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| import gradio as gr | |
| import ipdb | |
| from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns | |
| import pandas as pd | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from huggingface_hub import snapshot_download | |
| from src.about import ( | |
| CITATION_BUTTON_LABEL, | |
| CITATION_BUTTON_TEXT, | |
| EVALUATION_QUEUE_TEXT, | |
| INTRODUCTION_TEXT, | |
| LLM_BENCHMARKS_TEXT, | |
| TITLE, | |
| ) | |
| from src.display.css_html_js import custom_css | |
| from src.display.utils import ( | |
| BENCHMARK_COLS, | |
| EVAL_COLS, | |
| EVAL_TYPES, | |
| ModelInfoColumn, | |
| ModelType, | |
| fields, | |
| WeightType, | |
| Precision | |
| ) | |
| from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN | |
| from src.populate import get_evaluation_queue_df, get_leaderboard_df, get_model_info_df, get_merged_df | |
| from src.submission.submit import add_new_eval | |
| from src.utils import norm_sNavie, pivot_df, get_grouped_dfs, pivot_existed_df, rename_metrics, format_df | |
| # import ipdb | |
| def restart_space(): | |
| API.restart_space(repo_id=REPO_ID) | |
| # ## Space initialisation | |
| # try: | |
| # print(EVAL_REQUESTS_PATH) | |
| # snapshot_download( | |
| # repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, | |
| # token=TOKEN | |
| # ) | |
| # except Exception: | |
| # restart_space() | |
| # try: | |
| # print(EVAL_RESULTS_PATH) | |
| # snapshot_download( | |
| # repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, | |
| # token=TOKEN | |
| # ) | |
| # except Exception: | |
| # restart_space() | |
| # # LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) | |
| # df = pd.read_csv('LOTSAv2_EvalBenchmark(Long).csv') | |
| # # Step 2: Pivot the DataFrame | |
| # LEADERBOARD_DF = df.pivot_table(index='model', | |
| # columns='dataset', | |
| # values='eval_metrics/MAE[0.5]', | |
| # aggfunc='first') | |
| # LEADERBOARD_DF.drop(columns=['ALL'], inplace=True) | |
| # | |
| # # Reset the index if you want the model column to be part of the DataFrame | |
| # LEADERBOARD_DF.reset_index(inplace=True) | |
| # # Step 3: noramlize the values | |
| # # ipdb.set_trace() | |
| # LEADERBOARD_DF = norm_sNavie(LEADERBOARD_DF) | |
| # | |
| # # LEADERBOARD_DF['Average'] = LEADERBOARD_DF.mean(axis=1) | |
| # # LEADERBOARD_DF.insert(1, 'Average', LEADERBOARD_DF.pop('Average')) | |
| # # LEADERBOARD_DF = LEADERBOARD_DF.sort_values(by=['Average'], ascending=True) | |
| # print(f"The leaderboard is {LEADERBOARD_DF}") | |
| # print(f'Columns: ', LEADERBOARD_DF.columns) | |
| # LEADERBOARD_DF = pd.read_csv('pivoted_df.csv') | |
| # domain_df = pivot_df('results/grouped_results_by_domain.csv', tab_name='domain') | |
| # print(f'Domain dataframe is {domain_df}') | |
| # freq_df = pivot_df('results/grouped_results_by_frequency.csv', tab_name='frequency') | |
| # print(f'Freq dataframe is {freq_df}') | |
| # term_length_df = pivot_df('results/grouped_results_by_term_length.csv', tab_name='term_length') | |
| # print(f'Term length dataframe is {term_length_df}') | |
| # variate_type_df = pivot_df('results/grouped_results_by_univariate.csv', tab_name='univariate') | |
| # print(f'Variate type dataframe is {variate_type_df}') | |
| # model_info_df = get_model_info_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH) | |
| grouped_dfs = get_grouped_dfs() | |
| domain_df, freq_df, term_length_df, variate_type_df, overall_df = grouped_dfs['domain'], grouped_dfs['frequency'], grouped_dfs['term_length'], grouped_dfs['univariate'], grouped_dfs['overall'] | |
