{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential, Model, load_model\n",
    "\n",
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import scipy.sparse as sp\n",
    "import scipy.io as spio\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scrambler.models import *\n",
    "from scrambler.utils import OneHotEncoder, get_sequence_masks\n",
    "from scrambler.visualizations import plot_dna_logo, plot_dna_importance_scores\n",
    "\n",
    "from optimus5_utils import load_optimus5_data, load_optimus5_predictor, animate_optimus5_examples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train.shape = (15008, 1, 50, 4)\n",
      "x_test.shape = (3200, 1, 50, 4)\n",
      "y_train.shape = (15008, 1)\n",
      "y_test.shape = (3200, 1)\n"
     ]
    }
   ],
   "source": [
    "#Load Optimus-5 data and predictor\n",
    "\n",
    "encoder = OneHotEncoder(seq_length=50, channel_map={'A' : 0, 'C' : 1, 'G' : 2, 'T' : 3})\n",
    "\n",
    "train_data_path = 'bottom5KIFuAUGTop5KIFuAUG.csv'\n",
    "test_data_path = 'randomSampleTestingAllAUGtypes.csv'\n",
    "\n",
    "x_train, y_train, x_test, y_test = load_optimus5_data(train_data_path, test_data_path)\n",
    "\n",
    "predictor_path = 'saved_models/optimusRetrainedMain.hdf5'\n",
    "\n",
    "predictor = load_optimus5_predictor(predictor_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define sequence template and background\n",
    "\n",
    "sequence_template = '$' * 50\n",
    "\n",
    "pseudo_count = 1.0\n",
    "\n",
    "onehot_template = encoder(sequence_template)[None, ...]\n",
    "sequence_mask = get_sequence_masks([sequence_template])[0]\n",
    "\n",
    "x_mean = (np.sum(x_train, axis=(0, 1)) + pseudo_count) / (x_train.shape[0] + 4. * pseudo_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize background sequence distribution\n",
    "\n",
    "plot_dna_logo(np.copy(x_mean), sequence_template=sequence_template, figsize=(10, 1), logo_height=1.0, plot_start=0, plot_end=50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean KL Div against background (bits) = 1.9679329305814974\n"
     ]
    }
   ],
   "source": [
    "#Calculate mean training set kl-divergence against background\n",
    "\n",
    "x_train_clipped = np.clip(np.copy(x_train[:, 0, :, :]), 1e-8, 1. - 1e-8)\n",
    "\n",
    "kl_divs = np.sum(x_train_clipped * np.log(x_train_clipped / np.tile(np.expand_dims(x_mean, axis=0), (x_train_clipped.shape[0], 1, 1))), axis=-1) / np.log(2.0)\n",
    "\n",
    "x_mean_kl_divs = np.sum(kl_divs * sequence_mask, axis=-1) / np.sum(sequence_mask)\n",
    "x_mean_kl_div = np.mean(x_mean_kl_divs)\n",
    "\n",
    "print(\"Mean KL Div against background (bits) = \" + str(x_mean_kl_div))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Build scrambler\n",
    "\n",
