2023-06-28_dataset2_laurent_logistic-regression-with-NaNs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1fec7f6-4c47-4c18-90ec-11e430dab517",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "89a0dc87",
   "metadata": {},
   "source": [
    "## a standard Logistic regression\n",
    "https://laurentperrinet.github.io/sciblog/posts/2021-12-12-logistic-regression.html"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "53906c27",
   "metadata": {},
   "source": [
    "### generative model for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1a86876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4, 256), (4, 10))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_input = 256 # dimension of X_input\n",
    "n_classes = 10 # number of classes\n",
    "N_batch = 4\n",
    "seed = 1973 # release year of https://en.wikipedia.org/wiki/Ring_Ring_(ABBA_song)\n",
    "\n",
    "np.random.seed(seed)\n",
    "W = np.random.randn(N_input+1, n_classes) # FIXED design matrix\n",
    "\n",
    "def psychometric_function(W, X_input):\n",
    "    # print(W.shape, X_input.shape)\n",
    "    logit = (X_input @ W[:-1, :]) + W[-1, :]\n",
    "    return 1 / (1 + np.exp(-logit))\n",
    "\n",
    "def get_data(W, seed, N_batch):\n",
    "    N_input, n_classes = W.shape[0]-1, W.shape[1]\n",
    "    np.random.seed(seed)\n",
    "    X_input = np.random.randn(N_batch, N_input)\n",
    "    p = psychometric_function(W, X_input)\n",
    "    y = p > np.random.rand(N_batch, n_classes)  # generate data\n",
    "    return X_input, p, y\n",
    "\n",
    "X_input, p, y = get_data(W, seed, N_batch)\n",
    "X_input.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "640f75cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4, 10), 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape, N_batch"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "84c23c11",
   "metadata": {},
   "source": [
    "### inference model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0bb4ce15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "if torch.backends.mps.is_available():\n",
    "    device = torch.device('mps')\n",
    "elif torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = 'cpu'\n",
    "\n",
    "device = 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fc49264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cpu'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9af0c5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/torch/__init__.py:615: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:453.)\n",
      "  _C._set_default_tensor_type(t)\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "torch.set_default_tensor_type(\"torch.DoubleTensor\") # -> torch.tensor([1.2, 3]).dtype = torch.float64\n",
    "# see https://sebastianraschka.com/faq/docs/pytorch-crossentropy.html#pytorch-loss-input-confusion-cheatsheet\n",
    "criterion = torch.nn.BCELoss(reduction=\"mean\") # loss divided by output size\n",
    "#criterion = torch.nn.NLLLoss(reduction=\"mean\") # loss divided by output size\n",
    "\n",
    "class LogisticRegressionModel(torch.nn.Module):\n",
    "    #torch.nn.Module -> Base class for all neural network modules\n",
    "    def __init__(self, N, n_classes, logit0=torch.log(1.*torch.ones(1)), logit_wt=torch.log(0.1*torch.ones(1))):\n",
    "        super(LogisticRegressionModel, self).__init__() \n",
    "        # self.linear = torch.nn.Linear(N, n_classes, bias=bias)\n",
    "\n",
    "        self.bias = torch.nn.Parameter(logit0 * torch.ones(1, n_classes))\n",
    "        self.weight = torch.nn.Parameter(logit_wt * torch.ones((N_input, n_classes)))\n",
    "        # self.nl = torch.nn.LogSoftmax(n_classes)\n",
    "        self.nl = torch.nn.Sigmoid()\n",
    "\n",
    "    def forward(self, X_input):\n",
    "        # X_input.shape = N_batch, N_input\n",
    "        return self.nl(X_input @ self.weight +  self.bias)\n",
    "        # return self.nl(self.linear(X_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "232b502f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0 - Loss: 18.67803\n",
      "Iteration: 16 - Loss: 6.93610\n",
      "Iteration: 32 - Loss: 0.74322\n",
      "Iteration: 48 - Loss: 0.10833\n",
      "Iteration: 64 - Loss: 0.07094\n",
      "Iteration: 80 - Loss: 0.06663\n",
