# # 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(diff1, diff2, diff3, threshold=5.0): return not any(diff < (-threshold) for diff in diff1 + diff2 + diff3 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 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() 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" diffs_within_normal_range = True 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) last1=['']*len(latest_csv.index) diff1=['']*len(latest_csv.index) last2=['']*len(latest_csv.index) diff2=['']*len(latest_csv.index) last3=['']*len(latest_csv.index) diff3=['']*len(latest_csv.index) best_last1=['']*len(latest_csv.index) best_diff1=['']*len(latest_csv.index) best_last2=['']*len(latest_csv.index) best_diff2=['']*len(latest_csv.index) best_last3=['']*len(latest_csv.index) best_diff3=['']*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] key1=latest_csv_model+'-'+latest_csv_precision+'-'+'Arc' key2=latest_csv_model+'-'+latest_csv_precision+'-'+'TruthfulQA' key3=latest_csv_model+'-'+latest_csv_precision+'-'+'Winogrande' best_last1_value=best_in_dict(csv_dict, key1, latest_arc) best_last2_value=best_in_dict(csv_dict, key2, latest_truthfulqa) best_last3_value=best_in_dict(csv_dict, key3, latest_winogrande) best_last1[latest_csv_ind]=best_last1_value best_diff1[latest_csv_ind]=round((best_last1_value-latest_arc)*100/best_last1_value,2) best_last2[latest_csv_ind]=best_last2_value best_diff2[latest_csv_ind]=round((best_last2_value-latest_truthfulqa)*100/best_last2_value,2) best_last3[latest_csv_ind]=best_last3_value best_diff3[latest_csv_ind]=round((best_last3_value-latest_winogrande)*100/best_last3_value,2) 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: last1[latest_csv_ind]=previous_arc diff1[latest_csv_ind]=round((previous_arc-latest_arc)*100/previous_arc,2) last2[latest_csv_ind]=previous_truthfulqa diff2[latest_csv_ind]=round((previous_truthfulqa-latest_truthfulqa)*100/previous_truthfulqa,2) last3[latest_csv_ind]=previous_winogrande diff3[latest_csv_ind]=round((previous_winogrande-latest_winogrande)*100/previous_winogrande,2) in_previous_flag=True if not in_previous_flag: last1[latest_csv_ind]=pd.NA diff1[latest_csv_ind]=pd.NA last2[latest_csv_ind]=pd.NA diff2[latest_csv_ind]=pd.NA last3[latest_csv_ind]=pd.NA diff3[latest_csv_ind]=pd.NA latest_csv.insert(loc=5,column='last1',value=last1) latest_csv.insert(loc=6,column='diff1(%)',value=diff1) latest_csv.insert(loc=7,column='last2',value=last2) latest_csv.insert(loc=8,column='diff2(%)',value=diff2) latest_csv.insert(loc=9,column='last3',value=last3) latest_csv.insert(loc=10,column='diff3(%)',value=diff3) latest_csv.insert(loc=11,column='best 1',value=best_last1) latest_csv.insert(loc=12,column='best diff1(%)',value=best_diff1) latest_csv.insert(loc=13,column='best 2',value=best_last2) latest_csv.insert(loc=14,column='best diff2(%)',value=best_diff2) latest_csv.insert(loc=15,column='best 3',value=best_last3) latest_csv.insert(loc=16,column='best diff3(%)',value=best_diff3) diffs_within_normal_range = is_diffs_within_normal_range(diff1, diff2, diff3, threshold=highlight_threshold) subset1=['diff1(%)','diff2(%)','diff3(%)' ] subset2=['best diff1(%)','best diff2(%)','best diff3(%)'] columns={'Arc': '{:.2f}', 'TruthfulQA': '{:.2f}', 'Winogrande': '{:.2f}', 'last1': '{:.2f}', 'diff1(%)': '{:.2f}', 'last2': '{:.2f}', 'diff2(%)': '{:.2f}', 'last3': '{:.2f}', 'diff3(%)': '{:.2f}', 'best 1': '{:.2f}', 'best diff1(%)': '{:.2f}', 'best 2': '{:.2f}', 'best diff2(%)': '{:.2f}', 'best 3': '{:.2f}', 'best diff3(%)': '{:.2f}'} styled_df = latest_csv.style.format(columns).applymap(lambda val: highlight_vals(val, max=3.0, color1='red', color2='green'), subset=subset1) styled_df = styled_df.applymap(lambda val: highlight_vals(val, max=3.0, color1='yellow'), subset=subset2) html_output = styled_df.set_table_attributes("border=1").render() 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())