| overall_df = rename_metrics(overall_df) | |
| overall_df = format_df(overall_df) | |
| overall_df = overall_df.sort_values(by=['MASE_Rank']) | |
| domain_df = pivot_existed_df(domain_df, tab_name='domain') | |
| print(f'Domain dataframe is {domain_df}') | |
| freq_df = pivot_existed_df(freq_df, tab_name='frequency') | |
| print(f'Freq dataframe is {freq_df}') | |
| term_length_df = pivot_existed_df(term_length_df, tab_name='term_length') | |
| print(f'Term length dataframe is {term_length_df}') | |
| variate_type_df = pivot_existed_df(variate_type_df, tab_name='univariate') | |
| print(f'Variate type dataframe is {variate_type_df}') | |
| model_info_df = get_model_info_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH) | |
| # ( | |
| # finished_eval_queue_df, | |
| # running_eval_queue_df, | |
| # pending_eval_queue_df, | |
| # ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) | |
| def init_leaderboard(ori_dataframe, model_info_df, sort_val: str | list | None = None): | |
| if ori_dataframe is None or ori_dataframe.empty: | |
| raise ValueError("Leaderboard DataFrame is empty or None.") | |
| model_info_col_list = [c.name for c in fields(ModelInfoColumn) if c.displayed_by_default if c.name not in ['#Params (B)', 'available_on_hub', 'hub', 'Model sha','Hub License']] | |
| col2type_dict = {c.name: c.type for c in fields(ModelInfoColumn)} | |
| default_selection_list = list(ori_dataframe.columns) + model_info_col_list | |
| # print('default_selection_list: ', default_selection_list) | |
| # ipdb.set_trace() | |
| # default_selection_list = [col for col in default_selection_list if col not in ['#Params (B)', 'available_on_hub', 'hub', 'Model sha','Hub License']] | |
| merged_df = get_merged_df(ori_dataframe, model_info_df) | |
| new_cols = ['T'] + [col for col in merged_df.columns if col != 'T'] | |
| merged_df = merged_df[new_cols] | |
| if sort_val: | |
| if isinstance(sort_val, list): | |
| assert sort_val[0] == 'TestData Leakage' | |
| # ipdb.set_trace() | |
| leakage_order = pd.Categorical(merged_df[sort_val[0]], categories=['No', 'Yes', 'N/A'], ordered=True) | |
| merged_df['leakage_order'] = leakage_order | |
| merged_df = merged_df.sort_values(by=['leakage_order', sort_val[1]]) | |
| merged_df = merged_df.drop(columns=['leakage_order']) | |
| elif sort_val in merged_df.columns: | |
| merged_df = merged_df.sort_values(by=[sort_val]) | |
| else: | |
| print(f'Warning: cannot sort by {sort_val}') | |
| print('Merged df: ', merged_df) | |
| # ipdb.set_trace() | |
| # get the data type | |
| datatype_list = [col2type_dict[col] if col in col2type_dict else 'number' for col in merged_df.columns] | |
| # print('datatype_list: ', datatype_list) | |
| # print('merged_df.column: ', merged_df.columns) | |
| # ipdb.set_trace() | |
| return Leaderboard( | |
| value=merged_df, | |
| datatype=datatype_list, | |
| select_columns=SelectColumns( | |
| default_selection=default_selection_list, | |
| # default_selection=[c.name for c in fields(ModelInfoColumn) if | |
| # c.displayed_by_default and c.name not in ['params', 'available_on_hub', 'hub', | |
| # 'Model sha', 'Hub License']], | |
| # default_selection=list(dataframe.columns), | |
| cant_deselect=[c.name for c in fields(ModelInfoColumn) if c.never_hidden], | |
| label="Select Columns to Display:", | |
| # How to uncheck?? | |
| ), | |
