Demo_conv-pivae.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5dfc38dc",
   "metadata": {},
   "source": [
    "# Technical: conv-piVAE\n",
    "\n",
    "- this notebook will demo conv-piVAE on hippocampus data, implemented based on piVAE (Zhou, D., Wei, X. Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE. NeurIPS 2020. https://arxiv.org/abs/2011.04798)\n",
    "\n",
    "- This has only been tested on Ubuntu and MacOS"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a5d77bc6",
   "metadata": {},
   "source": [
    "- Install note: be sure you have cebra, and the demo dependencies, installed to use this notebook: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ee0ccde",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --pre 'cebra[datasets,demos]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70d7c62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib as jl\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras import backend as K\n",
    "\n",
    "sys.path.insert(0, '..')\n",
    "import cebra.datasets\n",
    "\n",
    "sys.path.insert(0, '../third_party/pivae')\n",
    "import pivae_code.datasets, pivae_code.conv_pi_vae, pivae_code.pi_vae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee6278b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "###Set your own seed\n",
    "seed = 42\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f36bb47f",
   "metadata": {},
   "source": [
    "## Load and generate dataset:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "813788a8",
   "metadata": {},
   "source": [
    "- The data will be automatically downloaded into a `/data` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aeb14cdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10.0M/10.0M [00:01<00:00, 9.00MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download complete. Dataset saved in 'data/rat_hippocampus/achilles.jl'\n"
     ]
    }
   ],
   "source": [
    "DATA_NAME = 'rat-hippocampus-achilles-3fold-trial-split-0'\n",
    "offset_right = 5\n",
    "offset_left = 5\n",
    "\n",
    "def _call_dataset(offset_right, offset_left, split):\n",
    "    dataset = cebra.datasets.init(DATA_NAME, split = split)\n",
    "    dataset.offset.right = offset_right\n",
    "    dataset.offset.left = offset_left\n",
    "    return dataset\n",
    "\n",
    "train_set = _call_dataset(offset_right, offset_left, 'train')\n",
    "valid_set=_call_dataset(offset_right, offset_left, 'valid')\n",
    "test_set=_call_dataset(offset_right, offset_left, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "532369c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_loader(dataset, batch_size):\n",
    "    def _to_batch_list(x, y, batch_size):\n",
    "        if x is not None and y is not None:\n",
    "            x = x.squeeze()\n",
    "            if len(x.shape) == 3:\n",
    "                x = x.transpose(0,2,1) \n",
    "            x_batch_list = np.array_split(x, int(len(x) / batch_size))\n",
    "            y_batch_list = np.array_split(y, int(len(y) / batch_size))\n",
    "        else:\n",
    "            return None, None\n",
    "        return x_batch_list, y_batch_list\n",
    "    \n",
    "    x,u = _to_batch_list(dataset[torch.arange(len(dataset))].numpy(), dataset.index.numpy(), batch_size)\n",
    "    \n",
    "    loader = pivae_code.pi_vae.custom_data_generator(x, u)\n",
    "    _len = len(x)\n",
    "    return x, u, loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a5f5c477",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 200\n",
    "train_x, train_u, train_loader = make_loader(train_set, batch_size)\n",
    "valid_x, valid_u, valid_loader = make_loader(valid_set, batch_size)\n",
    "test_x, test_u, test_loader = make_loader(test_set, batch_size)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cbe85ca0",
   "metadata": {},
   "source": [
    "## Visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e267dbd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9,3))\n",
    "plt.subplots_adjust(wspace = 0.3)\n",
    "ax = plt.subplot(121)\n",
    "ax.imshow(train_set.neural.numpy()[:1000].T, aspect = 'auto', cmap = 'gray_r')\n",
    "plt.ylabel('Neuron #')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.xticks(np.linspace(0, 1000, 5), np.linspace(0, 0.025*1000, 5, dtype = int))\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "ax2.scatter(np.arange(1000), train_set.continuous_index[:1000,0], c = 'gray', s=1)\n",
    "plt.ylabel('Position [m]')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.xticks(np.linspace(0, 1000, 5), np.linspace(0, 0.025*1000, 5, dtype = int))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4819ceac",
