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Added results for multimodal benchmark into FEDOT.docs (#1115)
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@@ -8,3 +8,4 @@ We make a comparison with existing state-of-the-art AutoML libraries on various | |
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forecasting | ||
tabular | ||
multimodal |
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Multimodal data | ||
--------------- | ||
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Here are overall classification and regression problems results on multimodal datasets across state-of-the-art AutoML frameworks | ||
using `AutoML Multimodal Benchmark <https://github.com/sxjscience/automl_multimodal_benchmark>`__ test suite: | ||
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.. raw:: html | ||
:file: multimodal_res.html | ||
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The results are obtained using sever based on Xeon Cascadelake (2900MHz) | ||
with 12 cores and 24GB memory for experiments with the local infrastructure. |
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Dataset name,Metric name,AutoGluon,FEDOT | ||
prod,accuracy,0.895,0.897 | ||
salary,accuracy,0.528,0.402 | ||
airbnb,accuracy,0.466,0.434 | ||
channel,accuracy,0.550,0.542 | ||
wine,accuracy,0.842,0.784 | ||
imdb,auc,0.874,0.872 | ||
fake,auc,0.968,0.958 | ||
kick,auc,0.775,0.752 | ||
jigsaw,auc,0.915,0.998 | ||
qaa,r2,0.383,0.504 | ||
qaq,r2,0.426,0.554 | ||
book,r2,0.6,0.994 | ||
jc,r2,0.612,0.619 | ||
cloth,r2,0.654,0.733 | ||
ae,r2,0.979,0.974 | ||
pop,r2,0.02,0.02 | ||
house,r2,0.943,0.928 | ||
mercari,r2,0.569,0.52 |
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<style type="text/css"> | ||
#T_benchmark table { | ||
border: 1px solid #e1e4e5; | ||
text-align: center; | ||
font-size: .9rem; | ||
} | ||
#T_benchmark th { | ||
border: 1px solid #e1e4e5; | ||
text-align: center; | ||
font-size: .9rem; | ||
} | ||
#T_benchmark td { | ||
border: 1px solid #e1e4e5; | ||
text-align: center; | ||
font-size: .9rem; | ||
} | ||
#T_benchmark th { | ||
padding: 8px 16px; | ||
} | ||
#T_benchmark td { | ||
padding: 8px 16px; | ||
} | ||
#T_benchmark tr { | ||
background-color: #fff; | ||
} | ||
#T_benchmark tbody tr:nth-child(odd) { | ||
background-color: #f3f6f6; | ||
} | ||
#T_benchmarkrow0_col1, #T_benchmarkrow1_col0, #T_benchmarkrow2_col0, #T_benchmarkrow3_col0, #T_benchmarkrow4_col0, #T_benchmarkrow5_col0, #T_benchmarkrow6_col0, #T_benchmarkrow7_col0, #T_benchmarkrow8_col1, #T_benchmarkrow9_col1, #T_benchmarkrow10_col1, #T_benchmarkrow11_col1, #T_benchmarkrow12_col1, #T_benchmarkrow13_col1, #T_benchmarkrow14_col0, #T_benchmarkrow15_col0, #T_benchmarkrow15_col1, #T_benchmarkrow16_col0, #T_benchmarkrow17_col0 { | ||
color: blue; | ||
font-weight: bold; | ||
} | ||
</style> | ||
<div style="overflow: auto;"> | ||
<table id="T_benchmark" style="width: 100%; border-collapse: collapse; font-family: Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;"> | ||
<thead> | ||
<tr> | ||
<th class="blank"> | ||
</th> | ||
<th class="index_name level0"> | ||
framework | ||
</th> | ||
<th class="col_heading level0 col0"> | ||
AutoGluon | ||
</th> | ||
<th class="col_heading level0 col1"> | ||
FEDOT | ||
</th> | ||
</tr> | ||
<tr> | ||
<th class="index_name level0"> | ||
