{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and evaluating encoding models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial introduces a typical encoding framework for mapping stimulus features onto brain activity during natural language comprehension. \n",
    "\n",
    "The previous tutorial walked through extracting two types of linguistic features: syntactic features and language model word embeddings. The `podcast-ecog` dataset comes with several other feature spaces as well. For this tutorial, we will use the LLM contextual embeddings. Encoding models ([Naselaris et al., 2011](https://www.sciencedirect.com/science/article/pii/S1053811910010657?via%3Dihub)) use linear regression to map these features onto brain activity. Here, we use the [Himalaya](https://gallantlab.org/himalaya/index.html) package ([Dupré La Tour et al., 2022](https://doi.org/10.1016/j.neuroimage.2022.119728)) to train encoding models using a ridge L2 penalty (also called ridge regression).\n",
    "\n",
    "Acknowledgments: This tutorial draws heavily on the [encling tutorial](https://github.com/snastase/encling-tutorial/blob/main/encling_tutorial.ipynb) by Samuel A. Nastase.\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hassonlab/podcast-ecog-tutorials/blob/main/notebooks/04-encoding.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only run this cell in colab\n",
    "!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124\n",
    "!pip install mne mne_bids himalaya scikit-learn pandas matplotlib nilearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mne\n",
    "import h5py\n",
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from nilearn.plotting import plot_markers\n",
    "from mne_bids import BIDSPath\n",
    "\n",
    "from himalaya.backend import set_backend, get_backend\n",
    "from himalaya.ridge import RidgeCV\n",
    "from himalaya.scoring import correlation_score\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will set the [Himalaya backend](https://gallantlab.org/himalaya/_generated/himalaya.backend.set_backend.html#himalaya.backend.set_backend) to `torch_cuda` so we can utilize a GPU for training, if available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    set_backend(\"torch_cuda\")\n",
    "    print(\"Using cuda!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now load two contextual word embeddings from GPT-2 ([Radford et al., 2019](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)). The loaded features should be a numpy array with a shape of (number of tokens * feature dimensions). Note that the numbers of tokens are different for the two features because of different tokenization schemas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2025-01-09 15:08:13--  https://s3.amazonaws.com/openneuro.org/ds005574/stimuli/gpt2-xl/features.hdf5\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.123.120, 52.216.43.152, 52.217.199.56, ...\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.123.120|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1721998080 (1.6G) [application/x-hdf5]\n",
      "Saving to: ‘features.hdf5’\n",
      "\n",
      "features.hdf5        12%[=>                  ] 197.25M  47.8MB/s    eta 42s    ^C\n",
      "Using embedding file path: features.hdf5\n"
     ]
    }
   ],
   "source": [
    "bids_root = \"\"  # if using a local dataset, set this variable accordingly\n",
    "\n",
    "# Download the transcript, if required\n",
    "embedding_path = f\"{bids_root}stimuli/gpt2-xl/features.hdf5\"\n",
    "if not len(bids_root):\n",
    "    !wget -nc https://s3.amazonaws.com/openneuro.org/ds005574/$embedding_path\n",
    "    embedding_path = \"features.hdf5\"\n",
    "\n",
    "print(f\"Using embedding file path: {embedding_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LLM embedding matrix has shape: (5491, 1600)\n"
     ]
    }
   ],
   "source": [
    "modelname, layer = 'gpt2-xl', 24\n",
    "with h5py.File(embedding_path, \"r\") as f:\n",
    "    contextual_embeddings = f[f\"layer-{layer}\"][...]\n",
