categorical.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3623bf32-7b4e-48d1-9231-2e361106a7e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes, load_breast_cancer\n",
    "from typing import Optional, Tuple\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "    \"font.family\": \"sans-serif\",\n",
    "    \"font.sans-serif\": [\"Helvetica\"]\n",
    "})\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "cp = sns.color_palette()\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "import FrEIA.framework as ff\n",
    "import FrEIA.modules as fm\n",
    "from tqdm.notebook import tqdm\n",
    "from IPython import display\n",
    "import latent_dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20e878f7-6b68-4094-b761-795f0723c489",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_name = \"diabetes\"  # \"breast_cancer\"\n",
    "exec(f\"ds = load_{ds_name}()\")\n",
    "x, y = torch.from_numpy(ds[\"data\"]).cuda().float(), torch.from_numpy(ds[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db5ad2ae-5ff2-41ae-a0bc-b06ec44dcf10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.return_types.max(\n",
       "values=tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], device='cuda:0'),\n",
       "indices=tensor([204,   0, 367, 340, 230, 123,  58, 123,  23,  23], device='cuda:0'))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x / x.max(0, keepdim=True)[0]\n",
    "x.max(dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e100eeae-25d4-4603-9aea-906164f630a8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[\"feature_names\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "218bd486-a864-4c49-8892-0405df617b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if ds_name == \"diabetes\":\n",
    "    fig, axs = plt.subplots(2, 5, figsize=(15, 6))\n",
    "elif ds_name == \"breast_cancer\":\n",
    "    fig, axs = plt.subplots(5, 6, figsize=(40,30))\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    hist, bin_edges = np.histogram(x[:,i].cpu().numpy())#, density=True)\n",
    "    # hist_rev, _ = np.histogram(rev_dp[:,i].cpu().numpy(), range=(bin_edges.min(), bin_edges.max()), density=True)\n",
    "    ax.bar(bin_edges[:-1], hist, width=bin_edges[1]-bin_edges[0], align=\"edge\", label=\"true\")\n",
    "    # ax.bar(bin_edges[:-1], hist_rev, width=bin_edges[1]-bin_edges[0], align=\"edge\", alpha=0.7, label=\"dp\")\n",
    "    ax.set_title(ds[\"feature_names\"][i], fontsize=50)\n",
    "    ax.xaxis.set_tick_params(labelsize=20)\n",
    "    ax.yaxis.set_tick_params(labelsize=20)\n",
    "# ax.legend(fontsize=40, loc=\"upper center\", bbox_to_anchor=(1., 1.15)) #, ncol=2)\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.savefig(f\"./experiments/categorical/features_diabetes.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "109c169e-fbdd-4c5d-bf29-6f13f09bede4",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_blocks = 4\n",
    "n_epochs = 5000\n",
    "lr = 1e-4\n",
    "noise_std = 0.02\n",
    "rev_loss = False # True\n",
    "register_forward_hook = True\n",
    "plot_every = 50\n",
    "coupling_block = fm.GINCouplingBlock # fm.GLOWCouplingBlock\n",
    "\n",
    "def subnet_fc(dims_in: int, dims_out: int) -> nn.Sequential:\n",
    "    return nn.Sequential(\n",
    "        nn.Linear(dims_in, 128),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(128, 128),\n",
    "        nn.ReLU(),\n",
