2023-06-30_dataset2_laurent_decoding_clean.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9008a877",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 2267760\n",
      "-rw-r--r--  1 laurentperrinet  staff   211M Jun 14 10:42 H_Ready-Set_bin1ms_alignedReady.mat\n",
      "-rw-r--r--  1 laurentperrinet  staff   407M Jun 14 10:55 G_Ready-Set_bin1ms_alignedReady.mat\n",
      "-rw-r--r--@ 1 laurentperrinet  staff   2.0K Jun 15 12:29 README.md\n",
      "-rw-r--r--@ 1 laurentperrinet  staff   183M Jun 27 15:52 G_Ready-Set_bin1ms_alignedReady_session=0.npz\n",
      "-rw-r--r--@ 1 laurentperrinet  staff   212M Jun 27 15:52 G_Ready-Set_bin1ms_alignedReady_session=1.npz\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    17M Jun 27 15:52 H_Ready-Set_bin1ms_alignedReady_session=0.npz\n",
      "-rw-r--r--@ 1 laurentperrinet  staff    68M Jun 27 15:52 H_Ready-Set_bin1ms_alignedReady_session=1.npz\n"
     ]
    }
   ],
   "source": [
    "%ls -ltrh /Users/laurentperrinet/quantic/grants/Polychronies/RSG-spikepatterns/Data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16476f59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1200, 107, 1498)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_session = 0\n",
    "monkey = 'G'\n",
    "from pathlib import Path\n",
    "path = Path.home()/'quantic'/'grants'/'polychronies'/'RSG-spikepatterns'/'Data'/f'{monkey}_Ready-Set_bin1ms_alignedReady_session={i_session}.npz'\n",
    "\n",
    "dictdata = np.load(path)\n",
    "data, id_neuron, id_short, t_s, t_p = dictdata['data'], dictdata['id_neuron'], dictdata['id_short'], dictdata['t_s'], dictdata['t_p']\n",
    "data = data.astype(float)\n",
    "data[data==255] = np.nan\n",
    "N_time, N_neuron, N_trial = data.shape\n",
    "\n",
    "N_time, N_neuron, N_trial"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "62b0b690",
   "metadata": {},
   "source": [
    "### computing the firing rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa340cf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 100,  140,  180,  220,  260,  300,  340,  380,  420,  460,  500,\n",
       "         540,  580,  620,  660,  700,  740,  780,  820,  860,  900,  940,\n",
       "         980, 1020, 1060, 1100, 1140, 1180]),\n",
       " 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_s_max = int(t_s.max())\n",
    "DeltaT = 100 # number of time bins on which we average\n",
    "dt = 40 # temporal distance between points on which we compute the firing rate\n",
    "\n",
    "timepoints = np.arange(DeltaT, t_s_max+1, dt)\n",
    "N_timepoint = len(timepoints)\n",
    "timepoints, N_timepoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ec9a4e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "fr = np.empty((N_timepoint, N_neuron, N_trial)) * np.nan\n",
    "for i_timepoint, timepoint in enumerate(timepoints):\n",
    "    fr[i_timepoint, :, :] = np.nansum(data[(timepoint-DeltaT):timepoint, :, :], axis=0) / (DeltaT/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb35aebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 107, 1498)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f825ab78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([934, 157, 392, ..., 826, 542, 868])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_train = 80/100\n",
    "N_trial_train = int(N_trial*ratio_train)\n",
    "N_trial_test = N_trial - N_trial_train\n",
    "idx_trial = np.random.permutation(N_trial)\n",
    "idx_trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b4b0c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x117085c10>"
      ]
     },
     "execution_count": 8,
     "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.imshow(fr[:, :, 102], cmap='viridis')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e9714952",
   "metadata": {},
   "source": [
    "### logistic regression model with NaNs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4c568a01",
   "metadata": {},
   "source": [
    "### inference model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6abe4441",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\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": 10,
   "id": "ff55e8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "torch.set_default_dtype(torch.float64) # -> torch.tensor([1.2, 3]).dtype = torch.float64\n",
    "criterion = torch.nn.BCELoss(reduction=\"mean\") \n",
    "\n",
    "class LogisticRegressionModel(torch.nn.Module):\n",
    "    def __init__(self, N_neuron, N_timepoint, logit_0=-2., logit_wt=0.1):\n",
    "        super(LogisticRegressionModel, self).__init__() \n",
    "        self.bias = torch.nn.Parameter(logit_0*torch.ones(1, N_timepoint))\n",
    "        self.weight = torch.nn.Parameter(logit_wt * torch.randn((N_neuron, N_timepoint)))\n",
