lzy/sci-gui-agent-benchmark
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

ver Dec27th

Browse Source 
merged zdy into main
This commit is contained in:
David Chang
2023-12-27 20:40:23 +08:00
parent 50b82167d0 7320f0aec4
commit 2a9e5cc373
4 changed files with 183 additions and 107 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
desktop_env/envs/desktop_env.py
View File

@@ -79,6 +79,7 @@ class DesktopEnv(gym.Env):
self.metric: Metric = getattr(metrics, self.evaluator["func"])
self.result_getter: Getter = getattr(getters, "get_{:}".format(self.evaluator["result"]["type"]))
self.expected_getter: Getter = getattr(getters, "get_{:}".format(self.evaluator["expected"]["type"]))
self.metric_options: Dict[str, Any] = self.evaluator.get("options", {})
# Initialize emulator and controller
print("Initializing...")
@@ -165,6 +166,7 @@ class DesktopEnv(gym.Env):
self.metric: Metric = getattr(metrics, self.evaluator["func"])
self.result_getter: Getter = getattr(getters, "get_{:}".format(self.evaluator["result"]["type"]))
self.expected_getter: Getter = getattr(getters, "get_{:}".format(self.evaluator["expected"]["type"]))
self.metric_options = self.evaluator.get("options", {})
self.setup_controller.reset_cache_dir(self.cache_dir)
@@ -237,7 +239,7 @@ class DesktopEnv(gym.Env):
result = self.result_getter(self, self.evaluator["result"])
expected = self.expected_getter(self, self.evaluator["expected"])
metric: float = self.metric(result, expected)
metric: float = self.metric(result, expected, **self.metric_options)
return metric

