examples/estimator.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Example: Using the High-Level Estimator\n",
    "\n",
    "In this example, we want to show you how to use the `NaturalPosteriorNetwork` estimator to train and evaluate both single NatPN models and ensembles thereof (NatPE).\n",
    "\n",
    "Although the following is not required, we first want to hide some useless output generated by PyTorch Lightning and fix the seed for reproducibility..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e32be8f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from natpn import suppress_pytorch_lightning_logs\n",
    "suppress_pytorch_lightning_logs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9fdd23f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytorch_lightning as pl\n",
    "pl.seed_everything(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ba5291",
   "metadata": {},
   "source": [
    "## Load the Data\n",
    "\n",
    "Then, we can define the data that we want to use. For simplicity, we will use the Sensorless Drive dataset. This is one of 8 datasets that are provided directly by this repository and which can be found in the `natpn.datasets` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9586889",
   "metadata": {},
   "outputs": [],
   "source": [
    "from natpn.datasets import SensorlessDriveDataModule\n",
    "dm = SensorlessDriveDataModule()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f920f52",
   "metadata": {},
   "source": [
    "If you want to use your own dataset with the `NaturalPosteriorNetwork` estimator, you can simply subclass `natpn.datasets.DataModule` which is a simple extension of `pytorch_lightning.LightningDataModule`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34df2caa",
   "metadata": {},
   "source": [
    "## NatPN\n",
    "\n",
    "Having defined the data that we want to use, we can proceed to initialize the `NaturalPosteriorNetwork` estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "605b3145",
   "metadata": {},
   "outputs": [],
   "source": [
    "from natpn import NaturalPosteriorNetwork"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1845094",
   "metadata": {},
   "source": [
    "This estimator provides plenty of initialization options and you should consult the documentation of the `__init__` method. In the following, we initalize an estimator which is configured as follows:\n",
    "\n",
    "- It uses a tabular encoder (which is required for tabular data)\n",
    "- It uses 16 layers of radial transforms (the default) for the evidence-producing normalizing flow\n",
    "- It runs both training and fine-tuning for at most 150 epochs\n",
    "- It uses a learning rate of `3e-3` and decays it by a factor `0.25` if the validation loss plateaus for at least 1/20-th of the training (i.e. 7 epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe277044",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = NaturalPosteriorNetwork(\n",
    "    encoder=\"tabular\",\n",
    "    flow_num_layers=16,\n",
    "    learning_rate=3e-3,\n",
    "    learning_rate_decay=True,\n",
    "    trainer_params=dict(max_epochs=150),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59c1dc37",
   "metadata": {},
   "source": [
    "Note that you can pass more options to the estimator itself and configure specifics of the training procedure by passing additional options to the `trainer_params` flag which configures a `pytorch_ligthning.Trainer`. For example, if you wanted to use a GPU, you should pass `gpus=1` as an additional option to `trainer_params`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0c76fce",
   "metadata": {},
   "source": [
    "### Train the Estimator\n",
    "\n",
    "Using the defined estimator, we can simply call `fit` to train it on the data - just like for any Scikit-learn estimator! For the Sensorless Drive dataset, fitting should take a few minutes only..."