    "        nn.Linear(128, dims_out)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8bbfa7a-e71f-4bbd-91bd-b3de00e20d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = [ff.InputNode(x.size(1), name=\"inp\")]\n",
    "cond = ff.ConditionNode(1)\n",
    "for i in range(1, n_blocks + 1):\n",
    "    nodes.append(ff.Node([nodes[-1].out0], fm.PermuteRandom, {\"seed\": i}, name=f\"permute_{i}\"))\n",
    "    nodes.append(ff.Node([nodes[-1].out0], coupling_block, {\"clamp\": 2.0, \"subnet_constructor\": subnet_fc}, conditions=cond, name=f\"coupling_{i}\"))\n",
    "nodes.append(ff.OutputNode([nodes[-1].out0], name=\"out\"))\n",
    "nodes.append(cond)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be5ab875-b91d-4819-ada1-dfdae9d92965",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ff.ReversibleGraphNet(nodes, verbose=False).cuda()\n",
    "# x, y = torch.cuda.FloatTensor(x), torch.cuda.FloatTensor(y)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "54615557-6509-44e6-9358-112bfee1c150",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "961b7bee88664b8bb75ac3d9bddc355f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forward_plot_titles = [r\"$\\mathbf{x}$\", *[f\"t{i}\" for i in range(1, 4)], r\"$f_{\\theta}(\\mathbf{x})$\"]\n",
    "backward_plot_titles = [r\"$f_{\\theta}(\\mathbf{z})$\", *[f\"t{i}\" for i in range(1, 4)], r\"$\\mathbf{z} \\sim \\mathcal{N}(\\mathbf{0},\\mathbf{I})$\"]\n",
    "\n",
    "z = torch.randn(x.shape, device=\"cuda\")\n",
    "\n",
    "pbar = tqdm(range(n_epochs))\n",
    "for e in pbar:\n",
    "    feature_maps = []\n",
    "    \n",
    "    optimizer.zero_grad()\n",
    "    x_noisy = x + torch.randn(x.shape, device=x.device) * noise_std\n",
    "    if ds_name == \"diabetes\":\n",
    "        x[:,1] = (x[:,1] > 0).float()\n",
    "        cond = (x[:,1:2] > 0).float()\n",
    "    elif ds_name == \"breast_cancer\":\n",
    "        cond = y[:,None].cuda()\n",
    "    out, jac = model(x_noisy, cond)\n",
    "    zz = 0.5 * torch.sum(out**2, dim=1)\n",
    "    nll = zz - jac\n",
    "    loss = torch.mean(nll)\n",
    "    loss.backward(retain_graph=rev_loss)\n",
    "    \n",
    "    #if rev_loss:\n",
    "    #    z_noisy = torch.randn((x.size(0), 10), device=\"cuda\")\n",
    "        # z_noisy = z + torch.randn(x.shape, device=x.device) * noise_std\n",
    "    #    recon, _ = model(z_noisy, rev=True)\n",
    "    #    mse = F.mse_loss(recon, x)\n",
    "    #    mse.backward()\n",
    "    \n",
    "    nn.utils.clip_grad_norm_(model.parameters(), 2)\n",
    "    \n",
    "    optimizer.step()\n",
    "    out = out.cpu().detach()\n",
    "    \n",
    "    # if i % plot_every == 0:\n",
    "    pbar.set_description(f\"nll: {loss.item():.4f}\") # | MSE: {mse.item():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e37bfb6d-9b19-48e1-a26f-7ff581efe5c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "revs_dp = []\n",
    "for epsilon in [.5, 1., 3.]:\n",
    "    laplace_dp = latent_dp.LaplaceMechanism(epsilon, .1)\n",
    "    with torch.no_grad():\n",
    "        if ds_name == \"diabetes\":\n",
    "            cond = (x[:,1:2] > 0).float()\n",
    "        elif ds_name == \"breast_cancer\":\n",
    "            cond = y[:,None].cuda()\n",
    "        z, _ = model(x, cond)\n",
    "        # gm_sigma = sigma_gaussian_mechanism(z, epsilon, delta)\n",
    "        z_dp = laplace_dp(z)\n",
    "        cond = torch.randint_like(cond, 2)\n",
    "        # z_dp = z + gm_sigma.cuda() * torch.randn(z.size()).cuda()\n",