    "#Scrambler network configuration\n",
    "network_config = {\n",
    "    'n_groups' : 5,\n",
    "    'n_resblocks_per_group' : 4,\n",
    "    'n_channels' : 32,\n",
    "    'window_size' : 3,\n",
    "    'dilation_rates' : [1, 2, 4, 2, 1],\n",
    "    'drop_rate' : 0.0,\n",
    "    'norm_mode' : 'instance',\n",
    "    'mask_smoothing' : False,\n",
    "    'mask_smoothing_window_size' : 5,\n",
    "    'mask_smoothing_std' : 1.,\n",
    "    'mask_drop_scales' : [1, 5],\n",
    "    'mask_min_drop_rate' : 0.0,\n",
    "    'mask_max_drop_rate' : 0.5,\n",
    "    'label_input' : False\n",
    "}\n",
    "\n",
    "#Initialize scrambler\n",
    "scrambler = Scrambler(\n",
    "    scrambler_mode='inclusion',\n",
    "    input_size_x=1,\n",
    "    input_size_y=50,\n",
    "    n_out_channels=4,\n",
    "    input_templates=[onehot_template],\n",
    "    input_backgrounds=[x_mean],\n",
    "    batch_size=32,\n",
    "    n_samples=32,\n",
    "    sample_mode='gumbel',\n",
    "    zeropad_input=False,\n",
    "    mask_dropout=False,\n",
    "    network_config=network_config\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded scrambler model from saved_models/optimus5_inclusion_scrambler_bits_0125_epochs_10.h5 \n"
     ]
    }
   ],
   "source": [
    "#Load pre-trained scrambler model\n",
    "save_dir = 'saved_models'\n",
    "\n",
    "model_name = 'optimus5_inclusion_scrambler_bits_0125_epochs_10'\n",
    "\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "\n",
    "model_path = os.path.join(save_dir, model_name + '.h5')\n",
    "\n",
    "scrambler.load_model(model_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128/128 [==============================] - 2s 12ms/step\n"
     ]
    }
   ],
   "source": [
    "#Interpret the test set using the trained scrambler\n",
    "pretrained_pwm_test, pretrained_sample_test, pretrained_importance_scores_test = scrambler.interpret(x_test[:128])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning batch 0...\n",
      "Epoch 1/1\n",
      "500/500 [==============================] - 19s 39ms/step - loss: 40.8811 - ft_nll_loss: 25.2364 - ft_entropy_loss: 15.6447\n",
      "Epoch 1/1\n",
      "500/500 [==============================] - 18s 36ms/step - loss: 33.4170 - ft_nll_loss: 21.3800 - ft_entropy_loss: 12.0370\n",
      "Epoch 1/1\n",
      "500/500 [==============================] - 18s 37ms/step - loss: 33.1691 - ft_nll_loss: 20.9606 - ft_entropy_loss: 12.2084\n",
      "Epoch 1/1\n",
      "500/500 [==============================] - 18s 36ms/step - loss: 35.5454 - ft_nll_loss: 21.3625 - ft_entropy_loss: 14.1829\n"
     ]
    }
   ],
   "source": [
    "#Interpret the test set using the trained scrambler\n",
    "finetuned_pwm_test, finetuned_sample_test, finetuned_importance_scores_test, finetuned_histories = scrambler.optimize(\n",
