# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Python program to convert CSV to HTML Table import os import sys import argparse import pandas as pd def highlight_vals(val, max=3.0, color1='red', color2='green'): if isinstance(val, float): if val > max: return 'background-color: %s' % color2 elif val <= -max: return 'background-color: %s' % color1 else: return '' def nonzero_min(lst): non_zero_lst = [num for num in lst if num > 0.0] return min(non_zero_lst) if non_zero_lst else None def is_diffs_within_normal_range(diff_Arc, diff_TruthfulQA, diff_Winogrande, threshold=5.0): return not any(diff < (-threshold) for diff in diff_Arc + diff_TruthfulQA + diff_Winogrande if isinstance(diff, float)) def add_to_dict(dict, key, value): if key not in dict: dict[key] = [] dict[key].append(value) def best_in_dict(dict, key, value): if key in dict: best_value = nonzero_min(dict[key]) if best_value < value or value <= 0.0: return best_value return value return value def create_fp16_dict(fp16_path): fp16_df = pd.read_csv(fp16_path) fp16_dict = {} for _, row in fp16_df.iterrows(): model = row['Model'] # Formalize the data to have 2 decimal places fp16_dict[model] = { 'Arc': "{:.2f}".format(row['Arc']), 'TruthfulQA': "{:.2f}".format(row['TruthfulQA']), 'Winogrande': "{:.2f}".format(row['Winogrande']) } return fp16_dict def calculate_percentage_difference(current, fp16): if fp16 != 'N/A' and current != 'N/A' and float(fp16) != 0: return (float(current) - float(fp16)) / float(fp16) * 100 else: return 'N/A' def main(): parser = argparse.ArgumentParser(description="convert .csv file to .html file") parser.add_argument("-f", "--folder_path", type=str, dest="folder_path", help="The directory which stores the .csv file", default="/home/arda/yibo/BigDL/python/llm/dev/benchmark/harness") parser.add_argument("-t", "--threshold", type=float, dest="threshold", help="the threshold of highlight values", default=3.0) parser.add_argument("-b", "--baseline_path", type=str, dest="baseline_path", help="the baseline path which stores the baseline.csv file") args = parser.parse_args() fp16_dict = create_fp16_dict('fp16.csv') csv_files = [] for file_name in os.listdir(args.folder_path): file_path = os.path.join(args.folder_path, file_name) if os.path.isfile(file_path) and file_name.endswith(".csv"): csv_files.append(file_path) csv_files.sort(reverse=True) highlight_threshold=args.threshold latest_csv = pd.read_csv(csv_files[0], index_col=0) daily_html=csv_files[0].split(".")[0]+".html" # Reset index latest_csv.reset_index(inplace=True) diffs_within_normal_range = True # Add display of FP16 values for each model and add percentage difference column for task in ['Arc', 'TruthfulQA', 'Winogrande']: latest_csv[f'{task}_FP16'] = latest_csv['Model'].apply(lambda model: fp16_dict.get(model, {}).get(task, 'N/A')) latest_csv[f'{task}_diff_FP16(%)'] = latest_csv.apply(lambda row: calculate_percentage_difference(row[task], row[f'{task}_FP16']), axis=1) if len(csv_files)>1: if args.baseline_path: previous_csv = pd.read_csv(args.baseline_path, index_col=0) else: previous_csv = pd.read_csv(csv_files[1], index_col=0) last_Arc=['']*len(latest_csv.index) diff_Arc=['']*len(latest_csv.index) last_TruthfulQA=['']*len(latest_csv.index) diff_TruthfulQA=['']*len(latest_csv.index) last_Winogrande=['']*len(latest_csv.index) diff_Winogrande=['']*len(latest_csv.index) Arc='Arc' TruthfulQA='TruthfulQA' Winogrande='Winogrande' csv_dict = {} for csv_file in csv_files: current_csv = pd.read_csv(csv_file, index_col=0) for current_csv_ind,current_csv_row in