example-1d-lightning.ipynb

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   "source": [
    "## Testing NP implementation on 1D functions sampled from a Gaussian Process\n"
   ],
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  },
  {
   "source": [
    "This notebook provides a usage example with the full pyTorch lightning module infrastructure"
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   "metadata": {}
  },
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "\n",
    "from pl_modules import *\n",
    "from pl_datasets import *"
   ]
  },
  {
   "source": [
    "#### Generate and visualise data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    " dm = NPDataModule(dataset_type='gpdata',\n",
    "                      num_workers=4,\n",
    "                      batch_size=16,\n",
    "                      kernel='rbf',\n",
    "                      num_samples=1000,\n",
    "                      num_points=100,\n",
    "                      lengthscale_range=(0.25, 0.5),\n",
    "                      sigma_range=(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Visualize data samples\n",
    "for i in range(10):\n",
    "    x, y = dm.train_loader.dataset[i] \n",
    "    plt.plot(x.squeeze(), y.squeeze(), c='b', alpha=0.5)\n",
    "    plt.xlim(-1, 1)"
   ]
  },
  {
   "source": [
    "#### Initialise module and visualise prior samples"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_dim = 50  # Dimension of representation of context points\n",
    "z_dim = 50  # Dimension of sampled latent variable\n",
    "h_dim = 50  # Dimension of hidden layers in encoder and decoder\n",
    "h_dim_enc = [50, 50] # Encoder hidden layers\n",
    "h_dim_dec = [50, 50, 50] # Decoder hidden layers\n",
    "training_type = 'VI' # training type to use, VI or MLE\n",
    "num_context = 16\n",
    "num_target = 32\n",
    "model = PLNeuralProcess(x_dim=dm.x_dim,\n",
    "                        y_dim=dm.y_dim,\n",
    "                        lr=1e-3,\n",
    "                        num_context=num_context,\n",
    "                        num_target=num_target,\n",
    "                        fix_n_context_and_target_points=False,\n",
    "                        r_dim=r_dim,\n",
    "                        z_dim=z_dim,\n",
    "                        h_dim=h_dim,\n",
    "                        h_dim_enc=h_dim_enc,\n",
    "                        h_dim_dec=h_dim_dec,\n",
    "                        n_repeat=1,\n",
    "                        training_type=training_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Create a set of 100 target points, with shape \n",
    "# (batch_size, num_points, x_dim), which in this case is\n",
    "# (1, 100, 1)\n",
    "x_target = torch.Tensor(np.linspace(-1, 1, 100))\n",
    "x_target = x_target.unsqueeze(1).unsqueeze(0)\n",
    "\n",
    "for i in range(64):\n",
    "    z_sample = torch.randn((1, z_dim))  # Shape (batch_size, z_dim)\n",
    "    # Map x_target and z to the target distribution (which is parameterized by a \n",
    "    # normal with mean mu and std dev sigma)\n",
    "    mu, _ = model.model.decoder(x_target, z_sample)\n",
    "    # Plot predicted mean at each target point (note we could also\n",
    "    # sample from distribution but plot mean for simplicity)\n",
    "    plt.plot(x_target.numpy()[0], mu.detach().numpy()[0], \n",
    "             c='b', alpha=0.5)\n",
    "    plt.xlim(-1, 1)"
   ]
  },
  {
   "source": [
    "#### Create and run trainer"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: None, using: 0 TPU cores\n"
     ]
    }
   ],
   "source": [
    "trainer = pl.Trainer(gpus=0 if not torch.cuda.is_available() else 1,\n",
    "                    max_epochs=100,\n",
    "                    checkpoint_callback=False, \n",