    "    predictor,\n",
    "    x_test[:128],\n",
    "    y_test[:128],\n",
    "    batch_size=32,\n",
    "    n_iters=500,\n",
    "    norm_mode='instance',\n",
    "    adam_lr=0.01,\n",
    "    adam_beta_1=0.5,\n",
    "    adam_beta_2=0.9,\n",
    "    nll_mode='reconstruction',\n",
    "    predictor_task='regression',\n",
    "    entropy_mode='maximization',\n",
    "    entropy_bits=0.125,\n",
    "    entropy_weight=10.\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def _rolling_average(x, window=1) :\n",
    "    x_avg = []\n",
    "    \n",
    "    for j in range(x.shape[0]) :\n",
    "        j_min = max(j - window + 1, 0)\n",
    "        x_avg.append(np.mean(x[j_min:j+1]))\n",
    "    \n",
    "    return np.array(x_avg)\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(2 * 4, 3))\n",
    "\n",
    "n_epochs_actual = len(finetuned_histories[0]['ft_nll'])\n",
    "\n",
    "nll_rolling_window = 25\n",
    "entropy_rolling_window = 25\n",
    "\n",
    "for i in range(len(finetuned_histories)) :\n",
    "    ax1.plot(np.arange(1, n_epochs_actual + 1), _rolling_average(np.array(finetuned_histories[i]['ft_nll']), window=nll_rolling_window), linewidth=3)\n",
    "\n",
    "plt.sca(ax1)\n",
    "plt.xlabel(\"Epochs\", fontsize=14)\n",
    "plt.ylabel(\"NLL\", fontsize=14)\n",
    "plt.xlim(1, n_epochs_actual)\n",
    "plt.xticks([1, n_epochs_actual], [1, n_epochs_actual], fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "for i in range(len(finetuned_histories)) :\n",
    "    ax2.plot(np.arange(1, n_epochs_actual + 1), _rolling_average(np.array(finetuned_histories[i]['ft_entropy']), window=entropy_rolling_window), linewidth=3)\n",
    "\n",
    "plt.sca(ax2)\n",
    "plt.xlabel(\"Epochs\", fontsize=14)\n",
    "plt.ylabel(\"Entropy Loss\", fontsize=14)\n",
    "plt.xlim(1, n_epochs_actual)\n",
    "plt.xticks([1, n_epochs_actual], [1, n_epochs_actual], fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sequence 0:\n",
      " - Prediction (original) = -1.0\n",
      " - Predictions (scrambled, pretrained) = [-1.2, -1.5, -0.8, -0.8, -1.4, -1.1, -1.2, -0.5, -1.0, -1.2, -0.7, -1.6, -0.8, -1.1, -0.7, -1.3, -1.0, -1.1, -1.5, -0.9, -0.3, -0.7, -1.0, -1.3, -1.0, -0.9, -1.6, -1.1, -0.9, -1.5, -1.3, -0.5]\n",
      " - Predictions (scrambled, finetuned) = [-0.2, -0.2, -0.5, -0.7, -0.7, -0.4, -0.7, -0.9, -1.0, -1.0, -1.1, -0.6, 0.74, -1.4, -0.7, -1.0, -1.4, -0.3, -1.1, -0.5, -0.7, -1.0, -0.9, -1.0, -0.9, -0.3, -0.9, -1.0, 0.44, -0.6, 0.91, 