notebooks/fourier_prior_example_keras.ipynb

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    "## A standalone example of the Fourier-based prior (Keras/TensorFlow 2)\n",
    "In this notebook, we will be running a simple example of the Fourier-based prior, in order to show how it can be applied. We will train a very simple model with and without the Fourier-based prior, and at the end we will show the importance scores on a random sample of input sequences using three different methods of computing attributions.\n",
    "\n",
    "The goal of this notebook is to present a very simple, standalone example of models trained with and without the Fourier-based prior. None of the code in this notebook will rely on libraries/repositories other than very standard and ubiquitous ones (e.g. Keras, NumPy, etc.). Note that this example uses a Keras backend of TensorFlow 2.\n",
    "\n",
    "We'll be training a simple binary model to predict binding of the SPI1 transcription factor. For the sake of simplicity and efficiency, we'll be training with only one output task (i.e. single-task models), with a slightly simpler data processing. Thus, these results won't fully match those presented in the paper (the results in the paper can be reproduced from the other code/notebooks in this repository).\n",
    "\n",
    "For more results, see the corresponding paper [here](https://proceedings.neurips.cc/paper/2020/hash/1487987e862c44b91a0296cf3866387e-Abstract.html).\n",
    "\n",
    "Some of these cells can take awhile to run, and the entire notebook can take on the order of an 30 minutes to complete. Several intermediates are saved along the way, so feel free to run the notebook in pieces, commenting out certain cells that have already been run before."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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     "text": [
      "/users/amtseng/miniconda3/envs/att-priors-keras/lib/python3.7/site-packages/ipykernel_launcher.py:15: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
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       "|<bar/>| 0/? [00:00<?, ?it/s]"
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   "source": [
    "import keras.models as km\n",
    "import keras.layers as kl\n",
    "import keras.optimizers as ko\n",
    "import keras.backend as kb\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.special\n",
    "import scipy.stats\n",
    "import scipy.ndimage\n",
    "import sklearn.metrics\n",
    "import pyfaidx\n",
    "import tqdm\n",
    "tqdm.tqdm_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the data\n",
    "We'll need the following files to train our model:\n",
    "1. Locations of SPI1 binding in the human genome (using the hg38 annotation). We will download the called peaks from the [ENCODE project](https://www.encodeproject.org/). Specifically, we'll be fetching the IDR-thresholded optimal peaks (i.e. peaks that the replicates agreed upon the most), using these regions as our positive (binding) set. We will be using the experiment [ENCSR000BGQ](https://www.encodeproject.org/experiments/ENCSR000BGQ/), which measures SPI1 binding in the GM12878 cell line.\n",
    "\n",
    "2. The hg38 human reference genome. We will be downloading this from the UCSC genome portal.\n",
    "\n",
    "3. The hg38 chromosome sizes. We will also be downloading this from the UCSC genome portal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a directory to store everything\n",
    "!mkdir -p prior_example/data\n",
    "!mkdir -p prior_example/models\n",
    "!mkdir -p prior_example/aux_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "--2020-11-17 13:52:20--  https://www.encodeproject.org/files/ENCFF071ZMW/@@download/ENCFF071ZMW.bed.gz\n",
      "Resolving www.encodeproject.org (www.encodeproject.org)... 34.211.244.144\n",
      "Connecting to www.encodeproject.org (www.encodeproject.org)|34.211.244.144|:443... connected.\n",
      "HTTP request sent, awaiting response... 307 Temporary Redirect\n",
      "Resolving encode-public.s3.amazonaws.com (encode-public.s3.amazonaws.com)... 52.218.160.10\n",
      "Connecting to encode-public.s3.amazonaws.com (encode-public.s3.amazonaws.com)|52.218.160.10|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 733979 (717K) [binary/octet-stream]\n",
      "Saving to: ‘prior_example/data/peaks.bed.gz’\n",
      "\n",
      "prior_example/data/ 100%[===================>] 716.78K   993KB/s    in 0.7s    \n",
      "\n",
      "2020-11-17 13:52:21 (993 KB/s) - ‘prior_example/data/peaks.bed.gz’ saved [733979/733979]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Download the peaks from ENCODE\n",
    "!wget https://www.encodeproject.org/files/ENCFF071ZMW/@@download/ENCFF071ZMW.bed.gz -O prior_example/data/peaks.bed.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "--2020-11-17 13:52:21--  https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz\n",
      "Resolving hgdownload.soe.ucsc.edu (hgdownload.soe.ucsc.edu)... 128.114.119.163\n",
      "Connecting to hgdownload.soe.ucsc.edu (hgdownload.soe.ucsc.edu)|128.114.119.163|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 983659424 (938M) [application/x-gzip]\n",
      "Saving to: ‘prior_example/data/hg38.fasta.gz’\n",
      "\n",
      "prior_example/data/ 100%[===================>] 938.09M   109MB/s    in 13s     \n",
      "\n",
      "2020-11-17 13:52:35 (70.7 MB/s) - ‘prior_example/data/hg38.fasta.gz’ saved [983659424/983659424]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Download the hg38 reference genome, and unzip it\n",
    "!wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz -O prior_example/data/hg38.fasta.gz\n",
    "!gunzip prior_example/data/hg38.fasta.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "--2020-11-17 13:53:48--  https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes\r\n",
      "Resolving hgdownload.soe.ucsc.edu (hgdownload.soe.ucsc.edu)... 128.114.119.163\r\n",
      "Connecting to hgdownload.soe.ucsc.edu (hgdownload.soe.ucsc.edu)|128.114.119.163|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 11672 (11K) [text/plain]\r\n",
      "Saving to: ‘prior_example/data/hg38.chrom.sizes’\r\n",
      "\r\n",
      "\r",
      "          prior_exa   0%[                    ]       0  --.-KB/s               \r",
      "prior_example/data/ 100%[===================>]  11.40K  --.-KB/s    in 0s      \r\n",
      "\r\n",
      "2020-11-17 13:53:48 (117 MB/s) - ‘prior_example/data/hg38.chrom.sizes’ saved [11672/11672]\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "# Download the hg38 chromosome sizes\n",
    "!wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes -O prior_example/data/hg38.chrom.sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"prior_example/aux_code/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "peaks_bed_path = \"prior_example/data/peaks.bed.gz\"\n",
    "reference_fasta_path = \"prior_example/data/hg38.fasta\"\n",
    "chrom_sizes_path = \"prior_example/data/hg38.chrom.sizes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the training data\n",
