        history)
+                if vet[1] != "original":
+                    idx_stack.append(stack.size())
+                    sql_stack.append(current_sql)
+                    kw_stack.append(kw)
+                else:
+                    idx_stack.append(stack.size())
+                    sql_stack.append(sql_stack[-1])
+                    kw_stack.append(kw)
+                if "sql" in current_sql:
+                    current_sql["nested_sql"] = {}
+                    current_sql["nested_label"] = nested_label
+                    current_sql = current_sql["nested_sql"]
+                elif isinstance(vet[1], dict):
+                    vet[1]["sql"] = {}
+                    current_sql = vet[1]["sql"]
+                elif vet[1] != "original":
+                    current_sql["sql"] = {}
+                    current_sql = current_sql["sql"]
+                # print("q_emb_var:{} hs_emb_var:{} mkw_emb_var:{}".format(q_emb_var.size(),hs_emb_var.size(),mkw_emb_var.size()))
+                if vet[1] == "nested" or vet[1] == "original":
+                    stack.push("none")
+                    history[0].append("none")
+                else:
+                    score = self.multi_sql.forward(
+                        q_emb_var, q_len, hs_emb_var, hs_len, mkw_emb_var, mkw_len)
+                    label = np.argmax(score[0].data.cpu().numpy())
+                    label = SQL_OPS[label]
+                    history[0].append(label)
+                    stack.push(label)
+                if label != "none":
+                    nested_label = label
+
+            elif vet in ('intersect', 'except', 'union'):
+                stack.push(("root", "nested"))
+                stack.push(("root", "original"))
+                # history[0].append("root")
+            elif vet == "none":
+                score = self.key_word.forward(
+                    q_emb_var, q_len, hs_emb_var, hs_len, kw_emb_var, kw_len)
+                kw_num_score, kw_score = [x.data.cpu().numpy() for x in score]
+                # print("kw_num_score:{}".format(kw_num_score))
+                # print("kw_score:{}".format(kw_score))
+                num_kw = np.argmax(kw_num_score[0])
+                kw_score = list(np.argsort(-kw_score[0])[:num_kw])
+                kw_score.sort(reverse=True)
+                # print("num_kw:{}".format(num_kw))
+                for kw in kw_score:
+                    stack.push(KW_OPS[kw])
+                stack.push("select")
+            elif vet in ("select", "orderBy", "where", "groupBy", "having"):
+                kw = vet
+                current_sql[kw] = []
+                history[0].append(vet)
+                stack.push(("col", vet))
+                # score = self.andor.forward(q_emb_var,q_len,hs_emb_var,hs_len)
+                # label = score[0].data.cpu().numpy()
+                # andor_cond = COND_OPS[label]
+                # history.append("")
+            # elif vet == "groupBy":
+            #     score = self.having.forward(q_emb_var,q_len,hs_emb_var,hs_len,col_emb_var,col_len,)
+            elif isinstance(vet, tuple) and vet[0] == "col":
+                # print("q_emb_var:{} hs_emb_var:{} col_emb_var:{}".format(q_emb_var.size(), hs_emb_var.size(),col_emb_var.size()))
+                score = self.col.forward(
+                    q_emb_var,
+                    q_len,
+                    hs_emb_var,
+                    hs_len,
+                    col_emb_var,
+                    col_len,
+                    col_name_len)
+                col_num_score, col_score = [
+                    x.data.cpu().numpy() for x in score]
+                col_num = np.argmax(col_num_score[0]) + 1  # double check
+                cols = np.argsort(-col_score[0])[:col_num]
+                # print(col_num)
+                # print("col_num_score:{}".format(col_num_score))
+                # print("col_score:{}".format(col_score))
+                for col in cols:
+                    if vet[1] == "where":
+                        stack.push(("op", "where", col))
+                    elif vet[1] != "groupBy":
+                        stack.push(("agg", vet[1], col))
+                    elif vet[1] == "groupBy":
+                        history[0].append(index_to_column_name(col, tables))
+                        current_sql[kw].append(
+                            index_to_column_name(col, tables))
+                # predict and or or when there is multi col in where condition
+                if col_num > 1 and vet[1] == "where":
+                    score = self.andor.forward(
+                        q_emb_var, q_len, hs_emb_var, hs_len)
+                    label = np.argmax(score[0].data.cpu().numpy())
+                    andor_cond = COND_OPS[label]
+                    current_sql[kw].append(andor_cond)
+                if vet[1] == "groupBy" and col_num > 0:
+                    score = self.having.forward(
+                        q_emb_var,
+                        q_len,
+                        hs_emb_var,
+                        hs_len,
+                        col_emb_var,
+                        col_len,
+                        col_name_len,
+                        np.full(
+                            B,
+                            cols[0],
+                            dtype=np.int64))
+                    label = np.argmax(score[0].data.cpu().numpy())
+                    if label == 1:
+                        has_having = (label == 1)
+                        # stack.insert(-col_num,"having")
+                        stack.push("having")
+                # history.append(index_to_column_name(cols[-1], tables[0]))
+            elif isinstance(vet, tuple) and vet[0] == "agg":
+                history[0].append(index_to_column_name(vet[2], tables))
+                if vet[1] not in ("having", "orderBy"):  # DEBUG-ed 20180817
+                    try:
+                        current_sql[kw].append(
+                            index_to_column_name(vet[2], tables))
+                    except Exception as e:
+                        # print(e)
+                        traceback.print_exc()
+                        print(
+                            "history:{},current_sql:{} stack:{}".format(
+                                history[0], current_sql, stack.items))
+                        print("idx_stack:{}".format(idx_stack))
+                        print("sql_stack:{}".format(sql_stack))
+                        exit(1)
+                hs_emb_var, hs_len = self.embed_layer.gen_x_history_batch(
+                    history)
+
+                score = self.agg.forward(
+                    q_emb_var,
+                    q_len,
+                    hs_emb_var,
+                    hs_len,
+                    col_emb_var,
+                    col_len,
+                    col_name_len,
+                    np.full(
+                        B,
+                        vet[2],
+                        dtype=np.int64))
+                agg_num_score, agg_score = [
+                    x.data.cpu().numpy() for x in score]
+                agg_num = np.argmax(agg_num_score[0])  # double check
+                agg_idxs = np.argsort(-agg_score[0])[:agg_num]
+                # print("agg:{}".format([AGG_OPS[agg] for agg in agg_idxs]))
+                if len(agg_idxs) > 0:
+                    history[0].append(AGG_OPS[agg_idxs[0]])
+                    if vet[1] not in ("having", "orderBy"):
+                        current_sql[kw].append(AGG_OPS[agg_idxs[0]])
+                    elif vet[1] == "orderBy":
+                        # DEBUG-ed 20180817
+                        stack.push(("des_asc", vet[2], AGG_OPS[agg_idxs[0]]))
+                    else:
+                        stack.push(
+                            ("op", "having", vet[2], AGG_OPS[agg_idxs[0]]))
+                for agg in agg_idxs[1:]:
+                    history[0].append(index_to_column_name(vet[2], tables))
+                    history[0].append(AGG_OPS[agg])
+                    if vet[1] not in ("having", "orderBy"):
+                        current_sql[kw].append(
+                            index_to_column_name(vet[2], tables))
+                        current_sql[kw].append(AGG_OPS[agg])
+                    elif vet[1] == "orderBy":
+                        stack.push(("des_asc", vet[2], AGG_OPS[agg]))
+                    else:
+                        stack.push(("op", "having", vet[2], agg_idxs))
+                if len(agg_idxs) == 0:
+                    if vet[1] not in ("having", "orderBy"):
+                        current_sql[kw].append("none_agg")
+                    elif vet[1] == "orderBy":
+                        stack.push(("des_asc", vet[2], "none_agg"))
+                    else:
+                        stack.push(("op", "having", vet[2], "none_agg"))
+                # current_sql[kw].append([AGG_OPS[agg] for agg in agg_idxs])
+                # if vet[1] == "having":
+                #     stack.push(("op","having",vet[2],agg_idxs))
+                # if vet[1] == "orderBy":
+                #     stack.push(("des_asc",vet[2],agg_idxs))
+                # if vet[1] == "groupBy" and has_having:
+                #     stack.push("having")
+            elif isinstance(vet, tuple) and vet[0] == "op":
+                if vet[1] == "where":
+                    # current_sql[kw].append(index_to_column_name(vet[2], tables))
+                    history[0].append(index_to_column_name(vet[2], tables))
+                    hs_emb_var, hs_len = self.embed_layer.gen_x_history_batch(
+                        history)
+
+                score = self.op.forward(
+                    q_emb_var,
+                    q_len,
+                    hs_emb_var,
+                    hs_len,
+                    col_emb_var,
+                    col_len,
+                    col_name_len,
+                    np.full(
+                        B,
+                        vet[2],
+                        dtype=np.int64))
+
+                op_num_score, op_score = [x.data.cpu().numpy() for x in score]
+                # num_score 0 maps to 1 in truth, must have at least one op
+                op_num = np.argmax(op_num_score[0]) + 1
+                ops = np.argsort(-op_score[0])[:op_num]
+                # current_sql[kw].append([NEW_WHERE_OPS[op] for op in ops])
+                if op_num > 0:
+                    history[0].append(NEW_WHERE_OPS[ops[0]])
+                    if vet[1] == "having":
+                        stack.push(("root_teminal", vet[2], vet[3], ops[0]))
+                    else:
+                        stack.push(("root_teminal", vet[2], ops[0]))
+                    # current_sql[kw].append(NEW_WHERE_OPS[ops[0]])
+                for op in ops[1:]:
+                    history[0].append(index_to_column_name(vet[2], tables))
+                    history[0].append(NEW_WHERE_OPS[op])
+                    # current_sql[kw].append(index_to_column_name(vet[2], tables))
+                    # current_sql[kw].append(NEW_WHERE_OPS[op])
+                    if vet[1] == "having":
+                        stack.push(("root_teminal", vet[2], vet[3], op))
+                    else:
+                        stack.push(("root_teminal", vet[2], op))
+                # stack.push(("root_teminal",vet[2]))
+            elif isinstance(vet, tuple) and vet[0] == "root_teminal":
+                score = self.root_teminal.forward(
+                    q_emb_var,
+                    q_len,
+                    hs_emb_var,
+                    hs_len,
+                    col_emb_var,
+                    col_len,
+                    col_name_len,
+                    np.full(
+                        B,
+                        vet[1],
+                        dtype=np.int64))
+
+                label = np.argmax(score[0].data.cpu().numpy())
+                label = ROOT_TERM_OPS[label]
+                if len(vet) == 4:
+                    current_sql[kw].append(
+                        index_to_column_name(vet[1], tables))
+                    current_sql[kw].append(vet[2])
+                    current_sql[kw].append(NEW_WHERE_OPS[vet[3]])
+                else:
+                    # print("kw:{}".format(kw))
+                    try:
+                        current_sql[kw].append(
+                            index_to_column_name(vet[1], tables))
+                    except Exception as e:
+                        # print(e)
+                        traceback.print_exc()
+                        print(
+                            "history:{},current_sql:{} stack:{}".format(
+                                history[0], current_sql, stack.items))
+                        print("idx_stack:{}".format(idx_stack))
+                        print("sql_stack:{}".format(sql_stack))
+                        exit(1)
+                    current_sql[kw].append(NEW_WHERE_OPS[vet[2]])
+                if label == "root":
+                    history[0].append("root")
+                    current_sql[kw].append({})
+                    # current_sql = current_sql[kw][-1]
+                    stack.push(("root", current_sql[kw][-1]))
+                else:
+                    current_sql[kw].append("terminal")
+            elif isinstance(vet, tuple) and vet[0] == "des_asc":
+                current_sql[kw].append(index_to_column_name(vet[1], tables))
+                current_sql[kw].append(vet[2])
+                score = self.des_asc.forward(
+                    q_emb_var,
+                    q_len,
+                    hs_emb_var,
+                    hs_len,
+                    col_emb_var,
+                    col_len,
+                    col_name_len,
+                    np.full(
+                        B,
+                        vet[1],
+                        dtype=np.int64))
+                label = np.argmax(score[0].data.cpu().numpy())
+                dec_asc, has_limit = DEC_ASC_OPS[label]
+                history[0].append(dec_asc)
+                current_sql[kw].append(dec_asc)
+                current_sql[kw].append(has_limit)
+        # print("{}".format(current_sql))
+
+        if failed:
+            return None
+        print("history:{}".format(history[0]))
+        if len(sql_stack) > 0:
+            current_sql = sql_stack[0]
+        # print("{}".format(current_sql))
+        return current_sql
+
+    def gen_col(self, col, table, table_alias_dict):
+        colname = table["column_names_original"][col[2]][1]
+        table_idx = table["column_names_original"][col[2]][0]
+        if table_idx not in table_alias_dict:
+            return colname
+        return "T{}.{}".format(table_alias_dict[table_idx], colname)
+
+    def gen_group_by(self, sql, kw, table, table_alias_dict):
+        ret = []
+        for i in range(0, len(sql)):
+            # if len(sql[i+1]) == 0:
+            # if sql[i+1] == "none_agg":
+            ret.append(self.gen_col(sql[i], table, table_alias_dict))
+            # else:
+            #     ret.append("{}({})".format(sql[i+1], self.gen_col(sql[i], table, table_alias_dict)))
