first draft

app.py

@@ -0,0 +1,127 @@
+import functools
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+from typing import List
+
+_ORIGINAL_DF = pd.read_csv('./data/benchmark.csv')
+_METRICS = {'MCC', 'F1', 'ACC'}
+_AGGREGATION_METHODS = {'mean', 'max', 'min', 'median'}
+_DATASETS = 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},
+  journal={bioRxiv},
+  year={2023},
+  url={https://api.semanticscholar.org/CorpusID:255943445}
+}
+"""
+_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>'
+
+_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."
+
+
+def retrieve_array_from_text(text):
+    return np.fromstring(text.replace('[', '').replace(']', ''), dtype=float, sep=',')
+
+
+def format_number(x):
+    return float(f'{x:.3}')
+
+
+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 = df[tasks]
+    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)
+
+    leaderboard_table = gr.components.Dataframe.update(
+        value=df,
+        # datatype=TYPES,
+        max_rows=None,
+        interactive=False,
+        visible=True,
+    )
+    return leaderboard_table
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Image(banner_url, height=160, scale=1)
+        gr.Textbox(_INTRODUCTION_TEXT, scale=5)
+
+    with gr.Row():
+        metric_choice = gr.Dropdown(
+            choices=list(_METRICS),
+            value="MCC",
+            label="Metric displayed.",
+        )
+        aggr_choice = gr.Dropdown(
+            choices=list(_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."
+        )
+    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",)
+
+        with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
+            gr.Markdown('Hey hey hey', elem_classes="markdown-text")
+
+        # with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table",
+        #                 id=2):
+        #     with gr.Column():
+        #         gr.Markdown("# ✉️✨ Request results for a new model here!",
+        #                     elem_classes="markdown-text")
+        #     with gr.Column():
+        #         gr.Markdown("Select a dataset:", elem_classes="markdown-text")
+        #         with gr.Column():
+        #             model_name_textbox = gr.Textbox(
+        #                 label="Model name (user_name/model_name)")
+        #             chb_coco2017 = gr.Checkbox(label="COCO validation 2017 dataset",
+        #                                        visible=False, value=True,
+        #                                        interactive=False)
+        #         with gr.Column():
+        #             mdw_submission_result = gr.Markdown()
+        #             btn_submitt = gr.Button(value="🚀 Request")
+
+    gr.Markdown(f"Last updated on **{_LAST_UPDATED}**", elem_classes="markdown-text")
+
+    with gr.Row():
+        with gr.Accordion("📙 Citation", open=False):
+            gr.Textbox(
+                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)
+
+demo.launch()

data/benchmark.csv

@@ -0,0 +1,125 @@
+Dataset,Model,MCC,F1,ACC
+H3,DNABERT-1,"[0.7482815508350361, 0.7596683085405548, 0.7660969858447341, 0.7689077565801784, 0.7673008977336083, 0.7597957786257659, 0.7633501430617131, 0.7218073101900512, 0.7622792762786613, 0.7664427056201113]","[0.8732730140417825, 0.8794300328827183, 0.8828391589301577, 0.8843396323383185, 0.8835491421331787, 0.8796714021177261, 0.880602904040404, 0.8588902383809929, 0.8807840047276667, 0.8830125562650173]","[0.8736631274223328, 0.8796791434288025, 0.8830214142799377, 0.884358286857605, 0.8836898803710938, 0.8796791434288025, 0.8810160756111145, 0.8596256971359253, 0.8810160756111145, 0.8830214142799377]"