   "metadata": {},
   "source": [
    "## Define and train a conv-piVAE model\n",
    "- Note that conv-piVAE use 10 time bins (250ms) receptive field, while piVAE uses 1 time bin (25ms). \n",
    "- If training takes too long, use smaller training epochs (usually the loss converges around ~300 epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b3c71c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-03 14:33:40.035925: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-10-03 14:33:40.035974: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-10-03 14:33:40.036015: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (stes-t14s): /proc/driver/nvidia/version does not exist\n",
      "2022-10-03 14:33:40.036392: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "2022-10-03 14:33:40.051370: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 1697430000 Hz\n",
      "2022-10-03 14:33:40.053336: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fd814000b70 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
      "2022-10-03 14:33:40.053404: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vae\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 10, 120)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_3 (InputLayer)            (None, 3)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "encoder (Model)                 [(None, 2), (None, 2 35020       input_1[0][0]                    \n",
      "                                                                 input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "decoder (Model)                 (None, 10, 120)      2977794     encoder[1][2]                    \n",
      "==================================================================================================\n",
      "Total params: 3,012,814\n",
      "Trainable params: 3,012,814\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stes/.conda/envs/cebra-full/lib/python3.8/site-packages/keras/engine/training_utils.py:816: UserWarning: Output encoder missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to encoder.\n",
      "  warnings.warn(\n",
      "/home/stes/.conda/envs/cebra-full/lib/python3.8/site-packages/keras/engine/training_utils.py:816: UserWarning: Output decoder missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to decoder.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "conv_pivae = pivae_code.conv_pi_vae.conv_vae_mdl(\n",
    "                dim_x=train_set.neural.shape[1],\n",
    "                dim_z=2,\n",
    "                dim_u=3,\n",
    "                time_window=10,\n",
    "                gen_nodes=60,\n",
    "                n_blk=2,\n",
    "                mdl=\"poisson\",\n",
    "                disc=False,\n",
    "                learning_rate=0.00025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77bcb091",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "34/34 [==============================] - 3s 100ms/step - loss: 509.8190 - val_loss: 212.0420\n",
      "Epoch 2/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 212.5554 - val_loss: 177.2430\n",
      "Epoch 3/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 192.3327 - val_loss: 165.8879\n",
      "Epoch 4/600\n",
      "34/34 [==============================] - 1s 42ms/step - loss: 186.7391 - val_loss: 164.7504\n",
      "Epoch 5/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 186.4515 - val_loss: 164.5008\n",
      "Epoch 6/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 186.4154 - val_loss: 164.6330\n",
      "Epoch 7/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 186.4084 - val_loss: 164.6232\n",
      "Epoch 8/600\n",
      "34/34 [==============================] - 1s 44ms/step - loss: 186.3944 - val_loss: 164.5316\n",
      "Epoch 9/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 186.2701 - val_loss: 164.2339\n",
      "Epoch 10/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 186.1861 - val_loss: 164.2754\n",
      "Epoch 11/600\n",
      "34/34 [==============================] - 1s 44ms/step - loss: 186.1450 - val_loss: 164.1155\n",
      "Epoch 12/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 186.0446 - val_loss: 164.0661\n",
      "Epoch 13/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 185.9379 - val_loss: 164.1499\n",
      "Epoch 14/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 185.8697 - val_loss: 164.0963\n",
      "Epoch 15/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 185.6543 - val_loss: 163.8571\n",
      "Epoch 16/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 185.2769 - val_loss: 163.7182\n",
