Mark Color Modification
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jenniew
parent
b151a9b672
commit
bb34c6e325
1 changed files with 97 additions and 85 deletions
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@ -28,7 +28,7 @@ def highlight_vals(val, max=3.0, color1='red', color2='green', color3='yellow',
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return 'background-color: %s' % color1
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elif val <= -max:
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return 'background-color: %s' % color2
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elif val != 0.0 and not pd.isna(val) and is_last:
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elif val != 0.0 and is_last:
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return 'background-color: %s' % color3
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else:
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return ''
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@ -38,7 +38,7 @@ def nonzero_min(lst):
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return min(non_zero_lst) if non_zero_lst else None
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def is_diffs_within_normal_range(diff_ppl_result, threshold=5.0):
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return not any(diff < (-threshold) for diff in diff_ppl_result + diff_ppl_result if isinstance(diff, float))#diff_en + diff_zh
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return not any(diff < (-threshold) for diff in diff_ppl_result if isinstance(diff, float))
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def add_to_dict(dict, key, value):
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if key not in dict:
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@ -58,17 +58,11 @@ def create_fp16_dict(fp16_path):
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fp16_dict = {}
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for _, row in fp16_df.iterrows():
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model = row['Model']
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# print("I want to test the ppl_result row", row['ppl_result'])
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# Formalize the data to have 2 decimal places
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# fp16_dict[model] = {
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# 'en': "{:.2f}".format(row['en']),
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# 'zh': "{:.2f}".format(row['zh'])
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# }
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fp16_dict[model] = {
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'ppl_result': "{:.2f}".format(row['ppl_result'])
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# 'zh': "{:.2f}".format(row['zh'])
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}
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# print(fp16_dict[model])
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return fp16_dict
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def calculate_percentage_difference(current, fp16):
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@ -112,10 +106,9 @@ def main():
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# Add display of FP16 values for each model and add percentage difference column
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for task in ['ppl_result']:#['en', 'zh']:
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latest_csv[f'{task}_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A'))
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# print("This is the stuff I want to check",latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A') == 'N/A'))
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latest_csv[f'{task}_diff_FP16(%)'] = latest_csv.apply(lambda row: calculate_percentage_difference(row[task], row[f'{task}_FP16']), axis=1)
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task = 'ppl_result'
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latest_csv[f'{task}_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A'))
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latest_csv[f'{task}_diff_FP16(%)'] = latest_csv.apply(lambda row: calculate_percentage_difference(row[task], row[f'{task}_FP16']), axis=1)
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print(csv_files)
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if len(csv_files)>1:
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@ -127,13 +120,8 @@ def main():
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last_ppl_result=['']*len(latest_csv.index)
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diff_ppl_result=['']*len(latest_csv.index)
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# last_en=['']*len(latest_csv.index)
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# diff_en=['']*len(latest_csv.index)
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# last_zh=['']*len(latest_csv.index)
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# diff_zh=['']*len(latest_csv.index)
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ppl_result = 'ppl_result'#en='en'
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#zh='zh'
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ppl_result = 'ppl_result'
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csv_dict = {}
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for csv_file in csv_files:
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@ -143,20 +131,15 @@ def main():
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current_csv_precision=current_csv_row['Precision'].strip()
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current_csv_model_ppl_result=current_csv_model+'-'+current_csv_precision+'-'+'ppl_result'
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# current_csv_model_en=current_csv_model+'-'+current_csv_precision+'-'+'en'
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# current_csv_model_zh=current_csv_model+'-'+current_csv_precision+'-'+'zh'
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add_to_dict(csv_dict, current_csv_model_ppl_result, current_csv_row[ppl_result])
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# add_to_dict(csv_dict, current_csv_model_en, current_csv_row[en])
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# add_to_dict(csv_dict, current_csv_model_zh, current_csv_row[zh])
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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['Model'].strip()
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latest_csv_precision=latest_csv_row['Precision'].strip()
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latest_ppl_result=latest_csv_row[ppl_result]#latest_en=latest_csv_row[en]
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latest_ppl_result=latest_csv_row[ppl_result]
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print("This is the latest_ppl_result",latest_ppl_result)
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# latest_zh=latest_csv_row[zh]
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in_previous_flag=False
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@ -167,84 +150,113 @@ def main():
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if latest_csv_model==previous_csv_model and latest_csv_precision==previous_csv_precision:
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previous_ppl_result=previous_csv_row[ppl_result] #previous_en=previous_csv_row[en]
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previous_ppl_result=previous_csv_row[ppl_result]
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print("This is the previous_ppl_result", previous_ppl_result)
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# previous_zh=previous_csv_row[zh]
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if previous_ppl_result > 0.0:# or previous_zh > 0.0:
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if previous_ppl_result > 0.0:
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last_ppl_result[latest_csv_ind]=previous_ppl_result
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diff_ppl_result[latest_csv_ind]=round((latest_ppl_result-previous_ppl_result)*100/previous_ppl_result,2)
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# last_zh[latest_csv_ind]=previous_zh
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# diff_zh[latest_csv_ind]=round((latest_zh-previous_zh)*100/previous_zh,2)
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in_previous_flag=True
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# last_en[latest_csv_ind]=previous_en
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# diff_en[latest_csv_ind]=round((latest_en-previous_en)*100/previous_en,2)
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# last_zh[latest_csv_ind]=previous_zh
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# diff_zh[latest_csv_ind]=round((latest_zh-previous_zh)*100/previous_zh,2)
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# in_previous_flag=True
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if not in_previous_flag:
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last_ppl_result[latest_csv_ind]=pd.NA
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diff_ppl_result[latest_csv_ind]=pd.NA
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# last_zh[latest_csv_ind]=pd.NA