    "        rev_dp, _ = model(z_dp, cond, rev=True)\n",
    "        rev, _ = model(z, cond, rev=True)\n",
    "        revs_dp.append(rev_dp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9cf8f4a7-3de0-495b-9b17-a1fd41778025",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x360 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if ds_name == \"diabetes\":\n",
    "    fig, axs = plt.subplots(2, 5, figsize=(15, 5))\n",
    "elif ds_name == \"breast_cancer\":\n",
    "    fig, axs = plt.subplots(5, 6, figsize=(40,30))\n",
    "for i, ax in enumerate(axs.flatten()):\n",
    "    hist, bin_edges = np.histogram(x[:,i].cpu().numpy(), density=True)\n",
    "    ax.bar(bin_edges[:-1], hist, width=bin_edges[1]-bin_edges[0], align=\"edge\", label=\"true\")\n",
    "    for rev_dp, epsilon in zip(revs_dp[1:2], [1]):#revs_dp[::-1], [9, 6, 3]):\n",
    "        hist_rev, _ = np.histogram(rev_dp[:,i].cpu().numpy(), range=(bin_edges.min(), bin_edges.max()), density=True)\n",
    "        ax.bar(bin_edges[:-1], hist_rev, width=bin_edges[1]-bin_edges[0], align=\"edge\", alpha=0.7, label=r\"$\\epsilon = %d$\" % int(epsilon))\n",
    "    ax.set_title(ds[\"feature_names\"][i], fontsize=20)\n",
    "    ax.xaxis.set_tick_params(labelsize=12)\n",
    "    ax.yaxis.set_tick_params(labelsize=12)\n",
    "    #if i == 6:\n",
    "    #    break\n",
    "    if i == 4:\n",
    "        ax.legend(fontsize=15, loc=\"upper center\", bbox_to_anchor=(1.1, 1.)) #, ncol=2)\n",
    "\n",
    "fig.tight_layout()\n",
    "#fig.savefig(f\"./experiments/categorical/features_{ds_name}.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9ee1b460-cbff-4c4b-bd40-eab63af75970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 5, figsize=(15, 3))\n",
    "if ds_name == \"diabetes\":\n",
    "    idx = [0, 1, 2, 4, 7]\n",
    "    #idx = [3, 5, 6, 8, 9]\n",
    "    #x_plot = x.cpu()[:,idx]\n",
    "    feature_names = np.array(ds[\"feature_names\"])[idx].tolist()\n",
    "elif ds_name == \"breast_cancer\":\n",
    "    fig, axs = plt.subplots(5, 6, figsize=(40,30))\n",
    "for i, ax in zip(idx, axs.flatten()):\n",
    "    hist, bin_edges = np.histogram(x[:,i].cpu().numpy(), density=True)\n",
    "    ax.bar(bin_edges[:-1], hist, width=bin_edges[1]-bin_edges[0], align=\"edge\", label=\"true\")\n",
    "    \n",
    "    for rev_dp, epsilon in zip(revs_dp[1:2], [1]):#revs_dp[::-1], [9, 6, 3]):\n",
    "        try:\n",
    "            hist_rev, _ = np.histogram(rev_dp[:,i].cpu().numpy(), range=(bin_edges.min(), bin_edges.max()), density=True)\n",
    "            ax.bar(bin_edges[:-1], hist_rev, width=bin_edges[1]-bin_edges[0], align=\"edge\", alpha=0.7, label=r\"$\\epsilon = %d$\" % int(epsilon))\n",
    "        except:\n",
    "            import pdb;pdb.set_trace()\n",
    "    ax.set_title(ds[\"feature_names\"][i], fontsize=20)\n",
    "    ax.xaxis.set_tick_params(labelsize=12)\n",
    "    ax.yaxis.set_tick_params(labelsize=12)\n",
    "    #if i == 6:\n",
    "    #    break\n",
    "    if i == idx[-1]:\n",
    "        ax.legend(fontsize=15, loc=\"upper center\", bbox_to_anchor=(.8, 1.)) #, ncol=2)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(f\"./experiments/categorical/features_{ds_name}_example_features.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0fb0ebe-82a6-43f3-9a51-c0ce145a071c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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