    "We're going to create a rather simple data loader for our binary dataset. We will split each chromosome into windows of length 1000 bp (i.e. the input sequence length for our models), strided across the chromosome with a stride of 50 bp. A 1000 bp window will be considered a \"positive\" binding example if the central 200 bp region overlaps a peak summit, and will be considered a \"negative\" otherwise.\n",
    "\n",
    "We'll save these labels as big BED files: one for training and one for testing. Our test set will consist of chr1. Our training set will consist of all other canonical (non-scaffold) chromosomes other than chrY and chrM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the chromosome sizes, ignoring the small scaffolds\n",
    "chrom_sizes = {}\n",
    "with open(chrom_sizes_path, \"r\") as f:\n",
    "    for line in f:\n",
    "        chrom, size = line.strip().split()\n",
    "        if len(chrom) > 5 or chrom in (\"chrY\", \"chrM\"):\n",
    "            continue\n",
    "        chrom_sizes[chrom] = int(size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_chroms = [\"chr1\"]\n",
    "train_chroms = [chrom for chrom in chrom_sizes.keys() if chrom not in test_chroms]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the peaks BED\n",
    "peaks_bed = pd.read_csv(\n",
    "    peaks_bed_path, sep=\"\\t\", header=None,  # Infer compression\n",
    "    names=[\n",
    "        \"chrom\", \"peak_start\", \"peak_end\", \"name\", \"score\",\n",
    "        \"strand\", \"signal\", \"pval\", \"qval\", \"summit_offset\"\n",
    "    ]\n",
    ")\n",
    "peaks_bed[\"summit\"] = peaks_bed[\"peak_start\"] + peaks_bed[\"summit_offset\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_length = 1000\n",
    "window_stride = 50\n",
    "center_overlap_length = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels_bed_path = \"prior_example/data/train_labels.bed\"\n",
    "test_labels_bed_path = \"prior_example/data/test_labels.bed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_chrom_labels(\n",
    "    chrom, chrom_size, summit_locs, window_length, window_stride,\n",
    "    window_center_length, labels_bed_fp\n",
    "):\n",
    "    \"\"\"\n",
    "    For a single chromosome, write its labels a BED file.\n",
    "    Arguments:\n",
    "        `chrom`: a single chromosome (e.g. \"chr1\")\n",
    "        `chrom_size`: size of the chromosome (e.g. 100000)\n",
    "        `summit_locs`: an iterable of locations of peak summits\n",
    "            in this chromosome\n",
    "        `window_length`: length of windows \n",
    "        `labels_bed_fp`: open file pointer of the file to write\n",
    "            the coordinates and labels\n",
    "    \"\"\"\n",
    "    coord_starts = np.arange(0, chrom_size - window_length, window_stride)\n",
    "    coord_ends = coord_starts + window_length\n",
    "    centers = coord_starts + (window_length // 2)\n",
    "    values = np.zeros_like(coord_starts)\n",
    "    for summit_loc in summit_locs:\n",
    "        delta = summit_loc - (window_length // 2)\n",
    "        values[np.abs(centers - summit_loc) < (window_center_length // 2)] = 1\n",
    "    for i in tqdm.notebook.trange(len(coord_starts), desc=(\"Writing \" + chrom)):\n",
    "        labels_bed_fp.write(\"%s\\t%d\\t%d\\t%d\\n\" % (chrom, coord_starts[i], coord_ends[i], values[i]))"
   ]
  },
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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      "\n"
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    {
     "name": "stdout",
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      "\n"
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   ],
   "source": [
    "# Create the label BEDs for each chromosome set\n",
    "\n",
    "# Clear the files first, if they already exist\n",
    "with open(train_labels_bed_path, \"w\") as f:\n",
    "    pass\n",
    "with open(test_labels_bed_path, \"w\") as f:\n",
    "    pass\n",
    "\n",
    "# Create the label BEDs\n",
    "with open(train_labels_bed_path, \"a\") as f:\n",
    "    for chrom in sorted(train_chroms):\n",
    "        summit_locs = peaks_bed[peaks_bed[\"chrom\"] == chrom][\"summit\"].values\n",
    "        write_chrom_labels(\n",
    "            chrom, chrom_sizes[chrom], summit_locs, input_length,\n",
    "            window_stride, center_overlap_length, f\n",
    "        )\n",
    "with open(test_labels_bed_path, \"a\") as f:\n",
    "    for chrom in sorted(test_chroms):\n",
    "        summit_locs = peaks_bed[peaks_bed[\"chrom\"] == chrom][\"summit\"].values\n",
    "        write_chrom_labels(\n",
    "            chrom, chrom_sizes[chrom], summit_locs, input_length,\n",
    "            window_stride, center_overlap_length, f\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dna_to_one_hot(seqs):\n",
    "    \"\"\"\n",
    "    Converts a list of DNA (\"ACGT\") sequences to one-hot encodings, where the\n",
    "    position of 1s is ordered alphabetically by \"ACGT\". `seqs` must be a list\n",
    "    of N strings, where every string is the same length L. Returns an N x L x 4\n",
    "    NumPy array of one-hot encodings, in the same order as the input sequences.\n",
    "    All bases will be converted to upper-case prior to performing the encoding.\n",
    "    Any bases that are not \"ACGT\" will be given an encoding of all 0s.\n",
    "    \"\"\"\n",
    "    seq_len = len(seqs[0])\n",
    "    assert np.all(np.array([len(s) for s in seqs]) == seq_len)\n",
    "\n",
    "    # Join all sequences together into one long string, all uppercase\n",
    "    seq_concat = \"\".join(seqs).upper()\n",
    "\n",
    "    one_hot_map = np.identity(5)[:, :-1]\n",
    "\n",
    "    # Convert string into array of ASCII character codes;\n",
    "    base_vals = np.frombuffer(bytearray(seq_concat, \"utf8\"), dtype=np.int8)\n",
    "\n",
    "    # Anything that's not an A, C, G, or T gets assigned a higher code\n",
    "    base_vals[~np.isin(base_vals, np.array([65, 67, 71, 84]))] = 85\n",
    "\n",
    "    # Convert the codes into indices in [0, 4], in ascending order by code\n",
    "    _, base_inds = np.unique(base_vals, return_inverse=True)\n",
    "\n",
    "    # Get the one-hot encoding for those indices, and reshape back to separate\n",
    "    return one_hot_map[base_inds].reshape((len(seqs), seq_len, 4))\n",
    "\n",
    "def one_hot_to_dna(one_hot):\n",
    "    \"\"\"\n",
    "    Converts a one-hot encoding into a list of DNA (\"ACGT\") sequences, where the\n",
    "    position of 1s is ordered alphabetically by \"ACGT\". `one_hot` must be an\n",
    "    N x L x 4 array of one-hot encodings. Returns a lits of N \"ACGT\" strings,\n",
    "    each of length L, in the same order as the input array. The returned\n",
    "    sequences will only consist of letters \"A\", \"C\", \"G\", \"T\", or \"N\" (all\n",
    "    upper-case). Any encodings that are all 0s will be translated to \"N\".\n",
    "    \"\"\"\n",
    "    bases = np.array([\"A\", \"C\", \"G\", \"T\", \"N\"])\n",
    "    # Create N x L array of all 5s\n",
    "    one_hot_inds = np.tile(one_hot.shape[2], one_hot.shape[:2])\n",
    "\n",