    "\n",
    "    def forward(self, X_input):\n",
    "        # X_input.shape = N_batch, N_neuron\n",
    "        X_input[torch.isnan(X_input)] = 0.\n",
    "        return F.softmax(torch.tensordot(X_input, self.weight, dims=1) +  self.bias, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98dce17f",
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 1.e-05\n",
    "logit_0 = -2.\n",
    "logit_wt = 0.01\n",
    "beta1, beta2 = 0.995, 0.999\n",
    "num_epochs = 2 ** 10 + 1\n",
    "amsgrad = True  # gives similar results\n",
    "amsgrad = False # gives similar results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "733ce849",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def fit_data(fr, logit_0=logit_0, logit_wt=logit_wt,\n",
    "                learning_rate=learning_rate,\n",
    "                num_epochs=num_epochs,\n",
    "                beta1=beta1, beta2=beta2, amsgrad=amsgrad,\n",
    "                verbose=False, \n",
    "        ):\n",
    "\n",
    "    logistic_model = LogisticRegressionModel(N_neuron, N_timepoint, logit_0=logit_0, logit_wt=logit_wt)\n",
    "    logistic_model = logistic_model.to(device)\n",
    "    logistic_model.train()\n",
    "    optimizer = torch.optim.Adam(\n",
    "        logistic_model.parameters(), lr=learning_rate, betas=(beta1, beta2), amsgrad=amsgrad\n",
    "    )\n",
    "    for epoch in range(int(num_epochs)):\n",
    "        logistic_model.train()\n",
    "        losses = []\n",
    "        for i_trial in idx_trial[range(N_trial_train)]:\n",
    "            X = torch.tensor(fr[:, :, i_trial], dtype=torch.float64)\n",
    "            time_labels = np.arange(N_timepoint)[X.sum(axis=1)>0]\n",
    "            X = X[time_labels, :]\n",
    "            labels = F.one_hot(torch.tensor(time_labels), num_classes=N_timepoint)*1.\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",
    "    return logistic_model, np.mean(losses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36eb6b45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0 - Loss: 0.15965\n",
      "Iteration: 32 - Loss: 0.13099\n",
      "Iteration: 64 - Loss: 0.13015\n",
      "Iteration: 96 - Loss: 0.12997\n",
      "Iteration: 128 - Loss: 0.12989\n",
      "Iteration: 160 - Loss: 0.12984\n",
      "Iteration: 192 - Loss: 0.12980\n",
      "Iteration: 224 - Loss: 0.12977\n",
      "Iteration: 256 - Loss: 0.12974\n",
      "Iteration: 288 - Loss: 0.12971\n",
      "Iteration: 320 - Loss: 0.12969\n",
      "Iteration: 352 - Loss: 0.12967\n",
      "Iteration: 384 - Loss: 0.12965\n",
      "Iteration: 416 - Loss: 0.12963\n",
      "Iteration: 448 - Loss: 0.12961\n",
      "Iteration: 480 - Loss: 0.12959\n",
      "Iteration: 512 - Loss: 0.12957\n",
      "Iteration: 544 - Loss: 0.12956\n",
      "Iteration: 576 - Loss: 0.12954\n",
      "Iteration: 608 - Loss: 0.12953\n",
      "Iteration: 640 - Loss: 0.12951\n",
      "Iteration: 672 - Loss: 0.12950\n",
      "Iteration: 704 - Loss: 0.12949\n",
      "Iteration: 736 - Loss: 0.12948\n",
      "Iteration: 768 - Loss: 0.12947\n",
      "Iteration: 800 - Loss: 0.12945\n",
      "Iteration: 832 - Loss: 0.12944\n",
      "Iteration: 864 - Loss: 0.12944\n",
      "Iteration: 896 - Loss: 0.12943\n",
      "Iteration: 928 - Loss: 0.12942\n",
      "Iteration: 960 - Loss: 0.12941\n",
      "Iteration: 992 - Loss: 0.12940\n",
      "Iteration: 1024 - Loss: 0.12940\n"
     ]
    }
   ],
   "source": [
    "logistic_model, loss = fit_data(fr, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0bb4ce15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28, 28),\n",
       " (28, 28),\n",
       " array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_pred = pd.DataFrame([], columns=['i_trial', 't_s', 't_pred', 't_p', 'id_short'], dtype=float)\n",
    "i_ = 0\n",
    "with torch.no_grad():\n",
    "    counts = np.zeros((N_timepoint, N_timepoint))\n",
    "    outputs = np.zeros((N_timepoint, N_timepoint))\n",
    "    for i_trial in idx_trial[range(N_trial_train, N_trial)]:\n",
    "        X = torch.tensor(fr[:, :, i_trial], dtype=torch.float64)\n",
    "        time_labels = np.arange(N_timepoint)[X.sum(axis=1)>0]\n",
    "        counts[time_labels, :] += 1\n",
    "        X = X[time_labels, :]\n",
    "        output = logistic_model(X).numpy()\n",
    "        outputs[time_labels, :] += output\n",
    "\n",
    "        proba = output[time_labels[-1], :]\n",
    "        t_pred = np.sum(proba * timepoints) / np.sum(proba)\n",
    "        df_pred.loc[i_] = {'i_trial':i_trial, 't_s':t_s[i_trial], 't_pred':t_pred, 't_p':t_p[i_trial], 'id_short':id_short[i_trial]}\n",
    "        i_ += 1\n",
    "\n",
    "    outputs = outputs/counts\n",
    "\n",
