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.gitattributes

@@ -56,3 +56,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+multihiertt_data/dev.json filter=lfs diff=lfs merge=lfs -text
+multihiertt_data/test.json filter=lfs diff=lfs merge=lfs -text
+multihiertt_data/train.json filter=lfs diff=lfs merge=lfs -text

multihiertt_data/dev.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0100d9d6832901d0c8ffa3141e548e51c98ecf3ac144407f90d8182ab3bcb0a7
+size 36118558

multihiertt_data/table_description_generation.py

@@ -0,0 +1,186 @@
+import numpy as np
+import pandas as pd
+from bs4 import BeautifulSoup
+import os
+import re
+from lxml.html.clean import Cleaner
+import json
+
+
+def clean_html(raw_html):
+    cleaner = Cleaner(remove_tags=["sup"])
+    return cleaner.clean_html(raw_html).decode("utf-8")
+
+EMPTY = "[EMPTY]"
+
+def isYear(value):
+    for i in range(1990, 2022):
+        if str(i) in value:
+            return True
+    return False
+
+def existTopHeaders(html):
+    first_row = html.tr
+    if first_row.td.string == None:
+        return True
+
+    for td in first_row.find_all("td"):
+        if not td.string:
+            continue
+        value = td.string.replace(",", "").strip()
+        if value:
+            try:
+                float(value[1:])
+                if isYear(value):
+                    return True
+                else:
+                    return False
+            except:
+                continue
+    return True
+
+def belongToTopHeaders(row):
+    for i, td in enumerate(row.find_all("td")):
+        if not td.string:
+            continue
+        value = td.string.replace(",", "").strip()
+        if value:
+            try:
+                float(value[1:])
+                if isYear(value):
+                    return True
+                else:
+                    return False
+            except:
+                continue
+    return True
+
+def handle_unnamed_single_topheader(columns, j):
+    tmp = j
+    while tmp < len(columns) and (columns[tmp].startswith("Unnamed") or columns[tmp] == EMPTY):
+        tmp += 1
+    if tmp < len(columns):
+        return columns[tmp]
+    
+    tmp = j
+    while tmp >= 0 and (columns[tmp].startswith("Unnamed") or columns[tmp] == EMPTY):
+        tmp -= 1
+    if tmp < 0:
+        return f"data {j}"
+    else:
+        return columns[tmp]
+
+def handle_unnamed_multi_topheader(columns, j):
+    tmp = j
+    while tmp < len(columns) and (columns[tmp][0].startswith("Unnamed") or columns[tmp][0] == EMPTY):
+        tmp += 1
+    if tmp < len(columns):
+        return columns[tmp][0]
+    
+    tmp = j
+    while tmp >= 0 and (columns[tmp][0].startswith("Unnamed") or columns[tmp][0] == EMPTY):
+        tmp -= 1
+    if tmp < 0:
+        return f"data {j}"
+    else:
+        return columns[tmp][0]
+
+def readHTML(html_string):
+    # file_path = html_path
+    html = BeautifulSoup(html_string, features='html.parser')
+    # remove superscripts and subscripts
+    for sup in html.select('sup'):
+        sup.extract()
+    for sup in html.select('sub'):
+        sup.extract()
+
+    # 1. locate top header
+    top_header_nonexist_flag = 0
+    if not existTopHeaders(html):
+        top_header_nonexist_flag = 1
+        new_tr_tag = html.new_tag("tr")
+        new_td_tag = html.new_tag("td")
+        new_tr_tag.insert(0, new_td_tag)
+        for i in range(len(html.tr.find_all("td")[1:])):
+            new_td_tag1 = html.new_tag("td")
+            new_td_tag1.string = f"data{i}"
+            new_tr_tag.insert(i+1, new_td_tag1)
+        html.table.insert(0, new_tr_tag)
+    else:
+        html.tr.td.string = ""
+
+    header = [0] 
+    top_header_flag = True
+    for i, tr in enumerate(html.find_all("tr")):
+        # # for locating top header
+        # if tr.td.string and ("in thousands" in tr.td.string.lower() or "in millions" in tr.td.string.lower()) and len(tr.td.string) < len("in thousands") + 5:
+        #     tr.td.replace_with(html.new_tag("td"))
+        if top_header_flag and i > 0 and not top_header_nonexist_flag:
+            if belongToTopHeaders(tr):
+                header.append(i)
+            else:
+                top_header_flag = False
+        # for locating left header
+        if tr.td.string != None:
+            for td in tr.find_all("td")[1:]:
+                if td.string == None:
+                    td.string = EMPTY
+
+    data = pd.read_html(str(html), header=header, index_col=0)[0]
+    return data, header, top_header_nonexist_flag
+
+
+def generateDescription(data, header, top_header_nonexist_flag):
+    describe_dict = {}
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            value = data.iloc[i, j]
+            if str(value).startswith("Unnamed") or str(value) == EMPTY or str(value) == "-" or str(value) == u'\u2014':
+                continue
+            describe = ""
+            if pd.isnull(data.index[i]):
+                describe += "total"
+            else:   
+                describe += f"{data.index[i]}"
+            temp_i = i - 1
+            while temp_i >= 0:
+                if (data.iloc[temp_i] == EMPTY).all():
+                    describe += f" {data.index[temp_i]}"
+                    break
+                temp_i -= 1
+            if not top_header_nonexist_flag:
+                describe += " of"
+                if len(header) == 1:
+                    describe += f" {handle_unnamed_single_topheader(data.columns, j)}"
+                else:
+                    describe += f" {handle_unnamed_multi_topheader(data.columns, j)}"
+                    prev = handle_unnamed_multi_topheader(data.columns, j)
+                    for temp_j in header[1:]:
+                        if data.columns[j][temp_j].startswith("Unnamed") or data.columns[j][temp_j] == EMPTY:
+                            continue
+                        if data.columns[j][temp_j] == prev:
+                            continue
+                        describe += f" {data.columns[j][temp_j]}"
+                        prev = data.columns[j][temp_j]
+            describe += f" is {data.iloc[i, j]}."
+            x_index = i+len(header)
+            y_index = j+1
+            if top_header_nonexist_flag == 1:
+                x_index -= 1 
+            describe_dict[f"{x_index}-{y_index}"] = describe
+    return describe_dict
+
+def generateDiscreptionCell(data, header, top_header_nonexist_flag):
+    discribe_dict = {}
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            value = data.iloc[i, j]
+            if str(value).startswith("Unnamed") or str(value) == "-" or str(value) == "[EMPTY]":
+                continue
+            discribe = f"{data.iloc[i, j]}"
+            x_index = i+len(header)
+            y_index = j+1
+            if top_header_nonexist_flag == 1:
+                x_index -= 1 
+            discribe_dict[f"{x_index}-{y_index}"] = discribe
+    return discribe_dict

multihiertt_data/test.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15bfe9cc1241e29050a5bcaf7b9639d6907e7895cd07a1f1b874c2fda905ad01
+size 52242696

multihiertt_data/train.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc17d75b5b3d155a44522bf8f9c2660afea60c7f5cf55048ccf0f927332f99c7
+size 262497396