0.48]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAABACAYAAAAH14HqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAC+tJREFUeJzt3XusXEd9wPHv79pxTBzHdR524jjXBpoIVKM4JQ1pWpSgIkoDTRNF4rHQSlAQSCAhlVYFAaKtWrVUlVr1n0qliSohlqhVQYQiigpNiqJYabEMKCFRBa2DnWfJy0nIg3vv8Me5m52dPed6X16v734/kuU9s7PnjCXPnN+ZM49IKSFJkiSpsnCyCyBJkiTNEgNkSZIkKWOALEmSJGUMkCVJkqSMAbIkSZKUMUCWJEmSMgbIkiRJUsYAWZIkScoYIEuSJEmZjSe7ANKaIq4AdmYpPyCle0/Y9drxZuCTwC8DjwJfAf6MVrr/hF1T0voUcQnwqizlx6R058kqjnTKiNgEXFukfpOUnp5aEdxqWjMt4gBwZZby96T0gb5s74+h/iOnz6boS2zHe4GbarI/DtxIK90+zDUkzbmIvwE+kqV8l5T2n6ziSKeMiIuB/ylSX09Kd0yrCA6x0KxbPM7xZLTjKuCzDd+eDXyFdrzmhFxb0no1nfZLWn/2DJh2wjjEQrOresVyQZE6+QrSjgD+irUfGM8EPga8a+LXl7Rele3VdiK2TvM1sTRtE3mjW/8wOdUHTHuQNct2A2XFWSSirjKN41qqMcfHY32RNIyTfpOXTlEnve54w9csq6sMW4DtE75O35hmSRpLxBnAuTXfGCBLx2eALK2hqTJMrpK0Ywvw6xM7nyRVmtqpqY6jlE5RJ30MsgGyZllTZZhkJbka2NTw3dIEryNpvpz4B3xp/aqrJ3tOwBDLRgbImmXTuMG8qSbtIHA+cBbwbuCxCV5P0nwwQJZGEbEAXFTzzZnAtmkVw1UsNMsGvsGUs2DLWbQNs2QBriiODwNvpZUeWT3+PO34DjC1tRclrQsGyNJozgNOb/huD/DkNAphD7JmWT6UIjWkj2tvcfwHtNLDPSmtdA/wzgleU9L6N432S1qPyjqykn2e2gOmPciaTdU4o7wi3APsW/08mQrSjs30rrP8BHBrbd5W+rfV9ZIlaRBN7deFRGwkJec4aF0a441uR153fgI8DLyi5rsTyh5kzapzgJdlxweyz5OqIOV5bqeVXmzM3Upfm9B1Ja1/efuSt18b6N8ASVJXXneOAkey46m9gTFA1qwqK0F+g7mAiKbxScPYWxw7zljS+PonGR0ocjjMQmqW14+jq3867EHW3MsrwTLw7eL73RO4xt7i+IcTOKcknQ+clh1/H3gmO3aintSs7EE2QJYyeSV4CLh/je9Htbc4PlqXSZKGVLZPRzhJN3npFGSALK0hf8VyhJSOAU83fD+qvcXxAxM4pyTl7dMS8Ai9N3mHWEjN8iC4fLjcRUTT5l4T5SoWmlXlE2Tn71fXfD+q87PPS8CjEzinJOXt04OktEyEPcjS8URsAc7NUo5SvUV+KQdwIfB/jedox0aqOQA7gWPAD9acgN/AHmTNqqYAue77UeUT/R6klVYac0rS4KbRfknrUbmDXjnEAprqTzsWaMfbgHuB/6WaHHsP8CTt+CLtuGqYgtiDrFk17QD5WM837dgN/ErNb75GKx2rSZekjnIWfv539X1EkFLP+rCS+u7tR4HHgZ/SnfjaP0SpHbuALwOX15zzZcANwA2043dopc8NUhADZM2eiM1Ur0Y66m8w48sD5PL1y+XALTW/eTVlMC1JvY73gL8V2MaUtsyVTiF53XkeeIyUEhEP0J031BtEV5t+3Qq8doDzD7wGuUMsNIvKVyydRcJ7e5Bj7J3t8oH+Px3zXJLUUU4ygkFfE0vzrfftS/cty1pvkD9Mf3C8DHyPaojFSPd3A2TNorpXLNC7m85megfyj8KtoyVNVsRWYHuWUtd+gQGyVKfu7Uv5uRtEt2MD8LHiHN8A9tFKl9JK+4AdwO8x5BsbA2TNovwJcoVqH3bo74EZd5jFC9nn0xpzSdLgmh7wnwCey9Jd6k3qN0iAnOfZD5yTHR8E3kIr3fdSSis9SSv9NXApVa/yQByDrFmU/+d/mJQ6r0fqXlGWO+wNIx93bIAsaRLqA+RqHOVR4OKGfJLqJ7hC7xuYxWyS6zXF7/+ocUm3VvoR8KNBC2IPsmZR3fg9qF6P/KQh3yjyHuQzxzyXJEFvu7RM7xquLvUmNYnYAOzOUvL7f153zgDOXv18TZa+DNw2qeIYIGsW1T9BVk+Lk1zJIg+Qd9Eee9KfJOXt0kOktJwdu5ue1GwnvW9zm4ZYQLf+vC5Lu5tWenZShXGIhWZR3rNyNhHvyY6jId8o8p3zTqd6In1s9fhWqoq6Dzg05nUkzY+8XVop2q8dDfkk9deJK4jojC8uJ+Uv0o5DdHuSoVqxoqsdv0B3992OFVrpi4MUxgBZsyVigd5l3t6w+qfOuDeYw8XxLjoBcrWr3grtWBrzGpLmy2Lx+eaGfLuIOC2bYyHNu/KtyifWyLtItZrVhiztmSLP24FPFWnPU20cclwOsdCs2UHvBh5rGfcV5f3F8YVjnk+SBm2Xgt7xltK8G6bTaw/VmOPcRIdJGiBr1gxTQc4jYqAnwQaHi+OXj3EuSfMuYiPDPWg7zELqGqY+LK6uVpGvWLG9KfMoDJA1a4a9YZS77g2j7EG+coxzSdIF9L7yPR4DZKlruAC5kq8Ss7/I8yXgPcA/jFIYA2TNmmFvGOPcYMoA+Q20wzohaVTTbL+k9WaYYZOdvHdkaT9PO7qT9lrpEK30j8C3RimMwYBmzbDjikcfh9xKz9BdtQKq3uhfHfl8kubd9Novaf0Z5oFxJxGbgf8s0n9rUoUxQNasmXYPzOHi+C9pjzWuWdL8sgdZGkXEVoYfQ7yb/gD5j2nHOXWZhzXyMm8RbKUa7/FyYCvVTmSbqJbQeIJqUecDKfHUBMqp+ZHfMO4FvlyT53eB82ryj+K/gddmx68DbqYdvw2sAG8d8/yS5kfeHj0F/F1Nnt8ALq3JL82zsi78M/DDIm0H8N7iN7cB9wGvWk27CPgO7fh94OtUq2JdPkqBhg6QI7