      "Iteration: 96 - Loss: 0.06826\n",
      "Iteration: 112 - Loss: 0.06702\n",
      "Iteration: 128 - Loss: 0.06653\n",
      "Iteration: 144 - Loss: 0.06621\n",
      "Iteration: 160 - Loss: 0.06732\n",
      "Iteration: 176 - Loss: 0.06674\n",
      "Iteration: 192 - Loss: 0.06671\n",
      "Iteration: 208 - Loss: 0.06614\n",
      "Iteration: 224 - Loss: 0.06583\n",
      "Iteration: 240 - Loss: 0.06700\n",
      "Iteration: 256 - Loss: 0.06651\n",
      "Iteration: 272 - Loss: 0.06624\n",
      "Iteration: 288 - Loss: 0.06570\n",
      "Iteration: 304 - Loss: 0.06641\n",
      "Iteration: 320 - Loss: 0.06726\n",
      "Iteration: 336 - Loss: 0.06571\n",
      "Iteration: 352 - Loss: 0.06568\n",
      "Iteration: 368 - Loss: 0.06616\n",
      "Iteration: 384 - Loss: 0.06607\n",
      "Iteration: 400 - Loss: 0.06732\n",
      "Iteration: 416 - Loss: 0.06709\n",
      "Iteration: 432 - Loss: 0.06543\n",
      "Iteration: 448 - Loss: 0.06617\n",
      "Iteration: 464 - Loss: 0.06609\n",
      "Iteration: 480 - Loss: 0.06658\n",
      "Iteration: 496 - Loss: 0.06529\n",
      "Iteration: 512 - Loss: 0.06612\n",
      "Final loss = 0.06539469463318472\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 0.005\n",
    "beta1, beta2 = 0.9, 0.999\n",
    "betas = (beta1, beta2)\n",
    "num_epochs = 2 ** 9 + 1\n",
    "batch_size = 256\n",
    "n_classes = 10\n",
    "amsgrad = False # gives similar results\n",
    "amsgrad = True  # gives similar results\n",
    "\n",
    "def fit_data(X_input, y, \n",
    "            learning_rate=learning_rate,\n",
    "            batch_size=batch_size,  # gamma=gamma,\n",
    "            num_epochs=num_epochs,\n",
    "            betas=betas,\n",
    "            verbose=False, **kwargs\n",
    "        ):\n",
    "\n",
    "    X, labels = torch.Tensor(X_input[:, None]), torch.Tensor(y[:, None])\n",
    "    X, labels = X.to(device), labels.to(device)\n",
    "    loader = DataLoader(\n",
    "        TensorDataset(X, labels), batch_size=batch_size, shuffle=True\n",
    "    )\n",
    "\n",
    "    N_batch = X_input.shape[0]\n",
    "    N = X_input.shape[1]\n",
    "    n_classes = y.shape[1]\n",
    "    logistic_model = LogisticRegressionModel(N, n_classes)\n",
    "    logistic_model = logistic_model.to(device)\n",
    "    logistic_model.train()\n",
    "    optimizer = torch.optim.Adam(\n",
    "        logistic_model.parameters(), lr=learning_rate, betas=betas, amsgrad=amsgrad\n",
    "    )\n",
    "    for epoch in range(int(num_epochs)):\n",
    "        logistic_model.train()\n",
    "        losses = []\n",
    "        for X_, labels_ in loader:\n",
    "            # X_, labels_ = X_.to(device), labels_.to(device)\n",
    "            outputs = logistic_model(X_)\n",
    "            loss = criterion(outputs, labels_)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            losses.append(loss.item())\n",
    "\n",
    "        if verbose and (epoch % (num_epochs // 32) == 0):\n",
    "            print(f\"Iteration: {epoch} - Loss: {np.mean(losses):.5f}\")\n",
    "\n",
    "    logistic_model.eval()\n",
    "    X, labels = torch.Tensor(X_input[:, None]), torch.Tensor(y[:, None])\n",
    "    outputs = logistic_model(X)\n",
    "    loss = criterion(outputs, labels).item()\n",
    "    return logistic_model, loss\n",
    "\n",
    "\n",
    "X_input, p, y = get_data(W, seed=seed, N_batch=10000)\n",
    "logistic_model, loss = fit_data(X_input, y, verbose=True)\n",
    "print(\"Final loss =\", loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b14de61c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'true bias')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1300x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 13))\n",
    "ax.plot([-3, 3], [-3, 3], 'r--')\n",
    "#ax.plot(logistic_model.linear.bias.detach().numpy(), 'r')\n",
    "# ax.plot(W[-1, :], 'r--')\n",
    "ax.scatter(logistic_model.bias.detach().numpy(), W[-1, :])\n",
    "ax.set_xlabel('learned bias')\n",