    "outputs.shape, counts.shape, outputs.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b7757287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0018392477224349068, 0.1846251473904052)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs.min(), outputs.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d4953e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'true time')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(outputs, cmap='viridis')\n",
    "ax.set_xlabel('predicted time')\n",
    "ax.set_ylabel('true time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37cd8f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 't_p')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "df_pred.plot.scatter(x='t_s', y='t_p', ax=ax)\n",
    "ax.set_xlabel('t_s')\n",
    "ax.set_ylabel('t_p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ce1427a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 't_p')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "df_pred.plot.scatter(x='t_p', y='t_pred', ax=ax)\n",
    "ax.set_xlabel('t_s')\n",
    "ax.set_ylabel('t_p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38bb69e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 't_p')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "df_pred.plot.scatter(x='t_s', y='t_pred', ax=ax)\n",
    "ax.set_xlabel('t_s')\n",
    "ax.set_ylabel('t_p')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "59d44e8f",
   "metadata": {},
   "source": [
    "## scan parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a8378946",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning_rate=1.000e-06:loss=1.299e-01\n",
      "learning_rate=3.162e-06:loss=1.296e-01\n",
      "learning_rate=1.000e-05:loss=1.294e-01\n",
      "learning_rate=3.162e-05:loss=1.294e-01\n",
      "learning_rate=1.000e-04:loss=1.300e-01\n"
     ]
    }
   ],
   "source": [
    "N_scan = 5\n",
    "for learning_rate_ in learning_rate*np.logspace(-1, 1, N_scan, base=10):\n",
    "    logistic_model, loss = fit_data(fr, learning_rate=learning_rate_, verbose=False)\n",
    "    print(f'learning_rate={learning_rate_:.3e}:{loss=:.3e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a2b59fd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logit_0=-2.000e-01:loss=1.294e-01\n",
      "logit_0=-6.325e-01:loss=1.294e-01\n",
      "logit_0=-2.000e+00:loss=1.294e-01\n",
      "logit_0=-6.325e+00:loss=1.294e-01\n",
      "logit_0=-2.000e+01:loss=1.294e-01\n"
     ]
    }
   ],
   "source": [
    "for logit_0_ in logit_0*np.logspace(-1, 1, N_scan, base=10):\n",
    "    logistic_model, loss = fit_data(fr, logit_0=logit_0_, verbose=False)\n",
    "    print(f'logit_0={logit_0_:.3e}:{loss=:.3e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ad355197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logit_wt=1.000e-03:loss=1.294e-01\n",
      "logit_wt=3.162e-03:loss=1.294e-01\n",
      "logit_wt=1.000e-02:loss=1.294e-01\n",
      "logit_wt=3.162e-02:loss=1.294e-01\n",
      "logit_wt=1.000e-01:loss=1.294e-01\n"
     ]
    }
   ],
   "source": [
    "for logit_wt_ in logit_wt*np.logspace(-1, 1, N_scan, base=10):\n",
    "    logistic_model, loss = fit_data(fr, logit_wt=logit_wt_, verbose=False)\n",
    "    print(f'logit_wt={logit_wt_:.3e}:{loss=:.3e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ab94a08c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beta1=9.900e-01:loss=1.294e-01\n",
      "beta1=9.982e-01:loss=1.293e-01\n",
      "beta1=9.997e-01:loss=1.293e-01\n",
      "beta1=9.999e-01:loss=1.293e-01\n",
      "beta1=1.000e+00:loss=1.293e-01\n"
     ]
    }
   ],
   "source": [
    "for beta1_ in 1-np.logspace(-2, -5, N_scan, base=10):\n",
    "    logistic_model, loss = fit_data(fr, beta1=beta1_, verbose=False)\n",
    "    print(f'beta1={beta1_:.3e}:{loss=:.3e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ce111043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beta2=9.000e-01:loss=1.295e-01\n",
      "beta2=9.822e-01:loss=1.295e-01\n",
      "beta2=9.968e-01:loss=1.294e-01\n",
      "beta2=9.994e-01:loss=1.294e-01\n",
      "beta2=9.999e-01:loss=1.294e-01\n"
     ]
    }
   ],
   "source": [
    "for beta2_ in 1-np.logspace(-1, -4, N_scan, base=10):\n",
    "    logistic_model, loss = fit_data(fr, beta2=beta2_, verbose=False)\n",
    "    print(f'beta2={beta2_:.3e}:{loss=:.3e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c796488e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amsgrad=True:loss=1.294e-01\n"
     ]
    }
   ],
   "source": [
    "logistic_model, loss = fit_data(fr, amsgrad=not(amsgrad), verbose=False)\n",
    "print(f'amsgrad={not(amsgrad)}:{loss=:.3e}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28ba5c9a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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