gW+FPgstWkr1NF8A8Cz64WZgfwm8CPgW+PUjDNrfyV47+T0sf7ckT8EvBrNflH8Q3gg0XaO4C3UM2OnciTqKS5kLdH9zW0X8t0A+Q9RMTqLqHSPCsD5M+Q0sGelIgL6A2Q99BKiXZ8iiqg7tgN3DJugYYKkCPYD/wr3bXm/iQlPj1uISQAIrbQG5AeaciZbzk5bg/MfwCJ/vUTt455XknzJ2+PBmm/zqB3B09pXpX38nJraah2v12iG7t2fvMvQBtoTbJAw45BPgz8V3b85gj2R/SfJ4JtEZw1TuE0d8ol2+oqCPTeeC5a3X1vNK30BCPOcJWkQn6Tb2q/ynSHWUi99eBF4P/7cqS0TDVaofc3rZSA9wF/SxVAN7kfuH7QAg3Vg5wST0ZwNXAd8HrgF4GvAksRPAs8t3rOjVT/wA/QG1BLaymHSwzSA7MJ2EnvWojDugm4eozfS5p3ET8HPZ1CTe1Xmb4HOHRCyiSdOvL7/wOktNKQ7wjdYLr7m1Z6jog/5Cru5Trex7m8gk1s4UWe4Xm+x1ncxAb+aXVzkYEMPQY5JV6gGuvx0niPeH8kEpA2wM1LC6k6ekkEQbWA+iKwDdgCHKMas9zZKrDzinsDcHD1OpovZU/KIAFy53fjBMhfAD4JXHKcfHfTv3ayJMF47Zc07wYZngTlEMuITcAbqeYOXc+dbOVO7gb+gmoy/7uBa4BXAvt5V9xCSgPNjRsuQG7HNuA1VD12W4DHqSbiVeFtLJM+H5fRro6ogt/vXrb34OKjT+34UEqxdyUtbNu57ZF7dm1/8NDpp73wdEqRItLyysrCQkSKlbSw4ePX/fn5cMBAZP7kFSTRHPSWlWcRuGvkq7bSEu34BL2D/EtPAzfSSs+NfB1J69kgYyihum8+D2xu+J00jwYZnlR+twhcecMb+SrAQ2fAXecBwT7gM51Mm5bh6oe4aMsSHwU++qX+OUe1YpjJsxHhTFtJkiSdklJKkw+QJUmSpPXOnfQkSZKkjAGyJEmSlDFAliRJkjIGyJIkSVLGAFmSJEnKGCBLkiRJGQNkSZIkKWOALEmSJGUMkCVJkqSMAbIkSZKU+RmRImACtJRUiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sequence 1:\n",
      " - Prediction (original) = -1.0\n",
      " - Predictions (scrambled, pretrained) = [-1.5, -0.9, -1.1, -0.8, -0.8, -1.3, -0.6, -1.1, -1.0, -0.7, -1.5, -1.0, -1.1, -1.6, -0.5, -1.3, -1.0, -0.7, -0.5, -1.1, -1.2, -1.3, -1.3, -1.1, -0.9, -1.2, -0.7, -1.0, -1.2, -1.1, -1.0, -0.1]\n",
      " - Predictions (scrambled, finetuned) = [-0.7, -1.4, -0.7, -1.4, -0.8, -1.0, -0.2, -0.9, -1.6, -1.0, 0.31, -1.6, -1.0, 0.19, -0.3, -0.7, -0.7, -1.2, -1.1, -1.3, -0.9, -0.9, -1.8, -1.0, -1.4, -0.9, -0.9, -0.7, -1.0, -0.4, -1.2, -0.3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sequence 2:\n",
      " - Prediction (original) = -0.9\n",
      " - Predictions (scrambled, pretrained) = [-0.9, -0.3, -0.5, -0.5, -0.6, -0.3, -0.8, -0.4, -0.8, -0.7, -0.6, -0.6, -0.2, -1.1, -0.7, -1.2, 0.32, -0.7, -0.6, -0.6, -0.9, -0.2, -0.7, -1.0, -0.8, 0.11, 0.28, -0.4, -0.5, -0.5, -0.1, -0.0]\n",
      " - Predictions (scrambled, finetuned) = [0.54, 0.44, -1.3, 0.61, -1.1, -0.0, 0.49, 0.77, 0.01, 0.47, 0.65, 0.54, 0.45, 0.77, 0.79, -0.3, 0.62, 0.64, 0.67, -0.4, -0.3, -0.8, 0.81, 0.67, 0.54, -0.9, 0.29, 0.79, 0.68, -0.8, 0.65, 0.14]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sequence 3:\n",
      " - Prediction (original) = -1.1\n",
      " - Predictions (scrambled, pretrained) = [-0.6, -0.2, -0.2, -0.5, -0.4, -0.8, -0.9, 0.78, -0.7, -0.4, -0.6, -0.7, -0.6, -0.7, -0.7, 0.6, -0.9, -0.6, -1.4, -0.5, -0.5, -0.4, -0.3, -1.2, -0.6, 0.2, 0.67, -0.5, -0.8, -0.3, -0.7, -0.8]\n",