    "print(f\"LLM embedding matrix has shape: {contextual_embeddings.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also load the stimuli transcripts associated with these features. Both transcripts should contain information about the word, token, start (onset), and end (offset). The contextual word embedding transcript should also include other prediction information extracted from GPT-2, like rank, probability, and entropy. For instance, we can calculate how accurate the model is in predicting the next token in the transcript based on the `rank` column, which are integers that represents the rank of the actual token in all the possible tokens of GPT-2.\n",
    "\n",
    "Note: Check that the transcript contains the same number of tokens as the features we loaded before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the transcript, if required\n",
    "transcript_path = f\"{bids_root}stimuli/gpt2-xl/transcript.tsv\"\n",
    "if not len(bids_root):\n",
    "    !wget -nc https://s3.amazonaws.com/openneuro.org/ds005574/$transcript_path\n",
    "    transcript_path = \"transcript.tsv\"\n",
    "\n",
    "# Load transcript\n",
    "df_contextual = pd.read_csv(transcript_path, sep=\"\\t\", index_col=0)\n",
    "if \"rank\" in df_contextual.columns:\n",
    "    model_acc = (df_contextual[\"rank\"] == 0).mean()\n",
    "    print(f\"Model accuracy: {model_acc*100:.3f}%\")\n",
    "\n",
    "df_contextual.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we extracted features, some words are split into separate tokens. Since we only have information of start and end for words, we will align the features from tokens to words for encoding models. Here, we simply average the token features across the same word. Now the features should be a numpy array with a shape of (number of words * feature dimensions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LLM embeddings matrix has shape: (5136, 1600)\n"
     ]
    }
   ],
   "source": [
    "aligned_embeddings = []\n",
    "for _, group in df_contextual.groupby(\"word_idx\"): # group by word index\n",
    "    indices = group.index.to_numpy()\n",
    "    average_emb = contextual_embeddings[indices].mean(0) # average features\n",
    "    aligned_embeddings.append(average_emb)\n",
    "aligned_embeddings = np.stack(aligned_embeddings)\n",
    "print(f\"LLM embeddings matrix has shape: {aligned_embeddings.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also construct a dataframe containing words with their start and end timestamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word_idx</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Act</td>\n",
       "      <td>3.710</td>\n",
       "      <td>3.790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one,</td>\n",
       "      <td>3.990</td>\n",
       "      <td>4.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>monkey</td>\n",
       "      <td>4.651</td>\n",
       "      <td>4.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>in</td>\n",
       "      <td>4.951</td>\n",
       "      <td>5.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>the</td>\n",
       "      <td>5.051</td>\n",
       "      <td>5.111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            word  start    end\n",
       "word_idx                      \n",
       "0            Act  3.710  3.790\n",
       "1           one,  3.990  4.190\n",
       "2         monkey  4.651  4.931\n",
       "3             in  4.951  5.011\n",
       "4            the  5.051  5.111"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_word = df_contextual.groupby(\"word_idx\").agg(dict(word=\"first\", start=\"first\", end=\"last\"))\n",
    "df_word.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading brain data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will load the preprocessed high-gamma ECoG data using MNE. Here, we will demonstrate loading data from our third subject."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "\n",
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       "    </th>\n",
       "    <th colspan=\"2\">\n",
       "        <strong>General</strong>\n",
       "    </th>\n",
       "</tr>\n",
       "\n",