    "ax.set_ylabel('true bias')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ed65c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((256, 10), (2560,), (256, 10), (2560,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model.weight.detach().numpy().shape, logistic_model.weight.detach().numpy().ravel().shape, W[:-1, :].shape, W[:-1, :].ravel().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0d73c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'true factors')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 13))\n",
    "ax.plot([-3, 3], [-3, 3], 'r--')\n",
    "ax.scatter(logistic_model.weight.detach().numpy().ravel(), W[:-1, :].ravel(), alpha=.01)\n",
    "ax.set_xlabel('learned factors')\n",
    "ax.set_ylabel('true factors')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3e07d361",
   "metadata": {},
   "source": [
    "## Logistic regression with NaNs\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "75c4b194",
   "metadata": {},
   "source": [
    "### generative model for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "233cd57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4, 256), (4, 10))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_input = 256 # number of X_input\n",
    "n_classes = 10 # number of classes\n",
    "N_batch = 4\n",
    "\n",
    "p_NaN = .005 # percentage of NaNs\n",
    "\n",
    "def get_data(W, seed, N_batch, p_NaN):\n",
    "    N_input, n_classes = W.shape[0]-1, W.shape[1]\n",
    "    np.random.seed(seed)\n",
    "    X_input = np.random.randn(N_batch, N_input)\n",
    "    p = psychometric_function(W, X_input)\n",
    "    y = (p > np.random.rand(N_batch, n_classes))  # generate data\n",
    "    X_input[np.random.rand(N_batch, N_input)<p_NaN] = np.NaN  # some parts of the data are not observable\n",
    "    return X_input, p, y\n",
    "\n",
    "X_input, p, y = get_data(W, seed, N_batch, p_NaN)\n",
    "X_input.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c29046ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.80834952, -0.13619906, -0.80648188, ...,  0.434049  ,\n",
       "         0.08255005, -0.78694834],\n",
       "       [-0.37632552, -0.59738574,  1.11696778, ...,  0.81501842,\n",
       "         0.21818331,         nan],\n",
       "       [-0.37634137, -0.43356338,  0.44584921, ...,  0.60073607,\n",
       "        -1.10058159,  0.013434  ],\n",
       "       [-0.89518271,  1.0845264 , -0.57140918, ..., -0.33543602,\n",
       "         0.2572201 , -0.52818111]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fd65b58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 4, 1]), 0, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(X_input).sum(axis=1), np.isnan(p).sum(), np.isnan(y).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf00d9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000000e+00, 5.16416203e-01, 1.02413819e-01, 9.99840792e-01,\n",
       "        9.77940787e-01, 9.43776930e-02, 5.06284936e-04, 8.99952836e-04,\n",
       "        1.00000000e+00, 1.92779293e-05],\n",
       "       [9.99999977e-01, 9.99998084e-01, 8.55022500e-09, 1.00000000e+00,\n",
       "        9.99999970e-01, 2.36069802e-01, 9.64523357e-01, 1.10640081e-12,\n",
       "        9.99999513e-01, 6.92597431e-05],\n",
       "       [2.57852128e-02, 9.99996196e-01, 6.20986426e-02, 9.11545234e-01,\n",
       "        6.48181739e-11, 9.99999999e-01, 1.02360082e-02, 9.99998219e-01,\n",
       "        9.99999977e-01, 1.41057295e-05],\n",
       "       [1.00000000e+00, 9.99999983e-01, 3.76846841e-04, 2.33812629e-04,\n",
       "        9.99889176e-01, 5.37469629e-01, 7.33585772e-08, 1.01885133e-07,\n",
       "        9.99999694e-01, 4.01410283e-03]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd1096f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True, False,  True,  True, False, False, False,  True,\n",
       "        False],\n",
       "       [ True,  True, False,  True,  True,  True,  True, False,  True,\n",
       "        False],\n",
       "       [False,  True, False,  True, False,  True, False,  True,  True,\n",
       "        False],\n",
       "       [ True,  True, False, False,  True, False, False, False,  True,\n",
       "        False]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "57077e27",
   "metadata": {},
   "source": [
    "### inference model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32b3a353",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "torch.set_default_tensor_type(\"torch.DoubleTensor\") # -> torch.tensor([1.2, 3]).dtype = torch.float64\n",