+            # for agg in sql[i+1]:
+            #     ret.append("{}({})".format(agg,gen_col(sql[i],table,table_alias_dict)))
+        return "{} {}".format(kw, ",".join(ret))
+
+    def gen_select(self, sql, kw, table, table_alias_dict):
+        ret = []
+        for i in range(0, len(sql), 2):
+            # if len(sql[i+1]) == 0:
+            if sql[i +
+                   1] == "none_agg" or not isinstance(sql[i +
+                                                          1], str):  # DEBUG-ed 20180817
+                ret.append(self.gen_col(sql[i], table, table_alias_dict))
+            else:
+                ret.append("{}({})".format(
+                    sql[i + 1], self.gen_col(sql[i], table, table_alias_dict)))
+            # for agg in sql[i+1]:
+            #     ret.append("{}({})".format(agg,gen_col(sql[i],table,table_alias_dict)))
+        return "{} {}".format(kw, ",".join(ret))
+
+    def gen_where(self, sql, table, table_alias_dict):
+        if len(sql) == 0:
+            return ""
+        start_idx = 0
+        andor = "and"
+        if isinstance(sql[0], str):
+            start_idx += 1
+            andor = sql[0]
+        ret = []
+        for i in range(start_idx, len(sql), 3):
+            # rewrite to stop a bug
+            if i + 2 < len(sql):
+                col = self.gen_col(sql[i], table, table_alias_dict)
+                op = sql[i + 1]
+                val = sql[i + 2]
+                where_item = ""
+            else:
+                break
+            if val == "terminal":
+                where_item = "{} {} '{}'".format(col, op, val)
+            else:
+                val = self.gen_sql(val, table)
+                where_item = "{} {} ({})".format(col, op, val)
+            if op == "between":
+                # TODO temprarily fixed
+                where_item += " and 'terminal'"
+            ret.append(where_item)
+        return "where {}".format(" {} ".format(andor).join(ret))
+
+    def gen_orderby(self, sql, table, table_alias_dict):
+        ret = []
+        limit = ""
+        if sql[-1]:
+            limit = "limit 1"
+        for i in range(0, len(sql), 4):
+            if sql[i +
+                   1] == "none_agg" or not isinstance(sql[i +
+                                                          1], str):  # DEBUG-ed 20180817
+                ret.append("{} {}".format(self.gen_col(
+                    sql[i], table, table_alias_dict), sql[i + 2]))
+            else:
+                ret.append("{}({}) {}".format(
+                    sql[i + 1], self.gen_col(sql[i], table, table_alias_dict), sql[i + 2]))
+        return "order by {} {}".format(",".join(ret), limit)
+
+    def gen_having(self, sql, table, table_alias_dict):
+        ret = []
+        for i in range(0, len(sql), 4):
+            if sql[i + 1] == "none_agg":
+                col = self.gen_col(sql[i], table, table_alias_dict)
+            else:
+                col = "{}({})".format(
+                    sql[i + 1], self.gen_col(sql[i], table, table_alias_dict))
+            op = sql[i + 2]
+            val = sql[i + 3]
+            if val == "terminal":
+                ret.append("{} {} '{}'".format(col, op, val))
+            else:
+                val = self.gen_sql(val, table)
+                ret.append("{} {} ({})".format(col, op, val))
+        return "having {}".format(",".join(ret))
+
+    def find_shortest_path(self, start, end, graph):
+        stack = [[start, []]]
+        visited = set()
+        while len(stack) > 0:
+            ele, history = stack.pop()
+            if ele == end:
+                return history
+            for node in graph[ele]:
+                if node[0] not in visited:
+                    stack.append((node[0], history + [(node[0], node[1])]))
+                    visited.add(node[0])
+        print("table {} table {}".format(start, end))
+        # print("could not find path!!!!!{}".format(self.path_not_found))
+        self.path_not_found += 1
+        # return []
+
+    def gen_from(self, candidate_tables, table):
+        def find(d, col):
+            if d[col] == -1:
+                return col
+            return find(d, d[col])
+
+        def union(d, c1, c2):
+            r1 = find(d, c1)
+            r2 = find(d, c2)
+            if r1 == r2:
+                return
+            d[r1] = r2
+
+        ret = ""
+        if len(candidate_tables) <= 1:
+            if len(candidate_tables) == 1:
+                ret = "from {}".format(
+                    table["table_names_original"][list(candidate_tables)[0]])
+            else:
+                ret = "from {}".format(table["table_names_original"][0])
+            # TODO: temporarily settings
+            return {}, ret
+        # print("candidate:{}".format(candidate_tables))
+        table_alias_dict = {}
+        uf_dict = {}
+        for t in candidate_tables:
+            uf_dict[t] = -1
+        idx = 1
+        graph = defaultdict(list)
+        for acol, bcol in table["foreign_keys"]:
+            t1 = table["column_names"][acol][0]
+            t2 = table["column_names"][bcol][0]
+            graph[t1].append((t2, (acol, bcol)))
+            graph[t2].append((t1, (bcol, acol)))
+            # if t1 in candidate_tables and t2 in candidate_tables:
+            #     r1 = find(uf_dict,t1)
+            #     r2 = find(uf_dict,t2)
+            #     if r1 == r2:
+            #         continue
+            #     union(uf_dict,t1,t2)
+            #     if len(ret) == 0:
+            #         ret = "from {} as T{} join {} as T{} on T{}.{}=T{}.{}".format(table["table_names"][t1],idx,table["table_names"][t2],
+            #                                                                       idx+1,idx,table["column_names_original"][acol][1],idx+1,
+            #                                                                       table["column_names_original"][bcol][1])
+            #         table_alias_dict[t1] = idx
+            #         table_alias_dict[t2] = idx+1
+            #         idx += 2
+            #     else:
+            #         if t1 in table_alias_dict:
+            #             old_t = t1
+            #             new_t = t2
+            #             acol,bcol = bcol,acol
+            #         elif t2 in table_alias_dict:
+            #             old_t = t2
+            #             new_t = t1
+            #         else:
+            #             ret = "{} join {} as T{} join {} as T{} on T{}.{}=T{}.{}".format(ret,table["table_names"][t1], idx,
+            #                                                                           table["table_names"][t2],
+            #                                                                           idx + 1, idx,
+            #                                                                           table["column_names_original"][acol][1],
+            #                                                                           idx + 1,
+            #                                                                           table["column_names_original"][bcol][1])
+            #             table_alias_dict[t1] = idx
+            #             table_alias_dict[t2] = idx + 1
+            #             idx += 2
+            #             continue
+            #         ret = "{} join {} as T{} on T{}.{}=T{}.{}".format(ret,new_t,idx,idx,table["column_names_original"][acol][1],
+            #                                                        table_alias_dict[old_t],table["column_names_original"][bcol][1])
+            #         table_alias_dict[new_t] = idx
+            #         idx += 1
+        # visited = set()
+        candidate_tables = list(candidate_tables)
+        start = candidate_tables[0]
+        table_alias_dict[start] = idx
+        idx += 1
+        ret = "from {} as T1".format(table["table_names_original"][start])
+        try:
+            for end in candidate_tables[1:]:
+                if end in table_alias_dict:
+                    continue
+                path = self.find_shortest_path(start, end, graph)
+                prev_table = start
+                if not path:
+                    table_alias_dict[end] = idx
+                    idx += 1
+                    ret = "{} join {} as T{}".format(
+                        ret, table["table_names_original"][end], table_alias_dict[end], )
+                    continue
+                for node, (acol, bcol) in path:
+                    if node in table_alias_dict:
+                        prev_table = node
+                        continue
+                    table_alias_dict[node] = idx
+                    idx += 1
+                    ret = "{} join {} as T{} on T{}.{} = T{}.{}".format(
+                        ret,
+                        table["table_names_original"][node],
+                        table_alias_dict[node],
+                        table_alias_dict[prev_table],
+                        table["column_names_original"][acol][1],
+                        table_alias_dict[node],
+                        table["column_names_original"][bcol][1])
+                    prev_table = node
+        except BaseException:
+            traceback.print_exc()
+            print("db:{}".format(table["db_id"]))
+            # print(table["db_id"])
+            return table_alias_dict, ret
+        # if len(candidate_tables) != len(table_alias_dict):
+        #     print("error in generate from clause!!!!!")
+        return table_alias_dict, ret
+
+    def gen_sql(self, sql, table):
+        select_clause = ""
+        from_clause = ""
+        groupby_clause = ""
+        orderby_clause = ""
+        having_clause = ""
+        where_clause = ""
+        nested_clause = ""
+        cols = {}
+        candidate_tables = set()
+        nested_sql = {}
+        nested_label = ""
+        parent_sql = sql
+        # if "sql" in sql:
+        #     sql = sql["sql"]
+        if "nested_label" in sql:
+            nested_label = sql["nested_label"]
+            nested_sql = sql["nested_sql"]
+            sql = sql["sql"]
+        elif "sql" in sql:
+            sql = sql["sql"]
+        for key in sql:
+            if key not in KW_WITH_COL:
+                continue
+            for item in sql[key]:
+                if isinstance(item, tuple) and len(item) == 3:
+                    if table["column_names"][item[2]][0] != -1:
+                        candidate_tables.add(table["column_names"][item[2]][0])
+        table_alias_dict, from_clause = self.gen_from(candidate_tables, table)
+        ret = []
+        if "select" in sql:
+            select_clause = self.gen_select(
+                sql["select"], "select", table, table_alias_dict)
+            if len(select_clause) > 0:
+                ret.append(select_clause)
+            else:
+                print("select not found:{}".format(parent_sql))
+        else:
+            print("select not found:{}".format(parent_sql))
+        if len(from_clause) > 0:
+            ret.append(from_clause)
+        if "where" in sql:
+            where_clause = self.gen_where(
+                sql["where"], table, table_alias_dict)
+            if len(where_clause) > 0:
+                ret.append(where_clause)
+        if "groupBy" in sql:  # DEBUG-ed order
+            groupby_clause = self.gen_group_by(
+                sql["groupBy"], "group by", table, table_alias_dict)
+            if len(groupby_clause) > 0:
+                ret.append(groupby_clause)
+        if "orderBy" in sql:
+            orderby_clause = self.gen_orderby(
+                sql["orderBy"], table, table_alias_dict)
+            if len(orderby_clause) > 0:
+                ret.append(orderby_clause)
+        if "having" in sql:
+            having_clause = self.gen_having(
+                sql["having"], table, table_alias_dict)
+            if len(having_clause) > 0:
+                ret.append(having_clause)
+        if len(nested_label) > 0:
+            nested_clause = "{} {}".format(
+                nested_label, self.gen_sql(
+                    nested_sql, table))
+            if len(nested_clause) > 0:
+                ret.append(nested_clause)
+        return " ".join(ret)
+
+    def check_acc(self, pred_sql, gt_sql):
+        pass
diff --git a/code/preprocess_data.py b/code/preprocess_data.py
new file mode 100644
index 0000000..8dbd9da
--- /dev/null
+++ b/code/preprocess_data.py
@@ -0,0 +1,789 @@
+# _*_ coding: utf_8 _*
+
+import argparse
+import codecs
+import json
+import os
+import sys
+from collections import defaultdict
+
+defaultencoding = 'utf-8'
+if sys.getdefaultencoding() != defaultencoding:
+    reload(sys)
+    sys.setdefaultencoding(defaultencoding)
+
+
+OLD_WHERE_OPS = (
+    'not',
+    'between',
+    '=',
+    '>',
+    '<',
+    '>=',
+    '<=',
+    '!=',
+    'in',
+    'like',
+    'is',
+    'exists')
+NEW_WHERE_OPS = (
+    '=',
+    '>',
+    '<',
+    '>=',
+    '<=',
+    '!=',
+    'like',
+    'not in',
+    'in',
+    'between',
+    'is')
+NEW_WHERE_DICT = {
+    '=': 0,
+    '>': 1,
+    '<': 2,
+    '>=': 3,
+    '<=': 4,
+    '!=': 5,
+    'like': 6,
+    'not in': 7,
+    'in': 8,
+    'between': 9,
+    'is': 10
+}
+# SQL_OPS = ('none','intersect', 'union', 'except')
+SQL_OPS = {
+    'none': 0,
+    'intersect': 1,
+    'union': 2,
+    'except': 3
+}
+KW_DICT = {
+    'where': 0,
+    'groupBy': 1,
+    'orderBy': 2
+}
+ORDER_OPS = {
+    'desc': 0,
+    'asc': 1}
+AGG_OPS = ('none', 'max', 'min', 'count', 'sum', 'avg')
+
+COND_OPS = {
+    'and': 0,
+    'or': 1
+}
+
+
+def convert_to_op_index(is_not, op):
+    op = OLD_WHERE_OPS[op]
+    if is_not and op == "in":
+        return 7
+    try:
+        return NEW_WHERE_DICT[op]
+    except BaseException:
+        print("Unsupport op: {}".format(op))
+        return -1
+
+
+def index_to_column_name(index, table):
+    column_name = table["column_names"][index][1]
+    table_index = table["column_names"][index][0]
+    table_name = table["table_names"][table_index]
+    return table_name, column_name, index
+
+
+def get_label_cols(with_join, fk_dict, labels):
+    # list(set([l[1][i][0][2] for i in range(min(len(l[1]), 3))]))
+    cols = set()
+    ret = []
+    for i in range(len(labels)):
+        cols.add(labels[i][0][2])
+        if len(cols) > 3:
+            break
+    for col in cols:
+        # ret.append([col])
+        if with_join and len(fk_dict[col]) > 0:
+            ret.append([col] + fk_dict[col])
+        else:
+            ret.append(col)
+    return ret
+
+
+class MultiSqlPredictor:
+    def __init__(self, question, sql, history):
+        self.sql = sql
+        self.question = question
+        self.history = history
+        self.keywords = ('intersect', 'except', 'union')
+
+    def generate_output(self):
+        for key in self.sql:
+            if key in self.keywords and self.sql[key]:
+                return self.history + ['root'], key, self.sql[key]
+        return self.history + ['root'], 'none', self.sql
+
+
+class KeyWordPredictor:
+    def __init__(self, question, sql, history):
+        self.sql = sql
+        self.question = question
