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bartzbeielsteinbartzbeielstein
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net regression
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‎.gitignore‎

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@@ -298,3 +298,4 @@ notebooks/data/spotPython/daten_sensitive.py
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notebooks/data/spotPython/daten_sensitive.csv
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notebooks/data/spotPython/data_sensitive.csv
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notebooks/data/spotPython/data_sensitive_rmNA.csv
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notebooks/runs_OLD/*

‎notebooks/00_spotPython_tests.ipynb‎

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@@ -653,58 +653,354 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from spotPython.light.netlightbase import NetLightBase\n",
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"from spotPython.utils.init import fun_control_init\n",
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"from spotPython.hyperdict.light_hyper_dict import LightHyperDict\n",
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"from spotPython.hyperparameters.values import add_core_model_to_fun_control\n",
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"fun_control = fun_control_init()\n",
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"add_core_model_to_fun_control(core_model=NetLightBase,\n",
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" fun_control=fun_control,\n",
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" hyper_dict=LightHyperDict)\n",
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"fun_control[\"core_model\"].__name__"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"# check if the fun_control[\"core_model_hyper_dict\"] is a LightHyperDict\n",
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"isinstance(fun_control[\"core_model_hyper_dict\"], dict)"
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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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"# Test netlightregression"
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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 42\n"
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"Seed set to 42\n",
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"/Users/bartz/miniforge3/envs/spotCondaEnv/lib/python3.11/site-packages/lightning/pytorch/utilities/parsing.py:198: 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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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"\n",
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" | Name | Type | Params\n",
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"--------------------------------------\n",
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"0 | layers | Sequential | 92.0 K\n",
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"--------------------------------------\n",
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"92.0 K Trainable params\n",
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"0 Non-trainable params\n",
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"92.0 K Total params\n",
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"0.368 Total estimated model params size (MB)\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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"| name | type | default | lower | upper | transform |\n",
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"|----------------|--------|-----------|---------|---------|-----------------------|\n",
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"| l1 | int | 3 | 5 | 8 | transform_power_2_int |\n",
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"| epochs | int | 4 | 6 | 13 | transform_power_2_int |\n",
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"| batch_size | int | 4 | 2 | 8 | 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 | 3 | None |\n",
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"| dropout_prob | float | 0.01 | 0 | 0.25 | None |\n",
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"| lr_mult | float | 1.0 | 0.1 | 10 | None |\n",
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"| patience | int | 2 | 2 | 6 | transform_power_2_int |\n",
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"| initialization | factor | Default | 0 | 2 | None |\n",
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"fun: Calling train_model\n",
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"torch.Size([2381, 133])\n",
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"torch.Size([2381])\n",
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"train_model(): Test set size: 1429\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/spotCondaEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:436: Consider setting `persistent_workers=True` in 'val_dataloader' to speed up the dataloader worker initialization.\n",
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"/Users/bartz/miniforge3/envs/spotCondaEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:436: Consider setting `persistent_workers=True` in 'train_dataloader' to speed up the dataloader worker initialization.\n",
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"/Users/bartz/miniforge3/envs/spotCondaEnv/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (12) 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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"data": {
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"text/plain": [
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"'NetLightBase'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
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" Validate metric DataLoader 0\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
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" hp_metric 166.65289306640625\n",
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" val_loss 166.65289306640625\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\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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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"\n",
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" | Name | Type | Params\n",
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"--------------------------------------\n",
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"0 | layers | Sequential | 5.2 K \n",
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"--------------------------------------\n",
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"5.2 K Trainable params\n",
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"0 Non-trainable params\n",
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"5.2 K Total params\n",
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"0.021 Total estimated model params size (MB)\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': 166.65289306640625, 'hp_metric': 166.65289306640625}\n",
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"fun: train_model returned\n",
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"fun: Calling train_model\n",
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"torch.Size([2381, 133])\n",
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"torch.Size([2381])\n",
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"train_model(): Test set size: 1429\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/spotCondaEnv/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (2) 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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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
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" Validate metric DataLoader 0\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
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" hp_metric 475733504.0\n",
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" val_loss 475733504.0\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\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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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"\n",
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" | Name | Type | Params\n",
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"--------------------------------------\n",
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"0 | layers | Sequential | 31.7 K\n",
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"--------------------------------------\n",
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"31.7 K Trainable params\n",
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"0 Non-trainable params\n",
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"31.7 K Total params\n",
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"0.127 Total estimated model params size (MB)\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': 475733504.0, 'hp_metric': 475733504.0}\n",
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"fun: train_model returned\n",
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"fun: Calling train_model\n",
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"torch.Size([2381, 133])\n",
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"torch.Size([2381])\n",
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"train_model(): Test set size: 1429\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/spotCondaEnv/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (48) 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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"source": [