    "                    num_sanity_val_steps=0,\n",
    "                    check_val_every_n_epoch=10,\n",
    "                    auto_lr_find=False,\n",
    "                    profiler='simple')"
   ]
  },
  {
   "source": [
    "_Note: we run the trainer for only 100 epochs, however more are needed to produce reasonable fits for GP samples_"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\n",
      "  | Name  | Type     | Params\n",
      "-----------------------------------\n",
      "0 | model | SimpleNP | 20.7 K\n",
      "-----------------------------------\n",
      "20.7 K    Trainable params\n",
      "0         Non-trainable params\n",
      "20.7 K    Total params\n",
      "0.083     Total estimated model params size (MB)\n",
      "Epoch 9: 100%|██████████| 63/63 [00:01<00:00, 59.12it/s, loss=107, v_num=2, training_loss=109.0, learning_rate=0.001]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 9: 100%|██████████| 63/63 [00:01<00:00, 45.17it/s, loss=107, v_num=2, training_loss=109.0, learning_rate=0.001, validation_loss=120.0]\n",
      "Epoch 19: 100%|██████████| 63/63 [00:01<00:00, 42.48it/s, loss=110, v_num=2, training_loss=108.0, learning_rate=0.001, validation_loss=120.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 19: 100%|██████████| 63/63 [00:02<00:00, 31.48it/s, loss=110, v_num=2, training_loss=107.0, learning_rate=0.001, validation_loss=115.0]\n",
      "Epoch 29: 100%|██████████| 63/63 [00:01<00:00, 36.32it/s, loss=106, v_num=2, training_loss=107.0, learning_rate=0.001, validation_loss=115.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 29: 100%|██████████| 63/63 [00:02<00:00, 23.63it/s, loss=106, v_num=2, training_loss=107.0, learning_rate=0.001, validation_loss=118.0]\n",
      "Epoch 39: 100%|██████████| 63/63 [00:04<00:00, 12.81it/s, loss=106, v_num=2, training_loss=107.0, learning_rate=0.001, validation_loss=118.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 39: 100%|██████████| 63/63 [00:05<00:00, 10.76it/s, loss=106, v_num=2, training_loss=107.0, learning_rate=0.001, validation_loss=127.0]\n",
      "Epoch 49: 100%|██████████| 63/63 [00:02<00:00, 22.48it/s, loss=106, v_num=2, training_loss=106.0, learning_rate=0.001, validation_loss=127.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 49: 100%|██████████| 63/63 [00:03<00:00, 17.02it/s, loss=106, v_num=2, training_loss=107.0, learning_rate=0.001, validation_loss=117.0]\n",
      "Epoch 59: 100%|██████████| 63/63 [00:03<00:00, 19.26it/s, loss=103, v_num=2, training_loss=104.0, learning_rate=0.001, validation_loss=117.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 59: 100%|██████████| 63/63 [00:04<00:00, 15.25it/s, loss=103, v_num=2, training_loss=105.0, learning_rate=0.001, validation_loss=122.0]\n",
      "Epoch 69: 100%|██████████| 63/63 [00:02<00:00, 29.55it/s, loss=102, v_num=2, training_loss=103.0, learning_rate=0.001, validation_loss=122.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 69: 100%|██████████| 63/63 [00:02<00:00, 21.72it/s, loss=102, v_num=2, training_loss=103.0, learning_rate=0.001, validation_loss=117.0]\n",
      "Epoch 79: 100%|██████████| 63/63 [00:01<00:00, 32.00it/s, loss=78.9, v_num=2, training_loss=82.60, learning_rate=0.001, validation_loss=117.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 79: 100%|██████████| 63/63 [00:02<00:00, 24.10it/s, loss=78.9, v_num=2, training_loss=80.90, learning_rate=0.001, validation_loss=115.0]\n",
      "Epoch 89: 100%|██████████| 63/63 [00:01<00:00, 35.62it/s, loss=73.8, v_num=2, training_loss=76.20, learning_rate=0.001, validation_loss=115.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 89: 100%|██████████| 63/63 [00:02<00:00, 25.56it/s, loss=73.8, v_num=2, training_loss=75.10, learning_rate=0.001, validation_loss=107.0]\n",