+        self.history = history
+        self.keywords = (
+            'select',
+            'where',
+            'groupBy',
+            'orderBy',
+            'limit',
+            'having')
+
+    def generate_output(self):
+        sql_keywords = []
+        for key in self.sql:
+            if key in self.keywords and self.sql[key]:
+                sql_keywords.append(key)
+        return self.history, [len(sql_keywords), sql_keywords], self.sql
+
+
+class ColPredictor:
+    def __init__(self, question, sql, table, history, kw=None):
+        self.sql = sql
+        self.question = question
+        self.history = history
+        self.table = table
+        self.keywords = ('select', 'where', 'groupBy', 'orderBy', 'having')
+        self.kw = kw
+
+    def generate_output(self):
+        ret = []
+        candidate_keys = self.sql.keys()
+        if self.kw:
+            candidate_keys = [self.kw]
+        for key in candidate_keys:
+            if key in self.keywords and self.sql[key]:
+                cols = []
+                sqls = []
+                if key == 'groupBy':
+                    sql_cols = self.sql[key]
+                    for col in sql_cols:
+                        cols.append(
+                            (index_to_column_name(
+                                col[1], self.table), col[2]))
+                        sqls.append(col)
+                elif key == 'orderBy':
+                    sql_cols = self.sql[key][1]
+                    for col in sql_cols:
+                        cols.append(
+                            (index_to_column_name(
+                                col[1][1], self.table), col[1][2]))
+                        sqls.append(col)
+                elif key == 'select':
+                    sql_cols = self.sql[key][1]
+                    for col in sql_cols:
+                        cols.append(
+                            (index_to_column_name(
+                                col[1][1][1],
+                                self.table),
+                                col[1][1][2]))
+                        sqls.append(col)
+                elif key == 'where' or key == 'having':
+                    sql_cols = self.sql[key]
+                    for col in sql_cols:
+                        if not isinstance(col, list):
+                            continue
+                        try:
+                            cols.append(
+                                (index_to_column_name(
+                                    col[2][1][1],
+                                    self.table),
+                                    col[2][1][2]))
+                        except BaseException:
+                            print(
+                                "Key:{} Col:{} Question:{}".format(
+                                    key, col, self.question))
+                        sqls.append(col)
+                ret.append((
+                    self.history + [key], (len(cols), cols), sqls
+                ))
+        return ret
+        # ret.append(history+[key],)
+
+
+class OpPredictor:
+    def __init__(self, question, sql, history):
+        self.sql = sql
+        self.question = question
+        self.history = history
+        # self.keywords = ('select', 'where', 'groupBy', 'orderBy', 'having')
+
+    def generate_output(self):
+        return self.history, convert_to_op_index(
+            self.sql[0], self.sql[1]), (self.sql[3], self.sql[4])
+
+
+class AggPredictor:
+    def __init__(self, question, sql, history, kw=None):
+        self.sql = sql
+        self.question = question
+        self.history = history
+        self.kw = kw
+
+    def generate_output(self):
+        label = -1
+        if self.kw:
+            key = self.kw
+        else:
+            key = self.history[-2]
+        if key == 'select':
+            label = self.sql[0]
+        elif key == 'orderBy':
+            label = self.sql[1][0]
+        elif key == 'having':
+            label = self.sql[2][1][0]
+        return self.history, label
+
+
+# class RootTemPredictor:
+#     def __init__(self, question, sql):
+#         self.sql = sql
+#         self.question = question
+#         self.keywords = ('intersect', 'except', 'union')
+#
+#     def generate_output(self):
+#         for key in self.sql:
+#             if key in self.keywords:
+#                 return ['ROOT'], key, self.sql[key]
+#         return ['ROOT'], 'none', self.sql
+
+
+class DesAscPredictor:
+    def __init__(self, question, sql, table, history):
+        self.sql = sql
+        self.question = question
+        self.history = history
+        self.table = table
+
+    def generate_output(self):
+        for key in self.sql:
+            if key == "orderBy" and self.sql[key]:
+                # self.history.append(key)
+                try:
+                    col = self.sql[key][1][0][1][1]
+                except BaseException:
+                    print("question:{} sql:{}".format(self.question, self.sql))
+                # self.history.append(index_to_column_name(col, self.table))
+                # self.history.append(self.sql[key][1][0][1][0])
+                if self.sql[key][0] == "asc" and self.sql["limit"]:
+                    label = 0
+                elif self.sql[key][0] == "asc" and not self.sql["limit"]:
+                    label = 1
+                elif self.sql[key][0] == "desc" and self.sql["limit"]:
+                    label = 2
+                else:
+                    label = 3
+                return self.history + \
+                    [index_to_column_name(col, self.table), self.sql[key][1][0][1][0]], label
+
+
+class AndOrPredictor:
+    def __init__(self, question, sql, table, history):
+        self.sql = sql
+        self.question = question
+        self.history = history
+        self.table = table
+
+    def generate_output(self):
+        if 'where' in self.sql and self.sql['where'] and len(
+                self.sql['where']) > 1:
+            return self.history, COND_OPS[self.sql['where'][1]]
+        return self.history, -1
+
+
+def parser_item_with_long_history(
+        question_tokens,
+        sql,
+        table,
+        history,
+        dataset):
+    table_schema = [
+        table["table_names"],
+        table["column_names"],
+        table["column_types"]
+    ]
+    stack = [("root", sql)]
+    with_join = False
+    fk_dict = defaultdict(list)
+    for fk in table["foreign_keys"]:
+        fk_dict[fk[0]].append(fk[1])
+        fk_dict[fk[1]].append(fk[0])
+    while len(stack) > 0:
+        node = stack.pop()
+        if node[0] == "root":
+            history, label, sql = MultiSqlPredictor(
+                question_tokens, node[1], history).generate_output()
+            dataset['multi_sql_dataset'].append({
+                "question_tokens": question_tokens,
+                "ts": table_schema,
+                "history": history[:],
+                "label": SQL_OPS[label]
+            })
+            history.append(label)
+            if label == "none":
+                stack.append((label, sql))
+            else:
+                node[1][label] = None
+                stack.append((label, node[1], sql))
+            # if label != "none":
+                # stack.append(("none",node[1]))
+        elif node[0] in ('intersect', 'except', 'union'):
+            stack.append(("root", node[1]))
+            stack.append(("root", node[2]))
+        elif node[0] == "none":
+            with_join = len(node[1]["from"]["table_units"]) > 1
+            history, label, sql = KeyWordPredictor(
+                question_tokens, node[1], history).generate_output()
+            label_idxs = []
+            for item in label[1]:
+                if item in KW_DICT:
+                    label_idxs.append(KW_DICT[item])
+            label_idxs.sort()
+            dataset['keyword_dataset'].append({
+                "question_tokens": question_tokens,
+                "ts": table_schema,
+                "history": history[:],
+                "label": label_idxs
+            })
+            if "having" in label[1]:
+                stack.append(("having", node[1]))
+            if "orderBy" in label[1]:
+                stack.append(("orderBy", node[1]))
+            if "groupBy" in label[1]:
+                if "having" in label[1]:
+                    dataset['having_dataset'].append({
+                        "question_tokens": question_tokens,
+                        "ts": table_schema,
+                        "history": history[:],
+                        "gt_col": node[1]["groupBy"][0][1],
+                        "label": 1
+                    })
+                else:
+                    dataset['having_dataset'].append({
+                        "question_tokens": question_tokens,
+                        "ts": table_schema,
+                        "history": history[:],
+                        "gt_col": node[1]["groupBy"][0][1],
+                        "label": 0
+                    })
+                stack.append(("groupBy", node[1]))
+            if "where" in label[1]:
+                stack.append(("where", node[1]))
+            if "select" in label[1]:
+                stack.append(("select", node[1]))
+        elif node[0] in ("select", "having", "orderBy"):
+            # if node[0] != "orderBy":
+            history.append(node[0])
+            if node[0] == "orderBy":
+                orderby_ret = DesAscPredictor(
+                    question_tokens, node[1], table, history).generate_output()
+                if orderby_ret:
+                    dataset['des_asc_dataset'].append({
+                        "question_tokens": question_tokens,
+                        "ts": table_schema,
+                        "history": orderby_ret[0],
+                        "gt_col": node[1]["orderBy"][1][0][1][1],
+                        "label": orderby_ret[1]
+                    })
+                    # history.append(orderby_ret[1])
+            col_ret = ColPredictor(
+                question_tokens,
+                node[1],
+                table,
+                history,
+                node[0]).generate_output()
+            agg_col_dict = dict()
+            op_col_dict = dict()
+            for h, l, s in col_ret:
+                if l[0] == 0:
+                    print("Warning: predicted 0 columns!")
+                    continue
+                dataset['col_dataset'].append({
+                    "question_tokens": question_tokens,
+                    "ts": table_schema,
+                    "history": history[:],
+                    "label": get_label_cols(with_join, fk_dict, l[1])
+                })
+                for col, sql_item in zip(l[1], s):
+                    key = "{}{}{}".format(col[0][0], col[0][1], col[0][2])
+                    if key not in agg_col_dict:
+                        agg_col_dict[key] = [(sql_item, col[0])]
+                    else:
+                        agg_col_dict[key].append((sql_item, col[0]))
+                    if key not in op_col_dict:
+                        op_col_dict[key] = [(sql_item, col[0])]
+                    else:
+                        op_col_dict[key].append((sql_item, col[0]))
+                for key in agg_col_dict:
+                    stack.append(
+                        ("col", node[0], agg_col_dict[key], op_col_dict[key]))
+        elif node[0] == "col":
+            history.append(node[2][0][1])
+            if node[1] == "where":
+                stack.append(("op", node[2], "where"))
+            else:
+                labels = []
+                for sql_item, col in node[2]:
+                    _, label = AggPredictor(
+                        question_tokens, sql_item, history, node[1]).generate_output()
+                    if label - 1 >= 0:
+                        labels.append(label - 1)
+
+                # print(node[2][0][1][2])
+                dataset['agg_dataset'].append({
+                    "question_tokens": question_tokens,
+                    "ts": table_schema,
+                    "history": history[:],
+                    "gt_col": node[2][0][1][2],
+                    "label": labels[:min(len(labels), 3)]
+                })
+                if node[1] == "having":
+                    stack.append(("op", node[2], "having"))
+                # if len(labels) == 0:
+                #     history.append("none")
+                # else:
+                if len(labels) > 0:
+                    history.append(AGG_OPS[labels[0] + 1])
+        elif node[0] == "op":
+            # history.append(node[1][0][1])
+            labels = []
+            # if len(labels) > 2:
+            #     print(question_tokens)
+            dataset['op_dataset'].append({
+                "question_tokens": question_tokens,
+                "ts": table_schema,
+                "history": history[:],
+                "gt_col": node[1][0][1][2],
+                "label": labels
+            })
+
+            for sql_item, col in node[1]:
+                _, label, s = OpPredictor(
+                    question_tokens, sql_item, history).generate_output()
+                if label != -1:
+                    labels.append(label)
+                    history.append(NEW_WHERE_OPS[label])
+                if isinstance(s[0], dict):
+                    stack.append(("root", s[0]))
+                    # history.append("root")
+                    dataset['root_tem_dataset'].append({
+                        "question_tokens": question_tokens,
+                        "ts": table_schema,
+                        "history": history[:],
+                        "gt_col": node[1][0][1][2],
+                        "label": 0
+                    })
+                else:
+                    dataset['root_tem_dataset'].append({
+                        "question_tokens": question_tokens,
+                        "ts": table_schema,
+                        "history": history[:],
+                        "gt_col": node[1][0][1][2],
+                        "label": 1
+                    })
+                    # history.append("terminal")
+            if len(labels) > 2:
+                print(question_tokens)
+            dataset['op_dataset'][-1]["label"] = labels
+        elif node[0] == "where":
+            history.append(node[0])
+            hist, label = AndOrPredictor(
+                question_tokens, node[1], table, history).generate_output()
+            if label != -1:
+                dataset['andor_dataset'].append({
+                    "question_tokens": question_tokens,
+                    "ts": table_schema,
+                    "history": history[:],
+                    "label": label
+                })
+            col_ret = ColPredictor(
+                question_tokens,
+                node[1],
+                table,
+                history,
+                "where").generate_output()
+            op_col_dict = dict()
+            for h, l, s in col_ret:
+                if l[0] == 0:
+                    print("Warning: predicted 0 columns!")