119
desktop_env/evaluators/metrics/table.py
View File

@@ -1,14 +1,10 @@
import pandas as pd
import zipfile
import lxml.etree
import lxml.cssselect
from lxml.etree import _Element
import xmltodict
#import pylightxl
import openpyxl
from openpyxl import Workbook
#from openpyxl import Workbook
from openpyxl.worksheet.worksheet import Worksheet
from openpyxl.chart._chart import ChartBase
from utils import load_charts, load_sparklines
from typing import Dict, List
from typing import Any
@@ -20,104 +16,35 @@ def compare_table(actual, expected):
# Compare the DataFrames
return 1 if df1.equals(df2) else 0
_xlsx_namespaces = [ ("x14", "http://schemas.microsoft.com/office/spreadsheetml/2009/9/main")
, ("xm", "http://schemas.microsoft.com/office/excel/2006/main")
]
_xlsx_ns_mapping = dict(_xlsx_namespaces)
_xlsx_ns_imapping = dict(map(lambda itm: (itm[1], itm[0]), _xlsx_namespaces))
_sparklines_selector = lxml.cssselect.CSSSelector("x14|sparkline", namespaces=_xlsx_ns_mapping)
#print(_sparklines_selector.css)
def _load_sparklines(xlsx_file: str) -> Dict[str, str]:
"""
This function modifies data_frame in-place
Args:
xlsx_file (str): path to xlsx
Returns:
List[Dict[str, str]]: sparkline definitions in form of
{
"F3": "Sheet1!C3:E3"
}
"""
# read xlsx
with zipfile.ZipFile(xlsx_file, "r") as z_f:
with z_f.open("xl/worksheets/sheet1.xml") as f:
sheet1: _Element = lxml.etree.fromstring(f.read())
sparklines: List[_Element] = _sparklines_selector(sheet1)
sparklines_dict: Dict[str, str] = {}
for sp_l in sparklines:
sparkline_xml: str = lxml.etree.tostring(sp_l, encoding="unicode")
sparkline: Dict[str, Dict[str, str]] = xmltodict.parse( sparkline_xml
, process_namespaces=True
, namespaces=_xlsx_ns_imapping
)
sparklines_dict[sparkline["x14:sparkline"]["xm:sqref"]] = sparkline["x14:sparkline"]["xm:f"]
return sparklines_dict
def compare_with_sparklines(actual: str, expected: str) -> float:
df1 = pd.read_excel(actual)
df2 = pd.read_excel(expected)
normal_content_metric: bool = df1.equals(df2)
print("Normal Contents Metric: {:}".format(normal_content_metric))
sp1 = _load_sparklines(actual)
sp2 = _load_sparklines(expected)
sp1 = load_sparklines(actual)
sp2 = load_sparklines(expected)
sparkline_metric: bool = sp1 == sp2
print("Sparkline Metric: {:}".format(sparkline_metric))
return float(normal_content_metric and sparkline_metric)
def _load_charts(xlsx_file: str) -> Dict[str, Any]:
def compare_with_charts(actual: str, expected: str, **options) -> float:
"""
Args:
xlsx_file (str): path to xlsx
Returns:
Dict[str, Any]: information of charts
actual (str): path to result xlsx
expected (str): path to gold xlsx
options (Dict[str, List[str]]): dict like {"chart_props": list of str}
giving the concerned chart properties
"""
workbook: Workbook = openpyxl.load_workbook(filename=xlsx_file)
worksheet: Worksheet = workbook.active
charts: List[ChartBase] = worksheet._charts
chart_set: Dict[str, Any] = {}
for ch in charts:
series: List[str] = []
for ser in ch.series:
value_num = ser.val.numRef.f\
if hasattr(ser.val, "numRef") and hasattr(ser.val.numRef, "f")\
else ""
value_str = ser.val.strRef.f\
if hasattr(ser.val, "strRef") and hasattr(ser.val.strRef, "f")\
else ""
categ_num = ser.cat.numRef.f\
if hasattr(ser.cat, "numRef") and hasattr(ser.cat.numRef, "f")\
else ""
categ_str = ser.cat.strRef.f\
if hasattr(ser.cat, "strRef") and hasattr(ser.cat.strRef, "f")\
else ""
series.append( "{:},{:},{:},{:}".format( value_num, value_str
, categ_num, categ_str
)
)
series: str = ";".join(series)
# TODO: maybe more aspects, like chart type
info: Dict[str, Any] = {}
chart_set[series] = info
return chart_set
def compare_with_charts(actual: str, expected: str) -> float:
df1 = pd.read_excel(actual)
df2 = pd.read_excel(expected)
normal_content_metric: bool = df1.equals(df2)
print("Normal Contents Metric: {:}".format(normal_content_metric))
charts1 = _load_charts(actual)
charts2 = _load_charts(expected)
charts1 = load_charts(actual, **options)
charts2 = load_charts(expected, **options)
chart_metric: bool = charts1==charts2
print("Chart Metric: {:}".format(chart_metric))
@@ -202,25 +129,5 @@ if __name__ == '__main__':
#print(check_sheet_list(path1, rule))
path1 = "../../../../../任务数据/LibreOffice Calc/Create_column_charts_using_statistics_gold.xlsx"
#workbook1: Workbook = openpyxl.load_workbook(filename=path1)
#worksheet1: Worksheet = workbook1.active
#charts: List[ChartBase] = worksheet1._charts
#print(len(charts))
#print(type(charts[0]))
#
#print(len(charts[0].series))
#print(type(charts[0].series[0]))
#print(type(charts[0].series[0].val))
##print(charts[0].series[0].val)
#print(charts[0].series[0].val.numRef.f)
#
#print(type(charts[0].series[0].cat))
##print(charts[0].series[0].cat)
#print(charts[0].series[0].cat.numRef)
#print(charts[0].series[0].cat.strRef)
#print(charts[0].series[0].cat.strRef.f)
#
#df1 = pd.read_excel(path1)
#print(df1)
path2 = "../../../../../任务数据/LibreOffice Calc/Create_column_charts_using_statistics_gold2.xlsx"
print(compare_with_charts(path1, path2))
print(compare_with_charts(path1, path2, chart_props=["type", "direction"]))