Dataset name | ||
</th> | ||
<th class="index_name level1"> | ||
Metric name | ||
</th> | ||
<th class="blank col0"> | ||
</th> | ||
<th class="blank col1"> | ||
</th> | ||
</tr> | ||
</thead> | ||
<tbody> | ||
<tr> | ||
<th class="row_heading level0 row0" id="T_benchmarklevel0_row0"> | ||
prod | ||
</th> | ||
<th class="row_heading level1 row0" id="T_benchmarklevel1_row0"> | ||
accuracy | ||
</th> | ||
<td class="data row0 col0" id="T_benchmarkrow0_col0"> | ||
0.895 | ||
</td> | ||
<td class="data row0 col1" id="T_benchmarkrow0_col1"> | ||
0.897 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row1" id="T_benchmarklevel0_row1"> | ||
salary | ||
</th> | ||
<th class="row_heading level1 row1" id="T_benchmarklevel1_row1"> | ||
accuracy | ||
</th> | ||
<td class="data row1 col0" id="T_benchmarkrow1_col0"> | ||
0.528 | ||
</td> | ||
<td class="data row1 col1" id="T_benchmarkrow1_col1"> | ||
0.402 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row2" id="T_benchmarklevel0_row2"> | ||
airbnb | ||
</th> | ||
<th class="row_heading level1 row2" id="T_benchmarklevel1_row2"> | ||
accuracy | ||
</th> | ||
<td class="data row2 col0" id="T_benchmarkrow2_col0"> | ||
0.466 | ||
</td> | ||
<td class="data row2 col1" id="T_benchmarkrow2_col1"> | ||
0.434 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row3" id="T_benchmarklevel0_row3"> | ||
channel | ||
</th> | ||
<th class="row_heading level1 row3" id="T_benchmarklevel1_row3"> | ||
accuracy | ||
</th> | ||
<td class="data row3 col0" id="T_benchmarkrow3_col0"> | ||
0.550 | ||
</td> | ||
<td class="data row3 col1" id="T_benchmarkrow3_col1"> | ||
0.542 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row4" id="T_benchmarklevel0_row4"> | ||
wine | ||
</th> | ||
<th class="row_heading level1 row4" id="T_benchmarklevel1_row4"> | ||
accuracy | ||
</th> | ||
<td class="data row4 col0" id="T_benchmarkrow4_col0"> | ||
0.842 | ||
</td> | ||
<td class="data row4 col1" id="T_benchmarkrow4_col1"> | ||
0.784 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row5" id="T_benchmarklevel0_row5"> | ||
imdb | ||
</th> | ||
<th class="row_heading level1 row5" id="T_benchmarklevel1_row5"> | ||
auc | ||
</th> | ||
<td class="data row5 col0" id="T_benchmarkrow5_col0"> | ||
0.874 | ||
</td> | ||
<td class="data row5 col1" id="T_benchmarkrow5_col1"> | ||
0.872 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row6" id="T_benchmarklevel0_row6"> | ||
fake | ||
</th> | ||
<th class="row_heading level1 row6" id="T_benchmarklevel1_row6"> | ||
auc | ||
</th> | ||
<td class="data row6 col0" id="T_benchmarkrow6_col0"> | ||
0.968 | ||
</td> | ||
<td class="data row6 col1" id="T_benchmarkrow6_col1"> | ||
0.958 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row7" id="T_benchmarklevel0_row7"> | ||
kick | ||
</th> | ||
<th class="row_heading level1 row7" id="T_benchmarklevel1_row7"> | ||
auc | ||
</th> | ||
<td class="data row7 col0" id="T_benchmarkrow7_col0"> | ||
0.775 | ||
</td> | ||
<td class="data row7 col1" id="T_benchmarkrow7_col1"> | ||
0.752 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row8" id="T_benchmarklevel0_row8"> | ||
jigsaw | ||
</th> | ||
<th class="row_heading level1 row8" id="T_benchmarklevel1_row8"> | ||
auc | ||
</th> | ||
<td class="data row8 col0" id="T_benchmarkrow8_col0"> | ||
0.915 | ||
</td> | ||
