| import pickle |
| from argparse import ArgumentParser |
| from dataclasses import dataclass |
| from math import isnan |
|
|
| import pandas as pd |
| from paperswithcode import PapersWithCodeClient |
| from paperswithcode.models import ( |
| EvaluationTableSyncRequest, |
| MetricSyncRequest, |
| ResultSyncRequest, |
| ) |
|
|
|
|
| @dataclass |
| class Task: |
| id: str |
| name: str |
| description: str |
| area: str |
| parent_task: str |
|
|
|
|
| _metrics = {"time": "training time (s)", "carbon_emission": "CO2 Emission (g)"} |
|
|
|
|
| def make_table(results_csv_list: list[str], metric: str): |
| df_list = [] |
| for results_csv in results_csv_list: |
| df = pd.read_csv(results_csv) |
| columns = ["score"] |
| if "time" in df.columns: |
| columns.append("time") |
| if "carbon_emission" in df.columns: |
| columns.append("carbon_emission") |
| df = ( |
| df.groupby(["dataset", "paradigm", "evaluation", "pipeline"])[columns] |
| .mean() |
| .reset_index() |
| ) |
| df.score = df.score * 100 |
| columns = dict(**_metrics, score=metric) |
| df.rename(columns=columns, inplace=True) |
| df.paradigm = df.paradigm.replace( |
| {"FilterBankMotorImagery": "MotorImagery", "LeftRightImagery": "MotorImagery"} |
| ) |
| print(df.head()) |
| df_list.append(df) |
| return pd.concat(df_list) |
|
|
|
|
| def upload_subtable(client, df, dataset, task, paper, evaluated_on): |
| kwargs = { |
| "task": task.id, |
| "dataset": dataset.id, |
| "description": task.description, |
| "external_id": f"{dataset.id}-{task.id}", |
| "mirror_url": "http://moabb.neurotechx.com/docs/benchmark_summary.html", |
| } |
| print(f"Uploading {kwargs=}") |
| |
|
|
| r = EvaluationTableSyncRequest( |
| **kwargs, |
| metrics=[ |
| MetricSyncRequest(name=metric, is_loss=metric in _metrics.values()) |
| for metric in df.columns |
| ], |
| results=[ |
| ResultSyncRequest( |
| metrics={k: str(v) for k, v in row.to_dict().items() if not isnan(v)}, |
| paper=paper, |
| methodology=pipeline, |
| external_id=f"{dataset.id}-{task.id}-{pipeline}", |
| evaluated_on=evaluated_on, |
| |
| |
| ) |
| for pipeline, row in df.iterrows() |
| ], |
| ) |
| print(r) |
| leaderboard_id = client.evaluation_synchronize(r) |
| print(f"{leaderboard_id=}") |
| return leaderboard_id |
|
|
|
|
| def upload_table(client, df, datasets, tasks, paper, evaluated_on, subsubtask): |
| gp_cols = ["dataset", "paradigm", "evaluation"] |
| df_gp = df.groupby(gp_cols) |
| ids = [] |
| for (dataset_name, paradigm_name, evaluation_name), sub_df in df_gp: |
| dataset = datasets[dataset_name] |
| task_key = (paradigm_name, evaluation_name) |
| if subsubtask is not None: |
| task_key += (subsubtask,) |
| task = tasks[task_key] |
| id = upload_subtable( |
| client, |
| sub_df.set_index("pipeline").drop( |
| columns=gp_cols |
| ), |
| dataset, |
| task, |
| paper, |
| evaluated_on, |
| ) |
| ids.append(id) |
| return ids |
|
|
|
|
| if __name__ == "__main__": |
| parser = ArgumentParser() |
| parser.add_argument("token", type=str, help="PapersWithCode API token") |
| parser.add_argument( |
| "metric", |
| type=str, |
| help="Metric used in the results CSV (see PapersWithCode metrics)", |
| ) |
| parser.add_argument( |
| "results_csv", type=str, help="CSV file with results to upload", nargs="+" |
| ) |
|
|
| parser.add_argument( |
| "-s", |
| "--subsubtask", |
| type=str, |
| default=None, |
| help="If relevant, the type of motor imagery task (see create_datasets_and_tasks.py)", |
| ) |
| parser.add_argument( |
| "-d", |
| "--datasets", |
| type=str, |
| help="Pickle file created by create_datasets_and_tasks.py", |
| default="paperswithcode_datasets_and_tasks.pickle", |
| ) |
| parser.add_argument( |
| "-o", |
| "--output", |
| type=str, |
| help="Pickle output file", |
| default="paperswithcode_results.pickle", |
| ) |
| parser.add_argument("-p", "--paper", type=str, help="Paper URL", default="") |
| parser.add_argument( |
| "-e", |
| "--evaluated_on", |
| type=str, |
| help="Results date YYYY-MM-DD", |
| default="2024-04-09", |
| ) |
| args = parser.parse_args() |
|
|
| with open(args.datasets, "rb") as f: |
| datasets = pickle.load(f) |
| summary_table = make_table(args.results_csv, metric=args.metric) |
|
|
| client = PapersWithCodeClient(token=args.token) |
|
|
| upload_table( |
| client, |
| summary_table, |
| datasets["datasets"], |
| datasets["tasks"], |
| args.paper, |
| args.evaluated_on, |
| args.subsubtask, |
| ) |
|
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