moving from default sets to lists

app.py

@@ -1,55 +1,56 @@
-import functools
+from typing import List
 
 import gradio as gr
-import pandas as pd
 import numpy as np
-from typing import List
+import pandas as pd
 
-_ORIGINAL_DF = pd.read_csv('./data/benchmark.csv')
-_METRICS = {'MCC', 'F1', 'ACC'}
-_AGGREGATION_METHODS = {'mean', 'max', 'min', 'median'}
-_DATASETS = set(_ORIGINAL_DF['Dataset'])
+_ORIGINAL_DF = pd.read_csv("./data/benchmark.csv")
+_METRICS = ["MCC", "F1", "ACC"]
+_AGGREGATION_METHODS = ["mean", "max", "min", "median"]
+_DATASETS = list(set(_ORIGINAL_DF["Dataset"]))
 
 _BIBTEX = """@article{DallaTorre2023TheNT,
-  title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
-  author={Hugo Dalla-Torre and Liam Gonzalez and Javier Mendoza Revilla and Nicolas Lopez Carranza and Adam Henryk Grzywaczewski and Francesco Oteri and Christian Dallago and Evan Trop and Hassan Sirelkhatim and Guillaume Richard and Marcin J. Skwark and Karim Beguir and Marie Lopez and Thomas Pierrot},
+  title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},  # noqa
+  author={Hugo Dalla-Torre and Liam Gonzalez and Javier Mendoza Revilla and Nicolas Lopez Carranza and Adam Henryk Grzywaczewski and Francesco Oteri and Christian Dallago and Evan Trop and Hassan Sirelkhatim and Guillaume Richard and Marcin J. Skwark and Karim Beguir and Marie Lopez and Thomas Pierrot},  # noqa
   journal={bioRxiv},
   year={2023},
   url={https://api.semanticscholar.org/CorpusID:255943445}
 }
 """
-_LAST_UPDATED = 'Aug 28, 2023'
+_LAST_UPDATED = "Aug 28, 2023"
 
 banner_url = "./assets/logo.png"
-_BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 600px;"> </div>'
+_BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 600px;"> </div>'  # noqa
 
-_INTRODUCTION_TEXT = "The 🤗 Nucleotide Transformer Leaderboard aims to track, rank and evaluate DNA foundational models on a set of curated downstream tasks with a standardized evaluation protocole."
+_INTRODUCTION_TEXT = "The 🤗 Nucleotide Transformer Leaderboard aims to track, rank and evaluate DNA foundational models on a set of curated downstream tasks with a standardized evaluation protocole."  # noqa
 
 
 def retrieve_array_from_text(text):
-    return np.fromstring(text.replace('[', '').replace(']', ''), dtype=float, sep=',')
+    return np.fromstring(text.replace("[", "").replace("]", ""), dtype=float, sep=",")
 
 
 def format_number(x):
-    return float(f'{x:.3}')
+    return float(f"{x:.3}")
 
 
-def get_dataset(tasks: List[str], target_metric: str = 'MCC', aggregation_method: str = 'mean'):
+def get_dataset(
+    tasks: List[str], target_metric: str = "MCC", aggregation_method: str = "mean"
+):
 
     aggr_fn = getattr(np, aggregation_method)
     scores = _ORIGINAL_DF[target_metric].apply(retrieve_array_from_text).apply(aggr_fn)
     scores = scores.apply(format_number)
-    df = _ORIGINAL_DF.drop(columns=list(_METRICS))
-    df['Score'] = scores
-    df = df.pivot(index='Model', columns='Dataset', values='Score')
+    df = _ORIGINAL_DF.drop(columns=_METRICS)
+    df["Score"] = scores
+    df = df.pivot(index="Model", columns="Dataset", values="Score")
     df = df[tasks]
-    df['All Tasks'] = df.agg('mean', axis='columns').apply(format_number)
+    df["All Tasks"] = df.agg("mean", axis="columns").apply(format_number)
     columns = list(df.columns.values)
     columns.sort()
     df = df[columns]
     df.reset_index(inplace=True)
-    df = df.rename(columns={'index': 'Model'})
-    df = df.sort_values(by=['All Tasks'], ascending=False)
+    df = df.rename(columns={"index": "Model"})
+    df = df.sort_values(by=["All Tasks"], ascending=False)
 
     leaderboard_table = gr.components.Dataframe.update(
         value=df,
@@ -68,29 +69,29 @@ with gr.Blocks() as demo:
 
     with gr.Row():
         metric_choice = gr.Dropdown(
-            choices=list(_METRICS),
+            choices=_METRICS,
             value="MCC",
             label="Metric displayed.",
         )
         aggr_choice = gr.Dropdown(
-            choices=list(_AGGREGATION_METHODS),
+            choices=_AGGREGATION_METHODS,
             value="mean",
             label="Aggregation used over 10-folds.",
         )
 
     with gr.Row():
         selected_tasks = gr.CheckboxGroup(
-            choices=list(_DATASETS),
-            value=list(_DATASETS),
-            label="Tasks",
-            info="Downstream tasks."
+            choices=_DATASETS, value=_DATASETS, label="Tasks", info="Downstream tasks."
         )
+
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0):
-            dataframe = gr.components.Dataframe(elem_id="leaderboard-table",)
+            dataframe = gr.components.Dataframe(
+                elem_id="leaderboard-table",
+            )
 
         with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
-            gr.Markdown('Hey hey hey', elem_classes="markdown-text")
+            gr.Markdown("Hey hey hey", elem_classes="markdown-text")
 
         # with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table",
         #                 id=2):
@@ -114,14 +115,31 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Accordion("📙 Citation", open=False):
             gr.Textbox(
-                value=_BIBTEX, lines=7,
+                value=_BIBTEX,
+                lines=7,
                 label="Copy the BibTeX snippet to cite this source",
                 elem_id="citation-button",
             ).style(show_copy_button=True)
 
-    selected_tasks.change(get_dataset, inputs=[selected_tasks, metric_choice, aggr_choice], outputs=dataframe)
-    metric_choice.change(get_dataset, inputs=[selected_tasks, metric_choice, aggr_choice], outputs=dataframe)
-    aggr_choice.change(get_dataset, inputs=[selected_tasks, metric_choice, aggr_choice], outputs=dataframe)
-    demo.load(fn=get_dataset, inputs=[selected_tasks, metric_choice, aggr_choice], outputs=dataframe)
+    selected_tasks.change(
+        get_dataset,
+        inputs=[selected_tasks, metric_choice, aggr_choice],
+        outputs=dataframe,
+    )
+    metric_choice.change(
+        get_dataset,
+        inputs=[selected_tasks, metric_choice, aggr_choice],
+        outputs=dataframe,
+    )
+    aggr_choice.change(
+        get_dataset,
+        inputs=[selected_tasks, metric_choice, aggr_choice],
+        outputs=dataframe,
+    )
+    demo.load(
+        fn=get_dataset,
+        inputs=[selected_tasks, metric_choice, aggr_choice],
+        outputs=dataframe,
+    )
 
 demo.launch()