    "    # Get indices of where the 1s are\n",
    "    batch_inds, seq_inds, base_inds = np.where(one_hot)\n",
    "\n",
    "    # In each of the locations in the N x L array, fill in the location of the 1\n",
    "    one_hot_inds[batch_inds, seq_inds] = base_inds\n",
    "\n",
    "    # Fetch the corresponding base for each position using indexing\n",
    "    seq_array = bases[one_hot_inds]\n",
    "    return [\"\".join(seq) for seq in seq_array]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a data loader which returns one-hot encoded sequences\n",
    "# and labels\n",
    "class BinaryDataLoader:\n",
    "    def __init__(\n",
    "        self, labels_npy_path, reference_genome_path, batch_size,\n",
    "        reverse_complement=True, seed=20200930\n",
    "    ):\n",
    "        labels_table = pd.read_csv(\n",
    "            labels_npy_path, header=None, sep=\"\\t\",\n",
    "            names=[\"chrom\", \"start\", \"end\", \"value\"]\n",
    "        )\n",
    "        \n",
    "        self.coords = labels_table[[\"chrom\", \"start\", \"end\"]].values\n",
    "        labels = labels_table[\"value\"].values\n",
    "        self.pos_inds = np.where(labels)[0]\n",
    "        self.neg_inds = np.where(~labels)[0]\n",
    "        print(\"Positive coordinates: %d\" % len(self.pos_inds))\n",
    "        print(\"Negative coordinates: %d\" % len(self.neg_inds))\n",
    "        print(\"Total: %d\" % len(labels))\n",
    "        \n",
    "        self.reference_genome_path = reference_genome_path\n",
    "        \n",
    "        self.shuffle_rng = np.random.RandomState(seed)\n",
    "        self.reverse_complement = reverse_complement\n",
    "        \n",
    "    def shuffle_data(self):\n",
    "        self.shuffle_rng.shuffle(self.pos_inds)\n",
    "        self.shuffle_rng.shuffle(self.neg_inds)\n",
    "        \n",
    "    def __len__(self):\n",
    "        return int(np.ceil(len(self.pos_inds) / batch_size))\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"\n",
    "        Returns batch of data: a B x L x 4 array of one-hot encoded\n",
    "        sequences, and a B-array of binary labels.\n",
    "        \"\"\"\n",
    "        batch_slice = slice(index * batch_size, (index + 1) * batch_size)\n",
    "        pos_coords = self.coords[self.pos_inds[batch_slice]]\n",
    "        neg_coords = self.coords[self.neg_inds[batch_slice]]\n",
    "        all_coords = np.concatenate([pos_coords, neg_coords])\n",
    "        \n",
    "        labels = np.ones(len(pos_coords) + len(neg_coords))\n",
    "        labels[len(pos_coords):] = 0\n",
    "        \n",
    "        genome_reader = pyfaidx.Fasta(self.reference_genome_path)\n",
    "        seqs = [\n",
    "            genome_reader[chrom][start:end].seq for\n",
    "            chrom, start, end in all_coords\n",
    "        ]\n",
    "        one_hot = dna_to_one_hot(seqs)\n",
    "        \n",
    "        if not self.reverse_complement:\n",
    "            return one_hot, labels\n",
    "        else:\n",
    "            return np.concatenate([one_hot, np.flip(one_hot, axis=(1, 2))]), \\\n",
    "                np.concatenate([labels, labels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive coordinates: 158549\n",
      "Negative coordinates: 55641291\n",
      "Total: 55641291\n",
      "Positive coordinates: 16296\n",
      "Negative coordinates: 4979109\n",
      "Total: 4979109\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "reverse_complement = True\n",
    "train_data_loader = BinaryDataLoader(\n",
    "    train_labels_bed_path, reference_fasta_path,\n",
    "    batch_size, reverse_complement\n",
    ")\n",
    "test_data_loader = BinaryDataLoader(\n",
    "    test_labels_bed_path, reference_fasta_path,\n",
    "    batch_size, reverse_complement\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the model\n",
    "We'll be using the same binary model architecture defined in the paper, except ported to Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smooth_tensor_1d(input_tensor, smooth_sigma):\n",
    "    \"\"\"\n",
    "    Smooths an input tensor along a dimension using a Gaussian filter.\n",
    "    Arguments:\n",
    "        `input_tensor`: a A x B tensor to smooth along the second dimension\n",
    "        `smooth_sigma`: width of the Gaussian to use for smoothing; this is the\n",
    "            standard deviation of the Gaussian to use, and the Gaussian will be\n",
    "            truncated after 1 sigma (i.e. the smoothing window is\n",
    "            1 + (2 * sigma); sigma of 0 means no smoothing\n",
    "    Returns an array the same shape as the input tensor, with the dimension of\n",
    "    `B` smoothed.\n",
    "    \"\"\"\n",
    "    # Generate the kernel\n",
    "    if smooth_sigma == 0:\n",
    "        sigma, truncate = 1, 0\n",
    "    else:\n",
    "        sigma, truncate = smooth_sigma, 1\n",
    "    base = np.zeros(1 + (2 * sigma))\n",
    "    base[sigma] = 1  # Center of window is 1 everywhere else is 0\n",
    "    kernel = scipy.ndimage.gaussian_filter(base, sigma=sigma, truncate=truncate)\n",
    "    kernel = kb.constant(kernel)\n",
    "\n",
    "    # Expand the input and kernel to 3D, with channels of 1\n",
    "    input_tensor = kb.expand_dims(input_tensor, axis=2)  # Shape: A x B x 1\n",
    "    kernel = kb.expand_dims(kb.expand_dims(kernel, axis=1), axis=2)  # Shape: (1 + 2s) x 1 x 1\n",
    "\n",
    "    smoothed = tf.nn.conv1d(\n",
    "        input_tensor, kernel, stride=1, padding=\"SAME\", data_format=\"NWC\"\n",
    "    )\n",
    "\n",
    "    return kb.squeeze(smoothed, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_logits_to_probs(logit_pred_vals):\n",
    "    \"\"\"\n",
    "    Converts the model's predicted binary logits into probabilities via a\n",
    "    sigmoid on all values.\n",
    "    Arguments:\n",
    "        `logit_pred_vals`: a tensor/array containing the predicted logits\n",
    "    Returns a tensor/array of the same shape, containing the predictions as\n",
    "    raw probabilities by doing a sigmoid. If the input is a tensor, the output\n",
    "    will be a tensor. If the input is a NumPy array, the output will be a NumPy\n",
    "    array.\n",
    "    \"\"\"\n",
    "    if type(logit_pred_vals) is np.ndarray:\n",
    "        return scipy.special.expit(logit_pred_vals)\n",
    "    else:\n",
    "        return kb.sigmoid(logit_pred_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_binary_predictor():\n",
    "    \"\"\"\n",
    "    Creates an binary TF binding site predictor from a DNA sequence.\n",
    "    \n",
    "    The model input is a B x L x 4 tensor, where B is the batch size,\n",
    "    and L is the sequence length. The output is the LOGITS of each input\n",
    "    as a B-tensor. Note that the logits are returned in the order\n",
    "    according to the input sequences.\n",
    "    \"\"\"\n",
    "    input_seq = kl.Input(shape=(input_length, 4), name=\"input_seq\")\n",
    "    \n",
    "    # Define the convolutional layers\n",
    "    depths = [64, 64, 64]\n",
    "    conv_filter_sizes = [15, 15, 13]\n",