161
desktop_env/evaluators/metrics/utils.py Normal file
View File

@@ -0,0 +1,161 @@
import zipfile
import lxml.etree
import lxml.cssselect
from lxml.etree import _Element
import xmltodict
import openpyxl
from openpyxl import Workbook
from openpyxl.worksheet.worksheet import Worksheet
from openpyxl.chart._chart import ChartBase
from typing import Dict, List, Set
from typing import Any
_xlsx_namespaces = [ ("x14", "http://schemas.microsoft.com/office/spreadsheetml/2009/9/main")
, ("xm", "http://schemas.microsoft.com/office/excel/2006/main")
]
_xlsx_ns_mapping = dict(_xlsx_namespaces)
_xlsx_ns_imapping = dict(map(lambda itm: (itm[1], itm[0]), _xlsx_namespaces))
_sparklines_selector = lxml.cssselect.CSSSelector("x14|sparkline", namespaces=_xlsx_ns_mapping)
#print(_sparklines_selector.css)
def load_sparklines(xlsx_file: str) -> Dict[str, str]:
"""
This function modifies data_frame in-place
Args:
xlsx_file (str): path to xlsx
Returns:
List[Dict[str, str]]: sparkline definitions in form of
{
"F3": "Sheet1!C3:E3"
}
"""
# read xlsx
with zipfile.ZipFile(xlsx_file, "r") as z_f:
with z_f.open("xl/worksheets/sheet1.xml") as f:
sheet1: _Element = lxml.etree.fromstring(f.read())
sparklines: List[_Element] = _sparklines_selector(sheet1)
sparklines_dict: Dict[str, str] = {}
for sp_l in sparklines:
sparkline_xml: str = lxml.etree.tostring(sp_l, encoding="unicode")
sparkline: Dict[str, Dict[str, str]] = xmltodict.parse( sparkline_xml
, process_namespaces=True
, namespaces=_xlsx_ns_imapping
)
sparklines_dict[sparkline["x14:sparkline"]["xm:sqref"]] = sparkline["x14:sparkline"]["xm:f"]
return sparklines_dict
# Available Chart Properties:
# title: str
# anchor: ["oneCell" | "twoCell" | "absolute", col0, row0, col1, row1]
# width: number
# height: number
# type: "scatterChart" | "lineChart" | "barChart"
# direction: "bar" (hori) | "col" (vert)
# xtitle, ytitle, ztitle: str
def load_charts(xlsx_file: str, **options) -> Dict[str, Any]:
"""
Args:
xlsx_file (str): path to xlsx
options (Dict[str, List[str]]): dict like {"chart_props": list of str}
giving the concerned chart properties
Returns:
Dict[str, Any]: information of charts
"""
workbook: Workbook = openpyxl.load_workbook(filename=xlsx_file)
worksheet: Worksheet = workbook.active
charts: List[ChartBase] = worksheet._charts
chart_set: Dict[str, Any] = {}
chart_props: Set[str] = set(options["chart_props"]) if "chart_props" in options else set()
for ch in charts:
series: List[str] = []
for ser in ch.series:
value_num = ser.val.numRef.f\
if hasattr(ser.val, "numRef") and hasattr(ser.val.numRef, "f")\
else ""
value_str = ser.val.strRef.f\
if hasattr(ser.val, "strRef") and hasattr(ser.val.strRef, "f")\
else ""
categ_num = ser.cat.numRef.f\
if hasattr(ser.cat, "numRef") and hasattr(ser.cat.numRef, "f")\
else ""
categ_str = ser.cat.strRef.f\
if hasattr(ser.cat, "strRef") and hasattr(ser.cat.strRef, "f")\
else ""
series.append( "{:},{:},{:},{:}".format( value_num, value_str
, categ_num, categ_str
)
)
series: str = ";".join(series)
# TODO: maybe more aspects, like chart type
info: Dict[str, Any] = {}
if "title" in chart_props:
info["title"] = ch.title.tx.rich.p[0].r[0].t
if "anchor" in chart_props:
info["anchor"] = [ ch.anchor.editAs
, ch.anchor._from.col, ch.anchor.to.row
, ch.anchor.to.col, ch.anchor.to.row
]
if "width" in chart_props:
info["width"] = ch.width
if "height" in chart_props:
info["height"] = ch.height
if "type" in chart_props:
info["type"] = ch.tagname
if "direction" in chart_props:
info["direction"] = ch.barDir
if "xtitle" in chart_props:
info["xtitle"] = ch.x_axis.title.tx.rich.p[0].r[0].t
if "ytitle" in chart_props:
info["ytitle"] = ch.y_axis.title.tx.rich.p[0].r[0].t
if "ztitle" in chart_props:
info["ztitle"] = ch.z_axis.title.tx.rich.p[0].r[0].t
chart_set[series] = info
return chart_set
if __name__ == "__main__":
path1 = "../../../../../任务数据/LibreOffice Calc/Create_column_charts_using_statistics_gold_line_scatter.xlsx"
workbook1: Workbook = openpyxl.load_workbook(filename=path1)
worksheet1: Worksheet = workbook1.active
charts: List[ChartBase] = worksheet1._charts
#print(len(charts))
#print(type(charts[0]))
#
#print(len(charts[0].series))
#print(type(charts[0].series[0]))
#print(type(charts[0].series[0].val))
##print(charts[0].series[0].val)
#print(charts[0].series[0].val.numRef.f)
#
#print(type(charts[0].series[0].cat))
##print(charts[0].series[0].cat)
#print(charts[0].series[0].cat.numRef)
#print(charts[0].series[0].cat.strRef)
#print(charts[0].series[0].cat.strRef.f)
#print(type(charts[0].title.tx.strRef))
#print(type(charts[0].title.tx.rich))
#print(type(charts[0].title.txPr))
#print(len(charts[0].title.tx.rich.p))
#print(len(charts[0].title.tx.rich.p[0].r))
#print(type(charts[0].title.tx.rich.p[0].r[0]))
#print(type(charts[0].title.tx.rich.p[0].r[0].t))
#print(charts[0].title.tx.rich.p[0].r[0].t)
#print(type(charts[0].anchor))
#print(charts[0].anchor.editAs)
#print(charts[0].anchor._from.col, charts[0].anchor.to.row)
#print(charts[0].anchor.to.col, charts[0].anchor.to.row)
#df1 = pd.read_excel(path1)
#print(df1)
print(load_charts(path1, chart_props=["title", "xtitle", "ytitle", "type"]))