       "<tr class=\"repr-element general-e2c6a011-dc66-4ad0-9abe-8b0011ec061b \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Filename(s)</td>\n",
       "    <td>\n",
       "        \n",
       "        sub-03_task-monkey_desc-highgamma_ieeg.fif\n",
       "        \n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "\n",
       "<tr class=\"repr-element general-e2c6a011-dc66-4ad0-9abe-8b0011ec061b \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>MNE object type</td>\n",
       "    <td>Raw</td>\n",
       "</tr>\n",
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       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Measurement date</td>\n",
       "    \n",
       "    <td>2019-03-11 at 10:54:21 UTC</td>\n",
       "    \n",
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       "    \n",
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       "    \n",
       "    \n",
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       "    <td>Experimenter</td>\n",
       "    \n",
       "    <td>Unknown</td>\n",
       "    \n",
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       "    \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
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       "<tr class=\"repr-section-header acquisition-f839ecd1-cb4a-4a24-bb3d-a60383c49811\" \n",
       "    title=\"Hide section\"  onclick=\"toggleVisibility('acquisition-f839ecd1-cb4a-4a24-bb3d-a60383c49811')\">\n",
       "    <th class=\"repr-section-toggle-col\">\n",
       "        <button>\n",
       "            \n",
       "            <span class=\"collapse-uncollapse-caret\"></span>\n",
       "        </button>\n",
       "    </th>\n",
       "    <th colspan=\"2\">\n",
       "        <strong>Acquisition</strong>\n",
       "    </th>\n",
       "</tr>\n",
       "\n",
       "<tr class=\"repr-element acquisition-f839ecd1-cb4a-4a24-bb3d-a60383c49811 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Duration</td>\n",
       "    <td>00:29:60 (HH:MM:SS)</td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<tr class=\"repr-element acquisition-f839ecd1-cb4a-4a24-bb3d-a60383c49811 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Sampling frequency</td>\n",
       "    <td>512.00 Hz</td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "<tr class=\"repr-element acquisition-f839ecd1-cb4a-4a24-bb3d-a60383c49811 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Time points</td>\n",
       "    <td>921,600</td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<tr class=\"repr-section-header channels-c91b88b8-4426-4bca-b950-7eac9d0e13f2\"  title=\"Hide section\" \n",
       "    onclick=\"toggleVisibility('channels-c91b88b8-4426-4bca-b950-7eac9d0e13f2')\">\n",
       "    <th class=\"repr-section-toggle-col\">\n",
       "        <button>\n",
       "            \n",
       "            <span class=\"collapse-uncollapse-caret\"></span>\n",
       "        </button>\n",
       "    </th>\n",
       "    <th colspan=\"2\">\n",
       "        <strong>Channels</strong>\n",
       "    </th>\n",
       "</tr>\n",
       "\n",
       "\n",
       "<tr class=\"repr-element channels-c91b88b8-4426-4bca-b950-7eac9d0e13f2 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>ECoG</td>\n",
       "    <td>\n",
       "        <button class=\"channel-names-btn\" onclick=\"alert('Good ECoG:\\n\\nLGA2, LGA3, LGA4, LGA5, LGA6, LGA7, LGA8, LGA9, LGA10, LGA11, LGA12, LGA13, LGA14, LGA15, LGA16, LGA17, LGA18, LGA19, LGA20, LGA21, LGA22, LGA23, LGA24, LGA25, LGA26, LGA27, LGA28, LGA29, LGA30, LGA31, LGA32, LGA33, LGA34, LGA35, LGA36, LGA37, LGA38, LGA39, LGA40, LGA41, LGA42, LGA43, LGA44, LGA45, LGA46, LGA47, LGA48, LGA49, LGA50, LGA51, LGA52, LGA53, LGA54, LGA55, LGA56, LGA57, LGA58, LGA59, LGA60, LGA61, LGA62, LGA63, LGA64, LGB65, LGB66, LGB67, LGB68, LGB69, LGB70, LGB71, LGB72, LGB73, LGB74, LGB75, LGB76, LGB77, LGB78, LGB79, LGB80, LGB81, LGB82, LGB83, LGB84, LGB85, LGB86, LGB87, LGB88, LGB89, LGB90, LGB91, LGB92, LGB93, LGB94, LGB95, LGB96, LGB97, LGB98, LGB99, LGB100, LGB101, LGB102, LGB103, LGB104, LGB105, LGB106, LGB107, LGB108, LGB109, LGB110, LGB111, LGB112, LGB113, LGB114, LGB115, LGB116, LGB117, LGB118, LGB119, LGB120, LGB121, LGB122, LGB123, LGB124, LGB125, LGB126, LGB127, LGB128, LAT1, LAT2, LAT3, LAT4, LMT1, LMT2, LMT3, LMT4, LPT1, LPT2, LPT3, LPT4, LO1, LO2, LO7, LO8, LP1, LP2, LP5, LP6, LP7, LF1, LF2, LF5, LF6, LOF1, LOF2, LOF3, LOF4, ROF1, ROF2, ROF3, ROF4, RAT2, RAT3, RAT4, RAT5, RAT6, RMT1, RMT2, RMT3, RMT4, RPT1, RPT2, RPT3, RPT4, RO1, RO2, RO7, RO8, DLAMT2, DLAMT3, DLAMT4, DLAMT5, DLAMT6, DLAMT8, DLPMT1, DLPMT2, DLPMT3, DLPMT4, DLPMT5, DLPMT6, DLPMT7, DLPMT8, DLAI1, DLAI2, DLAI3, DLAI4, DLPI1, DLPI2, DLPI3, DLPI4, DLAL2, DLAL3, DLAL5, DLAL6, DLAL7, DLPL1, DLPL2, DLPL3, DLPL4, DLPL5, DLPL6, DLPL7, DLPL9, DRAMT1, DRAMT2, DRAMT3, DRAMT4, DRAMT5, DRAMT6, DRAMT8, DRPMT1, DRPMT2, DRPMT3, DRPMT4, DRPMT5, DRPMT6, DRPMT7, DRPMT8, DRAI1, DRAI2, DRAI3, DRAI4, DRPI1, DRPI2, DRPI3, DRPI4')\" title=\"(Click to open in popup)&#13;&#13;LGA2, LGA3, LGA4, LGA5, LGA6, LGA7, LGA8, LGA9, LGA10, LGA11, LGA12, LGA13, LGA14, LGA15, LGA16, LGA17, LGA18, LGA19, LGA20, LGA21, LGA22, LGA23, LGA24, LGA25, LGA26, LGA27, LGA28, LGA29, LGA30, LGA31, LGA32, LGA33, LGA34, LGA35, LGA36, LGA37, LGA38, LGA39, LGA40, LGA41, LGA42, LGA43, LGA44, LGA45, LGA46, LGA47, LGA48, LGA49, LGA50, LGA51, LGA52, LGA53, LGA54, LGA55, LGA56, LGA57, LGA58, LGA59, LGA60, LGA61, LGA62, LGA63, LGA64, LGB65, LGB66, LGB67, LGB68, LGB69, LGB70, LGB71, LGB72, LGB73, LGB74, LGB75, LGB76, LGB77, LGB78, LGB79, LGB80, LGB81, LGB82, LGB83, LGB84, LGB85, LGB86, LGB87, LGB88, LGB89, LGB90, LGB91, LGB92, LGB93, LGB94, LGB95, LGB96, LGB97, LGB98, LGB99, LGB100, LGB101, LGB102, LGB103, LGB104, LGB105, LGB106, LGB107, LGB108, LGB109, LGB110, LGB111, LGB112, LGB113, LGB114, LGB115, LGB116, LGB117, LGB118, LGB119, LGB120, LGB121, LGB122, LGB123, LGB124, LGB125, LGB126, LGB127, LGB128, LAT1, LAT2, LAT3, LAT4, LMT1, LMT2, LMT3, LMT4, LPT1, LPT2, LPT3, LPT4, LO1, LO2, LO7, LO8, LP1, LP2, LP5, LP6, LP7, LF1, LF2, LF5, LF6, LOF1, LOF2, LOF3, LOF4, ROF1, ROF2, ROF3, ROF4, RAT2, RAT3, RAT4, RAT5, RAT6, RMT1, RMT2, RMT3, RMT4, RPT1, RPT2, RPT3, RPT4, RO1, RO2, RO7, RO8, DLAMT2, DLAMT3, DLAMT4, DLAMT5, DLAMT6, DLAMT8, DLPMT1, DLPMT2, DLPMT3, DLPMT4, DLPMT5, DLPMT6, DLPMT7, DLPMT8, DLAI1, DLAI2, DLAI3, DLAI4, DLPI1, DLPI2, DLPI3, DLPI4, DLAL2, DLAL3, DLAL5, DLAL6, DLAL7, DLPL1, DLPL2, DLPL3, DLPL4, DLPL5, DLPL6, DLPL7, DLPL9, DRAMT1, DRAMT2, DRAMT3, DRAMT4, DRAMT5, DRAMT6, DRAMT8, DRPMT1, DRPMT2, DRPMT3, DRPMT4, DRPMT5, DRPMT6, DRPMT7, DRPMT8, DRAI1, DRAI2, DRAI3, DRAI4, DRPI1, DRPI2, DRPI3, DRPI4\">\n",
       "            235\n",
       "        </button>\n",
       "\n",
       "        \n",
       "    </td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "<tr class=\"repr-element channels-c91b88b8-4426-4bca-b950-7eac9d0e13f2 \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Head & sensor digitization</td>\n",
       "    \n",
       "    <td>253 points</td>\n",
       "    \n",
       "</tr>\n",
       "    \n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<tr class=\"repr-section-header filters-4678d41e-846c-4029-be58-3a5fb889133f\"  title=\"Hide section\" \n",
       "    onclick=\"toggleVisibility('filters-4678d41e-846c-4029-be58-3a5fb889133f')\">\n",
       "    <th class=\"repr-section-toggle-col\">\n",
       "        <button>\n",
       "            \n",
       "            <span class=\"collapse-uncollapse-caret\"></span>\n",
       "        </button>\n",
       "    </th>\n",
       "    <th colspan=\"2\">\n",
       "        <strong>Filters</strong>\n",
       "    </th>\n",
       "</tr>\n",
       "\n",
       "<tr class=\"repr-element filters-4678d41e-846c-4029-be58-3a5fb889133f \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Highpass</td>\n",
       "    <td>70.00 Hz</td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "<tr class=\"repr-element filters-4678d41e-846c-4029-be58-3a5fb889133f \">\n",
       "    <td class=\"repr-section-toggle-col\"></td>\n",
       "    <td>Lowpass</td>\n",
       "    <td>200.00 Hz</td>\n",
       "</tr>\n",
       "\n",
       "\n",
       "</table>"
      ],
      "text/plain": [