    "    last_conv_out = input_seq\n",
    "    for i in range(3):\n",
    "        last_conv_out = kl.Conv1D(\n",
    "            filters=conv_filter_sizes[i], kernel_size=conv_filter_sizes[i],\n",
    "            padding=\"valid\", activation=\"relu\", name=(\"conv_%d\" % (i + 1))\n",
    "        )(last_conv_out)\n",
    "        last_conv_out = kl.BatchNormalization(\n",
    "             momentum=0.1, epsilon=1e-05\n",
    "        )(last_conv_out)\n",
    "\n",
    "    # Define the max pooling layer\n",
    "    pool_out = kl.MaxPooling1D(\n",
    "        pool_size=40, strides=40, padding=\"valid\"\n",
    "    )(last_conv_out)\n",
    "    \n",
    "    # Flatten the pooling output\n",
    "    pool_out_flat = kl.Flatten(name=\"flatten\")(pool_out)\n",
    "\n",
    "    # Define the fully connected layers\n",
    "    dims = [50, 15]\n",
    "    last_fc_out = pool_out_flat\n",
    "    for i in range(2):\n",
    "        last_fc_out = kl.Dense(\n",
    "            units=dims[i], activation=\"relu\", name=(\"dense_%d\" % (i + 1))\n",
    "        )(last_fc_out)\n",
    "        last_fc_out = kl.BatchNormalization(\n",
    "             momentum=0.1, epsilon=1e-05\n",
    "        )(last_fc_out)\n",
    "\n",
    "    # Map last fully connected layer to final outputs\n",
    "    output = kl.Dense(\n",
    "        units=1, name=\"dense_3\"\n",
    "    )(last_fc_out)\n",
    "    \n",
    "    model = km.Model(inputs=input_seq, outputs=output)\n",
    "    return model\n",
    "\n",
    "\n",
    "def correctness_loss(true_vals, logit_pred_vals):\n",
    "    \"\"\"\n",
    "    Computes the binary cross-entropy loss.\n",
    "    Arguments:\n",
    "        `true_vals`: a B-tensor of true binary values\n",
    "        `logit_pred_vals`: a B-tensor containing the predicted LOGITS\n",
    "    Returns a tensor scalar that is the loss for the batch.\n",
    "    \"\"\"\n",
    "    return kb.mean(kb.binary_crossentropy(\n",
    "        true_vals, logit_pred_vals, from_logits=True\n",
    "    ))\n",
    "\n",
    "\n",
    "def fourier_att_prior_loss(\n",
    "    status, input_grads, freq_limit, limit_softness,\n",
    "    att_prior_grad_smooth_sigma\n",
    "):\n",
    "    \"\"\"\n",
    "    Computes an attribution prior loss for some given training examples,\n",
    "    using a Fourier transform form.\n",
    "    Arguments:\n",
    "        `status`: a B-tensor, where B is the batch size; each entry is 1 if\n",
    "            that example is to be treated as a positive example, and 0\n",
    "            otherwise\n",
    "        `input_grads`: a B x L x 4 tensor, where B is the batch size, L is\n",
    "            the length of the input; this needs to be the gradients of the\n",
    "            input with respect to the output; this should be\n",
    "            *gradient times input*\n",
    "        `freq_limit`: the maximum integer frequency index, k, to consider for\n",
    "            the loss; this corresponds to a frequency cut-off of pi * k / L;\n",
    "            k should be less than L / 2\n",
    "        `limit_softness`: amount to soften the limit by, using a hill\n",
    "            function; None means no softness\n",
    "        `att_prior_grad_smooth_sigma`: amount to smooth the gradient before\n",
    "            computing the loss\n",
    "    Returns a single scalar Tensor consisting of the attribution loss for\n",
    "    the batch.\n",
    "    \"\"\"\n",
    "    abs_grads = kb.sum(kb.abs(input_grads), axis=2)\n",
    "\n",
    "    # Smooth the gradients\n",
    "    grads_smooth = smooth_tensor_1d(\n",
    "        abs_grads, att_prior_grad_smooth_sigma\n",
    "    )\n",
    "\n",
    "    # Only do the positives\n",
    "    pos_grads = grads_smooth[status == 1]\n",
    "\n",
    "    if pos_grads.numpy().size:\n",
    "        pos_fft = tf.signal.rfft(pos_grads)\n",
    "        pos_mags = tf.abs(pos_fft)\n",
    "        pos_mag_sum = kb.sum(pos_mags, axis=1, keepdims=True)\n",
    "        zero_mask = tf.cast(pos_mag_sum == 0, tf.float32)\n",
    "        pos_mag_sum = pos_mag_sum + zero_mask  # Keep 0s when the sum is 0  \n",
    "        pos_mags = pos_mags / pos_mag_sum\n",
    "\n",
    "        # Cut off DC\n",
    "        pos_mags = pos_mags[:, 1:]\n",
    "\n",
    "        # Construct weight vector\n",
    "        if limit_softness is None:\n",
    "            weights = tf.sequence_mask(\n",
    "                [freq_limit], maxlen=tf.shape(pos_mags)[1], dtype=tf.float32\n",
    "            )\n",
    "        else:\n",
    "            weights = tf.sequence_mask(\n",
    "                [freq_limit], maxlen=tf.shape(pos_mags)[1], dtype=tf.float32\n",
    "            )\n",
    "            x = tf.abs(tf.range(\n",
    "                -freq_limit + 1, tf.shape(pos_mags)[1] - freq_limit + 1, dtype=tf.float32\n",
    "            ))  # Take absolute value of negatives just to avoid NaN; they'll be removed\n",
    "            decay = 1 / (1 + tf.pow(x, limit_softness))\n",
    "            weights = weights + ((1.0 - weights) * decay)\n",
    "\n",
    "        # Multiply frequency magnitudes by weights\n",
    "        pos_weighted_mags = pos_mags * weights\n",
    "\n",
    "        # Add up along frequency axis to get score\n",
    "        pos_score = tf.reduce_sum(pos_weighted_mags, axis=1)\n",
    "        pos_loss = 1 - pos_score\n",
    "        return kb.mean(pos_loss)\n",
    "    else:\n",
    "        return kb.constant(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_model(model, save_path):\n",
    "    \"\"\"\n",
    "    Saves the given model at the given path. This saves the state of the model\n",
    "    (i.e. trained layers and parameters).\n",
    "    \"\"\"\n",
    "    model.save(save_path)\n",
    "\n",
    "\n",
    "def restore_model(load_path):\n",
    "    \"\"\"\n",
    "    Restores a model from the given path. It will restore the learned\n",
    "    parameters to the model.\n",
    "    \"\"\"\n",
    "    custom_objects = {\"kb\": kb, \"tf\": tf}\n",
    "    return km.load_model(load_path, custom_objects=custom_objects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the models\n",
    "We'll train two models, one with the Fourier-based prior, and the other without. Note that the first batch might take some time to load, while the reference Fasta is being indexed.\n",
    "\n",
    "While the model trained without the prior can converge in just 1 - 2 epochs, the model trained with the prior often requires a few more epochs (due to optimizing over multiple objectives). To keep it simple, we'll just train both models for 1 epoch each. This puts the Fourier-based prior at a slight disadvantage in the comparisons, but we will see that the interpretability of the model trained with the prior is still cleaner. We will also verify that the predictive performances between the two models are reasonably close."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "freq_limit = 150\n",
    "limit_softness = 0.2\n",
    "att_prior_grad_smooth_sigma = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(\n",
    "    data_loader, model, num_epochs, learning_rate, use_prior=False\n",