      "Epoch 99: 100%|██████████| 63/63 [00:02<00:00, 30.71it/s, loss=65.6, v_num=2, training_loss=72.80, learning_rate=0.001, validation_loss=107.0]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 99: 100%|██████████| 63/63 [00:03<00:00, 20.83it/s, loss=65.6, v_num=2, training_loss=72.70, learning_rate=0.001, validation_loss=112.0]\n",
      "Epoch 99: 100%|██████████| 63/63 [00:03<00:00, 20.79it/s, loss=65.6, v_num=2, training_loss=72.70, learning_rate=0.001, validation_loss=112.0]\n",
      "\n",
      "Profiler Report\n",
      "\n",
      "Action                             \t|  Mean duration (s)\t|Num calls      \t|  Total time (s) \t|  Percentage %   \t|\n",
      "------------------------------------------------------------------------------------------------------------------------------------\n",
      "Total                              \t|  -              \t|_              \t|  222.34         \t|  100 %          \t|\n",
      "------------------------------------------------------------------------------------------------------------------------------------\n",
      "run_training_epoch                 \t|  2.2018         \t|100            \t|  220.18         \t|  99.031         \t|\n",
      "run_training_batch                 \t|  0.027307       \t|6300           \t|  172.03         \t|  77.375         \t|\n",
      "optimizer_step_and_closure_0       \t|  0.026757       \t|6300           \t|  168.57         \t|  75.818         \t|\n",
      "training_step_and_backward         \t|  0.023905       \t|6300           \t|  150.6          \t|  67.736         \t|\n",
      "model_forward                      \t|  0.013139       \t|6300           \t|  82.773         \t|  37.229         \t|\n",
      "training_step                      \t|  0.012601       \t|6300           \t|  79.388         \t|  35.706         \t|\n",
      "model_backward                     \t|  0.0097855      \t|6300           \t|  61.649         \t|  27.728         \t|\n",
      "get_train_batch                    \t|  0.0033417      \t|6300           \t|  21.052         \t|  9.4687         \t|\n",
      "on_train_batch_end                 \t|  0.0021858      \t|6300           \t|  13.771         \t|  6.1936         \t|\n",
      "evaluation_step_and_end            \t|  0.008142       \t|630            \t|  5.1294         \t|  2.3071         \t|\n",
      "validation_step                    \t|  0.0077977      \t|630            \t|  4.9126         \t|  2.2095         \t|\n",
      "cache_result                       \t|  2.8278e-05     \t|33853          \t|  0.95728        \t|  0.43056        \t|\n",
      "training_step_end                  \t|  0.00011783     \t|6300           \t|  0.7423         \t|  0.33386        \t|\n",
      "on_epoch_start                     \t|  0.002892       \t|100            \t|  0.2892         \t|  0.13007        \t|\n",
      "on_batch_start                     \t|  3.6824e-05     \t|6300           \t|  0.23199        \t|  0.10434        \t|\n",
      "on_after_backward                  \t|  2.3194e-05     \t|6300           \t|  0.14612        \t|  0.065722       \t|\n",
      "on_batch_end                       \t|  2.1624e-05     \t|6300           \t|  0.13623        \t|  0.061273       \t|\n",
      "on_before_zero_grad                \t|  1.9941e-05     \t|6300           \t|  0.12563        \t|  0.056503       \t|\n",
      "on_train_batch_start               \t|  1.8815e-05     \t|6300           \t|  0.11853        \t|  0.053312       \t|\n",
      "on_validation_start                \t|  0.0079089      \t|10             \t|  0.079089       \t|  0.035572       \t|\n",
      "on_validation_end                  \t|  0.0065818      \t|10             \t|  0.065818       \t|  0.029603       \t|\n",