| hide_columns=[c.name for c in fields(ModelInfoColumn) if c.hidden], | |
| search_columns=['model'], | |
| # hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], | |
| # filter_columns=[ | |
| # ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"), | |
| # ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"), | |
| # ColumnFilter( | |
| # AutoEvalColumn.params.name, | |
| # type="slider", | |
| # min=0.01, | |
| # max=500, | |
| # label="Select the number of parameters (B)", | |
| # ), | |
| # ColumnFilter( | |
| # AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=False | |
| # ), | |
| # ], | |
| filter_columns=[ | |
| ColumnFilter(ModelInfoColumn.model_type.name, type="checkboxgroup", label="Model types"), | |
| ColumnFilter(ModelInfoColumn.testdata_leakage.name, type="checkboxgroup", label="TestData Leakage"), | |
| ], | |
| # bool_checkboxgroup_label="", | |
| column_widths=[40, 150] + [180 for _ in range(len(merged_df.columns)-2)], | |
| interactive=False, | |
| ) | |
| demo = gr.Blocks(css=custom_css) | |
| with demo: | |
| gr.HTML(TITLE) | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem('🏅 Overall', elem_id="llm-benchmark-tab-table", id=5): | |
| # leaderboard = init_leaderboard(overall_df, model_info_df, sort_val='Rank') | |
| leaderboard = init_leaderboard(overall_df, model_info_df, sort_val=['TestData Leakage', 'MASE_Rank']) | |
| print(f'FINAL Overall LEADERBOARD {overall_df}') | |
| with gr.TabItem("🏅 By Domain", elem_id="llm-benchmark-tab-table", id=0): | |
| leaderboard = init_leaderboard(domain_df, model_info_df) | |
| print(f"FINAL Domain LEADERBOARD 1 {domain_df}") | |
| with gr.TabItem("🏅 By Frequency", elem_id="llm-benchmark-tab-table", id=1): | |
| leaderboard = init_leaderboard(freq_df, model_info_df) | |
| print(f"FINAL Frequency LEADERBOARD 1 {freq_df}") | |
| with gr.TabItem("🏅 By Term Length", elem_id="llm-benchmark-tab-table", id=2): | |
| leaderboard = init_leaderboard(term_length_df, model_info_df) | |
| print(f"FINAL term length LEADERBOARD 1 {term_length_df}") | |
| with gr.TabItem("🏅 By Variate Type", elem_id="llm-benchmark-tab-table", id=3): | |
| leaderboard = init_leaderboard(variate_type_df, model_info_df) | |
| print(f"FINAL LEADERBOARD 1 {variate_type_df}") | |
| with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=4): | |
| gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") | |
| # Trigger the column filters once on initial load so default selections take effect | |
| demo.load( | |
| js=""" | |
| () => { | |
| // Make the JS fire one legitimate `input` event once the checkboxgroup | |
| // component is ready. `querySelector` looks for the *wrapper* div Gradio | |
| // puts around the checkbox-group. | |
| const target = document.querySelector( | |
| 'div[data-testid="checkboxgroup-model types"]'); | |
| if (!target) { return []; } // safety guard | |
| // Ask Gradio’s front-end to re-compute its filters: | |
| target.dispatchEvent(new Event('input', { bubbles: true })); | |
| return []; // load() must return something | |
| } | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Accordion("📙 Citation", open=False): | |
| citation_button = gr.Textbox( | |
| value=CITATION_BUTTON_TEXT, | |
| label=CITATION_BUTTON_LABEL, | |
| lines=20, | |
| elem_id="citation-button", | |
| show_copy_button=True, | |
| ) | |
| scheduler = BackgroundScheduler() | |
| # scheduler.add_job(restart_space, "interval", seconds=1800) | |
| scheduler.start() | |
| demo.queue(default_concurrency_limit=40).launch() |