       "<Raw | sub-03_task-monkey_desc-highgamma_ieeg.fif, 235 x 921600 (1800.0 s), ~275 kB, data not loaded>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = BIDSPath(root=f\"{bids_root}derivatives/ecogprep\",\n",
    "                    subject=\"03\", task=\"podcast\", datatype=\"ieeg\", description=\"highgamma\",\n",
    "                    suffix=\"ieeg\", extension=\".fif\")\n",
    "print(f\"File path within the dataset: {file_path}\")\n",
    "\n",
    "# You only need to run this if using Colab (i.e. if you did not set bids_root to a local directory)\n",
    "if not len(bids_root):\n",
    "    !wget -nc https://s3.amazonaws.com/openneuro.org/ds005574/$file_path\n",
    "    file_path = file_path.basename\n",
    "\n",
    "raw = mne.io.read_raw_fif(file_path, verbose=False)\n",
    "picks = mne.pick_channels_regexp(raw.ch_names, \"LG[AB]*\")\n",
    "raw = raw.pick(picks)\n",
    "raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will map the start information (in seconds) of each word in the dataframe onto the brain signal data by multiplying by the sampling rate. Here the first column of `events` mark the start of each word on the brain signal data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5136, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = np.zeros((len(df_word), 3), dtype=int)\n",
    "events[:, 0] = (df_word.start * raw.info['sfreq']).astype(int)\n",
    "events.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we'll take advantage of MNE's tools for creating epochs around stimulus events, which here are the starts (onsets) of each word, to visualize brain signal that respond to word onsets. Here, we take a fixed-width window ranging from -2 seconds to +2 seconds relative to word onset. Since the sampling rate is 512 Hz (512 samples per second), we have 2049 lags total. The ECoG data is a numpy array with the shape of (number of words * number of ECoG electrodes * number of lags)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "5136 matching events found\n",
      "No baseline correction applied\n",
      "Loading data for 5136 events and 2049 original time points ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3774742/3482921024.py:1: RuntimeWarning: The events passed to the Epochs constructor are not chronologically ordered.\n",
      "  epochs = mne.Epochs(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 bad epochs dropped\n",
      "Epochs object has a shape of: (5130, 235, 2049)\n"
     ]
    }
   ],
   "source": [
    "epochs = mne.Epochs(\n",
    "    raw,\n",
    "    events,\n",
    "    tmin=-2.0,\n",
    "    tmax=2.0,\n",
    "    baseline=None,\n",
    "    proj=False,\n",
    "    event_id=None,\n",
    "    preload=True,\n",
    "    event_repeated=\"merge\",\n",
    ")\n",
    "print(f\"Epochs object has a shape of: {epochs._data.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll downsample the temporal resolution to 32 Hz, which reduces the number of lags to 32 * 4 = 128.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "\n",
    "This code block may take ~3 minutes to run.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epochs object has a shape of: (5130, 235, 128)\n"
     ]
    }
   ],
   "source": [
    "epochs = epochs.resample(sfreq=32, npad='auto', method='fft', window='hamming')\n",
    "print(f\"Epochs object has a shape of: {epochs._data.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up feature and brain data\n",
    "\n",
    "Now we have both the features and the ECoG data ready. We plan to fit encoding models at each electrode and for each lag, so we'll reshape our target matrix `Y` to horizontally stack both electrodes and lags along the second dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ECoG data matrix shape: (5130, 30080)\n"
     ]
    }
   ],
   "source": [
    "epochs_data = epochs.get_data(copy=True)\n",
    "epochs_data = epochs_data.reshape(len(epochs), -1)\n",
    "print(f\"ECoG data matrix shape: {epochs_data.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also align our features with the ECoG data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5130, 1600)\n"