current_csv.iterrows(): current_csv_model=current_csv_row['Model'].strip() current_csv_precision=current_csv_row['Precision'].strip() current_csv_model_arc=current_csv_model+'-'+current_csv_precision+'-'+'Arc' current_csv_model_truthfulqa=current_csv_model+'-'+current_csv_precision+'-'+'TruthfulQA' current_csv_model_winogrande=current_csv_model+'-'+current_csv_precision+'-'+'Winogrande' add_to_dict(csv_dict, current_csv_model_arc, current_csv_row[Arc]) add_to_dict(csv_dict, current_csv_model_truthfulqa, current_csv_row[TruthfulQA]) add_to_dict(csv_dict, current_csv_model_winogrande, current_csv_row[Winogrande]) for latest_csv_ind,latest_csv_row in latest_csv.iterrows(): latest_csv_model=latest_csv_row['Model'].strip() latest_csv_precision=latest_csv_row['Precision'].strip() latest_arc=latest_csv_row[Arc] latest_truthfulqa=latest_csv_row[TruthfulQA] latest_winogrande=latest_csv_row[Winogrande] in_previous_flag=False for previous_csv_ind,previous_csv_row in previous_csv.iterrows(): previous_csv_model=previous_csv_row['Model'].strip() previous_csv_precision=previous_csv_row['Precision'].strip() if latest_csv_model==previous_csv_model and latest_csv_precision==previous_csv_precision: previous_arc=previous_csv_row[Arc] previous_truthfulqa=previous_csv_row[TruthfulQA] previous_winogrande=previous_csv_row[Winogrande] if previous_arc > 0.0 and previous_truthfulqa > 0.0 and previous_winogrande > 0.0: last_Arc[latest_csv_ind]=previous_arc diff_Arc[latest_csv_ind]=round((previous_arc-latest_arc)*100/previous_arc,2) last_TruthfulQA[latest_csv_ind]=previous_truthfulqa diff_TruthfulQA[latest_csv_ind]=round((previous_truthfulqa-latest_truthfulqa)*100/previous_truthfulqa,2) last_Winogrande[latest_csv_ind]=previous_winogrande diff_Winogrande[latest_csv_ind]=round((previous_winogrande-latest_winogrande)*100/previous_winogrande,2) in_previous_flag=True if not in_previous_flag: last_Arc[latest_csv_ind]=pd.NA diff_Arc[latest_csv_ind]=pd.NA last_TruthfulQA[latest_csv_ind]=pd.NA diff_TruthfulQA[latest_csv_ind]=pd.NA last_Winogrande[latest_csv_ind]=pd.NA diff_Winogrande[latest_csv_ind]=pd.NA latest_csv.insert(loc=5,column='last_Arc',value=last_Arc) latest_csv.insert(loc=6,column='diff_Arc(%)',value=diff_Arc) latest_csv.insert(loc=7,column='last_TruthfulQA',value=last_TruthfulQA) latest_csv.insert(loc=8,column='diff_TruthfulQA(%)',value=diff_TruthfulQA) latest_csv.insert(loc=9,column='last_Winogrande',value=last_Winogrande) latest_csv.insert(loc=10,column='diff_Winogrande(%)',value=diff_Winogrande) diffs_within_normal_range = is_diffs_within_normal_range(diff_Arc, diff_TruthfulQA, diff_Winogrande, threshold=highlight_threshold) subset1=['diff_Arc(%)','diff_TruthfulQA(%)','diff_Winogrande(%)' ] columns={'Arc': '{:.2f}', 'TruthfulQA': '{:.2f}', 'Winogrande': '{:.2f}', 'last_Arc': '{:.2f}', 'diff_Arc(%)': '{:.2f}', 'last_TruthfulQA': '{:.2f}', 'diff_TruthfulQA(%)': '{:.2f}', 'last_Winogrande': '{:.2f}', 'diff_Winogrande(%)': '{:.2f}'} latest_csv.drop('Index', axis=1, inplace=True) styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, color1='red', color2='green'), subset=subset1) for task in ['Arc', 'TruthfulQA', 'Winogrande']: styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=highlight_threshold, color1='red', color2='green'), subset=[f'{task}_diff_FP16(%)']) # add css style to restrict width and wrap text styled_df.set_table_styles([{ 'selector': 'th, td', 'props': [('max-width', '88px'), ('word-wrap', 'break-word')] }], overwrite=False) html_output = styled_df.set_table_attributes("border=1").to_html() with open(daily_html, 'w') as f: f.write(html_output) else: latest_csv.to_html(daily_html) if args.baseline_path and not diffs_within_normal_range: print("The diffs are outside the normal range: %" + str(highlight_threshold)) return 1 return 0 if __name__ == "__main__": sys.exit(main())