      "validation_step_end                \t|  9.1814e-05     \t|630            \t|  0.057843       \t|  0.026016       \t|\n",
      "on_validation_batch_end            \t|  4.934e-05      \t|630            \t|  0.031084       \t|  0.013981       \t|\n",
      "on_validation_batch_start          \t|  2.1233e-05     \t|630            \t|  0.013377       \t|  0.0060164      \t|\n",
      "on_train_epoch_end                 \t|  2.281e-05      \t|100            \t|  0.002281       \t|  0.0010259      \t|\n",
      "on_train_epoch_start               \t|  1.767e-05      \t|100            \t|  0.001767       \t|  0.00079472     \t|\n",
      "on_train_start                     \t|  0.0016246      \t|1              \t|  0.0016246      \t|  0.0007307      \t|\n",
      "on_epoch_end                       \t|  1.1753e-05     \t|110            \t|  0.0012928      \t|  0.00058147     \t|\n",
      "on_train_end                       \t|  0.00091751     \t|1              \t|  0.00091751     \t|  0.00041267     \t|\n",
      "on_validation_epoch_start          \t|  1.7827e-05     \t|10             \t|  0.00017827     \t|  8.018e-05      \t|\n",
      "on_validation_epoch_end            \t|  1.7561e-05     \t|10             \t|  0.00017561     \t|  7.8985e-05     \t|\n",
      "on_fit_start                       \t|  2.2016e-05     \t|1              \t|  2.2016e-05     \t|  9.9021e-06     \t|\n",
      "on_before_accelerator_backend_setup\t|  1.4506e-05     \t|1              \t|  1.4506e-05     \t|  6.5244e-06     \t|\n",
      "\n",
      "\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "trainer.fit(model, dm)"
   ]
  },
  {
   "source": [
    "#### Visualise samples from trained prior\n",
    "We can see the NP has learned to expect functions similar to those produced by GPs"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "model.eval()\n",
    "x_target = torch.Tensor(np.linspace(-1, 1, 100))\n",
    "x_target = x_target.unsqueeze(1).unsqueeze(0)\n",
    "\n",
    "for i in range(32):\n",
    "    z_sample = torch.randn((1, z_dim))\n",
    "    mu, _ = model.model.decoder(x_target, z_sample)\n",
    "    plt.plot(x_target.numpy()[0], mu.detach().numpy()[0], \n",
    "             c='b', alpha=0.5)\n",
    "    plt.xlim(-1, 1)"
   ]
  },
  {
   "source": [
    "#### Visualise posterior samples"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa97f76ff40>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from train import process_data_to_points\n",
    "# Extract a batch from data_loader\n",
    "# Use batch to create random set of context points\n",
    "rng = np.random.default_rng()\n",
    "x, y, lengthscale, sigma, period = dm.train_loader.dataset.generate_gp_sample(rng)\n",
    "\n",
    "model_x, model_y = torch.tensor(x).float().to(model.device).unsqueeze(0), torch.tensor(y).float().to(model.device).unsqueeze(0).unsqueeze(2)\n",
    "\n",
    "x_context, y_context, _, _ = process_data_to_points(model_x, model_y, num_context)\n",
    "\n",
    "# Create a set of target points corresponding to entire [-pi, pi] range\n",
    "x_target = torch.Tensor(np.linspace(-1, 1, 100))\n",
    "# x_target = torch.Tensor(np.linspace(-pi, pi, 100))\n",
    "x_target = x_target.unsqueeze(1).unsqueeze(0)\n",
    "\n",
    "model.eval()\n",
    "\n",
    "for i in range(64):\n",
    "    # Neural process returns distribution over y_target\n",
    "    p_y_pred, _, _ = model.model(x_context.float(), y_context.float(), x_target.float(), None)\n",
    "    # Extract mean of distribution\n",
    "    mu = p_y_pred[0].loc.detach()\n",
    "    plt.plot(x_target.numpy()[0], mu.numpy()[0], \n",
    "             alpha=0.05, c='b')\n",
    "\n",
    "plt.scatter(x_context[0].numpy(), y_context[0].numpy(), c='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}