+                    continue
+                dataset['col_dataset'].append({
+                    "question_tokens": question_tokens,
+                    "ts": table_schema,
+                    "history": history[:],
+                    "label": get_label_cols(with_join, fk_dict, l[1])
+                })
+                for col, sql_item in zip(l[1], s):
+                    key = "{}{}{}".format(col[0][0], col[0][1], col[0][2])
+                    if key not in op_col_dict:
+                        op_col_dict[key] = [(sql_item, col[0])]
+                    else:
+                        op_col_dict[key].append((sql_item, col[0]))
+                for key in op_col_dict:
+                    stack.append(("col", "where", op_col_dict[key]))
+        elif node[0] == "groupBy":
+            history.append(node[0])
+            col_ret = ColPredictor(
+                question_tokens,
+                node[1],
+                table,
+                history,
+                node[0]).generate_output()
+            agg_col_dict = dict()
+            for h, l, s in col_ret:
+                if l[0] == 0:
+                    print("Warning: predicted 0 columns!")
+                    continue
+                dataset['col_dataset'].append({
+                    "question_tokens": question_tokens,
+                    "ts": table_schema,
+                    "history": history[:],
+                    "label": get_label_cols(with_join, fk_dict, l[1])
+                })
+                for col, sql_item in zip(l[1], s):
+                    key = "{}{}{}".format(col[0][0], col[0][1], col[0][2])
+                    if key not in agg_col_dict:
+                        agg_col_dict[key] = [(sql_item, col[0])]
+                    else:
+                        agg_col_dict[key].append((sql_item, col[0]))
+                for key in agg_col_dict:
+                    stack.append(("col", node[0], agg_col_dict[key]))
+
+
+def parser_item(question_tokens, sql, table, history, dataset):
+    # try:
+    #     question_tokens = item['question_toks']
+    # except:
+    #     print(item)
+    # sql = item['sql']
+    table_schema = [
+        table["table_names"],
+        table["column_names"],
+        table["column_types"]
+    ]
+    history, label, sql = MultiSqlPredictor(
+        question_tokens, sql, history).generate_output()
+    dataset['multi_sql_dataset'].append({
+        "question_tokens": question_tokens,
+        "ts": table_schema,
+        "history": history[:],
+        "label": SQL_OPS[label]
+    })
+    history.append(label)
+    history, label, sql = KeyWordPredictor(
+        question_tokens, sql, history).generate_output()
+    label_idxs = []
+    for item in label[1]:
+        if item in KW_DICT:
+            label_idxs.append(KW_DICT[item])
+    label_idxs.sort()
+    dataset['keyword_dataset'].append({
+        "question_tokens": question_tokens,
+        "ts": table_schema,
+        "history": history[:],
+        "label": label_idxs
+    })
+    hist, label = AndOrPredictor(
+        question_tokens, sql, table, history).generate_output()
+    if label != -1:
+        dataset['andor_dataset'].append({
+            "question_tokens": question_tokens,
+            "ts": table_schema,
+            "history": hist[:] + ["where"],
+            "label": label
+        })
+    orderby_ret = DesAscPredictor(
+        question_tokens,
+        sql,
+        table,
+        history).generate_output()
+    if orderby_ret:
+        dataset['des_asc_dataset'].append({
+            "question_tokens": question_tokens,
+            "ts": table_schema,
+            "history": orderby_ret[0][:],
+            "label": orderby_ret[1]
+        })
+    col_ret = ColPredictor(
+        question_tokens,
+        sql,
+        table,
+        history).generate_output()
+    agg_candidates = []
+    op_candidates = []
+    for h, l, s in col_ret:
+        if l[0] == 0:
+            print("Warning: predicted 0 columns!")
+            continue
+        dataset['col_dataset'].append({
+            "question_tokens": question_tokens,
+            "ts": table_schema,
+            "history": h[:],
+            "label": list(set([l[1][i][0][2] for i in range(min(len(l[1]), 3))]))
+        })
+        for col, sql_item in zip(l[1], s):
+            if h[-1] in ('where', 'having'):
+                op_candidates.append((h + [col[0]], sql_item))
+            if h[-1] in ('select', 'orderBy', 'having'):
+                agg_candidates.append((h + [col[0]], sql_item))
+            if h[-1] == "groupBy":
+                label = 0
+                if sql["having"]:
+                    label = 1
+                dataset['having_dataset'].append({
+                    "question_tokens": question_tokens,
+                    "ts": table_schema,
+                    "history": h[:] + [col[0]],
+                    "label": label
+                })
+
+    op_col_dict = dict()
+    for h, sql_item in op_candidates:
+        _, label, s = OpPredictor(
+            question_tokens, sql_item, h).generate_output()
+        if label == -1:
+            continue
+        key = "{}{}".format(h[-2], h[-1][2])
+        label = NEW_WHERE_OPS[label]
+        if key in op_col_dict:
+            op_col_dict[key][1].append(label)
+        else:
+            op_col_dict[key] = [h[:], [label]]
+        # dataset['op_dataset'].append({
+        #     "question_tokens": question_tokens,
+        #     "ts": table_schema,
+        #     "history": h[:],
+        #     "label": label
+        # })
+        if isinstance(s[0], dict):
+            dataset['root_tem_dataset'].append({
+                "question_tokens": question_tokens,
+                "ts": table_schema,
+                "history": h[:] + [label],
+                "label": 0
+            })
+            parser_item(question_tokens, s[0], table, h[:] + [label], dataset)
+        else:
+            dataset['root_tem_dataset'].append({
+                "question_tokens": question_tokens,
+                "ts": table_schema,
+                "history": h[:] + [label],
+                "label": 1
+            })
+    for key in op_col_dict:
+        # if len(op_col_dict[key][1]) > 1:
+        #     print("same col has mult op ")
+        dataset['op_dataset'].append({
+            "question_tokens": question_tokens,
+            "ts": table_schema,
+            "history": op_col_dict[key][0],
+            "label": op_col_dict[key][1]
+        })
+    agg_col_dict = dict()
+    for h, sql_item in agg_candidates:
+        _, label = AggPredictor(question_tokens, sql_item, h).generate_output()
+        if label != 5:
+            key = "{}{}".format(h[-2], h[-1][2])
+            if key in agg_col_dict:
+                agg_col_dict[key][1].append(label)
+            else:
+                agg_col_dict[key] = [h[:], [label]]
+    for key in agg_col_dict:
+        # if 5 in agg_col_dict[key][1]:
+        #     print("none in agg label!!!")
+        dataset['agg_dataset'].append({
+            "question_tokens": question_tokens,
+            "ts": table_schema,
+            "history": agg_col_dict[key][0],
+            "label": agg_col_dict[key][1]
+        })
+
+
+def get_table_dict(table_data_path):
+    data = json.load(open(table_data_path))
+    table = dict()
+    for item in data:
+        table[item["db_id"]] = item
+    return table
+
+
+def parse_data(data, table_path, gen_data_path, history_option, part):
+    dataset = {
+        "multi_sql_dataset": [],
+        "keyword_dataset": [],
+        "col_dataset": [],
+        "op_dataset": [],
+        "agg_dataset": [],
+        "root_tem_dataset": [],
+        "des_asc_dataset": [],
+        "having_dataset": [],
+        "andor_dataset": []
+    }
+    table_dict = get_table_dict(table_path)
+    for item in data:
+        if history_option == "full":
+            # parser_item(item["question_toks"], item["sql"], table_dict[item["db_id"]], [], dataset)
+            parser_item_with_long_history(
+                item["question_toks"], item["sql"], table_dict[item["db_id"]], [], dataset)
+        else:
+            parser_item(item["question_toks"], item["sql"],
+                        table_dict[item["db_id"]], [], dataset)
+    print("\nfinished preprocess %s part" % (part))
+    for key in dataset:
+        print("dataset:{} size:{}".format(key, len(dataset[key])))
+        with open(os.path.join(gen_data_path,
+                    "{}_{}_{}.json".format(
+                        history_option,
+                        part,
+                        key)),"w", encoding='utf-8') as json_file:
+            json.dump(
+                dataset[key],
+                json_file,
+                indent=2,
+                ensure_ascii=False)
+        #print('done')
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '-s',
+        '--schema',
+        type=str,
+        default='char',
+        choices=[
+            'char',
+            'word'],
+        help='char for char-based schema and word for word-based schema.')
+
+    parser.add_argument(
+        '--history_option',
+        type=str,
+        default='full',
+        choices=[
+            'full',
+            'part',
+            'no'],
+        help='full, part, or no history')
+
+    args = parser.parse_args()
+
+    table_path = "./data/tables.json"
+
+    schema = args.schema
+    history_option = args.history_option
+    train_dev_test = ['train', 'dev']
+    for part in train_dev_test:
+        data_path = os.path.join('data', args.schema, part + '.json')
+        data = json.load(codecs.open(data_path, 'r', encoding='utf-8'))
+        gen_data_path = os.path.join('data', args.schema, 'generated_datasets')
+        print(gen_data_path)
+        if not os.path.exists(gen_data_path):
+            os.mkdir(gen_data_path)
+        parse_data(data, table_path, gen_data_path, history_option, part)
diff --git a/code/requirements.txt b/code/requirements.txt
new file mode 100644
index 0000000..6fa2de4
--- /dev/null
+++ b/code/requirements.txt
@@ -0,0 +1 @@
+nltk
\ No newline at end of file
diff --git a/code/test.py b/code/test.py
new file mode 100644
index 0000000..55893cd
--- /dev/null
+++ b/code/test.py
@@ -0,0 +1,102 @@
+import torch
+import argparse
+from utils.util import *
+from models.supermodel import SuperModel
+
+from config import global_config as cfg
+
+if __name__ == '__main__':
+    N_word = cfg.emb_size
+    N_col = cfg.col_emb_size
+    N_h = cfg.hidden_size
+    N_depth = cfg.num_layers
+    dropout = cfg.dropout
+    BATCH_SIZE = cfg.batch_size
+    learning_rate = cfg.learning_rate
+    train_emb = cfg.train_emb
+
+    history_type = cfg.history_type
+    table_type = cfg.table_type
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--models', type=str, help='path to saved model')
+    parser.add_argument('--test_data_path', type=str)
+    parser.add_argument('--output_path', type=str)
+    parser.add_argument(
+        '--emb_path',
+        type=str,
+        default='',
+        help='embedding path, multi-lingual or monolingual')
+    parser.add_argument(
+        '--col_emb_path',
+        type=str,
+        default='',
+        help='column embedding path')
+    parser.add_argument(
+        '--toy',
+        action='store_true',
+        help='If set, use small data; used for fast debugging.')
+    args = parser.parse_args()
+
+    use_hs = True
+    if history_type == "no":
+        use_hs = False
+
+    # default to use GPU, but have to check if GPU exists
+    GPU = True
+    if not cfg.nogpu:
+        if torch.cuda.device_count() == 0:
+            GPU = False
+
+    toy = args.toy
+    if toy:
+        USE_SMALL = True
+    else:
+        USE_SMALL = False
+
+    data = json.load(open(args.test_data_path))
+
+    emb_path = args.emb_path
+    word_emb = load_emb(emb_path, load_used=train_emb, use_small=USE_SMALL)
+    col_emb_path = args.col_emb_path
+    col_emb = None
+    if col_emb_path != 'None':
+        col_emb = load_emb(
+            col_emb_path,
+            load_used=train_emb,
+            use_small=USE_SMALL)
+    print("Finished load word embedding")
+
+    model = SuperModel(
+        word_emb,
+        col_emb,
+        N_word=N_word,
+        N_col = N_col,
+        N_h=N_h,
+        N_depth=N_depth,
+        dropout=dropout,
+        gpu=GPU,
+        trainable_emb=train_emb,
+        table_type=table_type,
+        use_hs=use_hs)
+
+    print("Loading from modules...")