    "):\n",
    "    \"\"\"\n",
    "    Trains the model for the given number of epochs.\n",
    "    \"\"\"\n",
    "    optimizer = ko.Adam(lr=learning_rate)\n",
    "    \n",
    "    for epoch_i in range(num_epochs):\n",
    "        losses = []\n",
    "        t_iter = tqdm.notebook.trange(\n",
    "            len(data_loader),\n",
    "            desc=(\"Epoch %d/%d: Loss: ---\" % (epoch_i + 1, num_epochs))\n",
    "        )\n",
    "        \n",
    "        data_loader.shuffle_data()\n",
    "        for i in t_iter:\n",
    "            input_seqs, output_vals = data_loader[i]\n",
    "            \n",
    "            input_seqs = tf.convert_to_tensor(input_seqs, dtype=tf.float32)\n",
    "            output_vals = tf.convert_to_tensor(output_vals, dtype=tf.float32)\n",
    "            \n",
    "            with tf.GradientTape() as opt_tape:\n",
    "                with tf.GradientTape() as input_grad_tape:\n",
    "                    input_grad_tape.watch(input_seqs)\n",
    "                    logit_pred_vals = model(input_seqs, training=True)\n",
    "\n",
    "                loss = correctness_loss(\n",
    "                    output_vals, kb.squeeze(logit_pred_vals, axis=1)\n",
    "                )\n",
    "                \n",
    "                # Compute gradients of the output with respect to the input\n",
    "                if use_prior:\n",
    "                    input_grads = input_grad_tape.gradient(\n",
    "                        logit_pred_vals, input_seqs\n",
    "                    )\n",
    "                    input_grads = input_grads * input_seqs  # Gradient * input\n",
    "                    loss = loss + fourier_att_prior_loss(\n",
    "                        output_vals, input_grads, freq_limit, limit_softness,\n",
    "                        att_prior_grad_smooth_sigma\n",
    "                    )\n",
    "            \n",
    "            # Compute gradient and update weights through backprop\n",
    "            opt_grads = opt_tape.gradient(loss, model.trainable_weights)\n",
    "            optimizer.apply_gradients(zip(opt_grads, model.trainable_weights))\n",
    "            \n",
    "            losses.append(float(loss))\n",
    "            t_iter.set_description(\n",
    "                \"Epoch %d/%d: Loss: %6.4f\" % (epoch_i + 1, num_epochs, float(loss))\n",
    "            )\n",
    "        print(\"Average loss: %6.4f\" % np.mean(losses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_model(data_loader, model):\n",
    "    \"\"\"\n",
    "    Predicts data from the model, and returns the true values\n",
    "    and the predicted probabilities.\n",
    "    \"\"\"\n",
    "    true_vals, pred_vals = [], []\n",
    "    \n",
    "    t_iter = tqdm.notebook.trange(\n",
    "        len(data_loader), desc=\"Loss: ---\"\n",
    "    )\n",
    "    for i in t_iter:\n",
    "        input_seqs, output_vals = data_loader[i]\n",
    "        true_vals.append(output_vals)\n",
    "\n",
    "        input_seqs = tf.convert_to_tensor(input_seqs, dtype=tf.float32)\n",
    "        output_vals = tf.convert_to_tensor(output_vals, dtype=tf.float32)\n",
    "\n",
    "        logit_pred_vals = model(input_seqs)\n",
    "        loss = correctness_loss(\n",
    "            output_vals, kb.squeeze(logit_pred_vals, axis=1)\n",
    "        )\n",
    "\n",
    "        t_iter.set_description(\"Loss: %6.4f\" % float(loss))\n",
    "        pred_vals.append(\n",
    "            binary_logits_to_probs(logit_pred_vals.numpy())\n",
    "        )\n",
    "    return np.concatenate(true_vals), np.concatenate(pred_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_performance(true_vals, pred_vals, acc_thresh=0.5):\n",
    "    \"\"\"\n",
    "    Shows accuracy, auROC, and auPRC.\n",
    "    \"\"\"\n",
    "    pos_mask = true_vals == 1\n",
    "    neg_mask = true_vals == 0\n",
    "    \n",
    "    pos_right = np.sum(pred_vals[pos_mask] > acc_thresh)\n",
    "    neg_right = np.sum(pred_vals[neg_mask] <= acc_thresh)\n",
    "    pos_acc = pos_right / np.sum(pos_mask)\n",
    "    neg_acc = neg_right / np.sum(neg_mask)\n",
    "    acc = (pos_right + neg_right) / len(true_vals)\n",
    "    \n",
    "    auroc = sklearn.metrics.roc_auc_score(true_vals, pred_vals)\n",
    "    \n",
    "    precis, recall, thresh = \\\n",
    "        sklearn.metrics.precision_recall_curve(true_vals, pred_vals)\n",
    "    auprc = sklearn.metrics.auc(recall, precis)\n",
    "    \n",
    "    print(\"Accuracy: %.2f%%\" % (acc * 100))\n",
    "    print(\"Positive accuracy: %.2f%%\" % (pos_acc * 100))\n",
    "    print(\"Negative accuracy: %.2f%%\" % (neg_acc * 100))\n",
    "    print(\"auROC: %.3f\" % auroc)\n",
    "    print(\"auPRC: %.3f\" % auprc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior_model_path = \"prior_example/models/prior_model.h5\"\n",
    "noprior_model_path = \"prior_example/models/noprior_model.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate new models\n",
    "prior_model = create_binary_predictor()\n",
    "noprior_model = create_binary_predictor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9f1581421fa4cacbe9792b0ace67912",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 1/1: Loss: ---', max=2478.0, style=ProgressStyle(de…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average loss: 0.3966\n"
     ]
    }
   ],
   "source": [
    "train_model(\n",
    "    train_data_loader, prior_model, 1, learning_rate, use_prior=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(prior_model, prior_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10186e9062cb4cb28d8533da70e97a60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch 1/1: Loss: ---', max=2478.0, style=ProgressStyle(de…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average loss: 0.2937\n"
     ]
    }
   ],
   "source": [
    "train_model(\n",
    "    train_data_loader, noprior_model, 1, learning_rate, use_prior=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(noprior_model, noprior_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "# Load in saved models\n",
    "prior_model = restore_model(prior_model_path)\n",
    "noprior_model = restore_model(noprior_model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97980f1471774e13949064627190a1b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Loss: ---', max=255.0, style=ProgressStyle(description_wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9425b796d234657803317cf241482e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Loss: ---', max=255.0, style=ProgressStyle(description_wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "prior_true_vals, prior_pred_vals = predict_model(test_data_loader, prior_model)\n",
    "noprior_true_vals, noprior_pred_vals = predict_model(test_data_loader, noprior_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance with prior\n",
      "Accuracy: 88.27%\n",
      "Positive accuracy: 98.70%\n",
      "Negative accuracy: 77.86%\n",
      "auROC: 0.975\n",
      "auPRC: 0.972\n",
      "\n",
      "Performance without prior\n",
      "Accuracy: 91.18%\n",
