Result table

This table was generated on 2024-09-26 at 19:32. See more results here. See last results here.

results

project_name group_name hostname status Time RMSE Accuracy error_msg BestOf2023-1 profs coktailjet Success 43.53 0.803 0.0 None BestOf2023-2 profs coktailjet Success 182.25 0.859 26.12 None freshtomatoes Master-IASD coktailjet Success 1.36 0.872 0.28 None tetech Master-IASD coktailjet Success 90.39 0.874 0.0 None code-brain-fuel Master-IASD coktailjet Success 14.84 0.884 0.0 None closeai Master-IASD coktailjet Success 0.41 1.037 0.0 None elcoma Master-IASD coktailjet Success 0.44 1.037 0.0 None elon Master-IASD coktailjet Success 0.43 1.037 0.0 None esi Master-IASD coktailjet Success 0.47 1.037 0.0 None just-do-it Master-IASD coktailjet Success 0.43 1.037 0.0 None matrixe Master-IASD coktailjet Success 0.43 1.037 0.0 None palm Master-IASD coktailjet Success 0.46 1.037 0.0 None rimaya Master-IASD coktailjet Success 0.45 1.037 0.0 None theshawshankredemption Master-IASD coktailjet Success 0.49 1.037 0.0 None average profs coktailjet Success 0.42 1.037 0.0 None random profs coktailjet Success 0.46 1.839 14.16 None anxisa Master-IASD coktailjet Success 0.62 5.004 0.0 None alexandre-verinotableandefficientmodel Master-IASD coktailjet Error 0 100 0 bytes: b'Traceback (most recent call last):\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/alexandre-verinotableandefficientmodel/generate.py", line 39, in \n P, Q = torch_matrix_factorization_gradient_descent(sum_tensor, k, alpha, lambda_, mu, ratio)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/alexandre-verinotableandefficientmodel/tmf.py", line 28, in torch_matrix_factorization_gradient_descent\n R_t, R_val = utils.train_val(R_tensor, ratio)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File "/home/lamsade/testplatform/test-platform-a1/repos/Master-IASD/alexandre-verinotableandefficientmodel/utils.py", line 10, in train_val\n R_t = torch.where(mask, R, torch.full_like(R, float(\'nan\')))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!\n'

Plots