     ]
    }
   ],
   "source": [
    "selected_df = df_word.iloc[epochs.selection]\n",
    "averaged_embeddings = aligned_embeddings[epochs.selection]\n",
    "print(averaged_embeddings.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will change the float precision to float32 for all data to take advantage of the GPU memory and computational speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5130, 1600), (5130, 30080))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = averaged_embeddings\n",
    "Y = epochs_data\n",
    "\n",
    "if \"torch\" in get_backend().__name__:\n",
    "    X = X.astype(np.float32)\n",
    "    Y = Y.astype(np.float32)\n",
    "\n",
    "X.shape, Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building encoding models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will use ridge regression to estimate the encoding model. We create a model pipeline uisng `sklearn`, which includes a [StandardScaler](https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html) that standardizes features (X), and a [RidgeCV](https://gallantlab.org/himalaya/_generated/himalaya.ridge.RidgeCV.html) model, which performs ridge regression with cross-validation over our specificed alpha values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;ridgecv&#x27;,\n",
       "                 RidgeCV(alphas=array([1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08,\n",
       "       1.e+09, 1.e+10]),\n",
       "                         cv=KFold(n_splits=5, random_state=None, shuffle=False),\n",
       "                         fit_intercept=True))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;ridgecv&#x27;,\n",
       "                 RidgeCV(alphas=array([1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08,\n",
       "       1.e+09, 1.e+10]),\n",
       "                         cv=KFold(n_splits=5, random_state=None, shuffle=False),\n",
       "                         fit_intercept=True))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">RidgeCV</label><div class=\"sk-toggleable__content \"><pre>RidgeCV(alphas=array([1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08,\n",
       "       1.e+09, 1.e+10]),\n",
       "        cv=KFold(n_splits=5, random_state=None, shuffle=False),\n",
       "        fit_intercept=True)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('ridgecv',\n",
       "                 RidgeCV(alphas=array([1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07, 1.e+08,\n",
       "       1.e+09, 1.e+10]),\n",
       "                         cv=KFold(n_splits=5, random_state=None, shuffle=False),\n",
       "                         fit_intercept=True))])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphas = np.logspace(1, 10, 10) # specify alpha values\n",
    "inner_cv = KFold(n_splits=5, shuffle=False) # inner 5-fold cross-validation setup\n",
    "model = make_pipeline(\n",
    "    StandardScaler(), RidgeCV(alphas, fit_intercept=True, cv=inner_cv) # pipeline\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training encoding models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While `RidgeCV` contains an inner cross-validation setup to find the best alpha, we will also set up an outer cross-validation loop to evaluate our encoding model. Here, we will use k = 2, meaning we will train on half of the data and evaluate on the other half. Within each fold, we will split the train and test dataset. Then we will standardize `Y` the same way we standardize `X` in the pipeline. We will then fit our model on the training dataset and use it to predict for the testing dataset. For evaluation, we will calculate correlation scores between `Y_preds`, the ECoG signal predicted by our model, and `Y_test`, the actual ECoG signal. The encoding model is trained and evaluated for each electrode and each lag.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "This code block may take a while to run. Make sure you are using a GPU if you have one (verify by running `nvidia-smi`). You may also consider resampling the epochs even further to use fewer lags, and/or choose specific electrodes to run to use fewer electrodes.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoding performance correlating matrix shape: (2, 235, 128)\n"