   ]
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      "GPU available: True, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "Running warmup...\n",
      "Preparing 'Sensorless Drive'...\n"
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     "data": {
      "text/plain": [
       "<natpn.model.estimator.NaturalPosteriorNetwork at 0x7fb4f3ffdd90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a67f8a7",
   "metadata": {},
   "source": [
    "Just like for any Scikit-learn estimator, the `fit` method returns the estimator such that calls to its methods can be chained."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d1f08fc",
   "metadata": {},
   "source": [
    "### Evaluate the Performance\n",
    "\n",
    "Now that the estimator is trained, we can finally evaluate it. For this purpose, the estimator provides two methods:\n",
    "\n",
    "- `score` computes the scores on the test set that is provided by the data module. For classification, it computes accuracy and Brier score. For regression, it computes RMSE and calibration.\n",
    "- `score_ood_detection` measures the model's ability to distinguish the items in the test set from items from unrelated datasets as provided by the data module. For each such unrelated dataset (first level of the returned dictionary), it computes multiple metrics: the aleatoric and epistemic confidence, measured by both the area under the precision-recall curve (AUC-PR) and the area under the receiver operating characteristic curve (AUC-ROC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c29b370d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating on test set...\n"
     ]
    },
    {
     "data": {
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       "Testing: 0it [00:00, ?it/s]"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9950908422470093, 'brier_score': 0.00926720630377531}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.score(dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56e1fc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating in-distribution vs. sensorless_drive_left_out...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08a627c5433e48499ab193de835e8bf8",
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      },
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       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating in-distribution vs. sensorless_drive_oodom...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d72525b3a804dbf84d1ea6c41f0656d",
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       "Testing: 0it [00:00, ?it/s]"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "{'sensorless_drive_left_out': {'aleatoric_confidence_auc_pr': 0.7124916315078735,\n",
       "  'aleatoric_confidence_auc_roc': 0.7678268551826477,\n",
       "  'epistemic_confidence_auc_pr': 0.8626615405082703,\n",
       "  'epistemic_confidence_auc_roc': 0.8627698421478271},\n",
       " 'sensorless_drive_oodom': {'aleatoric_confidence_auc_pr': 0.9998900294303894,\n",
       "  'aleatoric_confidence_auc_roc': 0.999791145324707,\n",
       "  'epistemic_confidence_auc_pr': 0.9999999403953552,\n",
       "  'epistemic_confidence_auc_roc': 0.9999999403953552}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.score_ood_detection(dm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34cf51d5",
   "metadata": {},
   "source": [
    "## NatPE\n",
    "\n",
    "Using the `NaturalPosteriorNetwork` estimator to train an ensemble of models is straightforward: the only change that is required is to set `ensemble_size` to the desired number of ensemble members. For this example, we choose an ensemble size of 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0fccaba",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = NaturalPosteriorNetwork(\n",
    "    ensemble_size=5,\n",
    "    encoder=\"tabular\",\n",
    "    flow_num_layers=16,\n",
    "    learning_rate=3e-3,\n",
    "    learning_rate_decay=True,\n",
    "    trainer_params=dict(max_epochs=150),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3575adf5",
   "metadata": {},
   "source": [
    "### Train the Ensemble Members\n",
    "\n",
    "When the estimator is trained, it trains each ensemble member independently and one after the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0cab44a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting model 1/5...\n",
      "GPU available: True, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "Running warmup...\n"
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       "Validation sanity check: 0it [00:00, ?it/s]"
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       "Training: 0it [00:00, ?it/s]"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running training...\n"
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       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
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       "Validating: 0it [00:00, ?it/s]"
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     "data": {
      "text/plain": [
       "<natpn.model.estimator.NaturalPosteriorNetwork at 0x7fb4ea3778e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "estimator.fit(dm)"
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   "cell_type": "markdown",
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   "source": [
    "### Evaluate the Performance\n",
    "\n",
    "For evaluation, it then merges the individually trained ensemble members into a single model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4330868d",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating on test set...\n"
     ]
    },
    {
     "data": {
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       "model_id": "1b84f04413f6418ba3e627e76692071f",
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      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
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    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9957175850868225, 'brier_score': 0.007547394838184118}"
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     "execution_count": 11,
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     "output_type": "execute_result"
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   "source": [
    "estimator.score(dm)"
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   "cell_type": "code",
   "execution_count": 12,
   "id": "060f0797",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating in-distribution vs. sensorless_drive_left_out...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f6530071e604d309befddacb1b7415a",
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      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating in-distribution vs. sensorless_drive_oodom...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c116ed3f955e4e77a8ab56a3065b1523",
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      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "{'sensorless_drive_left_out': {'aleatoric_confidence_auc_pr': 0.6225915551185608,\n",
       "  'aleatoric_confidence_auc_roc': 0.7333374619483948,\n",
       "  'epistemic_confidence_auc_pr': 0.9484376907348633,\n",
       "  'epistemic_confidence_auc_roc': 0.9485479593276978},\n",
       " 'sensorless_drive_oodom': {'aleatoric_confidence_auc_pr': 0.9998624920845032,\n",
       "  'aleatoric_confidence_auc_roc': 0.999738872051239,\n",
       "  'epistemic_confidence_auc_pr': 0.9999999403953552,\n",
       "  'epistemic_confidence_auc_roc': 1.0}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "estimator.score_ood_detection(dm)"
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   "cell_type": "code",
   "execution_count": null,
   "id": "669b6157",
   "metadata": {},
   "outputs": [],
   "source": []
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