      "Positive accuracy: 98.14%\n",
      "Negative accuracy: 84.23%\n",
      "auROC: 0.980\n",
      "auPRC: 0.978\n"
     ]
    }
   ],
   "source": [
    "# Compare predictive performance\n",
    "print(\"Performance with prior\")\n",
    "show_performance(prior_true_vals, prior_pred_vals)\n",
    "print(\"\")\n",
    "print(\"Performance without prior\")\n",
    "show_performance(noprior_true_vals, noprior_pred_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare interpretability\n",
    "We'll show the importance score tracks for a random sample of the test-set input sequences, to visually confirm the improved signal-to-noise ratio. More sophisticated methods of quantifying the improved interpretability can be found in the other notebooks of this repository (see paper for details).\n",
    "\n",
    "We will show the importance scores using input gradients and _in silico_ mutagenesis.\n",
    "\n",
    "At the time of writing this, TensorFlow 2 is not yet supported by [DeepSHAP](https://github.com/slundberg/shap/).\n",
    "\n",
    "Note that SPI1 binding is a relatively simple task, because of the straightforward motif and binding mode. Although we will see an improvement in the interpretability of models trained with the Fourier-based prior, these improvements are still small compared to the improvements we get when we train on more complex tasks (see the paper for examples of these complex tasks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also need some code to visualize the importance score tracks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-11-17 14:23:04--  https://raw.githubusercontent.com/amtseng/fourier_attribution_priors/master/src/plot/viz_sequence.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.196.133\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.196.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 7081 (6.9K) [text/plain]\n",
      "Saving to: ‘prior_example/aux_code/viz_sequence.py’\n",
      "\n",
      "prior_example/aux_c 100%[===================>]   6.92K  --.-KB/s    in 0s      \n",
      "\n",
      "2020-11-17 14:23:04 (31.9 MB/s) - ‘prior_example/aux_code/viz_sequence.py’ saved [7081/7081]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Download code for visualizing importance scores\n",
    "!wget https://raw.githubusercontent.com/amtseng/fourier_attribution_priors/master/src/plot/viz_sequence.py -O prior_example/aux_code/viz_sequence.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import viz_sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpret_input_grad(model, input_seq):\n",
    "    \"\"\"\n",
    "    Computes input gradient x input for an L x 4 one-hot\n",
    "    encoded sequence.\n",
    "    \"\"\"\n",
    "    assert input_seq.shape == (input_length, 4)\n",
    "    input_seq_np = input_seq\n",
    "    input_seqs = tf.convert_to_tensor([input_seq], dtype=tf.float32)\n",
    "    with tf.GradientTape() as input_grad_tape:\n",
    "        input_grad_tape.watch(input_seqs)\n",
    "        logit_pred_vals = model(input_seqs)\n",
    "\n",
    "    input_grads = input_grad_tape.gradient(\n",
    "        logit_pred_vals, input_seqs\n",
    "    )\n",
    "    return input_grads.numpy()[0] * input_seq_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpret_ism(model, input_seq):\n",
    "    \"\"\"\n",
    "    Computes in silico mutagenesis for an L x 4 one-hot\n",
    "    encoded sequence.\n",
    "    \"\"\"\n",
    "    assert input_seq.shape == (input_length, 4)\n",
    "    mutations = np.tile(input_seq, (len(input_seq) + 1, 1, 1))\n",
    "    inds = np.arange(len(input_seq))\n",
    "    mutations[(inds + 1, inds)] = 0  # First one is the original\n",
    "\n",
    "    mutations = tf.convert_to_tensor(mutations, dtype=tf.float32)\n",
    "    pred_logits = np.squeeze(model(mutations).numpy(), axis=1)\n",
    "    return np.expand_dims(pred_logits[0] - pred_logits[1:], axis=1) * input_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pick a few random positive sequences from the test set\n",
    "rng = np.random.RandomState(20200930)\n",
    "rand_inds = rng.choice(test_data_loader.pos_inds, size=5, replace=False)\n",
    "pos_coords = test_data_loader.coords[rand_inds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chr1:208342950-208343950\n",
      "-------------------------------\n",
      "With prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z7rkH8vMrfpqIiNRMCvyIiIjI7gUDMOsycMdC/krw7/8V3/Ll0KIFREbufduSgDO4c6PYRjsWo9y4O77CzU4wqryY5B27YQV9FXb1ivHEsDkANvXzXQ8e1wp+uRm+PKpsXdEWiAld/Lqidn1OJRk5Enr3hltugby8vW8vUqW2lMuIM4Y8U4cvxhXz5ZcwYQK43TBjBixeXG0lFBGR/bBPgR9jzPnGmOXGmFXGmEerulAiIiJSQ2yaDEc9BS36wJF3QkSd/d5FVBT49zA0Tm6xE8QxlGXrNInbtU9Yy7otASiIaAiTjwRgwDYw/QyByMYQDM0I1uUpCHorHuMnIor1PmDTF7DggR0PUKedM/ZPzh/lVlooTXL6NBY+3mk2sH10+6TbeXr60zusy/fmk/hCIpO/KsLng8mT4dlnIaCJxaSG+YMu3NvXQ2wsBINwzTXOefrzz8r+ERE5FOw18GOMcQOvARcAnYHrjDGdq7pgIgK+gA9fwLf3DQFvwIs34K3iEtVglTgQ5bCfh2H6mR1mF9on2+bButGweSqXj76Mq8dc/ZfLIlJYCGvXVmMWSOEGqN8VXBHgiYOUFOe7ZoyzvA+KiyGigkm0SqXmOVO59+nQJ7wuITYB2DFo075RewC2epqEM36ezHIe+2L1NxDT3LlzTD+IaozPOOPxRLujw/uIdkezwQ8mUAR/Dt2xIHU7Qum2R4WCNFEJ4MsObRAM/Tme/P5JJi+fvMfXDs4U9SMXjqT/rP7Ycl3lXvjxBdIL03nunWXcfruTEVW/vvMnlSO/JJ8FmxdUdzH2S2oqrFgBvn37+a8UwSB88gm8+CIMGwabN4ce2LIFrMVHBLF1nO/Thx/C8OFw5plw443OGF5ffw0LFuzYEzSrKIsSf8nBexEiIrJb+zJC4QnAKmvtGgBjzGjgUmA383JULBiEnBxwuaBuXae9eMgIeKFok5NCHpN0QP/tlMpjrWV+2nw6J3QmNiJ2H58DixZBerpzv0UL6NgRZ8re/DXgLwRPLMS3AVMzekCm5qbSYlgLAHIfzaVO1O7Pu7ySPOoOqgvAxv9spHnd5nvc95a8LWwr2kaXJl0qr8AhwWAQl6tmvIdYC/4CCJRA9oJQNoCLldTFRjWhfWPnIhJfLvz5EjQ4Dn/Qx3+/+S8A531wJj9d+S4EipyLwbodwb2bvip/PA8tr4QWffhh/SwmLJ8IwG8rPuXYuHjAgjsempy2x8FhZ62fRZO4JnRs3LES34hDRHF6aLpsC1GNneyL4i2w6UtnzBUbgLjWkNRr9/sI+sC73QlSRNTb5x+bwkLYvt3JTGnYcDdPK51FyrVrBGPSJOf6qG5dZyzUCy6A9u336dC78hdC2lSwAV77sB3tTzia4483LFkCXbtCfPwenpuSAv36Ocv9HoIH7oOIuhBZ39nvyjedbkvWwpAP4bWpzrZxcVBQ4CwnJu7YzSOmKeQuh0YnwJi60Cn05uw01s9vW36jRVxjGm2eGPqdtM7sWG1vo2vXCFaudOrd6Gh2sX77egA6NO4AztjONIhpAECjmEZkFmbiC/o4qslRTFs7jYWuZDp56rChqCwads3Yayju1Qc2rA+9piPwZjuvI9JT9r2N9kRjgdy49tTPXwoYiG4CbHW+n4Fi8MTD0SnOE5peAMuHO+P8lDN6yWgG/DAAgOxHsqkfvftozUPfPhReHjFvBHf3uBtrLf1n9QdgTvoMvN5ueDyGYHB3e9nVypUwc6Zz3vp8zvnRo8e+P79GSUoqG0i49Bwsfz4//fQugcZRo5x2ZevWTpejNm3gvPN23G3ykGTyffkMPmcwD53qfA5z5jiBFWOg67gUjp7QDxoDY+6Ho64DXz6kz4KuT+/TYN45xTks2rqI01qehtlTnRP0Q/pM53cJnK6FDY4OP+z1Qt++8PjjkJDg3A8HTCt6fyrR888TDj5GRTnB2vKOZBUvjodu3ZxB2uPjnfd+2DBn4PbevSEjA2xSEiZ9K2NPrstVvXOJ9cRS8L+CPR982zzIX+ssGxck94aIPVV0+6aoCJYscd7HmBg46qidupsWZ8LGcc7vuw1AZGNocclfPu5+CXhhyQBIONUpx5bvoP3dTtfV/ZCen87sDbPp06kProrasTlLoWADYOHYGyAjFDGvgnPpUFBRW7U0KL/H73A5gYAzbl3HjhAb69Qrt94KzZqV36fztS0ogDp1nO/1Pu3eBqEoDQLF5BQ1ZF1aAwIBpxpoWgOHmgsGne9adrZTL3Ts6LxWKVNc7HzVXC7nc6yo63tenlNf1a2753+WHah9uTprBmwsdz81tG6vWj5TD9PPYO46lg8+gAYNYF3qQlz9Daafoe+Tl0OW81+YmYP/6TzJBpn77lOQtQBvxhw6vNiQU0aewgUDejr76mcY8sRVUOJUWLNeS3H2sf0P7r7TGfhxzZJNfPLOWqZPh48HfuTMyAF8M/QZyFoIeatp8EwUpp/B9VAiQ57dwh9/wJDX36Lhc3Vo+HxDBj39GMHUr2HrdFj0P4hvDXXbUfLrwwBsXLYWVr0NwOxR7zkVKuCtGxP+T2h8/whMP0Pkg00Z9cofzJwJk4aPdi5ugBmvDnAa4kDzAXHh15f8XBwNn2/IGf1PhtXvAfDDe29BrjPg5S8v3RJ+j1eNeQDy11G45U+mvT8ONozhzbfuJvqZSCKfieTslHbh/S5673bwFxD05XH8sCZc9dlVXDToXOoNjKHBoAbc979HKVw5GbZ8zzUDO9P7w94cM+IYcr+91XlfN64J7+uMx44mor8Hd3837z99jTMGBPDnqDud1+QvDG97ymPdYMM4AEakfMe6daHzo3/98DY505zPP3tLFix7EYCXR6WEX8dT/x3KjBnw++/g6u+ix9s9iBtY9p6d+tgplCx9F/JW8sTt34bfn7mv3+WcJ7OcivfccyG55WI6feo8b+b7Z0KdI6HB0bw9fDOkToKNEwnUD32OyQZWvQXAoh9XsOHHzyF1Ml8NeZ5g+o+Qv4bCqVeEjxf/jPOZt3wyibFvzmH8eLij333hch6XkhRe3jT+5rIvy8JHAFg8/WenWwVw9LCyC/9Bj74PGyfAxonh5zd/uCmBZS+Dv4AzXzohvO0VQ7qHt+k4qAFxA+OIG1CXdwbOgpVvMOCNq0gemsxRbxzFebf8B+/qcbBhPFcMbsc/J/2T3iNP5LfRr0DGz3w6/Ec2bHD2+8KIB7hj3FX8e8q/uemZroyYO4JbJt5C9xea0H54e454/ghMP4P7GTcnPdyRCR+l8vnn8NlzbzFurI8JE2Dpe7eFy+n76V7ns/n5O2IHRBI3MI4pb10afvyXV+8NL2d97ZwfhdmZ/PTpONj4OfM/GgZbvne+S8Megk3Oxd6KFy5ynpTxI2wcDxHxbJs9EJLPhWYXc9HHA2n/zul0eK0DXfo1cs6fofWgUXdofjEfTbgufNxHg0Fo0BUan8DJr12OGeDUG0c8Wyf8Hgfn9AUgPS0XG9kAgl4uH/U3AKINHFuwEJpdxMbgxTz5Wi9weVi7fg3Hv9yca8dey4X9Ty377j8dT8/3e9LptU60GBBfdox5/wFg2Y8LYd0nAMx843ko3ARA8bdXhsuc9/2dAGSuXwdrP3Tey7ETYJszVfa4wSNh89eQPouNY0LnYHE6a6e8BNvms/TLj5xzDVj32R3h/V7zt3Xh5Yh+Lkw/Q9NHE50LYmDZ2IHhx2cO/U94OTC9LIOj9DxfNGM+/lUfQeFmZrz0uFOfe3NgxauQcDIknELR78PAGHLmvAxH3g5t/sFPH74DUc6Avy8+8I2TXbX+U7Z87tRRgQ2TnC460Qn8PmsxpE1xzrHhj4aDNpl9jnW+27GGaW99BGnf8tKTc4iNdRpTwz+7l9hno4keEM2jd33A4sWw4c8NsOQZcEWwKTXIyGErmT0bnh3yBu2GNuOo149izGcF3HEHXHstHBf9Iu0b/kogYwGTX36f0aMh5cVJ4c/zlkev5fPxhUyfDsf0axpev+3r2wHwLh0ByedByyv57efNnHuuITYWMheMJXb715DxE1mXH1uWdTP/3wCsWrAUbmoDwJahZ8FTgyGuJXPeCQUsshZAvU7Q6u/kfncP3N0LgLnHHA+xTgtkxlU9YfMmrLU88Go3NuVuYuDaP8j8/jHY+j0rUpvCic5v369vPxn+aOP6RdDtzW5c+UYLqNcR2txE1rQH+fTrDrDqLe468TGuvtLP/ffDg8+fzx/pf/D5n5+z9tObAFif43x/j2x4JNEBpyylgf1G0XWpF+XM8NW0jtPinTb+ZfDn8VGWE2w5Pvl4SpZcwLoi5zU98ABQ/+iyWb3KDQQd44mBTd1ZU+IEeL9bfb0zYxdw520Fzgxe/nwKZtwNwOaSE9i6cApMv4CfV54M588H4LpxZXXFwPfOJLMwk8E/DuZ/jxbBsiEsnPgpG36aABsnMXzO8PC2R8/7DiibxaxH0x40OeE1br3Fz+rV8PT9c8gNDV/U55mjqfNcHeo+V5e0z536M7cgN3zOXHnXFG6/3cm6aFM8iB7HOk889rkETD9DvWcMM977ALZOZ853I7jknWN4dtazXP/MOcQMcM7zU/s1C++vaeh3NeIpT3hdt9t7M3u2U57br1sbfh3LR/0zvNxiUB2SXkzirGdOC7dbHh9+M67QPlad2yV8vjYK1aOxT8Q43ytg/hffQNDJWJ1xec9wfb663frwMYKpX4ExrGreFtaOAuCbL7dzww1w6qlwXPCecNAn2DAWjGFC52jyfU7m5vQxnzrBHGDEG35uuAGuvx7qXOPUoxwJHHsHND6ZFaP/C+8tBXcET13bgRPfPJYT3j6Bzwb3h60zYNscYvo771Hd/8VT//n6nPH+GVzynxPhodvAGO599FhSZqRw4/gbaRX6zbjxpQiIawnNL2HCH4V4hnfD09/DKbe9TvbCURQteof4uCAtWjgXiU89lEZaGiz7LR0CTnvxl+4nwB9OO2HGC3eF35/UsTeHl1nwIABz58L0cXNhyQC+GfxIuP151z9SYfVIWDqYghl38vbbkLp2O9u2WdxumDtuDPWDO7bPk9nC9uXTGDrU6dp1/z0lbN0KKxen06qVJS8Plo4bjMtYggau6u2ci4VFQca+PJaJE2Hq+A3kLBjJoEEwfkiofW4trHkPjrgK5t+Pee9KzMA6uG47lS+/dF7O5x/8EW47znh9UDjbLzir7LevZLbzfZ02OY15zleLu2/bRo8ezvlRPPt+50KraCssdEat8K/6CI64GtrcxJyP3yIz0wdbvuf2q5eF95v4TGz4u1Dabpn4ySZWOE1yfvjgvXD7PO+rsvKE2y1ZaSz4fAxs/oYFHw9zrkOAGS895vxTqjgNohtD0954U2fxwKj+EJPs1Oeh83z0m0vIWfwZrB/DdReVlS17qvMbPXflXBKHJHLFmCs45cFOHPdqe0579zT+r9/fnSvMBONczzQ9H5pegA04Ub0pR7Wk3v251B9UnxtSTufuiTdy5+Q7mf3K9eFj5E5zjlG+fT73i9mM+zSXGTPg+X9NYtYs56L/pMHJJA9JJmFwQrjdMmHqaCL6u/H093DLHSksWWLJzYWfXy47d0vbLctWLnWuzfq5OP+pE8Lv+2P/fQFSv4DUyWwe7/xmBG2QxOfiSB6SzCX9LqZgySjI/o1/Diy7XjxxYDLu/m5cKRE8dF8qqakwcdqscFv1xgfOhEfuBGPo8mRDXP1duPq76DWkKb3e70X74e055ZVm3PPlPdwz+MZw+3zGq/3giUfY5mmCtXDZZU7g8+ymQ5g6FQa++Av3f9yL79d+zxVXLyQpCdq2hadHnsZHi/6P+7+6n+yvnfc4kL+ZZV+OhPQf+H3cm077DMj9+u8Q24ztgba8OLwBxxzjBC5/HTOGcePgwxEVX4f+NPTW8Pua+YXzm1FSWAKLnAzWhVOmQ9o3APR59rjwe3X24Ba0e6UdTYeUtUmOeLhs+cL/HU9Efw+e/h6uuajsOqvT4IY0er4Rja54iCOOgJ49YeWmYfQYlUTbl9vyfr/rnfMcOHZQAhHPRNBggIv1Xw2EnKWsnjHe+ece8NGQ75kzx5KZCTNeuDt8jDaD6hI3MI7m/ZvBH4MAeGNg2eQMT/xzOt+yO+gAABn6SURBVN5Qh4dGz8SEy9z3X06776vvJhD1jIeoAVE8mtKXzyeUMHMm/OeGmaSmOhmLD9+7lrVrYfZP64jo5ybymUjufupyyPjJ+cxfvC98vCtCl30bNzqBb4DJn6xi1Spn+cFh1xL7bAxxA+M4/7lWtBjWgkb/OYcxY6BVK1j623YWOU0OXnj3MTz93bj6ubji8jzq1IFGjaDuc87vS+NnDD9+8hlsngqbp/LJC+MPaIKNUvuS8VNRXHKXIxpj7gDuAGjZ0umDf1J6JDYC/JEriP5+OFPmtiQ1sAGTDNZAcNMm7MDhmCHv4zr5CHjEgIHjvr8CGvanYN4jrCjIhoKf6ZzXiqh4F8FgEPfGdKwvF/r24ZheEdAwBRalEFFyH8XF0cycHcXZFzajZUv4OSMKO/BlzIvv0npwN2j4JCxKoWWBG68bXLnxxLVNoksX+OnPIrIznAZC9/g5uJo/B78/zdQhK+l93I1QdzERD6+A7BGYxk2x1xyDee0OXCedjF3yE+aFd/DFRVIawDuyIIK1Lj/ujR1pfmEXevaE+b4OeBePJbLgZ9iyAft0NmbgULr0rUNRlJNAvoVCoJC03E0EA15MUhKmczvsn/Mxg99iydPnc2KgGO49jyOubgrxrfBMbc85NywETxz+3JcpCV3kROf4cNczuAOWRP9q8MRRMP8xFuRmsCB3LC0Lk8mNLQaK6dPkZ2LbDYJFKfyau4r1a5wfl2CDDlCynbg5N9Mg4MGU+KmTU4A/KgAGSjZtxfpyoG8fGp0Q4WTOLEohPuCimCCRWXkEC7fhKsnGk/EtRYVn4fXCWZujmefKocQD8Vd9BNvfwcTWxf6jO2bEQ/h7t6HkZOd1LJ/dmf6hjPybRzam7W+ZzGkGUzo4s7Yk5m8lqklnqNOOYxNeY/pXp5KYECR+4ky413AqbiZe8QgT3ulCBulEdjbYoGXoFy66uZ+grvcLbjm5HbQYA4tSCAJugHzI/HUZjYs/J2HZUyTf/jsASeYNXE0egUUpLJpSxElxb0HeFGK9UOSCwhl3ceUPTjBmfU4D6qz3EPT5OXZjMaubgctCns9gk5LAn09gyt24k5IwTRKwZ3XEvHwJD1zbmVPadCLn63H0eTcNWtwPi1Lo4I0mr7iYNnlB3BGx4Injn8FEHilKJuHT6bx2diFzm4M7CJkFeRR6AlwdB7fdHAv1bqRr/ptc6W1Oq7mpPNl3PZFth8GiFL7IX4V34Spae+DYPn0g4WR6xF/O7Dc9NEuIZFraHL7Z6rT+472GUV85lbLbQqAwA4/fTbdgHE3XFlC07lEuG+xkHV0/+h989JgTtl5/8a9wq1Ol2Jt7wKkGb4ckiq7zQcDHqJkBerV6iejsSUSO3QD3O9u6r+kGFxo8p3s4Zep6iGmKK/MK7OC5mGFnwyU9sXfnwJ2nE58XujBJncyKLwpp3/QHsrJ+pXj+v2jmTqdnTi7N6Yzrt6VkNvOzsRH8UQxT7xhF76MGcUprFy/lHMk1b63i1a7RnO55jAbBrziuMAJfRCK+9K1ExwfZEg3GQvDNX3GdaMgwnci7ZwNt3Yt5rE47ugbbYD+bzCez53PdKRfTqGEaTVf1ZeZLXVmd/ycLAptYsP1TTsptz9Fx9Yncsp3eWdFEZBawvh4sah8kPdqpgO2IX2CkwXVEO+z0pzDBAGbbL1h/H7jrDOZ64jmdYVD8Pa5P0+BsA/Ua4v2qL5H1luNa8x72l4aYIR9wxqhe0HQ6LEoh6pOFcLWBJGg98zVodDwmdQD2Gxfmlcv544SraJb0GhGZk0jOvppVf9zg/DPWF0EmXhoVWOwrkzGv/4uMjr1ok1CHqNxvcI1dDA+8BMC6wZfSttUM2Pg+wf9bgettQ0Tn9nimj4TYppi8P7H9+mOee5lJl13EJSdcBQ3/xPwYA2cY8iKbkvVgFq1jt2BMANvhTExmFg/OuQ9aDYfxSUSOToY+BhMPa594gNZ13sCVvRjb/kF46gn87hXY/PWY1W+z6Lz2nPnLFkjO4ZxzApB8Lle2/xufP3A7ntwgm6M2UpzgNFLWzEuma1cgkMiCR1dyXOcHWbtmFUknTeS002DuxixWrdgMeZu5sWEnRt/zHq51G7nk3hnQ+EHGv/gZHXvfTNeu8PPcBrw5ORJT4CX3t79x6SAnqHHUGD9r6zt1gueDBXChIRARxYq+f9K+0TZevfILxlw9nKJNQbpfPg1X87GwKIUVF3bhxJ+2gD+P4swiYgrScC18gOAZV2ASE5n3Xj3OajGA2JzxLM9qSo9AMTz1ONPSmnLuUYMxrQME/D5ciYmYSIP97RPo/yy2RVOsL4eCf/+NoUm/MXSY8z3uNq0vF7x7Hq5ebQj6izFP/o+CtBUEN0zDtXk8d2xoQNzaDJYmwPe/jOSsLs8T1bqQq6+vD3VvosnvzZnaq5gx77h5JGUqK/49lckdYGGbG7BJSfSJyCNweSuuPel2Fl/YgrPaNuX4i59ky5WduKWkK75Rn/HdmUnc+OLDtG4JPvcwmAPnx1gand6JS1+Yz1NNkyBmA6yAOqlnwVvTObEFPHtGCy467lqGHA+xJ7bhb92u4b+duhGxbQ1kgyd6vdOmuPN0Onz7L/h+GSRA8WBDbFISFEey5pS/k2ifx+U7AXtXAdx1Bi/mNKP395v4tjX894Lf2HhjAqOPhsxGI2Brd5KzPyGpj3MFOtCcTK+SFuR++hn5fV3YpCS62Dyeu6Y5dz03l097Qs/Gd/L5kfVp2d7Pj98dRdu2hrz0jeQ3dtolUfmZEPQRvex5Ggc8BEv8HN/+Wsbe8jo2K4Avdh02NQXz3DBi/hsLdeFIN/Q6vQQSzyRt8hNM3rSIyZsW0SO/NcXxznneeFMupqnzmxHp9UOks3xkMIb4rUV0yspj6yYfmRluTu28mC/fjaauyafluJ/hJkPQQOrTQEk+cfmR4XaLq1c96Oi0Imec2pK2U+bDvedRr+hXcjwQ7bMEX/0O15tP4ep8DHbhh/DM89AYp01x75VkdG9Bm8RE8OdTfPPJxHyxAVesC3vC/ZjMm3jlxAaMvnkkNnUbnYPT4N7Q70v9GACO2erm+dw23PDWGj47I4h9/i3M0J48cm5HxjzwNmbNOlpdWkSrxERYmcevdw3mpNbrcDXwErzvYkzdTix3j2bOFue3v3fn+pD4FCxKIbnEzRYToH6Ri3OCCZw8M4PC5AD21esgbyXfBuawcqaTrXRMQQypMTAhBxY/8QJdm2Zj/YsJBINgYORpn1H/qC8wWM59vDMftv87tn4DcjrfQXIyRAQNwelPYM4ZivEYrM85B2fHuejpL4T7zmf76a1pFmpT+L+6C09SEnUSG3L8272h7cNE5d/gnOf/vpJ7kzLAlQJNujL4Bg9DFxXSpO42rsgYwA9LE3E1Xstp5yTBnafzU/pKzggUw52nk5J9KzOfPIMSorj1ukw+TmrDZa1XMvUf55Czzo0neRl21a+YB//BgA2r+fv4zQxrejzRz1/JxReDzTf0aHM6wUQfR9U1XPaPAri7J28vCnJHz3Og83bOyatD3vY8soOrSZ/yHbPWJZC3dg2z8xLJOTqC9E0W682F+y9mTkwDTioaCsXfEXHLdMgeQesGbZhx/xT8G+O4rMs0Jj7SHLN5G8nrv4K+r4IHFj98PV03PEcw7z3+GLGYLolbSGyQTeNuV8CiFPq0XcmXIwdTJ5hH921R/OYuImjAvj0XTjV0aXgkP903kfS20WxcnIddOgQz+C1+vvVszrODwD8L9ydb4EKDu7uH476Z72R2ZbxNMKsZrjUfQuYCbLMWmK0ZfHLKJVzX4/8wEXFE5t7MhjVJZKYHafveWMyrN3HRR+dTp+vXsCiFFts38vtPLYiKcdH0g/lwviGpXixX35lAz+8z+KZrEZ/X3QjbVnJlmgeLxWTClMe/4/xu06Dhckom/5Ooy0dTWMeQ6y8CfxG/FfzJH787bbyzoi4Jt09dvTvAuWaH9vk3bZ/jf6tOA2DEQ24eecVpdq38NIMsj/OP4NJ2S7BbC/yXOmmMOSvzSE421K0L9b+cCf92vq9xDWIg+y1sYn24C6yx2G3ZuBMMQSxP9/4Bmj8Ei1JwZWaCN4eSRf1J9xaCtxBP4TLiWp4IdTsQ78vBWOc31Z2ZS7BeEIKGnOIkmjeH/ILGHOWLIyG1gDbFhdiXb4CcZVyw4U/qe6DIAzOT0yA/zXlNwM/z3qBHTieC/r9hnnoCPEuwj75EQubPHDP9BD6eeB2LS9rT+6X/cNMZljGz1/C/GTN5deXZnNf6GCY9NRzXpgw+avozIyb+CMATLS6FgjTM/P/S6Zi/Q5PTcaUNw/pvgKeeYIqvGX28z1IvcyztFxzHpGvPYt7Wllww8CpOPhlSl7sI+kPXiJ2OxF7YBe46g4WNozk5dI2Yd157GiUlgT+X4BXdcb3VH1fHo7Hf9Ie7zqBZk+3EuyJwlfhY7E0jo8j57Fr5IinwemmWC9ti3Fh/gIjtOfibOI/X3biMbZlnO228gu0UuyxRnd7k+3+3w+Wtx8Louaxv6WQnZm9qgX36CcyzL+J7KBp/nJ9INxzRwgv1OmPWPo6//pl44lazbelMzr3xLBo3hgZffwcPO+dHxpMuCt1BXD5DcPhUXG8+xgltj2NM/ESaJQTJ3lREcbHF5YJu2ZEscRcTBEwgQDDowUcAbzAABFj682kMSnH+ETT8PwGaN3cytzLzm9O6NQRcPvwEIRikIG09dtAIzNBT4aQj4KHXADju4vf58dOzKCh2E1iRzvxpLQlsy2L2uA1kdYliU2YeRZFOcHVNQRqpcV6IyyDy6xf4cnprsgsDpB9xBIXz65K2NodAtPM70Gv7zUy57RpsZBSnxcaxzpfLifXh1DMbQdLZ8G0vrjvGD+ZyDpi1do9/wMnA1HL3HwMe29Nzjj/+eFvlnn7aWnBuD6bEROe44CxXtdLXWf61VrTOX2Rt6mRr149z/gLeve66oKSg4geCQWuttSX+EhsIBvavnAf6eRzs97Ui/hJrt/9pbfZia4P+irfxFVpbuMVab84ed1VSYu306dZ++aW1U6ZY669od+U/x549d/1Md9jhdmsz51ubOXf3ZduTtR9bu22+td48a0fHOX/WWluSZe2WmdamfWdtoMRu3L7RFvuKd7ubXzf+ametm2WDwaB94ccX7LTV0yrcLhi0Ni3N2uXLrV261Noff7R21ixrvXs/LfeuONPa7cuszV0VPlcPRYFgwOYU7/k82qvqqgerU2ld0TPZ2jWjrP2sXtn5vLOK6srygkFrfQVO/VkF8vOtfecda997z9qRI60tLKySw+zd3t6H3Wx/5dOdLClYUrCBwD7+FpS3ZJDzffXmWDs6Nvw5kYKt91w9Swr21s9vLStbTVRR2Sr43pW+T10GYoO//c85r8Y2sXZsgrV5a60t2lpWX+3v51EZQsdOzUm1X634ygarsu6sjNe3t7rtILcZAsHAvreHypn05yQ7f/P8vW/4w7VOPbTgEWu/6bn37Xd+f0qXy7/vYO3/GpfVbx9h7eVYG4O18x+x1ldgg4GA7dQ2y+bmWrtx436/vH2WnW3tkiXWjhlj7QMPOG2BZcts2ee4v59hRedHTWhH7k7pb83ObfN9ff15a61dP8ba9WP36ffqQM/XOalz7LxN8w5+HbW3zy4YtLZ4m7WFmw9au6/0N88f8NsN2zc4K/fwvqxYYW1KirWZmdZu3mxtvxn9bIuhLWxmQWZ4G3/Ab2+dcKt9c96bez74bn4Pg0FrFyxwriu++cbawJPlyhMX59y6XLuu27m81fEbJAcu3Fbd8foMmGf3EsfZ+c/YvaQLGWM8wArgbGATMBf4u7X2j909p3v37nZeaZ6liNQMW2dCzjJwuaHtrWAObGYaETk8+IN+Or7akdcvep3z2p639yfsLBhwusp6s5zxM7o8Di4PiS8kkl7odDkZfM5gHjrN6UL9V9KXq0zpYAx7Kdtloy9j4vKJ3FMPXrtjI8Q2h49Dz/17DXxdUrMUbYU17wNB6PAvZxD1fbGncX+Mgf5N4fHVztgxH7sAA5eshvQfoM2N/D7mDb5dezv3943A63W6l1Wlfv3gjjsgOdkZy6JOu1D5D9NxZqR2CQRg9WpnbJZWrQ5wPNu9jGu2W6V1gctFeKA4fa9qNWPMfGtt9/15zl67ellr/caY+4CpOL1f3t1T0EdEaqjEns6fiMg+8Lg8rPrXqgPfgcvtDFK6k+Q6yeHAT4WDkB6CBp49kInLJ3Ju8+4Q2ai6iyOHmphE6PJI5e93wma49gM48p9w7o/OGGqBEtj8FbS8nLXu2+h9QQRRUc6AzlVt69ayAV89HnRRKrWK2/0XJnUolZKy78Ge8vRdkn2wTy0ua+1X1tr21tq21tpnq7pQIiIiUjtd3P7i8PIBZRLVQJ0SOnFai9M4pfW54UF4nSZW7QhsSQ0Vmmodayu+8FuIM6Pf7Gsg4wdnO3cUNL8Ufr6Z/KVjqVfv4BXX7y9LRqgpk3+KiBwu9mVwZxEREZFKcWG7C3n2B+d/SF2adKnm0lSeH279wZlpMHUStL4ers5zJjsQOdiSksqWT+y7a1Co1bWQeCYRqwL4/ZaK53GpfPHxZYGfkpKDk2UkIiIOxdtFRETkoGnXsF142ZXctOyB8herh6rEXvDnUFjzAfz2WHWXRmT3YhKp37Qpq1Y5QR+/v+oPeeKJMGWKMxaKW8MMiogcVAr8iIiIyEHTOLZxdReh6hgXnDER1n/mDKBfEwesltpvb13AQnr0gPfeg8JC8PmqvlgXXQQPPQSjRsEtt1T98UREpIy6eomIiMhBY0JTnUR7omvngJR12sLZ31Z3KUT2qmFDWLcOBgyArCwYMaJqjxcfDy+/DDfdBBde6MSlDmjmIxER2W8K/IiIiMhBVT+6Pj2P0CyDItVt5Eg46SSoW/fgHO/CCyEj4+AcS0REyijwIyIiIgdV9iPZ1V0EEQE6d4bsbGXeiIjUdhrjR0RERGRn5Qebrg0DT4vshtut6dVFRGo7VfMiIiIiO9u6teJlERERkUOMAj8iIiIiIiIiIrWUAj8iIiIiO0tMrHhZRERE5BCjwZ1FREREdlYbp5oXERGRw5IyfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREaikFfkREREREREREailjra38nRqTAawHGgOZlX4AETmcqV4RkcqmekVEKpPqFBGpbOXrlSOstQn78+QqCfyEd27MPGtt9yo7gIgcdlSviEhlU70iIpVJdYqIVLa/Wq+oq5eIiIiIiIiISC2lwI+IiIiIiIiISC1V1YGft6p4/yJy+FG9IiKVTfWKiFQm1SkiUtn+Ur1SpWP8iIiIiIiIiIhI9VFXLxERERERERGRWqrKAj/GmPONMcuNMauMMY9W1XFEpHYxxrxrjEk3xiwpt66hMeZbY8zK0G2D0HpjjHklVM8sMsYcV30lF5GayBjTwhgz3RizzBjzhzGmb2i96hUROSDGmGhjzBxjzO+heqVfaH1rY8yvoXrlU2NMZGh9VOj+qtDjraqz/CJSMxlj3MaYhcaYL0L3K61OqZLAjzHGDbwGXAB0Bq4zxnSuimOJSK3zPnD+TuseBb6z1rYDvgvdB6eOaRf6uwN44yCVUUQOHX7gAWttJ+Ak4N5Qm0T1iogcqBLgLGvtMcCxwPnGmJOA54FhoXolG7gttP1tQLa19khgWGg7EZGd9QWWlbtfaXVKVWX8nACsstausdZ6gdHApVV0LBGpRay1s4CsnVZfCnwQWv4A6FNu/Sjr+AWob4xJPjglFZFDgbU2zVq7ILSch9OgaobqFRE5QKH6IT90NyL0Z4GzgLGh9TvXK6X1zVjgbGOMOUjFFZFDgDGmOXAR8E7ovqES65SqCvw0AzaWu58aWiciciASrbVp4FzEAU1C61XXiMg+C6VCdwN+RfWKiPwFoS4ZvwHpwLfAamC7tdYf2qR83RGuV0KP5wCNDm6JRaSGewl4GAiG7jeiEuuUqgr8VBRt0vRhIlLZVNeIyD4xxsQD44B/W2tz97RpBetUr4jIDqy1AWvtsUBznN4OnSraLHSrekVEdssYczGQbq2dX351BZsecJ1SVYGfVKBFufvNgc1VdCwRqf22lna1CN2mh9arrhGRvTLGROAEfT6y1o4PrVa9IiJ/mbV2OzADZwyx+sYYT+ih8nVHuF4JPV6PXbu1i8jh61TgEmPMOpxhcs7CyQCqtDqlqgI/c4F2oVGoI4FrgUlVdCwRqf0mATeFlm8CPi+3/h+hWXhOAnJKu26IiEC4j/xIYJm1dmi5h1SviMgBMcYkGGPqh5ZjgHNwxg+bDlwZ2mzneqW0vrkS+N5aq4wfEQHAWvuYtba5tbYVTuzke2vt9VRinWKqqs4xxlyIE6VyA+9aa5+tkgOJSK1ijPkE6AU0BrYCTwMTgc+AlsAG4CprbVbogu5VnFnACoFbrLXzqqPcIlIzGWNOA34AFlPWb/5xnHF+VK+IyH4zxhyNM7CqG+cf6Z9Za/sbY9rg/Le+IbAQuMFaW2KMiQY+xBljLAu41lq7pnpKLyI1mTGmF/CgtfbiyqxTqizwIyIiIiIiIiIi1auqunqJiIiIiIiIiEg1U+BHRERERERERKSWUuBHRERERERERKSWUuBHRERERERERKSWUuBHRERERERERKSWUuBHRERERERERKSWUuBHRERERERERKSWUuBHRERERERERKSW+n8T7KlICQEPnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "chr1:234880850-234881850\n",
      "-------------------------------\n",
      "With prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "chr1:110259400-110260400\n",
      "-------------------------------\n",
      "With prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "chr1:88647100-88648100\n",
      "-------------------------------\n",
      "With prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n",
      "chr1:35506700-35507700\n",
      "-------------------------------\n",
      "With prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No prior:\n",
      "Input gradients\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ISM\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===============================\n"
     ]
    }
   ],
   "source": [
    "# Show the importance scores\n",
    "genome_reader = pyfaidx.Fasta(reference_fasta_path)\n",
    "center_slice = slice(300, 700)\n",
    "for chrom, start, end in pos_coords:\n",
    "    print(\"%s:%d-%d\" % (chrom, start, end))\n",
    "    print(\"-------------------------------\")\n",
    "    \n",
    "    input_seq = dna_to_one_hot([genome_reader[chrom][start:end].seq])[0]\n",
    "    \n",
    "    print(\"With prior:\")\n",
    "    print(\"Input gradients\")\n",
    "    viz_sequence.plot_weights(\n",
    "        interpret_input_grad(prior_model, input_seq)[center_slice],\n",
    "        subticks_frequency=100\n",
    "    )\n",
    "    print(\"ISM\")\n",
    "    viz_sequence.plot_weights(\n",
    "        interpret_ism(prior_model, input_seq)[center_slice],\n",
    "        subticks_frequency=100\n",
    "    )\n",
    "    print(\"No prior:\")\n",
    "    print(\"Input gradients\")\n",
    "    viz_sequence.plot_weights(\n",
    "        interpret_input_grad(noprior_model, input_seq)[center_slice],\n",
    "        subticks_frequency=100\n",
    "    )\n",
    "    print(\"ISM\")\n",
    "    viz_sequence.plot_weights(\n",
    "        interpret_ism(noprior_model, input_seq)[center_slice],\n",
    "        subticks_frequency=100\n",
    "    )\n",
    "    print(\"===============================\")"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}