+    model.multi_sql.load_state_dict(torch.load(
+        "{}/multi_sql_models.dump".format(args.models)))
+    model.key_word.load_state_dict(torch.load(
+        "{}/keyword_models.dump".format(args.models)))
+    model.col.load_state_dict(torch.load(
+        "{}/col_models.dump".format(args.models)))
+    model.op.load_state_dict(torch.load(
+        "{}/op_models.dump".format(args.models)))
+    model.agg.load_state_dict(torch.load(
+        "{}/agg_models.dump".format(args.models)))
+    model.root_teminal.load_state_dict(torch.load(
+        "{}/root_tem_models.dump".format(args.models)))
+    model.des_asc.load_state_dict(torch.load(
+        "{}/des_asc_models.dump".format(args.models)))
+    model.having.load_state_dict(torch.load(
+        "{}/having_models.dump".format(args.models)))
+
+    test_acc(model, BATCH_SIZE, data, args.output_path)
+    # test_exec_acc()
diff --git a/code/test_gen.sh b/code/test_gen.sh
new file mode 100644
index 0000000..7a489d8
--- /dev/null
+++ b/code/test_gen.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+#export CUDA_VISIBLE_DEVICES=1
+schema="char"  # char or word
+#schema="word"
+
+toy=""
+
+embedding="multi"  # multi for multi-lingual or mono for monolingual
+#embedding="mono"
+
+emb_path="embedding/${schema}_emb.txt"
+col_emb_path="embedding/glove.42B.300d.txt"
+if [[ ${embedding} == "multi" ]]; then col_emb_path="None"; fi
+
+TEST_DATA="data/${schema}/dev.json"
+
+SAVE_PATH="data/${schema}/generated_datasets/saved_models_multi_2022-12-05-06:04:27"
+python -u test.py \
+   --test_data_path  ${TEST_DATA} \
+   --models          ${SAVE_PATH} \
+   --output_path     ${SAVE_PATH}/test_result.txt \
+   --emb_path    ${emb_path} \
+   --col_emb_path    ${col_emb_path} \
+   ${toy} \
+   > "${SAVE_PATH}/test_result.out.txt" \
+   2>&1 &
diff --git a/code/train.py b/code/train.py
new file mode 100644
index 0000000..538a71a
--- /dev/null
+++ b/code/train.py
@@ -0,0 +1,273 @@
+import argparse
+import datetime
+import torch
+from config import global_config as cfg
+from utils.util import *
+from utils.word_embedding import WordEmbedding
+from models.agg_predictor import AggPredictor
+from models.col_predictor import ColPredictor
+from models.desasc_limit_predictor import DesAscLimitPredictor
+from models.having_predictor import HavingPredictor
+from models.keyword_predictor import KeyWordPredictor
+from models.multisql_predictor import MultiSqlPredictor
+from models.op_predictor import OpPredictor
+from models.root_teminal_predictor import RootTeminalPredictor
+from models.andor_predictor import AndOrPredictor
+from tqdm import *
+
+TRAIN_COMPONENTS = (
+    'multi_sql',
+    'keyword',
+    'col',
+    'op',
+    'agg',
+    'root_tem',
+    'des_asc',
+    'having',
+    'andor')
+SQL_TOK = ['<UNK>', '<END>', 'WHERE', 'AND', 'EQL', 'GT', 'LT', '<BEG>']
+
+
+def lr_decay(optimizer, epoch, decay_rate, init_lr):
+    lr = init_lr * ((1 - decay_rate)**epoch)
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+    return optimizer
+
+
+if __name__ == '__main__':
+    np.random.seed(100)
+    torch.manual_seed(100)
+    torch.cuda.manual_seed(100)
+    torch.backends.cudnn.enabled = False
+    torch.backends.cudnn.deterministic = True
+
+    device = 'cuda:2'
+
+    N_word = cfg.emb_size
+    N_col = cfg.col_emb_size
+    N_h = cfg.hidden_size
+    N_depth = cfg.num_layers
+    dropout = cfg.dropout
+    BATCH_SIZE = cfg.batch_size
+    learning_rate = cfg.learning_rate
+    train_emb = cfg.train_emb
+    epoch = cfg.epoch
+
+    history_type = cfg.history_type
+    table_type = cfg.table_type
+
+    use_hs = True
+    if history_type == "no":
+        use_hs = False
+
+    # default to use GPU, but have to check if GPU exists
+    GPU = True
+    if not cfg.nogpu:
+        if torch.cuda.device_count() == 0:
+            GPU = False
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--data_root',
+        type=str,
+        default='',
+        help='root path for generated_data')
+    parser.add_argument(
+        '--save_dir',
+        type=str,
+        default='',
+        help='set model save directory.')
+    parser.add_argument(
+        '--train_component',
+        type=str,
+        default='',
+        help='set train components,available:[multi_sql,keyword,col,op,agg,root_tem,des_asc,having,andor].')
+    parser.add_argument(
+        '--emb_path',
+        type=str,
+        default='',
+        help='embedding path, multi-lingual or monolingual')
+    parser.add_argument(
+        '--col_emb_path',
+        type=str,
+        default='',
+        help='column embedding path')
+    parser.add_argument(
+        '--toy',
+        action='store_true',
+        help='If set, use small data; used for fast debugging.')
+    args = parser.parse_args()
+
+    toy = args.toy
+    if toy:
+        USE_SMALL = True
+    else:
+        USE_SMALL = False
+
+    data_root = args.data_root
+    save_dir = args.save_dir
+
+    train_component = args.train_component
+    if train_component not in TRAIN_COMPONENTS:
+        print("Invalid train component")
+        exit(1)
+    train_data = load_dataset(
+        train_component,
+        "train",
+        history_type,
+        data_root)
+    dev_data = load_dataset(
+        train_component,
+        "dev",
+        history_type,
+        data_root)
+
+    emb_path = args.emb_path
+    word_emb = load_emb(emb_path, load_used=train_emb, use_small=USE_SMALL)
+    col_emb_path = args.col_emb_path
+    col_emb = None
+    if col_emb_path != 'None':
+        col_emb = load_emb(
+            col_emb_path,
+            load_used=train_emb,
+            use_small=USE_SMALL)
+    print("Finished load word embedding")
+
+    embed_layer = WordEmbedding(
+        word_emb,
+        N_word,
+        gpu=GPU,
+        SQL_TOK=SQL_TOK,
+        trainable=train_emb)
+    q_embed_layer = embed_layer
+
+    if not col_emb:
+        N_col = None
+    else:
+        embed_layer = WordEmbedding(
+            col_emb, N_col, gpu=GPU, SQL_TOK=SQL_TOK, trainable=train_emb)
+
+    model = None
+    if train_component == "multi_sql":
+        model = MultiSqlPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            gpu=GPU,
+            dropout=dropout,
+            use_hs=use_hs)
+    elif train_component == "keyword":
+        model = KeyWordPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=GPU,
+            use_hs=use_hs)
+    elif train_component == "col":
+        model = ColPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=GPU,
+            use_hs=use_hs)
+    elif train_component == "op":
+        model = OpPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=GPU,
+            use_hs=use_hs)
+    elif train_component == "agg":
+        model = AggPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=GPU,
+            use_hs=use_hs)
+    elif train_component == "root_tem":
+        model = RootTeminalPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=GPU,
+            use_hs=use_hs)
+    elif train_component == "des_asc":
+        model = DesAscLimitPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=GPU,
+            use_hs=use_hs)
+    elif train_component == "having":
+        model = HavingPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=GPU,
+            use_hs=use_hs)
+    elif train_component == "andor":
+        model = AndOrPredictor(
+            N_word=N_word,
+            N_col=N_col,
+            N_h=N_h,
+            N_depth=N_depth,
+            dropout=dropout,
+            gpu=GPU,
+            use_hs=use_hs)
+    parameters = filter(lambda p: p.requires_grad, model.parameters())
+    optimizer = torch.optim.Adam(
+        model.parameters(),
+        lr=learning_rate,
+        weight_decay=0)
+    print("Finished build model")
+
+    print_flag = False
+
+    model.to(device)
+
+    print("Start training")
+    best_acc = 0.0
+    for i in tqdm(range(epoch)):
+        print('Epoch %d @ %s' % (i + 1, datetime.datetime.now()))
+        print(
+            ' Loss = %s' %
+            epoch_train(
+                model,
+                optimizer,
+                BATCH_SIZE,
+                train_component,
+                train_data,
+                table_type,
+                q_embed_layer,
+                embed_layer))
+        acc = epoch_acc(
+            model,
+            BATCH_SIZE,
+            train_component,
+            dev_data,
+            table_type,
+            q_embed_layer,
+            embed_layer)
+        if acc > best_acc:
+            best_acc = acc
+            print("Save model...")
+            torch.save(
+                model.state_dict(),
+                save_dir + "/{}_models.dump".format(
+                    train_component))
diff --git a/code/train_all.sh b/code/train_all.sh
new file mode 100644
index 0000000..e1bb04c
--- /dev/null
+++ b/code/train_all.sh
@@ -0,0 +1,49 @@
+#!/bin/bash
+
+DATE=`date '+%Y-%m-%d-%H:%M:%S'`
+
+schema="char"  # char or word
+#schema="word"
+
+toy=""
+
+embedding="multi"  # multi for multi-lingual or mono for monolingual
+#embedding="mono"
+
+data_root="data/${schema}/generated_datasets"
+emb_path="embedding/${schema}_emb.txt"
+col_emb_path="embedding/glove.42B.300d.txt"
+if [[ ${embedding} == "multi" ]]; then col_emb_path="None"; fi
+save_dir="${data_root}/saved_models_${embedding}_${DATE}"
+log_dir="${save_dir}/train_log"
+mkdir -p ${save_dir}
+mkdir -p ${log_dir}
+
+export CUDA_VISIBLE_DEVICES=0
+for module in col
+do
+  nohup python -u train.py \
+    --data_root    ${data_root} \
+    --save_dir     ${save_dir} \
+    --train_component ${module} \
+    --emb_path    ${emb_path} \
+    --col_emb_path    ${col_emb_path} \
+    ${toy} \
+    > "${log_dir}/train_${module}_${DATE}.txt" \
+    2>&1 &
+done
+
+export CUDA_VISIBLE_DEVICES=1
+for module in keyword op des_asc multi_sql agg having root_tem andor
+do
+  nohup python -u train.py \
+    --data_root    ${data_root} \
+    --save_dir     ${save_dir} \
+    --train_component ${module} \
+    --emb_path    ${emb_path} \
+    --col_emb_path    ${col_emb_path} \
+    ${toy} \
+    > "${log_dir}/train_${module}_${DATE}.txt" \
+    2>&1 &
+done
+
diff --git a/code/utils/__init__.py b/code/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/code/utils/process_sql.py b/code/utils/process_sql.py
new file mode 100644
index 0000000..0ce1f6c
--- /dev/null
+++ b/code/utils/process_sql.py
@@ -0,0 +1,564 @@
+################################
+# Assumptions:
+#   1. sql is correct
+#   2. only table name has alias
+#   3. only one intersect/union/except
+#
+# val: number(float)/string(str)/sql(dict)
+# col_unit: (agg_id, col_id, isDistinct(bool))
+# val_unit: (unit_op, col_unit1, col_unit2)
+# table_unit: (table_type, col_unit/sql)
+# cond_unit: (not_op, op_id, val_unit, val1, val2)
+# condition: [cond_unit1, 'and'/'or', cond_unit2, ...]
+# sql {
+#   'select': (isDistinct(bool), [(agg_id, val_unit), (agg_id, val_unit), ...])
+#   'from': {'table_units': [table_unit1, table_unit2, ...], 'conds': condition}
+#   'where': condition
+#   'groupBy': [col_unit1, col_unit2, ...]
+#   'orderBy': ('asc'/'desc', [val_unit1, val_unit2, ...])