     ]
    }
   ],
   "source": [
    "epochs_shape = epochs._data.shape[1:] # number of electrodes * number of lags\n",
    "\n",
    "def train_encoding(X, Y):\n",
    "\n",
    "    corrs = [] # empty array to store correlation results\n",
    "    kfold = KFold(2, shuffle=False) # outer 2-fold cross-validation setup\n",
    "    for train_index, test_index in kfold.split(X): # loop through folds\n",
    "\n",
    "        # Split train and test datasets\n",
    "        X1_train, X1_test = X[train_index], X[test_index]\n",
    "        Y_train, Y_test = Y[train_index], Y[test_index]\n",
    "\n",
    "        # Standardize Y\n",
    "        scaler = StandardScaler()\n",
    "        Y_train = scaler.fit_transform(Y_train)\n",
    "        Y_test = scaler.transform(Y_test)\n",
    "\n",
    "        model.fit(X1_train, Y_train) # Fit pipeline with transforms and ridge estimator\n",
    "        Y_preds = model.predict(X1_test) # Use trained model to predict on test set\n",
    "        corr = correlation_score(Y_test, Y_preds).reshape(epochs_shape) # Compute correlation score\n",
    "\n",
    "        if \"torch\" in get_backend().__name__: # if using gpu, transform tensor back to numpy\n",
    "            corr = corr.numpy(force=True)\n",
    "\n",
    "        corrs.append(corr) # append fold correlation results to final results\n",
    "    return np.stack(corrs)\n",
    "\n",
    "# set_backend(\"torch\") # resort to torch or numpy if cuda out of memory\n",
    "corrs_embedding = train_encoding(X, Y)\n",
    "print(f\"Encoding performance correlating matrix shape: {corrs_embedding.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting encoding performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We trained and evaluated many encoding models. We have 235 electrodes and 128 lags for each, and on top of that we split the podcast into two 15-minute chunks to train on one half and test on the other. Thus, we have correlations for each of these in one array. Below, we will summarize these results in two ways: spatially and temporally.\n",
    "\n",
    "First we summarize spatially by averaging over all electrodes and looking at the average temporal pattern of correlations. We notice that encoding performance increases after word onset and is lower near ± 2 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lags = np.arange(-2 * 512, 2 * 512, 16) / 512 # specify the lags\n",
    "mean = corrs_embedding.mean((0,1))\n",
    "err = corrs_embedding.std((0,1)) / np.sqrt(np.product(corrs_embedding.shape[:2]))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(lags, mean, color='black')\n",
    "ax.fill_between(lags, mean - err, mean + err, alpha=0.1, color='black')\n",
    "ax.set_xlabel(\"lag (s)\")\n",
    "ax.set_ylabel(\"encoding performance (r ± sem)\")\n",
    "ax.axvline(0, c=(.9, .9, .9), ls=\"--\")\n",
    "ax.axhline(0, c=(.9, .9, .9), ls=\"--\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we summarize temporally by selecting the maximum correlation across lags per electrode. Now that we have one correlation per electrode, we plot the results on the brain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinate matrix shape:  (235, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x350 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = corrs_embedding.mean(0).max(-1)\n",
    "\n",
    "ch2loc = {ch['ch_name']: ch['loc'][:3] for ch in raw.info['chs']}\n",
    "coords = np.vstack([ch2loc[ch] for ch in raw.info['ch_names']])\n",
    "coords *= 1000  # nilearn likes to plot in meters, not mm\n",
    "print(\"Coordinate matrix shape: \", coords.shape)\n",
    "\n",
    "order = values.argsort()\n",
    "plot_markers(values[order], coords[order],\n",
    "             node_size=30, display_mode='lzr',\n",
    "             node_vmin=0, node_cmap='inferno_r', colorbar=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mne",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}