      "Epoch 17/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 184.5498 - val_loss: 163.2224\n",
      "Epoch 18/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 182.9443 - val_loss: 163.1278\n",
      "Epoch 19/600\n",
      "34/34 [==============================] - 2s 47ms/step - loss: 180.7850 - val_loss: 161.5421\n",
      "Epoch 20/600\n",
      "34/34 [==============================] - 2s 44ms/step - loss: 178.7081 - val_loss: 159.5752\n",
      "Epoch 21/600\n",
      "34/34 [==============================] - 1s 44ms/step - loss: 176.8280 - val_loss: 158.1187\n",
      "Epoch 22/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 174.9552 - val_loss: 155.8834\n",
      "Epoch 23/600\n",
      "34/34 [==============================] - 1s 44ms/step - loss: 173.0196 - val_loss: 152.2670\n",
      "Epoch 24/600\n",
      "34/34 [==============================] - 2s 48ms/step - loss: 170.3790 - val_loss: 149.4572\n",
      "Epoch 25/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 168.4063 - val_loss: 147.2516\n",
      "Epoch 26/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 166.8510 - val_loss: 144.7695\n",
      "Epoch 27/600\n",
      "34/34 [==============================] - 1s 44ms/step - loss: 165.3624 - val_loss: 143.2796\n",
      "Epoch 28/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 164.1374 - val_loss: 142.2919\n",
      "Epoch 29/600\n",
      "34/34 [==============================] - 1s 44ms/step - loss: 163.0654 - val_loss: 141.1287\n",
      "Epoch 30/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 162.3048 - val_loss: 140.3004\n",
      "Epoch 31/600\n",
      "34/34 [==============================] - 1s 44ms/step - loss: 161.2343 - val_loss: 139.6303\n",
      "Epoch 32/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 160.4963 - val_loss: 138.8364\n",
      "Epoch 33/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 159.7594 - val_loss: 137.9424\n",
      "Epoch 34/600\n",
      "34/34 [==============================] - 1s 44ms/step - loss: 159.0830 - val_loss: 137.6932\n",
      "Epoch 35/600\n",
      "34/34 [==============================] - 1s 43ms/step - loss: 158.5559 - val_loss: 137.3128\n",
      "Epoch 36/600\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "34/34 [==============================] - 2s 46ms/step - loss: 133.5697 - val_loss: 149.5192\n",
      "Epoch 542/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.4706 - val_loss: 148.3766\n",
      "Epoch 543/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.8055 - val_loss: 148.3919\n",
      "Epoch 544/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 133.7280 - val_loss: 147.1725\n",
      "Epoch 545/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.6027 - val_loss: 147.6811\n",
      "Epoch 546/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.4496 - val_loss: 147.0526\n",
      "Epoch 547/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.6999 - val_loss: 146.1245\n",
      "Epoch 548/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.6330 - val_loss: 146.5956\n",
      "Epoch 549/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 133.3718 - val_loss: 147.9286\n",
      "Epoch 550/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.6493 - val_loss: 149.0562\n",
      "Epoch 551/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.1895 - val_loss: 149.0026\n",
      "Epoch 552/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.5352 - val_loss: 147.2954\n",
      "Epoch 553/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.4619 - val_loss: 147.7376\n",
      "Epoch 554/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.1738 - val_loss: 146.2086\n",
      "Epoch 555/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 132.8803 - val_loss: 146.4775\n",
      "Epoch 556/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 132.7602 - val_loss: 147.0419\n",
      "Epoch 557/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 132.8004 - val_loss: 147.7082\n",
      "Epoch 558/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 132.7452 - val_loss: 148.4892\n",
      "Epoch 559/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 132.8828 - val_loss: 148.6580\n",
      "Epoch 560/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.1773 - val_loss: 148.2873\n",
      "Epoch 561/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.8202 - val_loss: 148.7892\n",
      "Epoch 562/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.1894 - val_loss: 148.3109\n",
      "Epoch 563/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 135.0070 - val_loss: 148.3951\n",
      "Epoch 564/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.4832 - val_loss: 146.6408\n",
