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bartzbeielsteinbartzbeielstein
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0.24.4
print instead of show
1 parent 9fb562e commit 7d19b05

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Lines changed: 407 additions & 6 deletions

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‎notebooks/00_spotPython_tests.ipynb‎

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"S_loaded_2.run()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## print_exp_table()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 123\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"module_name: light\n",
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"submodule_name: regression\n",
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"model_name: NNLinearRegressor\n",
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"| name | type | default | lower | upper | transform |\n",
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"|----------------|--------|-----------|---------|---------|-----------------------|\n",
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"| l1 | int | 3 | 1 | 2 | transform_power_2_int |\n",
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"| epochs | int | 4 | 2 | 2 | transform_power_2_int |\n",
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"| batch_size | int | 4 | 4 | 11 | transform_power_2_int |\n",
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"| act_fn | factor | ReLU | 0 | 5 | None |\n",
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"| optimizer | factor | SGD | 0 | 2 | None |\n",
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"| dropout_prob | float | 0.01 | 0 | 0.025 | None |\n",
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"| lr_mult | float | 1.0 | 0.1 | 10 | None |\n",
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"| patience | int | 2 | 1 | 2 | transform_power_2_int |\n",
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"| batch_norm | factor | 0 | 0 | 1 | None |\n",
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"| initialization | factor | Default | 0 | 4 | None |\n"
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]
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}
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],
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"source": [
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"from spotpython.data.diabetes import Diabetes\n",
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"from spotpython.hyperdict.light_hyper_dict import LightHyperDict\n",
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"from spotpython.fun.hyperlight import HyperLight\n",
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"from spotpython.utils.init import fun_control_init\n",
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"from spotpython.utils.eda import print_exp_table, print_res_table\n",
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"from spotpython.hyperparameters.values import set_hyperparameter\n",
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"\n",
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"fun_control = fun_control_init(\n",
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" PREFIX=\"show_exp_table\",\n",
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" fun_evals=5,\n",
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" max_time=1,\n",
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" data_set = Diabetes(),\n",
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" core_model_name=\"light.regression.NNLinearRegressor\",\n",
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" hyperdict=LightHyperDict,\n",
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" _L_in=10,\n",
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" _L_out=1)\n",
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"\n",
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"set_hyperparameter(fun_control, \"optimizer\", [ \"Adadelta\", \"Adam\", \"Adamax\"])\n",
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"set_hyperparameter(fun_control, \"l1\", [1,2])\n",
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"set_hyperparameter(fun_control, \"epochs\", [2,2])\n",
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"set_hyperparameter(fun_control, \"batch_size\", [4,11])\n",
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"set_hyperparameter(fun_control, \"dropout_prob\", [0.0, 0.025])\n",
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"set_hyperparameter(fun_control, \"patience\", [1,2])\n",
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"\n",
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"fun = HyperLight().fun\n",
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"\n",
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"print_exp_table(fun_control)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## print_res_table()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 123\n",
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"Seed set to 123\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'act_fn' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['act_fn'])`.\n",
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"GPU available: True (mps), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n",
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"\n",
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" | Name | Type | Params | Mode | In sizes | Out sizes\n",
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"----------------------------------------------------------------------\n",
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"0 | layers | Sequential | 14.2 K | train | [512, 10] | [512, 1] \n",
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"----------------------------------------------------------------------\n",
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"14.2 K Trainable params\n",
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"0 Non-trainable params\n",
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"14.2 K Total params\n",
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"0.057 Total estimated model params size (MB)\n",
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"24 Modules in train mode\n",
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"0 Modules in eval mode\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=23` in the `DataLoader` to improve performance.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"module_name: light\n",
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"submodule_name: regression\n",
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"model_name: NNLinearRegressor\n",
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"Experiment saved to show_exp_table_exp.pkl\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=23` in the `DataLoader` to improve performance.\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
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"`Trainer.fit` stopped: `max_epochs=4` reached.\n",
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"GPU available: True (mps), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n",
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"\n",
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" | Name | Type | Params | Mode | In sizes | Out sizes\n",
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"-----------------------------------------------------------------------\n",
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"0 | layers | Sequential | 4.0 K | train | [1024, 10] | [1024, 1]\n",
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"-----------------------------------------------------------------------\n",
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"4.0 K Trainable params\n",
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"0 Non-trainable params\n",
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"4.0 K Total params\n",
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"0.016 Total estimated model params size (MB)\n",
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"24 Modules in train mode\n",
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"0 Modules in eval mode\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"train_model result: {'val_loss': 24008.939453125, 'hp_metric': 24008.939453125}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"GPU available: True (mps), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n",
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"\n",
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" | Name | Type | Params | Mode | In sizes | Out sizes\n",
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"---------------------------------------------------------------------\n",
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"0 | layers | Sequential | 3.7 K | train | [32, 10] | [32, 1] \n",
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"---------------------------------------------------------------------\n",
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"3.7 K Trainable params\n",
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"0 Non-trainable params\n",
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"3.7 K Total params\n",
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"0.015 Total estimated model params size (MB)\n",
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"17 Modules in train mode\n",
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"0 Modules in eval mode\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"train_model result: {'val_loss': 24078.296875, 'hp_metric': 24078.296875}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