      " - Predictions (scrambled, finetuned) = [-0.7, -0.6, -0.8, -0.8, -0.6, -1.2, -0.8, -0.3, -0.8, -0.9, -0.4, -1.2, -1.1, -0.8, -1.1, -0.5, -0.7, -1.0, -0.8, -0.3, -0.9, -0.9, -0.6, -0.9, -0.8, -0.5, -0.6, -1.0, -0.9, -0.8, -0.7, -0.3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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JLwHvIdPTimc/rSkj+Puyq/yudvkHxtrXu5DcuDZc7eQWpL9NQTahhUyomiHeRFd4xUFFGVdDcvfb2IRqeMWLwPaFLEeQ7BY2XHpnHrJSOA3ZW+HKC+2S5e8Zk8lOSM5mG65x6Ebku6Ygm9fic8eakNzmdiinRsK4piGbySsZsWr08V5aMxWRtSsFrYvmQUT205AJmisvtEuW1yP9Ziqy6vGw4x2XLK9hTPfMdDyPQ67WoBruuBgJC52G9KFKJo0is47dfzTw/kKWmwN/7qycolvsce0eZAycgoSyDGLy4ETMQSFvpqoEP2XkwLtGKd4H/NZ6xzaWHkTy5vZyal5BrrZDcm2asFO0PIk09J4h97UihdJYKhTpWHYowQ+BI4sk688DXyFX9yGp6ur4XYQkPl8MzFOKf0DyfZpYnaoS/KrW/Lj4fUdxwEkpb2klT6qVy68mV59dxpeAvbUe7YRnKsUrVL/NroOrgQOM5P+nkas/A2ZS+RlUvYn/DRxXyPI54DhyNYdyB9ucqqf0UDJ9TvH7MeCj5Opx3DvOoYWS70OWtkzuAnY3cmpeRK7upLldPozkq+wdSnFMMZnct4HuRiRp/SvAU0qxP1YqsGKQsst5JvBvWqOB+4u0cHUH0bQxRGxejwM7k+le+7qu6Kt2P3DlGt1Ta3p9/JRie2UprVRk3b2O6iTx62S6Z8g9A3yeXM3FnxIoeALmzNNplLNFX9XAflrTOwjjAqXYB8fEu0BdfO96gO9ADX99K6UoJ8DfiGrM3/FkuldPTwOHkqt5SAaJHlypLvcfPZQi03PJ1d7A9/Fvsm4zcbP5zUXy2D9eXF9Jrralua8+heiQnkf3G8VGylLmosh2uQlVb+KRZLp3+M+TwKfI1XzKG0xdzoSMTMvEN9P3k6vdkTF9nxq6BYhM7i2uby42Nl5JOaORTTcHyRfey5v/80B9/Diie3qe0llKMQn49OiHNPQd3zuIQWc78Q43zkl4HDi40CFmNp21qMb8f1lrfgKgNbcpxXup5ha3ZfJXZNy7v7i+qTg85GrKNpBNdzeSL7zXvi4p6K613rNlOQ/YR+tR59axhUd1NN1sH+OQK3Xjp8n0+cXv+eTqQKR9mtmaRqgmLDhKay4F0JqbXbLs0LZ5AdEhjxXXpyqFxpoY9cGvFjEhFiPW9e32oRlacxvV2Y9Nd7ShcAWZnot4VMwQCJvum1pXPM2zoHQqjk2zEPhM5QQiMdZGB/zCoLFn8YcWxjEAWhfJ08seU5vf01i7PrXmEbpL12LzO0vr8oSkODDivNEbEmtr5yn8VOVkrExfSzkLgc1rAWLo2rL8AWUvk013O25D5QjwngBnKs/Fnvv9YsS6/lzlIIRM34MoJfObbboTHKehfRExuOv4fdY8watoawdRXpqbQXlWvQg5OU8bdM8ipxT60EaWdhm/UshgDCKj3QDzlDqb7hTHYUHfozwBi4XN6zEka00Zmf4O7gMlYMm0r4u15hfmjeLadVCDqyy65pmPzvy25bFTP1bL+BDu7CBfQ3LR++iuINPlg2oyvRjZlHpLy3KGtMuZlVM/Rac06ZBva81frHsnUT/BDIXN6z7gK473jofSiaw23SVkuux9F1l+BDBPZLPpvkumy7KWQ5h2h9KhUTbdkWT6EYvuAWQzuFkvNt1JWvOgde946ORkNZvXnbhzqB9JeYJp0z2BtaKnNQ9RzZRg032TzDq1VU7e3Y3ySqtNd4QxaevR3UuzDplV2AkmjkIONOsXNq+byfTJpTui0w9HDrfx0c3Das9acz/u9HUxsPmdWtiTJv4H+HlH/GoRYyDbM3vnqXfISW0SaiHL9CbdC0iy6ioy/XvAPKWmkV9hVJiHLoxYr1xozIxtHMeYEWMPyjdqzb3WPbTmj5SVrl3Gn3iOLf0pVc96DELr4EjGFIVN83syfbuH7jQYPfFvxHp2vvfUO0llNcdDd4bziFS590/YISHVTUZmWbsxYOQIcnMz0OPISUJVZPoqxHPegynPxTjac3GS1Gh6ryI1n5mB4yGqXtjeZKquD/xSayonw2nNDcipkWXIYQymR6VJliPmn6V6Gpsg03dTngiH9FWN/4CUNhixrnPvSW2SHs91lPNg25cgtK8ehzssyizLY5S/I9RAtvu5TTdiXZ/pPG5W+uohiJfJRec+7ECOVP4odhiWUr3QLlc5y2UUHWLyWwijK3Q2v5sp56SN0SGxGLGuf1AYtmWILP+FsfAam84ny4XIhKM3lobSzaOXz1gO1DDluwBfKEymf0v58AtblpWTO4tT31z50dtixLqu0yGfYGzPiF3G83qn+ln4MfWONZ8sH6D3fRKeZB6Q8yTuUBiK1QBzImyWU+Noz0W5qxP/9hixrn3fthjJbtKLzbdl+WPPybA/pDrOxKCNDukmlKcGMSEW9gdc6XqpWGruxeusQTn103WuowxHkWnzb5r87nfM/Hv8TMPTLqPPQwmZfoGxZSSb7lc+sgZ+TrqiYrs2kHtnxLv4md7LEetxnUw0Y9/Qhu45xr6vDZ0rbnB1ysu+NzB2KlVXMcj237mqUOZuFO2ySLlmLn3fZiwBlVAYrV5+5upECzpnnyvoXO3LPozBL8vqgH+r95hmsPvqiPH7Gdyxj2hdUkqxGLGu69rXk4wZdQJJI2bGz5oyWRulJqJr2kI4Rqxr39jgPma6bMjMRYzMNR3P6uj+gGwqXd549jvjeZvxcj6MTs5sOv/KQKZdbcGmN+vA/rbVKG8qu951tLDBz6dD5mrN3S4ST99pizayfJix2FTbWPplDZ2EM1YnDXOpiwcdk8nalHX/NYUerKUrYnvNct7j8B4DnclyxLquk+X9xlWoPl5Mb9VANkmadHdbf9Pm15Ol3U6vrpmom7IcT3n8ucOxAtkr5w2u+y0xYl3XydJcLQyV5SLgppiCWTD5LaA8Tpn8fCtSnaLfEIuXqcbxNNGAx6CzoRRTKJ/IFdpQbH4VL10gXSy/Lhp0KL9bjZilUBoIrIOO6J6l/SZFe+AxZdq0QSkUI9Z16LetQ9ngvN73YgO/WLq27ctliPSwLqp0ROt6lGPHQ/vqOMp1dqPH09AVRqzr0LrrYQ3KYSumTBT95fY1Ycp+jmMJtQ39XMoboOsmiuazBxvoRozfmrhx797KEn0zzPaiKSvYujJCeLucTDm0bJhj88uEGw0m3R01K54mplL2wP/G6WGt5wXhfWc1yplRhinL5wgP2xixrkPGWTuMbdD6cS3KseqhuiAWI8bvp3Bvsm6igwGW0zEBu6nYm7PE0G+IxYOeUAIbI9a10wvcwAuohjsE8pszYH72cnsov9ZQipUoB83HyiS0Dky6ReD2GDTQzXEuM9bDVJ6LqM4kuzBgRqzrQbfLYdP1YMryBcqTlQmUjysfsWhDZWIP+G3L2BYjxu+nyHRoWrEe6iZgruet4RjwY2Rie5Dnep71Qzdi/H6kdnWvh+oyfWg7MWHSP0I5A8WqKGWeGmmWsQ2/fvtOW4wYv+fWehN7qK7axIzNw6BbkrK83xn244ZZzpdxZ9Wq4wWvblneFziRAsuj6wrt6xB2pMGgZdKIVgZyMeCPGLce8rxqY8S6DmmwLroYfo+R6dBcl13we3TAsx67Yw2zDh4OGqSqA34oLxPmdz4M2MtdXYRZjFjXw2yXsXQaWnshTUPkQaqTHFOWI9azUJnEtstYjBi/Y9qXKZNXkH0ICzzPYzGdcphQjEzsums2kKup4ZroRozfobK0J0T99nG7jFBegh6xng26r7ZHda9NaBntVZvYbxs23SBlOZGyE6RN+xoxfj/sC2OroWnDryu6QcrSTj4QK8thjunD4NeIth5kOy7UldvPhRHrOlbpxvBrI2ST30LKu36dcHiJQssYiy5kssBIQeNHdcAPlaU94PdrGMxF62co7xzuwoAZsa5DyxlbB13QxUzAbKX9GOVckqYsRyzaWON/cP2gurkypn2V+6zW9urIICZg7WRS3ag6l3IZ1y/esWGvrvhDMyQl5hTr3RCMOHi0hf1tdj0OYuI2yPF5BmUP2LIw4W5DN0xZrkvZPgkqYx/6eMS6fjXJMsp54dhrs7TaNgNDWwPZPv4ztDEMjy5Xy1GOW24zcJeUrplGqwaTKMeBDXrW04UsQ2WyCnEDvl3GLpSn/Xe6MJDNcr4M7o12DXQQ3pFNuoXYG8fC6GIGDXuysZhyuzGfx9ZdrExiYKdiXBbaF7SXie2QsA1dV8o2qJa9LsSiq/ruvw60XkD51LhBlHOQ4/OwZTnsvrosyHIycU68LmS5CHe2nBB+g564mQj9tmmUN3MO2mAdpkyC0NZAtjdGhX6ASffXmjRhXfCzadp0YpM25tva0MWiizoIlUmsLPupgx6GYcCU6zs8TrqLOpgXOAGz6fo3kMv/28/tb/NlV7AxzH6wLLYvaC+TJkPX9Y597yUkfaFJNx2lekbEstLHTX5hK2BVOlg62+WSpHu8RQjisijLWD0ew29eizjpV7MsYzFsfo1om+bN/oCYQco+ynhN5FhLG5eAjuFn09j8tqZ63voCMn2eRRs7AIfSxSKWn3nyjy2TjZHjq23YqYZsum2pnlj4V8qHR7jodqV6aMn9ZNpMIeOK5QvdwR+KOpmsD+zgoPmp1S4Xa03zpiZBTPvqh66HJkPElKUpkxdLm4xyNQ05cMDGLyP7aizskzztunsX1SOQHyPTlxjXS8JA9h8p7YZdhoeoHolsp2yz6eaitUYpV3zvXY4y2rLckfJ+ApAY+Ca6fSinZQP4E5mWzZBKTcS9T2EusLHjO+p0yAxgZ6q4bMjtskkmO+P2wjbR7ed45w7q+oHEne5DFb+hftxbG/fRyRcvZX18F6onvD0A2k5zF6Mfy/zkkK29HDRXUy/L9XAfc36hQ5b+lIX9o0mWuyPeYhP3gbbHjKXVthkY+jWQQ8/ENulsmjciR5Ha2DqSXxPN/sjBFCbuR06dqytnLL+uMYg62IlyYv0e7ImETfcxRJ4m/ox57Leb7hiqR1+eTy/Honi3zGX0B63/oXsDxi7j5rjb5Q0NdLH8nCjSp5mbodq1L6XsVFA9GYZ46mxe6+KWya5UB+FB9oOmPnAIctqViRsA00C2N4iBPWmoHsncFv3KxCzjE2gt6RyVeoyx0ArXRNH1ba6NmS4D2S7jYVQNpqson6jnovsWVcP6u4xlC1mH8gqmq5zmd9S1y9fjbpfbMtzxuYnXEcDbrXuXUz0VzKb7LuUDhgC+3sBvCm6ZHEC9LN/kodvCwa+LPOE+NMnyKKrHEl+AtNc6uhB+i6zVxNVxy+SD1MtyUw/dxhbdyy1WE2PQJMvjGMs/3sPZyImIdXRdY9i2VCPahljYA35oBzHp2nSqGH6xZbRpY76tLb8YDLMOuuDVhq4H1yYj83/wb1Bqgy7a5aDpJlrXbWXpWqY3/7ffGWZfjUV/vJRaifIKhksmK1I1StqiX5m4vNz276YQi967