      "Epoch 565/600\n",
      "34/34 [==============================] - 2s 47ms/step - loss: 134.3996 - val_loss: 148.3154\n",
      "Epoch 566/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.6765 - val_loss: 147.3045\n",
      "Epoch 567/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.9406 - val_loss: 148.5090\n",
      "Epoch 568/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.9962 - val_loss: 149.2270\n",
      "Epoch 569/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.7897 - val_loss: 150.7587\n",
      "Epoch 570/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.4867 - val_loss: 150.0631\n",
      "Epoch 571/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.7373 - val_loss: 148.6393\n",
      "Epoch 572/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.9860 - val_loss: 148.3361\n",
      "Epoch 573/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.2844 - val_loss: 150.0474\n",
      "Epoch 574/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.3064 - val_loss: 149.2645\n",
      "Epoch 575/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.0796 - val_loss: 149.6770\n",
      "Epoch 576/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.0362 - val_loss: 149.3986\n",
      "Epoch 577/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 132.9999 - val_loss: 148.3777\n",
      "Epoch 578/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.0929 - val_loss: 148.6705\n",
      "Epoch 579/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.1316 - val_loss: 149.0501\n",
      "Epoch 580/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.2899 - val_loss: 149.8145\n",
      "Epoch 581/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.6173 - val_loss: 148.6873\n",
      "Epoch 582/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.9034 - val_loss: 148.3769\n",
      "Epoch 583/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.4010 - val_loss: 149.4266\n",
      "Epoch 584/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.4090 - val_loss: 150.5802\n",
      "Epoch 585/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.8262 - val_loss: 151.9098\n",
      "Epoch 586/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.2415 - val_loss: 151.2908\n",
      "Epoch 587/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 133.5606 - val_loss: 152.0659\n",
      "Epoch 588/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.0117 - val_loss: 152.8749\n",
      "Epoch 589/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.8595 - val_loss: 153.4059\n",
      "Epoch 590/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.4200 - val_loss: 154.0671\n",
      "Epoch 591/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.4285 - val_loss: 151.3421\n",
      "Epoch 592/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.0065 - val_loss: 152.1399\n",
      "Epoch 593/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.3750 - val_loss: 151.7432\n",
      "Epoch 594/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.6496 - val_loss: 152.9641\n",
      "Epoch 595/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.7530 - val_loss: 153.7097\n",
      "Epoch 596/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.0605 - val_loss: 151.3371\n",
      "Epoch 597/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.3471 - val_loss: 152.9345\n",
      "Epoch 598/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 133.4139 - val_loss: 153.0237\n",
      "Epoch 599/600\n",
      "34/34 [==============================] - 2s 45ms/step - loss: 133.4027 - val_loss: 153.5298\n",
      "Epoch 600/600\n",
      "34/34 [==============================] - 2s 46ms/step - loss: 134.0882 - val_loss: 155.1112\n"
     ]
    }
   ],
   "source": [
    "model_chk_path = 'conv_pivae_model0210.h5'\n",
    "mcp = ModelCheckpoint(model_chk_path, monitor=\"val_loss\", save_best_only=True, save_weights_only=True)\n",
    "s_n = conv_pivae.fit_generator(train_loader,\n",
    "              steps_per_epoch=len(train_x), epochs=600, \n",
    "              verbose=1,\n",
    "              validation_data = valid_loader,\n",
    "              validation_steps = len(valid_x), callbacks=[mcp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2aeee19f",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_pivae.load_weights(model_chk_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "15f17008",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = conv_pivae.predict([np.concatenate(train_x),\n",
    "                            np.concatenate(train_u)])\n",
    "# Outputs: post_mean, post_log_var, z_sample,fire_rate, lam_mean, lam_log_var, z_mean, z_log_var\n",