+promoter_all,DNABERT-2,"[0.942971328310832, 0.9443154441811433, 0.9422633380369486, 0.9439208581301246, 0.9450230928153951, 0.9479738385448059, 0.9452704860460633, 0.9403212484352065, 0.9400711074334984, 0.937036586896011]","[0.9714517048352591, 0.9721275122940127, 0.9711143497325996, 0.9719594306559163, 0.9724648954953812, 0.9739864746103335, 0.9726351320118622, 0.9700994666990601, 0.9699291341647567, 0.9684085844386766]","[0.9714527130126953, 0.9721283912658691, 0.9711148738861084, 0.9719594717025757, 0.972466230392456, 0.9739865064620972, 0.9726351499557495, 0.9701013565063477, 0.9699324369430542, 0.9684121608734131]"
+H3K9ac,DNABERT-2,"[0.5344027571413028, 0.5421810214039127, 0.5525069250087552, 0.5230910315246928, 0.5470623476976447, 0.546623540561198, 0.5332566783854417, 0.5492637147331741, 0.5441396668344004, 0.5469794572721413]","[0.7627188923569993, 0.7701550506208662, 0.7762236805855545, 0.7612916023060107, 0.7720750491460242, 0.7731645993965807, 0.7663299663299663, 0.7696261072333728, 0.769833967371391, 0.7718082807913815]","[0.7629683017730713, 0.77485591173172, 0.7791786789894104, 0.7651296854019165, 0.7730547189712524, 0.7752161026000977, 0.7701728940010071, 0.7698126435279846, 0.7705331444740295, 0.7726945281028748]"
+enhancers,DNABERT-2,"[0.5300265019876657, 0.530424509479351, 0.502037385505508, 0.45665264819882306, 0.5353279888705554, 0.53, 0.5197580741108034, 0.4950556968990279, 0.5550069376300808, 0.5030527316443673]","[0.7649941248531213, 0.764905962384954, 0.7494927227635964, 0.7270069062243678, 0.767428775062363, 0.765, 0.7563900521752234, 0.7474857960760293, 0.7774986093663085, 0.7492414554025928]","[0.7649999856948853, 0.7649999856948853, 0.75, 0.7274999618530273, 0.7674999833106995, 0.7649999856948853, 0.7574999928474426, 0.7475000023841858, 0.7774999737739563, 0.75]"
+promoter_tata,DNABERT-2,"[0.924929152226695, 0.9119134996017575, 0.9020979020979021, 0.9119933319783221, 0.9025866340988751, 0.9086866248128785, 0.9282583367319251, 0.9184388198135769, 0.8859031012806026, 0.9030598486418306]","[0.9624620129242165, 0.9559542063574034, 0.951048951048951, 0.9559737939249553, 0.9511317960651575, 0.9543331603528801, 0.9641189117058343, 0.9592168577383364, 0.942928992124942, 0.9511669203450026]","[0.9626623392105103, 0.9561688303947449, 0.951298713684082, 0.9561688303947449, 0.951298713684082, 0.9545454382896423, 0.9642857313156128, 0.9594155550003052, 0.9431818127632141, 0.951298713684082]"
+H3K4me2,DNABERT-2,"[0.35721353834799807, 0.3246037981397263, 0.3324692839025147, 0.3274332701274811, 0.3337975902080194, 0.32035591932649876, 0.34226417543937215, 0.3268680972860338, 0.33779551349306935, 0.36079003567941575]","[0.6785181610711811, 0.65959746489474, 0.6643472485059154, 0.6636408984564366, 0.6649174898440713, 0.6599316206535575, 0.6701657201377897, 0.6617570512464604, 0.6661338523741951, 0.6798982814048143]","[0.6906005144119263, 0.6798303127288818, 0.670039176940918, 0.6736292243003845, 0.6834203600883484, 0.6690600514411926, 0.6860313415527344, 0.667754590511322, 0.6860313415527344, 0.6938642263412476]"