+#   'having': condition
+#   'limit': None/limit value
+#   'intersect': None/sql
+#   'except': None/sql
+#   'union': None/sql
+# }
+################################
+
+import json
+import sqlite3
+from nltk import word_tokenize
+import pdb
+
+CLAUSE_KEYWORDS = ('select', 'from', 'where', 'group', 'order', 'limit', 'intersect', 'union', 'except')
+JOIN_KEYWORDS = ('join', 'on', 'as')
+
+WHERE_OPS = ('not', 'between', '=', '>', '<', '>=', '<=', '!=', 'in', 'like', 'is', 'exists')
+UNIT_OPS = ('none', '-', '+', "*", '/')
+AGG_OPS = ('none', 'max', 'min', 'count', 'sum', 'avg')
+TABLE_TYPE = {
+    'sql': "sql",
+    'table_unit': "table_unit",
+}
+
+COND_OPS = ('and', 'or')
+SQL_OPS = ('intersect', 'union', 'except')
+ORDER_OPS = ('desc', 'asc')
+
+
+
+class Schema:
+    """
+    Simple schema which maps table&column to a unique identifier
+    """
+    def __init__(self, schema):
+        self._schema = schema
+        self._idMap = self._map(self._schema)
+
+    @property
+    def schema(self):
+        return self._schema
+
+    @property
+    def idMap(self):
+        return self._idMap
+
+    def _map(self, schema):
+        idMap = {'*': "__all__"}
+        id = 1
+        for key, vals in schema.items():
+            for val in vals:
+                idMap[key.lower() + "." + val.lower()] = "__" + key.lower() + "." + val.lower() + "__"
+                id += 1
+
+        for key in schema:
+            idMap[key.lower()] = "__" + key.lower() + "__"
+            id += 1
+
+        return idMap
+
+
+def get_schema(db):
+    """
+    Get database's schema, which is a dict with table name as key
+    and list of column names as value
+    :param db: database path
+    :return: schema dict
+    """
+
+    schema = {}
+    conn = sqlite3.connect(db)
+    cursor = conn.cursor()
+
+    # fetch table names
+    cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
+    tables = [str(table[0].lower()) for table in cursor.fetchall()]
+
+    # fetch table info
+    for table in tables:
+        cursor.execute("PRAGMA table_info({})".format(table))
+        schema[table] = [str(col[1].lower()) for col in cursor.fetchall()]
+
+    return schema
+
+
+def get_schema_from_json(fpath):
+    with open(fpath) as f:
+        data = json.load(f)
+
+    schema = {}
+    for entry in data:
+        table = str(entry['table'].lower())
+        cols = [str(col['column_name'].lower()) for col in entry['col_data']]
+        schema[table] = cols
+
+    return schema
+
+
+def tokenize(string):
+    string = str(string)
+    string = string.replace("\'", "\"")  # ensures all string values wrapped by "" problem??
+    quote_idxs = [idx for idx, char in enumerate(string) if char == '"']
+    assert len(quote_idxs) % 2 == 0, "Unexpected quote"
+
+    # keep string value as token
+    vals = {}
+    for i in range(len(quote_idxs)-1, -1, -2):
+        qidx1 = quote_idxs[i-1]
+        qidx2 = quote_idxs[i]
+        val = string[qidx1: qidx2+1]
+        key = "__val_{}_{}__".format(qidx1, qidx2)
+        string = string[:qidx1] + key + string[qidx2+1:]
+        vals[key] = val
+
+    toks = [word.lower() for word in word_tokenize(string)]
+    # replace with string value token
+    for i in range(len(toks)):
+        if toks[i] in vals:
+            toks[i] = vals[toks[i]]
+
+    # find if there exists !=, >=, <=
+    eq_idxs = [idx for idx, tok in enumerate(toks) if tok == "="]
+    eq_idxs.reverse()
+    prefix = ('!', '>', '<')
+    for eq_idx in eq_idxs:
+        pre_tok = toks[eq_idx-1]
+        if pre_tok in prefix:
+            toks = toks[:eq_idx-1] + [pre_tok + "="] + toks[eq_idx+1: ]
+
+    return toks
+
+
+def scan_alias(toks):
+    """Scan the index of 'as' and build the map for all alias"""
+    as_idxs = [idx for idx, tok in enumerate(toks) if tok == 'as']
+    alias = {}
+    for idx in as_idxs:
+        alias[toks[idx+1]] = toks[idx-1]
+    return alias
+
+
+def get_tables_with_alias(schema, toks):
+    tables = scan_alias(toks)
+    for key in schema:
+        assert key not in tables, "Alias {} has the same name in table".format(key)
+        tables[key] = key
+    return tables
+
+
+def parse_col(toks, start_idx, tables_with_alias, schema, default_tables=None):
+    """
+        :returns next idx, column id
+    """
+    tok = toks[start_idx]
+    if tok == "*":
+        return start_idx + 1, schema.idMap[tok]
+
+    if '.' in tok:  # if token is a composite
+        alias, col = tok.split('.')
+        key = tables_with_alias[alias] + "." + col
+        return start_idx+1, schema.idMap[key]
+
+    assert default_tables is not None and len(default_tables) > 0, "Default tables should not be None or empty"
+
+    for alias in default_tables:
+        table = tables_with_alias[alias]
+        if tok in schema.schema[table]:
+            key = table + "." + tok
+            return start_idx+1, schema.idMap[key]
+
+    assert False, "Error col: {}".format(tok)
+
+
+def parse_col_unit(toks, start_idx, tables_with_alias, schema, default_tables=None):
+    """
+        :returns next idx, (agg_op id, col_id)
+    """
+    idx = start_idx
+    len_ = len(toks)
+    isBlock = False
+    isDistinct = False
+    if toks[idx] == '(':
+        isBlock = True
+        idx += 1
+
+    if toks[idx] in AGG_OPS:
+        agg_id = AGG_OPS.index(toks[idx])
+        idx += 1
+        assert idx < len_ and toks[idx] == '('
+        idx += 1
+        if toks[idx] == "distinct":
+            idx += 1
+            isDistinct = True
+        idx, col_id = parse_col(toks, idx, tables_with_alias, schema, default_tables)
+        assert idx < len_ and toks[idx] == ')'
+        idx += 1
+        return idx, (agg_id, col_id, isDistinct)
+
+    if toks[idx] == "distinct":
+        idx += 1
+        isDistinct = True
+    agg_id = AGG_OPS.index("none")
+    idx, col_id = parse_col(toks, idx, tables_with_alias, schema, default_tables)
+
+    if isBlock:
+        assert toks[idx] == ')'
+        idx += 1  # skip ')'
+
+    return idx, (agg_id, col_id, isDistinct)
+
+
+def parse_val_unit(toks, start_idx, tables_with_alias, schema, default_tables=None):
+    idx = start_idx
+    len_ = len(toks)
+    isBlock = False
+    if toks[idx] == '(':
+        isBlock = True
+        idx += 1
+
+    col_unit1 = None
+    col_unit2 = None
+    unit_op = UNIT_OPS.index('none')
+
+    idx, col_unit1 = parse_col_unit(toks, idx, tables_with_alias, schema, default_tables)
+    if idx < len_ and toks[idx] in UNIT_OPS:
+        unit_op = UNIT_OPS.index(toks[idx])
+        idx += 1
+        idx, col_unit2 = parse_col_unit(toks, idx, tables_with_alias, schema, default_tables)
+
+    if isBlock:
+        assert toks[idx] == ')'
+        idx += 1  # skip ')'
+
+    return idx, (unit_op, col_unit1, col_unit2)
+
+
+def parse_table_unit(toks, start_idx, tables_with_alias, schema):
+    """
+        :returns next idx, table id, table name
+    """
+    idx = start_idx
+    len_ = len(toks)
+    #pdb.set_trace()
+    key = tables_with_alias[toks[idx]]
+
+    if idx + 1 < len_ and toks[idx+1] == "as":
+        idx += 3
+    else:
+        idx += 1
+
+    return idx, schema.idMap[key], key
+
+
+def parse_value(toks, start_idx, tables_with_alias, schema, default_tables=None):
+    idx = start_idx
+    len_ = len(toks)
+
+    isBlock = False
+    if toks[idx] == '(':
+        isBlock = True
+        idx += 1
+
+    if toks[idx] == 'select':
+        idx, val = parse_sql(toks, idx, tables_with_alias, schema)
+    elif "\"" in toks[idx]:  # token is a string value
+        val = toks[idx]
+        idx += 1
+    else:
+        try:
+            val = float(toks[idx])
+            idx += 1
+        except:
+            end_idx = idx
+            while end_idx < len_ and toks[end_idx] != ',' and toks[end_idx] != ')'\
+                and toks[end_idx] != 'and' and toks[end_idx] not in CLAUSE_KEYWORDS and toks[end_idx] not in JOIN_KEYWORDS:
+                    end_idx += 1
+
+            idx, val = parse_col_unit(toks[start_idx: end_idx], 0, tables_with_alias, schema, default_tables)
+            idx = end_idx
+
+    if isBlock:
+        assert toks[idx] == ')'
+        idx += 1
+
+    return idx, val
+
+
+def parse_condition(toks, start_idx, tables_with_alias, schema, default_tables=None):
+    idx = start_idx
+    len_ = len(toks)
+    conds = []
+
+    while idx < len_:
+        idx, val_unit = parse_val_unit(toks, idx, tables_with_alias, schema, default_tables)
+        not_op = False
+        if toks[idx] == 'not':
+            not_op = True
+            idx += 1
+
+        assert idx < len_ and toks[idx] in WHERE_OPS, "Error condition: idx: {}, tok: {}".format(idx, toks[idx])
+        op_id = WHERE_OPS.index(toks[idx])
+        idx += 1
+        val1 = val2 = None
+        if op_id == WHERE_OPS.index('between'):  # between..and... special case: dual values
+            idx, val1 = parse_value(toks, idx, tables_with_alias, schema, default_tables)
+            assert toks[idx] == 'and'
+            idx += 1
+            idx, val2 = parse_value(toks, idx, tables_with_alias, schema, default_tables)
+        else:  # normal case: single value
+            idx, val1 = parse_value(toks, idx, tables_with_alias, schema, default_tables)
+            val2 = None
+
+        conds.append((not_op, op_id, val_unit, val1, val2))
+
+        if idx < len_ and (toks[idx] in CLAUSE_KEYWORDS or toks[idx] in (")", ";") or toks[idx] in JOIN_KEYWORDS):
+            break
+
+        if idx < len_ and toks[idx] in COND_OPS:
+            conds.append(toks[idx])
+            idx += 1  # skip and/or
+
+    return idx, conds
+
+
+def parse_select(toks, start_idx, tables_with_alias, schema, default_tables=None):
+    idx = start_idx
+    len_ = len(toks)
+
+    assert toks[idx] == 'select', "'select' not found"
+    idx += 1
+    isDistinct = False
+    if idx < len_ and toks[idx] == 'distinct':
+        idx += 1
+        isDistinct = True
+    val_units = []
+
+    while idx < len_ and toks[idx] not in CLAUSE_KEYWORDS:
+        agg_id = AGG_OPS.index("none")
+        if toks[idx] in AGG_OPS:
+            agg_id = AGG_OPS.index(toks[idx])
+            idx += 1
+        idx, val_unit = parse_val_unit(toks, idx, tables_with_alias, schema, default_tables)
+        val_units.append((agg_id, val_unit))
+        if idx < len_ and toks[idx] == ',':
+            idx += 1  # skip ','
+
+    return idx, (isDistinct, val_units)
+
+
+def parse_from(toks, start_idx, tables_with_alias, schema):
+    """
+    Assume in the from clause, all table units are combined with join
+    """
+    assert 'from' in toks[start_idx:], "'from' not found"
+
+    len_ = len(toks)
+    idx = toks.index('from', start_idx) + 1
+    default_tables = []
+    table_units = []
+    conds = []
+
+    while idx < len_:
+        isBlock = False
+        if toks[idx] == '(':
+            isBlock = True
+            idx += 1
+
+        if toks[idx] == 'select':
+            idx, sql = parse_sql(toks, idx, tables_with_alias, schema)
+            table_units.append((TABLE_TYPE['sql'], sql))
+        else:
+            if idx < len_ and toks[idx] == 'join':
+                idx += 1  # skip join
+            idx, table_unit, table_name = parse_table_unit(toks, idx, tables_with_alias, schema)
+            table_units.append((TABLE_TYPE['table_unit'],table_unit))
+            default_tables.append(table_name)
+        if idx < len_ and toks[idx] == "on":
+            idx += 1  # skip on
+            idx, this_conds = parse_condition(toks, idx, tables_with_alias, schema, default_tables)
+            if len(conds) > 0:
+                conds.append('and')
+            conds.extend(this_conds)
+
+        if isBlock:
+            assert toks[idx] == ')'
+            idx += 1
+        if idx < len_ and (toks[idx] in CLAUSE_KEYWORDS or toks[idx] in (")", ";")):
+            break
+
+    return idx, table_units, conds, default_tables
+
+
+def parse_where(toks, start_idx, tables_with_alias, schema, default_tables):
+    idx = start_idx
+    len_ = len(toks)
+
+    if idx >= len_ or toks[idx] != 'where':
+        return idx, []
+
+    idx += 1
+    idx, conds = parse_condition(toks, idx, tables_with_alias, schema, default_tables)
+    return idx, conds
+
+
+def parse_group_by(toks, start_idx, tables_with_alias, schema, default_tables):
+    idx = start_idx
+    len_ = len(toks)
+    col_units = []
+
+    if idx >= len_ or toks[idx] != 'group':
+        return idx, col_units
+
+    idx += 1
+    assert toks[idx] == 'by'
+    idx += 1
+
+    while idx < len_ and not (toks[idx] in CLAUSE_KEYWORDS or toks[idx] in (")", ";")):
+        idx, col_unit = parse_col_unit(toks, idx, tables_with_alias, schema, default_tables)
+        col_units.append(col_unit)
+        if idx < len_ and toks[idx] == ',':
+            idx += 1  # skip ','
+        else:
+            break
+
+    return idx, col_units
+
+
+def parse_order_by(toks, start_idx, tables_with_alias, schema, default_tables):
+    idx = start_idx
+    len_ = len(toks)
+    val_units = []
+    order_type = 'asc' # default type is 'asc'
+
+    if idx >= len_ or toks[idx] != 'order':
+        return idx, val_units
+
+    idx += 1
+    assert toks[idx] == 'by'
+    idx += 1
+
+    while idx < len_ and not (toks[idx] in CLAUSE_KEYWORDS or toks[idx] in (")", ";")):
+        idx, val_unit = parse_val_unit(toks, idx, tables_with_alias, schema, default_tables)
+        val_units.append(val_unit)
+        if idx < len_ and toks[idx] in ORDER_OPS:
+            order_type = toks[idx]
+            idx += 1
+        if idx < len_ and toks[idx] == ',':
+            idx += 1  # skip ','
+        else:
+            break
+
+    return idx, (order_type, val_units)
+
+
+def parse_having(toks, start_idx, tables_with_alias, schema, default_tables):
+    idx = start_idx
+    len_ = len(toks)
+
+    if idx >= len_ or toks[idx] != 'having':
+        return idx, []
+
+    idx += 1
+    idx, conds = parse_condition(toks, idx, tables_with_alias, schema, default_tables)
+    return idx, conds
+
+
+def parse_limit(toks, start_idx):
+    idx = start_idx
+    len_ = len(toks)
+
+    if idx < len_ and toks[idx] == 'limit':
+        idx += 2
+        return idx, int(toks[idx-1])
+
+    return idx, None
+
+
+def parse_sql(toks, start_idx, tables_with_alias, schema):
+    isBlock = False # indicate whether this is a block of sql/sub-sql
+    len_ = len(toks)
+    idx = start_idx
+
+    sql = {}
+    if toks[idx] == '(':
+        isBlock = True
+        idx += 1
+
+    # parse from clause in order to get default tables
+    from_end_idx, table_units, conds, default_tables = parse_from(toks, start_idx, tables_with_alias, schema)
+    sql['from'] = {'table_units': table_units, 'conds': conds}
+    # select clause
+    _, select_col_units = parse_select(toks, idx, tables_with_alias, schema, default_tables)
+    idx = from_end_idx
+    sql['select'] = select_col_units
+    # where clause
+    idx, where_conds = parse_where(toks, idx, tables_with_alias, schema, default_tables)
+    sql['where'] = where_conds
+    # group by clause
+    idx, group_col_units = parse_group_by(toks, idx, tables_with_alias, schema, default_tables)
+    sql['groupBy'] = group_col_units
+    # having clause
+    idx, having_conds = parse_having(toks, idx, tables_with_alias, schema, default_tables)
+    sql['having'] = having_conds
+    # order by clause
+    idx, order_col_units = parse_order_by(toks, idx, tables_with_alias, schema, default_tables)
+    sql['orderBy'] = order_col_units
+    # limit clause
+    idx, limit_val = parse_limit(toks, idx)
+    sql['limit'] = limit_val
+
+    idx = skip_semicolon(toks, idx)
+    if isBlock:
+        assert toks[idx] == ')'
+        idx += 1  # skip ')'
+    idx = skip_semicolon(toks, idx)
+
+    # intersect/union/except clause
+    for op in SQL_OPS:  # initialize IUE
+        sql[op] = None
+    if idx < len_ and toks[idx] in SQL_OPS:
+        sql_op = toks[idx]
+        idx += 1
+        idx, IUE_sql = parse_sql(toks, idx, tables_with_alias, schema)
+        sql[sql_op] = IUE_sql
+    return idx, sql
+
+
+def load_data(fpath):
+    with open(fpath) as f:
+        data = json.load(f)
+    return data
+
+
+def get_sql(schema, query):
+    toks = tokenize(query)
+    tables_with_alias = get_tables_with_alias(schema.schema, toks)
+    _, sql = parse_sql(toks, 0, tables_with_alias, schema)
+
+    return sql
+
+
+def skip_semicolon(toks, start_idx):
+    idx = start_idx
+    while idx < len(toks) and toks[idx] == ";":
+        idx += 1
+    return idx
diff --git a/code/utils/util.py b/code/utils/util.py
new file mode 100644
index 0000000..8ad4932
--- /dev/null
+++ b/code/utils/util.py
@@ -0,0 +1,523 @@
+import re
+import io
+import json
+import numpy as np
+import os
+import signal
+
+
+def get_table_dict(table_data_path):
+    data = json.load(open(table_data_path))
+    table = dict()
+    for item in data:
+        table[item["db_id"]] = item
+    return table
+
+
+def load_dataset(component, train_dev, history, root):
+    return json.load(
+        open("{}/{}_{}_{}_dataset.json".format(root, history, train_dev, component)))
+
+
+def to_batch_seq(data, idxes, st, ed):
+    q_seq = []
+    history = []
+    label = []
+    for i in range(st, ed):
+        q_seq.append(data[idxes[i]]['question_tokens'])
+        history.append(data[idxes[i]]["history"])
+        label.append(data[idxes[i]]["label"])
+    return q_seq, history, label
+
+# CHANGED
+
+
+def to_batch_tables(data, idxes, st, ed, table_type):
+    # col_lens = []
+    col_seq = []
+    for i in range(st, ed):
+        ts = data[idxes[i]]["ts"]
+        tname_toks = [x.split(" ") for x in ts[0]]
+        col_type = ts[2]
+        cols = [x.split(" ") for xid, x in ts[1]]
+        tab_seq = [xid for xid, x in ts[1]]
+        cols_add = []
+        for tid, col, ct in zip(tab_seq, cols, col_type):
+            col_one = [ct]
+            if tid == -1:
+                tabn = ["all"]
+            else:
+                if table_type == "no":
+                    tabn = []
+                else:
+                    tabn = tname_toks[tid]
+            for t in tabn:
+                if t not in col:
+                    col_one.append(t)
+            col_one.extend(col)
+            cols_add.append(col_one)
+        col_seq.append(cols_add)
+
+    return col_seq
+
+# used for training in train.py
+
+
+def epoch_train(
+        model,
+        optimizer,
+        batch_size,
+        component,
+        data,
+        table_type,
+        q_embed_layer,
+        embed_layer):
+    model.train()
+    perm = np.random.permutation(len(data))
+    cum_loss = 0.0
+    st = 0
+
+    while st < len(data):
+        ed = st + batch_size if st + batch_size < len(perm) else len(perm)
+        q_seq, history, label = to_batch_seq(data, perm, st, ed)
+        q_emb_var, q_len = q_embed_layer.gen_x_q_batch(q_seq)
+        hs_emb_var, hs_len = embed_layer.gen_x_history_batch(history)
+        score = 0.0
+        loss = 0.0
+        if component == "multi_sql":
+            mkw_emb_var = embed_layer.gen_word_list_embedding(
+                ["none", "except", "intersect", "union"], (ed - st))
+            mkw_len = np.full(q_len.shape, 4, dtype=np.int64)
+            # print("mkw_emb:{}".format(mkw_emb_var.size()))
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                mkw_emb_var=mkw_emb_var,
+                mkw_len=mkw_len)
+        elif component == "keyword":
+            # where group by order by
+            # [[0,1,2]]
+            kw_emb_var = embed_layer.gen_word_list_embedding(
+                ["where", "group by", "order by"], (ed - st))
+            mkw_len = np.full(q_len.shape, 3, dtype=np.int64)
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                kw_emb_var=kw_emb_var,
+                kw_len=mkw_len)
+        elif component == "col":
+            # col word embedding
+            # [[0,1,3]]
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len)
+
+        elif component == "op":
+            # B*index
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            index = 0
+            for i in range(st, ed):
+                # print(i)
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == "agg":
+            # [[0,1,3]]
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(i)
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == "root_tem":
+            # B*0/1
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(data[perm[i]]["history"])
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == "des_asc":
+            # B*0/1
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(i)
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == 'having':
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(i)
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == "andor":
+            score = model.forward(q_emb_var, q_len, hs_emb_var, hs_len)
+        # score = model.forward(q_seq, col_seq, col_num, pred_entry,
+        #         gt_where=gt_where_seq, gt_cond=gt_cond_seq, gt_sel=gt_sel_seq)
+        # print("label {}".format(label))
+        loss = model.loss(score, label)
+        # print("loss {}".format(loss.data.cpu().numpy()))
+        cum_loss += loss.cpu().data.numpy() * (ed - st)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        st = ed
+
+    return cum_loss / len(data)
+
+# used for development evaluation in train.py
+
+
+def epoch_acc(
+        model,
+        batch_size,
+        component,
+        data,
+        table_type,
+        q_embed_layer,
+        embed_layer,
+        error_print=False,
+        train_flag=False):
+    model.eval()
+    perm = list(range(len(data)))
+    st = 0
+    total_number_error = 0.0
+    total_p_error = 0.0
+    total_error = 0.0
+    print("dev data size {}".format(len(data)))
+    while st < len(data):
+        ed = st + batch_size if st + batch_size < len(perm) else len(perm)
+
+        q_seq, history, label = to_batch_seq(data, perm, st, ed)
+        q_emb_var, q_len = q_embed_layer.gen_x_q_batch(q_seq)
+        hs_emb_var, hs_len = embed_layer.gen_x_history_batch(history)
+        score = 0.0
+
+        if component == "multi_sql":
+            # none, except, intersect,union
+            # truth B*index(0,1,2,3)
+            # print("hs_len:{}".format(hs_len))
+            # print("q_emb_shape:{} hs_emb_shape:{}".format(q_emb_var.size(), hs_emb_var.size()))
+            mkw_emb_var = embed_layer.gen_word_list_embedding(
+                ["none", "except", "intersect", "union"], (ed - st))
+            mkw_len = np.full(q_len.shape, 4, dtype=np.int64)
+            # print("mkw_emb:{}".format(mkw_emb_var.size()))
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                mkw_emb_var=mkw_emb_var,
+                mkw_len=mkw_len)
+        elif component == "keyword":
+            # where group by order by
+            # [[0,1,2]]
+            kw_emb_var = embed_layer.gen_word_list_embedding(
+                ["where", "group by", "order by"], (ed - st))
+            mkw_len = np.full(q_len.shape, 3, dtype=np.int64)
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                kw_emb_var=kw_emb_var,
+                kw_len=mkw_len)
+        elif component == "col":
+            # col word embedding
+            # [[0,1,3]]
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len)
+        elif component == "op":
+            # B*index
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(i)
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == "agg":
+            # [[0,1,3]]
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(i)
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == "root_tem":
+            # B*0/1
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(data[perm[i]]["history"])
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == "des_asc":
+            # B*0/1
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(i)
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == 'having':
+            col_seq = to_batch_tables(data, perm, st, ed, table_type)
+            col_emb_var, col_name_len, col_len = embed_layer.gen_col_batch(
+                col_seq)
+            gt_col = np.zeros(q_len.shape, dtype=np.int64)
+            # print(ed)
+            index = 0
+            for i in range(st, ed):
+                # print(i)
+                gt_col[index] = data[perm[i]]["gt_col"]
+                index += 1
+            score = model.forward(
+                q_emb_var,
+                q_len,
+                hs_emb_var,
+                hs_len,
+                col_emb_var,
+                col_len,
+                col_name_len,
+                gt_col=gt_col)
+
+        elif component == "andor":
+            score = model.forward(q_emb_var, q_len, hs_emb_var, hs_len)
+        # print("label {}".format(label))
+        if component in ("agg", "col", "keyword", "op"):
+            num_err, p_err, err = model.check_acc(score, label)
+            total_number_error += num_err
+            total_p_error += p_err
+            total_error += err
+        else:
+            err = model.check_acc(score, label)
+            total_error += err
+        st = ed
+
+    if component in ("agg", "col", "keyword", "op"):
+        print(
+            "Dev {} acc number predict acc:{} partial acc: {} total acc: {}".format(
+                component,
+                1 -
+                total_number_error *
+                1.0 /
+                len(data),
+                1 -
+                total_p_error *
+                1.0 /
+                len(data),
+                1 -
+                total_error *
+                1.0 /
+                len(data)))
+        return 1 - total_error * 1.0 / len(data)
+    else:
+        print(
+            "Dev {} acc total acc: {}".format(
+                component,
+                1 -
+                total_error *
+                1.0 /
+                len(data)))
+        return 1 - total_error * 1.0 / len(data)
+
+
+def timeout_handler(num, stack):
+    print("Received SIGALRM")
+    raise Exception("Timeout")
+
+## used in test.py
+
+
+def test_acc(model, batch_size, data, output_path):
+    table_dict = get_table_dict("./data/tables.json")
+    f = open(output_path, "w")
+    for item in data[:]:
+        db_id = item["db_id"]
+        if db_id not in table_dict:
+            print("Error %s not in table_dict" % db_id)
+        # signal.signal(signal.SIGALRM, timeout_handler)
+        # signal.alarm(2) # set timer to prevent infinite recursion in SQL
+        # generation
+        sql = model.forward([item["question_toks"]] *
+                            batch_size, [], table_dict[db_id])
+        if sql is not None:
+            print(sql)
+            sql = model.gen_sql(sql, table_dict[db_id])
+        else:
+            sql = "select a from b"
+        print(sql)
+        print("")
+        f.write("{}\n".format(sql))
+    f.close()