    "post_z = outputs[0]\n",
    "mean_z = outputs[6]\n",
    "labels = np.concatenate(train_u)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "65a3a15d",
   "metadata": {},
   "source": [
    "## Visualize the embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f3050ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd8cd1b7df0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = plt.subplot(111)\n",
    "r_ind = labels[:,1] == 1\n",
    "l_ind = labels[:,2] == 1\n",
    "ax.scatter(post_z[r_ind, 0], post_z[r_ind, 1], c=labels[r_ind,0], s=1, cmap = 'cool')\n",
    "ax.scatter(post_z[l_ind, 0], post_z[l_ind, 1], c=labels[l_ind,0], s=1, cmap = 'viridis')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "73a6fef1",
   "metadata": {},
   "source": [
    "## Decoding the position using Monte Carlo sampling\n",
    "\n",
    "- This method takes a while since it uses random sampling from p(z|u) to approximate p(u|x). \n",
    "- Below functions to compute marginal likelihood and decode are copied and adapted from the pi-VAE repo  https://github.com/zhd96/pi-vae/blob/main/code/util.py, https://github.com/zhd96/pi-vae/blob/main/examples/pi-vae_rat_data.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e9f52bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_marginal_lik_poisson(vae_mdl,\n",
    "                                 x_test,\n",
    "                                 u_fake,\n",
    "                                 n_sample,\n",
    "                                 log_opt=False):\n",
    "    lik_all = []\n",
    "\n",
    "    for jj in range(len(x_test)):  ## for each batch\n",
    "        lik_test = []\n",
    "        for ii in range(len(u_fake)):  ## for each unique u value\n",
    "            opts = vae_mdl.predict([x_test[jj], u_fake[ii][jj]])\n",
    "            lam_mean = opts[4]\n",
    "            lam_log_var = opts[5]\n",
    "            z_dim = lam_mean.shape\n",
    "            z_sample = np.random.normal(0,\n",
    "                                        1,\n",
    "                                        size=(n_sample, z_dim[0], z_dim[1]))\n",
    "            z_sample = z_sample * np.exp(0.5 * lam_log_var) + lam_mean\n",
    "\n",
    "            ## compute fire rate ##\n",
    "            get_fire_rate_output = K.function(\n",
    "                [vae_mdl.layers[-1].get_input_at(0)],\n",
    "                [vae_mdl.layers[-1].get_output_at(0)],\n",
    "            )\n",
    "            fire_rate = get_fire_rate_output([z_sample.reshape(-1,\n",
    "                                                               z_dim[-1])])[0]\n",
    "            fire_rate = fire_rate.reshape(n_sample, -1, fire_rate.shape[-2],\n",
    "                                          fire_rate.shape[-1])\n",
    "            ## compute p(x|z) poisson likelihood ##\n",
    "            loglik = x_test[jj] * np.log(np.clip(fire_rate, 1e-10,\n",
    "                                                 1e7)) - fire_rate\n",
    "            # n_sample*n_time*n_neuron\n",
    "            loglik = loglik.sum(axis=(-2, -1), dtype = np.float64)\n",
    "            ## sum across neurons and time\n",
    "            loglik_max = loglik.max(axis=0)\n",
    "            loglik -= loglik_max\n",
    "            if log_opt:\n",
    "                tmp = np.log(np.exp(loglik).mean(axis=0)) + (loglik_max)\n",
    "            else:\n",
    "                tmp = (np.exp(loglik).mean(axis=0)) * np.exp(loglik_max)\n",
    "            lik_test.append(tmp)\n",
    "        lik_all.append(np.array(lik_test))\n",
    "\n",
    "    return lik_all\n",
    "\n",
    "\n",
    "def decode_sampling_rat(test_x, test_y, model, sampling_num):\n",
    "    hd_bins = np.linspace(0, 1.6, 100)\n",
    "    hd_bins_dir = np.hstack([np.concatenate([np.linspace(0,1.6,100), np.linspace(0,1.6,100)])[...,None], np.zeros((200,2))]) \n",
    "    hd_bins_dir[:100][:,1]=1\n",
    "    hd_bins_dir[100:200][:,2]=1\n",
    "    nu_sample = 200\n",
    "    u_fake = []\n",
    "    for jj in range(nu_sample):\n",
    "        tmp_all = []\n",
    "        for ii in range(len(test_x)):\n",
    "            nn = test_x[ii].shape[0]\n",
    "            tmp = np.hstack((np.ones((nn, 1)) * hd_bins[jj % 100], np.zeros((nn, 2))))\n",
    "            if jj >= (nu_sample // 2):\n",
    "                tmp[:, 2] += 1\n",
    "            else:\n",
    "                tmp[:, 1] += 1\n",
    "            tmp_all.append(tmp)\n",
    "        u_fake.append(np.array(tmp_all))\n",
    "    u_fake = np.array(u_fake)\n",
    "\n",
    "    ## compute loglik\n",
    "\n",