+H3K79me3,DNABERT-2,"[0.6117759785529087, 0.604249802789691, 0.6196825585801692, 0.5989352893481307, 0.5949286486709787, 0.6182307315523825, 0.6229857830703471, 0.6207796890888668, 0.6244469343842699, 0.6097613473021399]","[0.8056615067349997, 0.8017275405953681, 0.8089450849796341, 0.7994652163577772, 0.7947827349256497, 0.8089169896159523, 0.8110067197610753, 0.8089543852255716, 0.8115871747366495, 0.8047739151408263]","[0.8069444894790649, 0.8020833730697632, 0.8107638955116272, 0.8003472685813904, 0.7947916984558105, 0.8093750476837158, 0.8125, 0.8111111521720886, 0.8131944537162781, 0.8059027791023254]"
+promoter_no_tata,DNABERT-2,"[0.9459995508809259, 0.9395958834035045, 0.9384624835461015, 0.9414869735569276, 0.9479940294223118, 0.9427165041026788, 0.9466540649772862, 0.943844142274305, 0.948327579772992, 0.9416690415029093]","[0.9729955769367432, 0.9697841069879958, 0.9692212543944716, 0.9707282189946287, 0.9739357251833259, 0.9712911670923462, 0.9731757389448441, 0.9718579644320853, 0.9741259504109008, 0.9707219798972195]","[0.9729984998703003, 0.9697884917259216, 0.9692220687866211, 0.9707326292991638, 0.9739425778388977, 0.9712991118431091, 0.9731873273849487, 0.9718655347824097, 0.9741314053535461, 0.9707326292991638]"
+enhancers_types,DNABERT-2,"[0.43801608774836776, 0.39480051938096566, 0.4462164296395021, 0.4312622270999757, 0.414066842442896, 0.43467170659923565, 0.3990018581867164, 0.4433902755086645, 0.4130127978947361, 0.41077423711145067]","[0.5389506172839507, 0.47044068641565184, 0.5515226895823911, 0.4851593555549507, 0.508013139098606, 0.4753282584022817, 0.48337454126151697, 0.5128506307083976, 0.5047920017607712, 0.5332482315292552]","[0.6349999904632568, 0.5924999713897705, 0.6424999833106995, 0.6200000047683716, 0.6274999976158142, 0.6100000143051147, 0.6074999570846558, 0.625, 0.625, 0.6150000095367432]"
+H3K14ac,DNABERT-2,"[0.5084164726509145, 0.5237832284623603, 0.5093886112122986, 0.501960340287159, 0.5284948468889121, 0.5164396036218774, 0.5301144900131555, 0.5226802144552319, 0.507813245571197, 0.5145272208871023]","[0.7513342429261288, 0.760903008201224, 0.754689744998905, 0.7496117488620481, 0.7597390676092636, 0.7553020937923157, 0.7632842748398323, 0.7612755884162556, 0.7537198505637146, 0.7543778874435569]","[0.7527239918708801, 0.7633172273635864, 0.7590799331665039, 0.7518160343170166, 0.7605932354927063, 0.7566586136817932, 0.7651332020759583, 0.7651332020759583, 0.7593826055526733, 0.7557506561279297]"
+H3K4me1,DNABERT-2,"[0.51249964601378, 0.5188190594812178, 0.49611859666701086, 0.5127816098911838, 0.5195626855253265, 0.5242783557643198, 0.5003138146873604, 0.5094205479723316, 0.5118343855737911, 0.5086587515380008]","[0.7545690893297465, 0.7562586555875146, 0.7475591410826705, 0.7563815494849977, 0.7583381854994958, 0.7598382070491866, 0.7499092359537535, 0.7546855289241304, 0.7545655326268823, 0.7524633593866452]","[0.7591540217399597, 0.7619949579238892, 0.7509469985961914, 0.7585227489471436, 0.7626262903213501, 0.7648358345031738, 0.7528409361839294, 0.7569444179534912, 0.7588383555412292, 0.7572600841522217]"
+splice_sites_all,DNABERT-2,"[0.9077191290446589, 0.9092377967798532, 0.9073885967607539, 0.9111764959523436, 0.9020013530030443, 0.9119105413072893, 0.9052997141265223, 0.9009547966433649, 0.9137222138057713, 0.9143449254871993]","[0.938146589687066, 0.9395277825953691, 0.9382584413196825, 0.9408004373031983, 0.9346720886788661, 0.9411024515098708, 0.9364755513540123, 0.933950653711554, 0.9425834627607914, 0.9429525797767894]","[0.937666654586792, 0.9393333196640015, 0.937999963760376, 0.9403333067893982, 0.934666633605957, 0.940666675567627, 0.9356666803359985, 0.9336666464805603, 0.9423333406448364, 0.9426666498184204]"
