17bdb1a60b
* LLM: add whisper models into nightly test * small fix * small fix * add more whisper models * test all cases * test specific cases * collect the csv * store the resut * to html * small fix * small test * test all cases * modify whisper_csv_to_html
113 lines
No EOL
4.2 KiB
Python
113 lines
No EOL
4.2 KiB
Python
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Python program to convert CSV to HTML Table
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import os
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import sys
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import argparse
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import pandas as pd
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def highlight_vals(val, max=3.0, color1='red', color2='green'):
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if isinstance(val, float):
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if val > max:
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return 'background-color: %s' % color2
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elif val <= -max:
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return 'background-color: %s' % color1
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else:
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return ''
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def main():
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parser = argparse.ArgumentParser(description="convert .csv file to .html file")
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parser.add_argument("-f", "--folder_path", type=str, dest="folder_path",
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help="The directory which stores the .csv file", default="/mnt/disk1/whisper_pr_gpu/")
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parser.add_argument("-t", "--threshold", type=float, dest="threshold",
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help="the threshold of highlight values", default=1.0)
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args = parser.parse_args()
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csv_files = []
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for file_name in os.listdir(args.folder_path):
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file_path = os.path.join(args.folder_path, file_name)
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if os.path.isfile(file_path) and file_name.endswith(".csv"):
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csv_files.append(file_path)
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csv_files.sort(reverse=True)
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latest_csv = pd.read_csv(csv_files[0], index_col=0)
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daily_html=csv_files[0].split(".")[0]+".html"
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if len(csv_files)>1:
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previous_csv = pd.read_csv(csv_files[1], index_col=0)
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last1=['']*len(latest_csv.index)
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diff1=['']*len(latest_csv.index)
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last2=['']*len(latest_csv.index)
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diff2=['']*len(latest_csv.index)
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WER='WER'
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RTF='RTF'
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for latest_csv_ind,latest_csv_row in latest_csv.iterrows():
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latest_csv_model=latest_csv_row['models'].strip()
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latest_csv_precision=latest_csv_row['precision'].strip()
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latest_WER=latest_csv_row[WER]
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latest_RTF=latest_csv_row[RTF]
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in_previous_flag=False
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for previous_csv_ind,previous_csv_row in previous_csv.iterrows():
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previous_csv_model=previous_csv_row['models'].strip()
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previous_csv_precision=previous_csv_row['precision'].strip()
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if latest_csv_model==previous_csv_model and latest_csv_precision==previous_csv_precision:
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previous_WER=previous_csv_row[WER]
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previous_RTF=previous_csv_row[RTF]
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if previous_WER > 0.0 and previous_RTF > 0.0:
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last1[latest_csv_ind]=previous_WER
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diff1[latest_csv_ind]=round((previous_WER-latest_WER)*100/previous_WER,2)
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last2[latest_csv_ind]=previous_RTF
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diff2[latest_csv_ind]=round((previous_RTF-latest_RTF)*100/previous_RTF,2)
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in_previous_flag=True
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if not in_previous_flag:
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last1[latest_csv_ind]=pd.NA
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diff1[latest_csv_ind]=pd.NA
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last2[latest_csv_ind]=pd.NA
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diff2[latest_csv_ind]=pd.NA
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latest_csv.insert(loc=4,column='last1',value=last1)
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latest_csv.insert(loc=5,column='diff1(%)',value=diff1)
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latest_csv.insert(loc=6,column='last2',value=last2)
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latest_csv.insert(loc=7,column='diff2(%)',value=diff2)
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subset1=['diff1(%)','diff2(%)']
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columns={'WER': '{:.6f}', 'RTF': '{:.6f}', 'last1': '{:.6f}', 'diff1(%)': '{:.6f}','last2': '{:.6f}', 'diff2(%)': '{:.6f}'}
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styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=1.0, color1='red', color2='green'), subset=subset1)
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html_output = styled_df.set_table_attributes("border=1").render()
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with open(daily_html, 'w') as f:
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f.write(html_output)
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else:
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latest_csv.to_html(daily_html)
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return 0
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if __name__ == "__main__":
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sys.exit(main()) |