    "# see https://sebastianraschka.com/faq/docs/pytorch-crossentropy.html#pytorch-loss-input-confusion-cheatsheet\n",
    "criterion = torch.nn.BCELoss(reduction=\"mean\") # loss divided by output size\n",
    "#criterion = torch.nn.NLLLoss(reduction=\"mean\") # loss divided by output size\n",
    "\n",
    "\n",
    "class LogisticRegressionModel(torch.nn.Module):\n",
    "    def __init__(self, N, n_classes, logit0=torch.log(1.*torch.ones(1)), logit_wt=torch.log(0.1*torch.ones(1))):\n",
    "        super(LogisticRegressionModel, self).__init__() \n",
    "        # self.linear = torch.nn.Linear(N, n_classes, bias=bias)\n",
    "\n",
    "        self.bias = torch.nn.Parameter(logit0 * torch.ones(1, n_classes))\n",
    "        self.weight = torch.nn.Parameter(logit_wt * torch.ones((N_input, n_classes)))\n",
    "        # self.nl = torch.nn.LogSoftmax(n_classes)\n",
    "        self.nl = torch.nn.Sigmoid()\n",
    "\n",
    "    def forward(self, X_input):\n",
    "        # X_input.shape = N_batch, N_input\n",
    "        mask = torch.isnan(X_input)\n",
    "        X_input[mask] = 0.\n",
    "        return self.nl(X_input @ self.weight +  self.bias)\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8d549ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2.8270e-11, 2.8270e-11, 2.8270e-11, 2.8270e-11, 2.8270e-11, 2.8270e-11,\n",
       "         2.8270e-11, 2.8270e-11, 2.8270e-11, 2.8270e-11],\n",
       "        [1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00,\n",
       "         1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00],\n",
       "        [7.4381e-01, 7.4381e-01, 7.4381e-01, 7.4381e-01, 7.4381e-01, 7.4381e-01,\n",
       "         7.4381e-01, 7.4381e-01, 7.4381e-01, 7.4381e-01],\n",
       "        [7.4768e-42, 7.4768e-42, 7.4768e-42, 7.4768e-42, 7.4768e-42, 7.4768e-42,\n",
       "         7.4768e-42, 7.4768e-42, 7.4768e-42, 7.4768e-42]],\n",
       "       grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model = LogisticRegressionModel(N_input, n_classes)\n",
    "X_input, p, y = get_data(W, seed, N_batch, p_NaN)\n",
    "outputs = logistic_model(torch.tensor(X_input.astype(float)))\n",
    "outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "279fc28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0 - Loss: 14.82075\n",
      "Iteration: 16 - Loss: 0.09913\n",
      "Iteration: 32 - Loss: 0.09765\n",
      "Iteration: 48 - Loss: 0.09737\n",
      "Iteration: 64 - Loss: 0.09741\n",
      "Iteration: 80 - Loss: 0.09732\n",
      "Iteration: 96 - Loss: 0.09739\n",
      "Iteration: 112 - Loss: 0.09733\n",
      "Iteration: 128 - Loss: 0.09736\n",
      "Iteration: 144 - Loss: 0.09736\n",
      "Iteration: 160 - Loss: 0.09732\n",
      "Iteration: 176 - Loss: 0.09735\n",
      "Iteration: 192 - Loss: 0.09733\n",
      "Iteration: 208 - Loss: 0.09732\n",
      "Iteration: 224 - Loss: 0.09739\n",
      "Iteration: 240 - Loss: 0.09736\n",
      "Iteration: 256 - Loss: 0.09734\n",
      "Iteration: 272 - Loss: 0.09732\n",
      "Iteration: 288 - Loss: 0.09732\n",
      "Iteration: 304 - Loss: 0.09736\n",
      "Iteration: 320 - Loss: 0.09741\n",
      "Iteration: 336 - Loss: 0.09733\n",
      "Iteration: 352 - Loss: 0.09736\n",
      "Iteration: 368 - Loss: 0.09731\n",
      "Iteration: 384 - Loss: 0.09739\n",
      "Iteration: 400 - Loss: 0.09738\n",
      "Iteration: 416 - Loss: 0.09738\n",
      "Iteration: 432 - Loss: 0.09731\n",
      "Iteration: 448 - Loss: 0.09730\n",
      "Iteration: 464 - Loss: 0.09733\n",
      "Iteration: 480 - Loss: 0.09735\n",
      "Iteration: 496 - Loss: 0.09730\n",
      "Iteration: 512 - Loss: 0.09739\n",
      "Final loss = 0.09494212373903074\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 0.005\n",
    "beta1, beta2 = 0.9, 0.999\n",
    "betas = (beta1, beta2)\n",
    "num_epochs = 2 ** 9 + 1\n",
    "batch_size = 256\n",
    "n_classes=10\n",
    "amsgrad = True  # gives similar results\n",
    "amsgrad = False # gives similar results\n",
    "\n",
    "def fit_data(X_input, y, \n",
    "            learning_rate=learning_rate,\n",
    "            batch_size=batch_size,  # gamma=gamma,\n",
    "            num_epochs=num_epochs,\n",
    "            betas=betas,\n",