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"`Trainer.fit` stopped: `max_epochs=4` reached.\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'act_fn' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['act_fn'])`.\n",
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"GPU available: True (mps), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n",
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"\n",
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" | Name | Type | Params | Mode | In sizes | Out sizes\n",
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"---------------------------------------------------------------------\n",
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"0 | layers | Sequential | 13.5 K | train | [32, 10] | [32, 1] \n",
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"---------------------------------------------------------------------\n",
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"13.5 K Trainable params\n",
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"0 Non-trainable params\n",
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"13.5 K Total params\n",
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"0.054 Total estimated model params size (MB)\n",
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"17 Modules in train mode\n",
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"0 Modules in eval mode\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=23` in the `DataLoader` to improve performance.\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=23` in the `DataLoader` to improve performance.\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"train_model result: {'val_loss': 23962.169921875, 'hp_metric': 23962.169921875}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_epochs=4` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"train_model result: {'val_loss': 17713.64453125, 'hp_metric': 17713.64453125}\n",
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"spotpython tuning: 17713.64453125 [########--] 80.00% \n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/utilities/parsing.py:208: Attribute 'act_fn' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['act_fn'])`.\n",
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"GPU available: True (mps), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"HPU available: False, using: 0 HPUs\n",
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"\n",
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" | Name | Type | Params | Mode | In sizes | Out sizes\n",
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"---------------------------------------------------------------------\n",
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"0 | layers | Sequential | 13.5 K | train | [32, 10] | [32, 1] \n",
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"---------------------------------------------------------------------\n",
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"13.5 K Trainable params\n",
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"0 Non-trainable params\n",
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"13.5 K Total params\n",
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"0.054 Total estimated model params size (MB)\n",
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"17 Modules in train mode\n",
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"0 Modules in eval mode\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=23` in the `DataLoader` to improve performance.\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:424: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=23` in the `DataLoader` to improve performance.\n",
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"/Users/bartz/miniforge3/envs/spot312/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
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"`Trainer.fit` stopped: `max_epochs=4` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"train_model result: {'val_loss': 18886.90625, 'hp_metric': 18886.90625}\n",
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"spotpython tuning: 17713.64453125 [##########] 100.00% Done...\n",
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"\n",
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"Experiment saved to show_exp_table_res.pkl\n",
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"| name | type | default | lower | upper | tuned | transform | importance | stars |\n",
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"|----------------|--------|-----------|---------|---------|----------------------|-----------------------|--------------|---------|\n",
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"| l1 | int | 3 | 1.0 | 2.0 | 2.0 | transform_power_2_int | 29.49 | * |\n",
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"| epochs | int | 4 | 2.0 | 2.0 | 2.0 | transform_power_2_int | 0.00 | |\n",
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"| batch_size | int | 4 | 4.0 | 11.0 | 5.0 | transform_power_2_int | 1.18 | * |\n",
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"| act_fn | factor | ReLU | 0.0 | 5.0 | ELU | None | 0.32 | . |\n",
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"| optimizer | factor | SGD | 0.0 | 2.0 | Adam | None | 0.08 | |\n",
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"| dropout_prob | float | 0.01 | 0.0 | 0.025 | 0.010464684336704316 | None | 0.27 | . |\n",
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"| lr_mult | float | 1.0 | 0.1 | 10.0 | 8.82569482726512 | None | 9.55 | * |\n",
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"| patience | int | 2 | 1.0 | 2.0 | 2.0 | transform_power_2_int | 100.00 | *** |\n",
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"| batch_norm | factor | 0 | 0.0 | 1.0 | 0 | None | 0.05 | |\n",
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"| initialization | factor | Default | 0.0 | 4.0 | kaiming_normal | None | 1.07 | * |\n"
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]
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}
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],
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"source": [
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"from spotpython.data.diabetes import Diabetes\n",
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"from spotpython.hyperdict.light_hyper_dict import LightHyperDict\n",
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"from spotpython.fun.hyperlight import HyperLight\n",
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"from spotpython.utils.init import fun_control_init, design_control_init\n",
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"from spotpython.spot import Spot\n",
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"from spotpython.utils.eda import print_res_table\n",
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"from spotpython.hyperparameters.values import set_hyperparameter\n",
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"\n",
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"fun_control = fun_control_init(\n",
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" PREFIX=\"show_exp_table\",\n",
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" fun_evals=5,\n",
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" max_time=1,\n",
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" data_set = Diabetes(),\n",
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" core_model_name=\"light.regression.NNLinearRegressor\",\n",
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" hyperdict=LightHyperDict,\n",
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" _L_in=10,\n",
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" _L_out=1)\n",
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"\n",
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"set_hyperparameter(fun_control, \"optimizer\", [ \"Adadelta\", \"Adam\", \"Adamax\"])\n",
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"set_hyperparameter(fun_control, \"l1\", [1,2])\n",
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"set_hyperparameter(fun_control, \"epochs\", [2,2])\n",
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"set_hyperparameter(fun_control, \"batch_size\", [4,11])\n",
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"set_hyperparameter(fun_control, \"dropout_prob\", [0.0, 0.025])\n",
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"set_hyperparameter(fun_control, \"patience\", [1,2])\n",
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"design_control = design_control_init(init_size=3)\n",
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"\n",
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"fun = HyperLight().fun\n",
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"\n",
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"S = Spot(fun=fun, fun_control=fun_control, design_control=design_control)\n",
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"\n",
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"S.run()\n",
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"\n",
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"print_res_table(S)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

‎pyproject.toml‎

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@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "spotpython"
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version = "0.24.3"
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version = "0.24.4"
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authors = [
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{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
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]

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