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Commit fea72d79 authored by Mohamed Sebabti's avatar Mohamed Sebabti
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model rn

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......@@ -403,3 +403,93 @@ Fitting 3 folds for each of 48 candidates, totalling 144 fits
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.0min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/numpy/_core/_methods.py:135: RuntimeWarning: overflow encountered in reduce
ret = umr_sum(arr, axis, dtype, out, keepdims, where=where)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:172: RuntimeWarning: overflow encountered in square
return ((y_true - y_pred) ** 2).mean() / 2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/utils/extmath.py:203: RuntimeWarning: overflow encountered in matmul
ret = a @ b
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_base.py:127: RuntimeWarning: invalid value encountered in multiply
delta *= 1 - Z**2
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
[CV] END activation=tanh, hidden_layer_sizes=(100, 100), learning_rate_init=0.1, max_iter=500, solver=sgd; total time= 3.0min
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:528: FitFailedWarning:
21 fits failed out of a total of 144.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.
Below are more details about the failures:
--------------------------------------------------------------------------------
21 fits failed with the following error:
Traceback (most recent call last):
File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_validation.py", line 866, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/base.py", line 1389, in wrapper
return fit_method(estimator, *args, **kwargs)
File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 754, in fit
return self._fit(X, y, incremental=False)
File "/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 496, in _fit
raise ValueError(
ValueError: Solver produced non-finite parameter weights. The input data may contain large values and need to be preprocessed.
warnings.warn(some_fits_failed_message, FitFailedWarning)
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ 0.54900442 0.55026229 0.55175494 0.54449637 0.54619006 nan
0.55486645 0.55397549 0.5576357 nan 0.54283811 nan
0.5408644 0.50266413 0.49902525 0.17836774 0.54044313 -0.00213426
0.51490498 0.44610184 0.47578625 nan 0.53347605 nan
0.55993497 0.5530103 0.55283927 0.5452355 0.52950521 0.20815397
0.55149549 0.54391725 0.5429399 0.53528469 0.53307444 nan
0.49646465 0.48566926 0.51478239 0.54206741 0.49009549 nan
0.48790796 0.40424641 0.4902751 0.53533079 0.48903065 nan]
warnings.warn(
/home/mohamed.sebabti/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
Meilleurs paramètres : {'activation': 'tanh', 'hidden_layer_sizes': (50,), 'learning_rate_init': 0.001, 'max_iter': 500, 'solver': 'adam'}
📊 MLPRegressor Optimisé - MAE: 9.16, RMSE: 12.46, R²: 0.578
id ... pred_mlp
0 7pQSmQ0l7QdBeL9X6CEMbH ... NaN
1 2LcJoQ5SDUZrC2qUjWMEdF ... NaN
2 6RzHyUtRNARYyn2AIuoLnY ... NaN
3 6Kd0I5es8911FZpYhFS053 ... NaN
4 0IhY390qx5QJEnRXpeuEwq ... NaN
5 1e7M98usgS7tK89PoEbqpz ... NaN
6 7mZwaEEaHSeTYC2hTAjUki ... NaN
7 0uzPDHGV0ZAjk8wLlryt7C ... NaN
8 45t91j2BnZBTBtechHEdEo ... 38.985588
9 0CIRYGtFan5C3t1udR9A2p ... NaN
10 4qgBYQNjvnuwxp9a68LG0t ... NaN
11 3PHtZBkUc9dDxnaVH9lpTd ... NaN
12 0PFRTA04YOjj4VT3O6MpgP ... NaN
13 1uRKT2LRANv4baowBWHfDS ... NaN
14 6OgG8LnH5gjxlOmBrogYo5 ... NaN
15 3UT1XINeKNAmRyaCHWBOVt ... NaN
16 1ckIrPqEU43EltNMg8HzlV ... NaN
17 4ZHPfdvTgCDGIeP8fAUVpG ... NaN
18 5U1cEuyrUubzj5gc5T7uND ... NaN
19 0RbSPGMjoqjO0GjPFozPkY ... NaN
20 0Cwl389eg6mvLxcaAbV24Z ... NaN
21 0W18O5YKX34dnMqN8MQFfK ... NaN
22 5hNX9VwbZXJ29NW79dDeDz ... NaN
23 3RDMd9JiKdVik8zjGhZ0wJ ... NaN
24 7MER2sjr6GgSohO2vpNO2g ... NaN
25 7GDciQOihRs8nQFEFOrc5K ... NaN
26 24ejNAm1vGCiaB9GBLos4n ... NaN
27 1oXJHuq86np471uQGQjfqg ... NaN
28 3jfdw2ubsW6gUXrf8MAggA ... NaN
29 0awP7BRdUvnzBCaU9YOpiJ ... NaN
30 3vsv5Rj6zpoIvzl8ciwIpd ... NaN
31 15CxpYrIBCl3PX2sVdgDzw ... NaN
32 6vAyOOwgDTH5LCLY2glRTl ... NaN
33 7JZUD0GPmCKtY5XJWL0dJl ... NaN
34 3Wqq9SNrWfdQWqQg4xeYLo ... NaN
35 3ZgQhe1Sv7mnQjpVTJnOFp ... NaN
36 6IEjcFyfMHwQbe26jLojUj ... NaN
37 5jkjpSsMOfsxgdGScPZVq2 ... NaN
38 42alprYTLPWivGUUbuR4dD ... NaN
39 7C8FT11joTPgBCsoDqG735 ... 30.940608
[40 rows x 28 columns]
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