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# diff_zh[latest_csv_ind]=pd.NA
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latest_csv.insert(loc=6,column='last_ppl_result',value=last_ppl_result)
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latest_csv.insert(loc=7,column='last_diff_ppl_result(%)',value=diff_ppl_result)
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# latest_csv.insert(loc=11,column='last_zh',value=last_zh)
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# latest_csv.insert(loc=12,column='diff_zh(%)',value=diff_zh)
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latest_csv.insert(loc=7,column='ppl_result_diff_last(%)',value=diff_ppl_result)
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diffs_within_normal_range = is_diffs_within_normal_range(diff_ppl_result, threshold=highlight_threshold)#en, diff_zh, threshold=highlight_threshold)
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diffs_within_normal_range = is_diffs_within_normal_range(diff_ppl_result, threshold=highlight_threshold)
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subset1=['last_diff_ppl_result(%)']#['diff_en(%)','diff_zh(%)']
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subset1='ppl_result_diff_last(%)'
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columns={'ppl_result': '{:.2f}', 'last_ppl_result': '{:.2f}', 'last_diff_ppl_result(%)': '{:.2f}'}
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#{'en': '{:.2f}', 'zh': '{:.2f}', 'last_en': '{:.2f}', 'diff_en(%)': '{:.2f}',
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#'last_zh': '{:.2f}', 'diff_zh(%)': '{:.2f}'}
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# columns will be different: columns_sec={'ppl_result': '{:.2f}'}
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columns={'ppl_result': '{:.2f}', 'last_ppl_result': '{:.2f}', 'ppl_result_diff_last(%)': '{:.2f}'}
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# This is the same
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latest_csv.drop('Index', axis=1, inplace=True)
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# subset is different
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styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True), subset=subset1)
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for task in ['ppl_result']:#['en', 'zh']:
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styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)'])
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# add css style to restrict width and wrap text
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styled_df.set_table_styles([{
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'selector': 'th, td',
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'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
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}], overwrite=False)
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html_output = styled_df.set_table_attributes("border=1").to_html()
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with open(daily_html, 'w') as f:
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f.write(html_output)
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styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=True), subset=['ppl_result_diff_last(%)'])
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styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=['ppl_result_diff_FP16(%)'])
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else:
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columns={'ppl_result': '{:.2f}'}
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latest_csv.drop('Index', axis=1, inplace=True)
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columns_sec={'ppl_result': '{:.2f}'}
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#{'en': '{:.2f}', 'zh': '{:.2f}', 'last_en': '{:.2f}', 'diff_en(%)': '{:.2f}',
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#'last_zh': '{:.2f}', 'diff_zh(%)': '{:.2f}'}
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styled_df = latest_csv.style.format(columns_sec).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True))#, subset=subset1)
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for task in ['ppl_result']:#['en', 'zh']:
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styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)'])
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# add css style to restrict width and wrap text
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styled_df.set_table_styles([{
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'selector': 'th, td',
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'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
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}], overwrite=False)
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html_output = styled_df.set_table_attributes("border=1").to_html()
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with open(daily_html, 'w') as f:
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f.write(html_output)
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# latest_csv.to_html(daily_html)
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styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=['ppl_result_diff_FP16(%)'])
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# add css style to restrict width and wrap text
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styled_df.set_table_styles([{
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'selector': 'th, td',
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'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
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}], overwrite=False)
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html_output = styled_df.set_table_attributes("border=1").to_html()
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with open(daily_html, 'w') as f:
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f.write(html_output)
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if args.baseline_path and not diffs_within_normal_range:
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print("The diffs are outside the normal range: %" + str(highlight_threshold))
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return 1
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return 1
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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# styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True), subset=subset1)
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# # These are the same:
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# task = 'ppl_result'
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# styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)'])
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# # add css style to restrict width and wrap text
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# styled_df.set_table_styles([{
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# 'selector': 'th, td',
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# 'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
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# }], overwrite=False)
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# html_output = styled_df.set_table_attributes("border=1").to_html()
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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.drop('Index', axis=1, inplace=True)
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# columns_sec={'ppl_result': '{:.2f}'}
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# styled_df = latest_csv.style.format(columns_sec).applymap(lambda val: highlight_vals(val, max=3.0, is_last=True))#, subset=subset1)
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# task = 'ppl_result'
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# styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, is_last=False), subset=[f'{task}_diff_FP16(%)'])
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# # add css style to restrict width and wrap text
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# styled_df.set_table_styles([{
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# 'selector': 'th, td',
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# 'props': [('max-width', '88px'), ('word-wrap', 'break-word')]
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# }], overwrite=False)
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# html_output = styled_df.set_table_attributes("border=1").to_html()
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# with open(daily_html, 'w') as f:
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# f.write(html_output)
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# if args.baseline_path and not diffs_within_normal_range:
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# print("The diffs are outside the normal range: %" + str(highlight_threshold))
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# return 1
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# return 0
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# if __name__ == "__main__":
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# sys.exit(main())
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