<td class="data row8 col1" id="T_benchmarkrow8_col1"> | ||
0.998 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row9" id="T_benchmarklevel0_row9"> | ||
qaa | ||
</th> | ||
<th class="row_heading level1 row9" id="T_benchmarklevel1_row9"> | ||
r2 | ||
</th> | ||
<td class="data row9 col0" id="T_benchmarkrow9_col0"> | ||
0.383 | ||
</td> | ||
<td class="data row9 col1" id="T_benchmarkrow9_col1"> | ||
0.504 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row10" id="T_benchmarklevel0_row10"> | ||
qaq | ||
</th> | ||
<th class="row_heading level1 row10" id="T_benchmarklevel1_row10"> | ||
r2 | ||
</th> | ||
<td class="data row10 col0" id="T_benchmarkrow10_col0"> | ||
0.426 | ||
</td> | ||
<td class="data row10 col1" id="T_benchmarkrow10_col1"> | ||
0.554 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row11" id="T_benchmarklevel0_row11"> | ||
book | ||
</th> | ||
<th class="row_heading level1 row11" id="T_benchmarklevel1_row11"> | ||
r2 | ||
</th> | ||
<td class="data row11 col0" id="T_benchmarkrow11_col0"> | ||
0.600 | ||
</td> | ||
<td class="data row11 col1" id="T_benchmarkrow11_col1"> | ||
0.994 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row12" id="T_benchmarklevel0_row12"> | ||
jc | ||
</th> | ||
<th class="row_heading level1 row12" id="T_benchmarklevel1_row12"> | ||
r2 | ||
</th> | ||
<td class="data row12 col0" id="T_benchmarkrow12_col0"> | ||
0.612 | ||
</td> | ||
<td class="data row12 col1" id="T_benchmarkrow12_col1"> | ||
0.619 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row13" id="T_benchmarklevel0_row13"> | ||
cloth | ||
</th> | ||
<th class="row_heading level1 row13" id="T_benchmarklevel1_row13"> | ||
r2 | ||
</th> | ||
<td class="data row13 col0" id="T_benchmarkrow13_col0"> | ||
0.654 | ||
</td> | ||
<td class="data row13 col1" id="T_benchmarkrow13_col1"> | ||
0.733 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row14" id="T_benchmarklevel0_row14"> | ||
ae | ||
</th> | ||
<th class="row_heading level1 row14" id="T_benchmarklevel1_row14"> | ||
r2 | ||
</th> | ||
<td class="data row14 col0" id="T_benchmarkrow14_col0"> | ||
0.979 | ||
</td> | ||
<td class="data row14 col1" id="T_benchmarkrow14_col1"> | ||
0.974 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row15" id="T_benchmarklevel0_row15"> | ||
pop | ||
</th> | ||
<th class="row_heading level1 row15" id="T_benchmarklevel1_row15"> | ||
r2 | ||
</th> | ||
<td class="data row15 col0" id="T_benchmarkrow15_col0"> | ||
0.020 | ||
</td> | ||
<td class="data row15 col1" id="T_benchmarkrow15_col1"> | ||
0.020 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row16" id="T_benchmarklevel0_row16"> | ||
house | ||
</th> | ||
<th class="row_heading level1 row16" id="T_benchmarklevel1_row16"> | ||
r2 | ||
</th> | ||
<td class="data row16 col0" id="T_benchmarkrow16_col0"> | ||
0.943 | ||
</td> | ||
<td class="data row16 col1" id="T_benchmarkrow16_col1"> | ||
0.928 | ||
</td> | ||
</tr> | ||
<tr> | ||
<th class="row_heading level0 row17" id="T_benchmarklevel0_row17"> | ||
mercari | ||
</th> | ||
<th class="row_heading level1 row17" id="T_benchmarklevel1_row17"> | ||
r2 | ||
</th> | ||
<td class="data row17 col0" id="T_benchmarkrow17_col0"> | ||
0.569 | ||
</td> | ||
<td class="data row17 col1" id="T_benchmarkrow17_col1"> | ||
0.520 | ||
</td> | ||
</tr> | ||
</tbody> | ||
</table> | ||
</div> |