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A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.2.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
Traceback (most recent call last): File "/home/desktop309/git/MI-DPC_Conference_paper/_5_test_models.py", line 3, in <module>
import neuromancer
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/neuromancer/__init__.py", line 10, in <module>
from neuromancer import dataset
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/neuromancer/dataset.py", line 7, in <module>
import pandas as pd
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/pandas/__init__.py", line 26, in <module>
from pandas.compat import (
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/pandas/compat/__init__.py", line 27, in <module>
from pandas.compat.pyarrow import (
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/pandas/compat/pyarrow.py", line 8, in <module>
import pyarrow as pa
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/pyarrow/__init__.py", line 65, in <module>
import pyarrow.lib as _lib
AttributeError: _ARRAY_API not found
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.2.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
Traceback (most recent call last): File "/home/desktop309/git/MI-DPC_Conference_paper/_5_test_models.py", line 3, in <module>
import neuromancer
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/neuromancer/__init__.py", line 10, in <module>
from neuromancer import dataset
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/neuromancer/dataset.py", line 7, in <module>
import pandas as pd
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/pandas/__init__.py", line 49, in <module>
from pandas.core.api import (
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/pandas/core/api.py", line 9, in <module>
from pandas.core.dtypes.dtypes import (
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/pandas/core/dtypes/dtypes.py", line 24, in <module>
from pandas._libs import (
File "/home/desktop309/git/.venv/lib/python3.11/site-packages/pyarrow/__init__.py", line 65, in <module>
import pyarrow.lib as _lib
AttributeError: _ARRAY_API not found
/home/desktop309/git/.venv/lib/python3.11/site-packages/torch/utils/_device.py:103: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
return func(*args, **kwargs)
####################################################################################################
####################################################################################################
CPLEX data analysis
----------------------------------------------------------------------------------------------------
N=10
L mean = 5.843378222325237
MIT = 0.0030634004230876053
FUP = 0.0
----------------------------------------------------------------------------------------------------
N=15
L mean = 4.412315152581246
MIT = 0.008544540812608971
FUP = 0.0
----------------------------------------------------------------------------------------------------
N=20
L mean = 4.015206334355445
MIT = 0.042125146222254585
FUP = 0.0
----------------------------------------------------------------------------------------------------
N=25
L mean = 3.888901348124384
MIT = 0.2507174808967578
FUP = 0.04271222651004791
----------------------------------------------------------------------------------------------------
N=30
L mean = 3.84705237148144
MIT = 1.3140732391658339
FUP = 2.442605495452881
####################################################################################################
####################################################################################################
Sigmoid STE data analysis
/home/desktop309/git/.venv/lib/python3.11/site-packages/torch/utils/_device.py:103: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1, 1874, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
return func(*args, **kwargs)
/home/desktop309/git/.venv/lib/python3.11/site-packages/torch/utils/_device.py:103: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([1, 1873, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
return func(*args, **kwargs)
----------------------------------------------------------------------------------------------------
N = 10
L mean = 6.81923
RSM = 14.3102
MIT = 0.00018
NTP = 82603
TT = 231.6466977596283
----------------------------------------------------------------------------------------------------
N = 15
L mean = 4.60304
RSM = 4.1435
MIT = 0.00018
NTP = 84003
TT = 347.5512089729309
----------------------------------------------------------------------------------------------------
N = 20
L mean = 4.19892
RSM = 4.3752
MIT = 0.00018
NTP = 85403
TT = 214.39045786857605
----------------------------------------------------------------------------------------------------
N = 25
L mean = 3.94508
RSM = 1.4240
MIT = 0.00018
NTP = 86803
TT = 440.8222222328186
----------------------------------------------------------------------------------------------------
N = 30
L mean = 3.89197
RSM = 1.1542
MIT = 0.00018
NTP = 88203
TT = 410.2597749233246
----------------------------------------------------------------------------------------------------
N = 40
L mean = 3.85037
RSM = None
MIT = 0.00018
NTP = 91003
TT = 444.1767337322235
####################################################################################################
####################################################################################################
Imitation Learning data analysis
----------------------------------------------------------------------------------------------------
N = 10
L mean = 5.87401
RSM = 0.5215
MIT = 0.00019
NTP = 82603
TT = 1826.9677298069
Number of data samples = 23933
----------------------------------------------------------------------------------------------------
N = 15
L mean = 4.48078
RSM = 1.5281
MIT = 0.00019
NTP = 84003
TT = 2335.1715965270996
Number of data samples = 23913
----------------------------------------------------------------------------------------------------
N = 20
L mean = 4.06216
RSM = 1.1560
MIT = 0.00019
NTP = 85403
TT = 3645.9436752796173
Number of data samples = 23899
----------------------------------------------------------------------------------------------------
N = 25
L mean = 3.96540
RSM = 1.9292
MIT = 0.00019
NTP = 86803
TT = 4405.656575679779
Number of data samples = 23879
----------------------------------------------------------------------------------------------------
N = 30
L mean = 3.89658
RSM = 1.2711
MIT = 0.00019
NTP = 88203
TT = 7053.167684793472
Number of data samples = 23859
----------------------------------------------------------------------------------------------------
N = 40
L mean = 4.99758
RSM = None
MIT = 0.00019
NTP = 91003
TT = 7238.995790243149
Number of data samples = 4424
####################################################################################################
####################################################################################################
Softmax STE data analysis
----------------------------------------------------------------------------------------------------
N = 10
L mean = 6.76038
RSM = 13.5643
MIT = 0.00018
NTP = 83026
TT = 223.2731556892395
----------------------------------------------------------------------------------------------------
N = 15
L mean = 4.79903
RSM = 8.0582
MIT = 0.00018
NTP = 84426
TT = 281.5616145133972
----------------------------------------------------------------------------------------------------
N = 20
L mean = 4.11252
RSM = 2.3663
MIT = 0.00018
NTP = 85826
TT = 373.3109791278839
----------------------------------------------------------------------------------------------------
N = 25
L mean = 3.93284
RSM = 1.1172
MIT = 0.00018
NTP = 87226
TT = 432.68623757362366
----------------------------------------------------------------------------------------------------
N = 30
L mean = 3.90025
RSM = 1.3639
MIT = 0.00018
NTP = 88626
TT = 317.33048963546753
----------------------------------------------------------------------------------------------------
N = 40
L mean = 3.86317
RSM = None
MIT = 0.00018
NTP = 91426
TT = 393.3625774383545
####################################################################################################
####################################################################################################
Learnable Threshold data analysis
----------------------------------------------------------------------------------------------------
N = 10
L mean = 6.41858
RSM = 8.9615
MIT = 0.00042
NTP = 78191
TT = 429.9897541999817
----------------------------------------------------------------------------------------------------
N = 15
L mean = 4.53471
RSM = 2.6990
MIT = 0.00042
NTP = 80091
TT = 573.8098156452179
----------------------------------------------------------------------------------------------------
N = 20
L mean = 4.08385
RSM = 1.6810
MIT = 0.00042
NTP = 81991
TT = 664.5666193962097
----------------------------------------------------------------------------------------------------
N = 25
L mean = 3.90683
RSM = 0.4590
MIT = 0.00042
NTP = 83891
TT = 845.7364530563354
----------------------------------------------------------------------------------------------------
N = 30
L mean = 3.86307
RSM = 0.4146
MIT = 0.00042
NTP = 85791
TT = 821.4241962432861
----------------------------------------------------------------------------------------------------
N = 40
L mean = 3.84013
RSM = None
MIT = 0.00042
NTP = 89591
TT = 1080.8954899311066
...DONE...