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"from spotPython.light.netlightbase import NetLightBase\n",
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"import torch\n",
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"import numpy as np\n",
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"from spotPython.spot import spot\n",
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"from math import inf\n",
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"from spotPython.utils.init import fun_control_init\n",
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"from spotPython.utils.file import get_experiment_name, get_spot_tensorboard_path\n",
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"from spotPython.utils.device import getDevice\n",
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"from spotPython.hyperparameters.values import set_data_set\n",
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"from spotPython.data.pkldataset import PKLDataset\n",
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"from spotPython.light.netlightregression import NetLightRegression\n",
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"from spotPython.hyperdict.light_hyper_dict import LightHyperDict\n",
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"from spotPython.hyperparameters.values import add_core_model_to_fun_control\n",
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"fun_control = fun_control_init()\n",
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"add_core_model_to_fun_control(core_model=NetLightBase,\n",
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"from spotPython.hyperparameters.values import modify_hyper_parameter_bounds\n",
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"from spotPython.hyperparameters.values import modify_hyper_parameter_levels\n",
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"from spotPython.fun.hyperlight import HyperLight\n",
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"from spotPython.hyperparameters.values import (get_bound_values,\n",
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" get_var_name,\n",
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" get_var_type,)\n",
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"from spotPython.utils.eda import gen_design_table\n",
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"from spotPython.light.utils import get_tuned_architecture\n",
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"from spotPython.light.traintest import test_model\n",
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"from spotPython.light.traintest import load_light_from_checkpoint\n",
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"\n",
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"MAX_TIME = 10\n",
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"INIT_SIZE = 5\n",
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"WORKERS = 10\n",
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"PREFIX=\"031\"\n",
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"\n",
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"experiment_name = get_experiment_name(prefix=PREFIX)\n",
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"fun_control = fun_control_init(\n",
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" spot_tensorboard_path=get_spot_tensorboard_path(experiment_name),\n",
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" num_workers=WORKERS,\n",
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" device=getDevice(),\n",
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" _L_in=133,\n",
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" _L_out=1,\n",
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" TENSORBOARD_CLEAN=True)\n",
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"\n",
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"dataset = PKLDataset(directory=\"/Users/bartz/workspace/spotPython/notebooks/data/spotPython/\", filename=\"data_sensitive.pkl\", target_column='N', feature_type=torch.float32, target_type=torch.float32, rmNA=True)\n",
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"set_data_set(fun_control=fun_control,\n",
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" data_set=dataset)\n",
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"\n",
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"\n",
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"\n",
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"add_core_model_to_fun_control(core_model=NetLightRegression,\n",
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" fun_control=fun_control,\n",
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" hyper_dict=LightHyperDict)\n",
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"fun_control[\"core_model\"].__name__"
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"modify_hyper_parameter_bounds(fun_control, \"l1\", bounds=[5,8])\n",
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"modify_hyper_parameter_bounds(fun_control, \"epochs\", bounds=[6,13])\n",
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"modify_hyper_parameter_bounds(fun_control, \"batch_size\", bounds=[2, 8])\n",
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"modify_hyper_parameter_levels(fun_control, \"optimizer\",[\"Adam\", \"AdamW\", \"Adamax\", \"NAdam\"])\n",
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"\n",
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"print(gen_design_table(fun_control))\n",
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"\n",
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"var_type = get_var_type(fun_control)\n",
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"var_name = get_var_name(fun_control)\n",
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"lower = get_bound_values(fun_control, \"lower\")\n",
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"upper = get_bound_values(fun_control, \"upper\")\n",
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"fun = HyperLight(log_level=10).fun\n",
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"spot_tuner = spot.Spot(fun=fun,\n",
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" log_level=10,\n",
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" lower = lower,\n",
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" upper = upper,\n",
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" fun_evals = inf,\n",
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" max_time = MAX_TIME,\n",
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" tolerance_x = np.sqrt(np.spacing(1)),\n",
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" var_type = var_type,\n",
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" var_name = var_name,\n",
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" show_progress= True,\n",
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" fun_control = fun_control,\n",
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" design_control={\"init_size\": INIT_SIZE},\n",
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" surrogate_control={\"noise\": True,\n",
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" \"min_theta\": -4,\n",
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" \"max_theta\": 3,\n",
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" \"n_theta\": len(var_name),\n",
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" \"model_fun_evals\": 10_000,\n",
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" })\n",
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"spot_tuner.run()"
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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": 12,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# check if the fun_control[\"core_model_hyper_dict\"] is a LightHyperDict\n",
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"isinstance(fun_control[\"core_model_hyper_dict\"], dict)"
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"spot_tuner.plot_progress(log_y=False, filename=None)"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(gen_design_table(fun_control=fun_control, spot=spot_tuner))"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"spot_tuner.plot_importance(threshold=0.025, filename=None)"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"config = get_tuned_architecture(spot_tuner, fun_control)"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_model(config, fun_control)"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_loaded = load_light_from_checkpoint(config, fun_control)"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_loaded"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"spot_tuner.plot_important_hyperparameter_contour(filename=None)"
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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,
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"metadata": {},
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"outputs": [],
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"source": [
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"spot_tuner.parallel_plot()"
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]
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},
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{

‎pyproject.toml‎

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[project]
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name = "spotPython"
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version = "0.6.50"
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version = "0.6.51"
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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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