+H3K4me3,DNABERT-2,"[0.37870896710333624, 0.3513947194651316, 0.36847622700841975, 0.35147105024900405, 0.34082595578847935, 0.35421783670077633, 0.3663167295979321, 0.34062305198888404, 0.3681662635259586, 0.30199180355693145]","[0.6831459310614767, 0.6738575561059538, 0.6832038091209844, 0.6576294020770173, 0.6650170308032185, 0.6768115848740637, 0.6758962955108079, 0.6702751539943513, 0.6801736138411489, 0.6505518847384464]","[0.6834238767623901, 0.6739130020141602, 0.6834238767623901, 0.6608695387840271, 0.6652173399925232, 0.677445650100708, 0.6763586401939392, 0.6720108389854431, 0.6864129900932312, 0.6510869264602661]"
+H4,DNABERT-2,"[0.7834511539671549, 0.7969540336190496, 0.8054146755924768, 0.7889943678042721, 0.78686063388446, 0.7963037703537222, 0.8011850086926835, 0.8108143024350066, 0.7981421606591401, 0.7921997638935698]","[0.8902474580754904, 0.8981629729301508, 0.9027003702197451, 0.8944541098190031, 0.8933049794170684, 0.8980271197778276, 0.9002540461850255, 0.9050906618464669, 0.8985931675273342, 0.8959603658536586]","[0.8907967209815979, 0.8990384936332703, 0.9038462042808533, 0.8956044316291809, 0.8949176073074341, 0.8990384936332703, 0.901098906993866, 0.905906617641449, 0.9004120826721191, 0.8969780206680298]"
+H3,DNABERT-2,"[0.7822219167778175, 0.8022541397589973, 0.7878534391942859, 0.772607332598298, 0.778300249683909, 0.7889369936963635, 0.771848222871967, 0.8046997389033943, 0.7712512400013819, 0.7940211327403449]","[0.8908613231466749, 0.9010481190883527, 0.8934899955446198, 0.886246529069934, 0.889021366658146, 0.8943661468276952, 0.8854371410338224, 0.9023498694516972, 0.8856252238283466, 0.896952738098646]","[0.8910427689552307, 0.9010695219039917, 0.89371657371521, 0.8863636255264282, 0.8890374302864075, 0.894385039806366, 0.885695219039917, 0.9024064540863037, 0.885695219039917, 0.8970588445663452]"
+splice_sites_acceptors,DNABERT-2,"[0.9476835107682485, 0.9530926717027766, 0.9485635204447879, 0.9503787652354947, 0.9504380355446014, 0.9413826101940671, 0.9494543665340462, 0.9531462054476831, 0.9494567370043685, 0.9530926717027766]","[0.9738219170158355, 0.9765336079007648, 0.9742767997350945, 0.9751796512240027, 0.9751743124610571, 0.9706673862022268, 0.9747271832670231, 0.9765341240006353, 0.9747275744287759, 0.9765336079007648]","[0.9738267064094543, 0.9765343070030212, 0.9742779731750488, 0.9751805067062378, 0.9751805067062378, 0.970667839050293, 0.9747292399406433, 0.9765343070030212, 0.9747292399406433, 0.9765343070030212]"
+splice_sites_donors,DNABERT-2,"[0.9242896811445462, 0.9271975593941848, 0.9237088903458133, 0.9327089196479408, 0.9299183835497035, 0.9247476984720868, 0.921589848986675, 0.9206871855228632, 0.9270329063083603, 0.9270086604319921]","[0.9621350286158002, 0.963501705029838, 0.961674237681005, 0.9662385993248053, 0.9648706177508861, 0.9621284077948835, 0.9607661294015102, 0.960309814217613, 0.9635036192521739, 0.9635035281032823]","[0.9621350169181824, 0.9635036587715149, 0.9616788029670715, 0.9662408828735352, 0.9648722410202026, 0.9621350169181824, 0.9607664346694946, 0.9603102207183838, 0.9635036587715149, 0.9635036587715149]"
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