    "            verbose=False, **kwargs\n",
    "        ):\n",
    "\n",
    "    X, labels = torch.Tensor(X_input[:, None]), torch.Tensor(y[:, None])\n",
    "    loader = DataLoader(\n",
    "        TensorDataset(X, labels), batch_size=batch_size, shuffle=True\n",
    "    )\n",
    "\n",
    "    N_batch = X_input.shape[0]\n",
    "    N = X_input.shape[1]\n",
    "    n_classes = y.shape[1]\n",
    "    logistic_model = LogisticRegressionModel(N, n_classes)\n",
    "    # logistic_model = logistic_model.to(device)\n",
    "    logistic_model.train()\n",
    "    optimizer = torch.optim.Adam(\n",
    "        logistic_model.parameters(), lr=learning_rate, betas=betas, amsgrad=amsgrad\n",
    "    )\n",
    "    for epoch in range(int(num_epochs)):\n",
    "        logistic_model.train()\n",
    "        losses = []\n",
    "        for X_, labels_ in loader:\n",
    "            # X_, labels_ = X_.to(device), labels_.to(device)\n",
    "            outputs = logistic_model(X_)\n",
    "            loss = criterion(outputs, labels_)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            losses.append(loss.item())\n",
    "\n",
    "        if verbose and (epoch % (num_epochs // 32) == 0):\n",
    "            print(f\"Iteration: {epoch} - Loss: {np.mean(losses):.5f}\")\n",
    "\n",
    "    logistic_model.eval()\n",
    "    X, labels = torch.Tensor(X_input[:, None]), torch.Tensor(y[:, None])\n",
    "    outputs = logistic_model(X)\n",
    "    loss = criterion(outputs, labels).item()\n",
    "    return logistic_model, loss\n",
    "\n",
    "\n",
    "X_input, p, y = get_data(W, seed=seed, N_batch=100000, p_NaN=p_NaN)\n",
    "logistic_model, loss = fit_data(X_input, y, verbose=True)\n",
    "print(\"Final loss =\", loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f76e8598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'X_input')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 13))\n",
    "ax.plot([-3, 3], [-3, 3], 'r--')\n",
    "#ax.plot(logistic_model.linear.bias.detach().numpy(), 'r')\n",
    "# ax.plot(W[-1, :], 'r--')\n",
    "ax.scatter(logistic_model.bias.detach().numpy(), W[-1, :])\n",
    "ax.set_xlabel('input dimensions')\n",
    "ax.set_ylabel('X_input')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eead96ff",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LogisticRegressionModel' object has no attribute 'linear'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m logistic_model\u001b[39m.\u001b[39;49mlinear\u001b[39m.\u001b[39mweight\u001b[39m.\u001b[39mdetach()\u001b[39m.\u001b[39mnumpy()\u001b[39m.\u001b[39mshape, logistic_model\u001b[39m.\u001b[39mlinear\u001b[39m.\u001b[39mweight\u001b[39m.\u001b[39mdetach()\u001b[39m.\u001b[39mnumpy()\u001b[39m.\u001b[39mravel()\u001b[39m.\u001b[39mshape, W[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, :]\u001b[39m.\u001b[39mshape, W[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, :]\u001b[39m.\u001b[39mravel()\u001b[39m.\u001b[39mshape\n",
      "File \u001b[0;32m/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py:1633\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1631\u001b[0m     \u001b[39mif\u001b[39;00m name \u001b[39min\u001b[39;00m modules:\n\u001b[1;32m   1632\u001b[0m         \u001b[39mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> 1633\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m object has no attribute \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m   1634\u001b[0m     \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m, name))\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'LogisticRegressionModel' object has no attribute 'linear'"
     ]
    }
   ],
   "source": [
    "logistic_model.linear.weight.detach().numpy().shape, logistic_model.linear.weight.detach().numpy().ravel().shape, W[:-1, :].shape, W[:-1, :].ravel().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a5fc8cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 13))\n",
    "ax.plot([-3, 3], [-3, 3], 'r--')\n",
    "ax.scatter(logistic_model.weight.detach().numpy().ravel(), W[:-1, :].ravel(), alpha=.01)\n",
    "ax.set_xlabel('input dimensions')\n",
    "ax.set_ylabel('X_input')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23c12275",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}