+
+
+def load_emb(file_name, load_used=False, use_small=False):
+    if not load_used:
+        print('Loading word embedding from %s' % file_name)
+        ret = {}
+        with open(file_name) as inf:
+            for idx, line in enumerate(inf):
+                if (use_small and idx >= 5000):
+                    break
+                info = line.strip().split(' ')
+                info[0] = info[0].encode('utf-8').decode('utf-8')
+                if info[0].lower() not in ret:
+                    ret[info[0]] = np.array(list(map(lambda x: float(x), info[1:])))
+        return ret
+    else:
+        print('Load used word embedding')
+        with open('../alt/glove/word2idx.json') as inf:
+            w2i = json.load(inf)
+        with open('../alt/glove/usedwordemb.npy') as inf:
+            word_emb_val = np.load(inf)
+        return w2i, word_emb_val
diff --git a/code/utils/word_embedding.py b/code/utils/word_embedding.py
new file mode 100644
index 0000000..aa5fe5a
--- /dev/null
+++ b/code/utils/word_embedding.py
@@ -0,0 +1,261 @@
+import json
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+
+AGG_OPS = ('none', 'maximum', 'minimum', 'count', 'sum', 'average')
+
+
+class WordEmbedding(nn.Module):
+    def __init__(self, word_emb, N_word, gpu, SQL_TOK,
+                 trainable=False):
+        super(WordEmbedding, self).__init__()
+        self.trainable = trainable
+        self.N_word = N_word
+        self.gpu = gpu
+        self.SQL_TOK = SQL_TOK
+
+        if trainable:
+            print("Using trainable embedding")
+            self.w2i, word_emb_val = word_emb
+            # tranable when using pretrained model, init embedding weights
+            # using prev embedding
+            self.embedding = nn.Embedding(len(self.w2i), N_word)
+            self.embedding.weight = nn.Parameter(
+                torch.from_numpy(word_emb_val.astype(np.float32)))
+        else:
+            # else use word2vec or glove
+            self.word_emb = word_emb
+            print("Using fixed embedding")
+
+    def gen_x_q_batch(self, q):
+        B = len(q)
+        val_embs = []
+        val_len = np.zeros(B, dtype=np.int64)
+        for i, one_q in enumerate(q):
+            q_val = []
+            for ws in one_q:
+                q_val.append(
+                    self.word_emb.get(
+                        ws,
+                        np.zeros(
+                            self.N_word,
+                            dtype=np.float32)))
+
+            val_embs.append([np.zeros(self.N_word,
+                                      dtype=np.float32)] + q_val + [np.zeros(self.N_word,
+                                                                             dtype=np.float32)])  # <BEG> and <END>
+            val_len[i] = 1 + len(q_val) + 1
+        max_len = max(val_len)
+
+        val_emb_array = np.zeros((B, max_len, self.N_word), dtype=np.float32)
+        for i in range(B):
+            for t in range(len(val_embs[i])):
+                val_emb_array[i, t, :] = val_embs[i][t]
+        val_inp = torch.from_numpy(val_emb_array)
+        if self.gpu:
+            val_inp = val_inp.cuda()
+        val_inp_var = Variable(val_inp)
+
+        return val_inp_var, val_len
+
+    def gen_x_history_batch(self, history):
+        B = len(history)
+        val_embs = []
+        val_len = np.zeros(B, dtype=np.int64)
+        for i, one_history in enumerate(history):
+            history_val = []
+            for item in one_history:
+                # col
+                if isinstance(item, list) or isinstance(item, tuple):
+                    emb_list = []
+                    ws = item[0].split() + item[1].split()
+                    ws_len = len(ws)
+                    for w in ws:
+                        emb_list.append(
+                            self.word_emb.get(
+                                w, np.zeros(
+                                    self.N_word, dtype=np.float32)))
+                    if ws_len == 0:
+                        raise Exception("word list should not be empty!")
+                    elif ws_len == 1:
+                        history_val.append(emb_list[0])
+                    else:
+                        history_val.append(sum(emb_list) / float(ws_len))
+                # ROOT
+                elif isinstance(item, str):
+                    if item == "ROOT":
+                        item = "root"
+                    elif item == "asc":
+                        item = "ascending"
+                    elif item == "desc":
+                        item == "descending"
+                    if item in (
+                        "none",
+                        "select",
+                        "from",
+                        "where",
+                        "having",
+                        "limit",
+                        "intersect",
+                        "except",
+                        "union",
+                        'not',
+                        'between',
+                        '=',
+                        '>',
+                        '<',
+                        'in',
+                        'like',
+                        'is',
+                        'exists',
+                        'root',
+                        'ascending',
+                            'descending'):
+                        history_val.append(
+                            self.word_emb.get(
+                                item, np.zeros(
+                                    self.N_word, dtype=np.float32)))
+                    elif item == "orderBy":
+                        history_val.append(
+                            (self.word_emb.get(
+                                "order",
+                                np.zeros(
+                                    self.N_word,
+                                    dtype=np.float32)) +
+                                self.word_emb.get(
+                                "by",
+                                np.zeros(
+                                    self.N_word,
+                                    dtype=np.float32))) /
+                            2)
+                    elif item == "groupBy":
+                        history_val.append(
+                            (self.word_emb.get(
+                                "group",
+                                np.zeros(
+                                    self.N_word,
+                                    dtype=np.float32)) +
+                                self.word_emb.get(
+                                "by",
+                                np.zeros(
+                                    self.N_word,
+                                    dtype=np.float32))) /
+                            2)
+                    elif item in ('>=', '<=', '!='):
+                        history_val.append(
+                            (self.word_emb.get(
+                                item[0],
+                                np.zeros(
+                                    self.N_word,
+                                    dtype=np.float32)) +
+                                self.word_emb.get(
+                                item[1],
+                                np.zeros(
+                                    self.N_word,
+                                    dtype=np.float32))) /
+                            2)
+                elif isinstance(item, int):
+                    history_val.append(
+                        self.word_emb.get(
+                            AGG_OPS[item], np.zeros(
+                                self.N_word, dtype=np.float32)))
+                else:
+                    print(
+                        "Warning: unsupported data type in history! {}".format(item))
+
+            val_embs.append(history_val)
+            val_len[i] = len(history_val)
+        max_len = max(val_len)
+
+        val_emb_array = np.zeros((B, max_len, self.N_word), dtype=np.float32)
+        for i in range(B):
+            for t in range(len(val_embs[i])):
+                val_emb_array[i, t, :] = val_embs[i][t]
+        val_inp = torch.from_numpy(val_emb_array)
+        if self.gpu:
+            val_inp = val_inp.cuda()
+        val_inp_var = Variable(val_inp)
+
+        return val_inp_var, val_len
+
+    def gen_word_list_embedding(self, words, B):
+        val_emb_array = np.zeros(
+            (B, len(words), self.N_word), dtype=np.float32)
+        for i, word in enumerate(words):
+            if len(word.split()) == 1:
+                emb = self.word_emb.get(
+                    word, np.zeros(
+                        self.N_word, dtype=np.float32))
+            else:
+                word = word.split()
+                emb = (
+                    self.word_emb.get(
+                        word[0],
+                        np.zeros(
+                            self.N_word,
+                            dtype=np.float32)) + self.word_emb.get(
+                        word[1],
+                        np.zeros(
+                            self.N_word,
+                            dtype=np.float32))) / 2
+            for b in range(B):
+                val_emb_array[b, i, :] = emb
+        val_inp = torch.from_numpy(val_emb_array)
+        if self.gpu:
+            val_inp = val_inp.cuda()
+        val_inp_var = Variable(val_inp)
+        return val_inp_var
+
+    def gen_col_batch(self, cols):
+        ret = []
+        col_len = np.zeros(len(cols), dtype=np.int64)
+
+        names = []
+        for b, one_cols in enumerate(cols):
+            names = names + one_cols
+            col_len[b] = len(one_cols)
+        # TODO: what is the diff bw name_len and col_len?
+        name_inp_var, name_len = self.str_list_to_batch(names)
+        return name_inp_var, name_len, col_len
+
+    def str_list_to_batch(self, str_list):
+        """get a list var of wemb of words in each column name in current bactch"""
+        B = len(str_list)
+
+        val_embs = []
+        val_len = np.zeros(B, dtype=np.int64)
+        for i, one_str in enumerate(str_list):
+            if self.trainable:
+                val = [self.w2i.get(x, 0) for x in one_str]
+            else:
+                val = [self.word_emb.get(x, np.zeros(
+                    self.N_word, dtype=np.float32)) for x in one_str]
+            val_embs.append(val)
+            val_len[i] = len(val)
+        max_len = max(val_len)
+
+        if self.trainable:
+            val_tok_array = np.zeros((B, max_len), dtype=np.int64)
+            for i in range(B):
+                for t in range(len(val_embs[i])):
+                    val_tok_array[i, t] = val_embs[i][t]
+            val_tok = torch.from_numpy(val_tok_array)
+            if self.gpu:
+                val_tok = val_tok.cuda()
+            val_tok_var = Variable(val_tok)
+            val_inp_var = self.embedding(val_tok_var)
+        else:
+            val_emb_array = np.zeros(
+                (B, max_len, self.N_word), dtype=np.float32)
+            for i in range(B):
+                for t in range(len(val_embs[i])):
+                    val_emb_array[i, t, :] = val_embs[i][t]
+            val_inp = torch.from_numpy(val_emb_array)
+            if self.gpu:
+                val_inp = val_inp.cuda()
+            val_inp_var = Variable(val_inp)
+
+        return val_inp_var, val_len
-- 
Gitee


From fff029845016ded3d4e1c9530deca554c7a681d9 Mon Sep 17 00:00:00 2001
From: baby123 <10339928+baby12@user.noreply.gitee.com>
Date: Fri, 9 Dec 2022 12:38:09 +0000
Subject: [PATCH 3/3] =?UTF-8?q?=E4=BB=A3=E7=A0=81?=
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Signed-off-by: baby123 <10339928+baby12@user.noreply.gitee.com>
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+# 基于openGauss的自然语言查询器
+
+### 数据预处理
+将训练语料中的question经过英文翻译和中文翻译生成新的语料，作为其中数据增强的方式。
+
+
+### 模型
+模型以官方提供的baseline为基础，更新为python3版本，并使用最新的torch函数替换。训练过程中，将输入问题用LSTM编码，结合关键词以及表头名称，通过注意力机制与问题结合得到表征信息。模型分为多个模块，针对不同训练任务分别训练。
+
+### 后续优化
+可以加入预训练模型，使用Multilingual BERT进行编码训练，但由于时间问题和硬件不支持，探索之后由于显卡不足未能在比赛截止前得到结果，后续会继续探索使用预训练模型生成SQL查询语句。
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+# 基于openGauss的自然语言查询器
+
+### 模型
+
+#### 数据, Embeddings 和 预训练模型
+
+1. 下载数据，embedding和数据库：
+    - 将 ``train.json`` 和 ``dev.json`` 放入 ``./data/char/``
+    - 将 ``char_emb.txt``放入``./embedding/``
+    - 将 ``database`` 放入 ``./``
+2. 使用 ``python preprocess_data.py -s char|word`` 生成每个模块的训练文件
+
+#### 训练
+运行``train_all.sh``训练所有模块
+```
+python train.py \
+    --data_root       path/to/char/or/word/based/generated_data \
+    --save_dir        path/to/save/trained/module \
+    --train_component <module_name> \
+    --emb_path        path/to/embeddings
+    --col_emb_path    path/to/corresponding/embeddings/for/column
+```
+
+#### 测试
+运行``test_gen.sh`` 生成SQL查询
+```
+python train.py \
+    --data_root       path/to/char/or/word/based/generated_data \
+    --save_dir        path/to/save/trained/module \
+    --train_component <module_name> \
+    --emb_path        path/to/embeddings
+    --col_emb_path    path/to/corresponding/embeddings/for/column
+```
+
+#### 评估
+运行``evaluation.sh``评估生成的SQL查询
+```
+python evaluation.py \
+    --gold            path/to/gold/dev/or/test/queries \
+    --pred            path/to/predicted/dev/or/test/queries \
+    --etype           evaluation/metric \
+    --db              path/to/database \
+    --table           path/to/tables \
+```
+
+
+
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