    "    lik_all = compute_marginal_lik_poisson(model, test_x, u_fake, sampling_num)\n",
    "    decode_use = np.array(\n",
    "        [\n",
    "            (lik_all[jj]).reshape(200, -1, order=\"F\").argmax(axis=0)\n",
    "            for jj in range(len(lik_all))\n",
    "        ]\n",
    "    )\n",
    "    median_err = np.median(\n",
    "        np.abs(\n",
    "            [\n",
    "                hd_bins_dir[np.concatenate(decode_use)[i],0] - np.concatenate(test_y)[i, 0]\n",
    "                for i in range(len(np.concatenate(test_y)))\n",
    "            ]\n",
    "        )\n",
    "    )\n",
    "\n",
    "    prediction = [\n",
    "        hd_bins_dir[np.concatenate(decode_use)[i]]\n",
    "        for i in range(len(np.concatenate(test_y)))\n",
    "    ]\n",
    "\n",
    "    return median_err, prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9492d507",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_165463/761218901.py:64: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  u_fake.append(np.array(tmp_all))\n",
      "/tmp/ipykernel_165463/761218901.py:70: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  decode_use = np.array(\n"
     ]
    }
   ],
   "source": [
    "## Set smaller sampling_num to reduce computing time\n",
    "median_err, prediction = decode_sampling_rat(test_x, test_u, conv_pivae, sampling_num=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1319394f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Err [m]: 0.09438572947425072\n"
     ]
    }
   ],
   "source": [
    "print(f'Median Err [m]: {median_err}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "272d0252",
   "metadata": {},
   "source": [
    "## Decoding with the embedding using kNN\n",
    "\n",
    "- Here we test kNN decoding on piVAE embedding without label prior, the same method we use for decoding with CERBA embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e93610f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_outputs = conv_pivae.predict([np.concatenate(train_x),\n",
    "                            np.concatenate(train_u)])\n",
    "valid_outputs = conv_pivae.predict([np.concatenate(valid_x),\n",
    "                            np.concatenate(valid_u)])\n",
    "test_outputs = conv_pivae.predict([np.concatenate(test_x),\n",
    "                            np.concatenate(test_u)])\n",
    "\n",
    "train_post_z = train_outputs[6]\n",
    "valid_post_z = valid_outputs[6]\n",
    "test_post_z = test_outputs[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a47b6f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier\n",
    "import sklearn.metrics\n",
    "\n",
    "def decoding_pos_dir(emb_train, emb_valid, emb_test, label_train, label_valid, label_test):\n",
    "    metric = 'cosine'\n",
    "    neighbors = np.power(np.arange(1,6, dtype = int),2)\n",
    "    valid_scores = []\n",
    "    for n in neighbors:\n",
    "        pos_decoder = KNeighborsRegressor(n, metric = metric)\n",
    "        dir_decoder = KNeighborsClassifier(n, metric = metric)\n",
    "        pos_decoder.fit(emb_train, label_train[:,0])\n",
    "        dir_decoder.fit(emb_train, label_train[:,1])\n",
    "        pos_pred = pos_decoder.predict(emb_valid)\n",
    "        dir_pred = dir_decoder.predict(emb_valid)\n",
    "        prediction =np.stack([pos_pred, dir_pred],axis = 1)\n",
    "        valid_score = sklearn.metrics.r2_score(label_valid[:,:2], prediction)\n",
    "        valid_scores.append(valid_score)\n",
    "    \n",
    "    best_n=neighbors[np.argmax(valid_scores)]\n",
    "    \n",
    "    pos_decoder = KNeighborsRegressor(best_n, metric = metric)\n",
    "    dir_decoder = KNeighborsClassifier(n, metric = metric)\n",
    "    pos_decoder.fit(emb_train, label_train[:,0])\n",
    "    dir_decoder.fit(emb_train, label_train[:,1])\n",
    "    pos_pred = pos_decoder.predict(emb_test)\n",
    "    dir_pred = dir_decoder.predict(emb_test)\n",
    "    prediction =np.stack([pos_pred, dir_pred],axis = 1)\n",
    "    pos_test_err = np.median(abs(prediction[:,0] - label_test[:, 0]))\n",
    "    \n",
    "    return pos_test_err\n",
    "\n",
    "train_label = np.concatenate(train_u)\n",
    "valid_label = np.concatenate(valid_u)\n",
    "test_label = np.concatenate(test_u)\n",
    "pos_test_err = decoding_pos_dir(train_post_z, valid_post_z, test_post_z, train_label, valid_label, test_label )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9269365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Err: 0.10224993526935577 m\n"
     ]
    }
   ],
   "source": [
    "print(f'Median Err: {pos_test_err} m')"
   ]
  }
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