j+PemLmk+nhql93ThS7vdyHLxQPOc74kZbm06oJYLKk+3hbD5teItgZyrEEyTLp+jKZh84vBsiDLfmVie8VcytO3QakNXu3tGcIMkako1fMyL6k6HyYvO/zCJRPof5Wiy7rrz0D2b8xcWvp4L97Q/DbzEJtXc7t8tdMNk9ernW6YvJYV22ZgaGsg24eC2N6tELpQmlh+sWW0aWO+rS2/GAyzDrrg1YauB1PBL2Ys72/XBkwX7XLQdPYyUz+yXAg8Ufy2l9zXN96J4TXMftBl+4KxduWTSSy6LKfZ9v2hRu7UcK7frvqGJdPHH0WPxrqbZTQPsXk1t8uu6MY736qni5XluCL8a1BYkrJcWnVBLJZUH2+LYfNrxJIwkG03etf8bJpYfq+2RrSsGsiPoHVvqeX/nIFcJLk3l5rayrK82XEsptYnS9Mgb8OrX0O+DfrlZZ982ZuAPQ2lY9v73Qhql7PNWGTz9xm6dhldqeHq6GLHy37GWQgz4sFdzkHrkFgMW5Zd0C0rshymzhpfnAHQli5aFxT5mweFZCBHoq2BbA/4qzjfqqdbecD8bJpYfjHf1oYuFsOsg1hZ9lMH4N5kBIM1YGLbyXilKrv1Q+jatJNYOqiexNbDfCgdOOIyRJYvTrQDBel2AAAUTElEQVQKgT24DbIf2Lzati9TJg+jtchBJg9dZrKIl4l/oyr4U7ZBtcw+OteECJZ8Hw+ZuA1ah8Ri2LLsgu7VLstYPT5sWU52vtUNhi3LWAybXyP69SDbu5RD6KYUKWgGxc+mWcv5VjNtzLe1oYtFF3UQKpNYWfZTB+DzLg3WgFkr0msgtBH8WngNYuquB58sF1HOM+kyRBTVtFQ+2INb23K2Qb+8fN5L+7rf9tVPOX0bVe3fUI6p9oWP2L/XQ6lxLH193HdipslvcpF6KwTDHJ+HLcsu6FYvDteK4ZdkWaZbk1yF2lT99p82WFJ9vC2Gza8RbQ1k+5SY0A7ip8v0VWRaUVUIcfwyvZByvtF1rOefKPgd3sBv7UAD5kXKA/qgDeQu6sCWydcLmexh0TyDfJ+P7oCC7ksNZbTptijozvaU1VSeq6HUQaP/yoH8/RowZjknYWYtyPQFRRn/roEO7O8L47c81dyTIXShvHowZbSGJcvFjvf835bp2wqZrEAVsTKJwfxaXpnevSjnNzz0pkyWt2Qy2fNeDPqRic17G6OM29S8a9PtatBtZNxfDvFQN/XV9xay/A/rvSa6dQq6q7FRjZNe16oDc8zxtUtTh1xT8HIp1NjxMgZNMtm+KOf/tKRbraD7bTBdpueRaVXQ2XnabboxuWV6dkHzGqpYmmT51qKcZ1nvPUudzorhl+n7DFnamTtMuomYuZkzfVFBsxFVDHO8bJLlm4ty/sR670nK3zvIMsJwZRKEtnmQH0MOgegpyNAPuN+6Xge4J5IulF8vr18bId+P5F8G+capwFN1BFqjleJ+xpLbD7pS+5FJL/fwVHK1Ipl+vo6gwANIntE2vOzNTuEyEa+W6RF7R/HPhX4NGJcsn3C92EAXqihcdCHHTd8PbFj8nqEUE7QuhUe4UT2MYUfchwDAmCz7aV8mBqc8M/08eSkXcNs+Zy7vb0rVYHG9F4N+ZGLzPjbwXZvu1Aa6GxGjoueR7WdMD8U0YCXjeg+qk/Meet/j4vfnAF79lLMtHkVWDXoe2UHLsks6+16X/GIwF5nA95x4QbwMfdzL4R87NvcjS3sCH0I3yMnGA9Z1qCxfUYq5jPXBQTv/himTILTyIBfJrM2PCG1Ec6zrYdLNaBFH2QW/NZVqPfFog9iOPKcDurWDwhDEi28u37cZSNegmjDch34NmDnW9bLQnscRHvZgH8ZQh54s51j3B/1tsTD5tZmAjce9WuXCmigV2hZdeJQ4bxa0m/zVeZDr6TKtiZPlw5Rj2Nt8W5t+2/ueOdb9pa9dyuESMfoxyoBh2Rm/2iPTL1NOS9iG1xzjd+hK8Bzr+tUky+cQ52YMrzmRdDGYY10vcQ9yTJoWcwBYT6mgXbC2UTcSwQvgtRF0imre00HyG0c3p7w5oTULKHs5Y8oIcXUwibG0S23oRlrE97aRnb1BqS2WZLscBl0bWa6DUhOoDlIjgfTzKGfbCC1jLOYYv9cgVyv5XrQwg3a7o0ON6QocDoU2MhmOgSwwy7g2ecCkINOvUI5pHong2+bdYffVWJT0I7lqdpZk+kXGMqlA/LcNm26YstyghQ4x6ZYjzND6vyTLNvrYpFtZqcrx3l1iPuU45EHLpBH9GsjLIUuUbWgAtgphpDVPUz6PO4gult8SoIuFyW9TpZwxoXU0MBZKMgy6VYG/DaRrO7kInfy4EPttD1GO311a21cbT904ZEnL9mYFyaQ4VcsMrdlywHlSbZm8NZCu7apDl2EWr1WqlJmiK751IRYhdGYZJyDHrIfApNuQXE0NpGvTx3uH2MTqkGcph8kNc2xeAfcehia6TQInfE9Sji8e9HjyOBJi2ZYuFmY5pwJvjKCDsHLaE/xB60d7BWaYspxBvCE/sHIWKU1N/bOlUsG5vQeCGAU2x7p+p+slpVheqdHBwW5827XYOWvye41SbgWgFG+pKeMOEbzA820B/HwyUUqxRWBZ6mDymwi8zcPvdUqNbjybYz2Olcmg6YZpwNid/50hu5C15mXK3rNNDTmXYLWTCj/f8l9M+3LQtZ1sbODwZm1BrkLT7cwxfk/FM3lWis2U6jvH5RzrOrR9tZVJv6tBc6zrd7heUooZSvXpCa6mhguj66aPK2D7QLq2fXZ9quEq2wZ5ugVzzL+llNszZfWdWMyxrmNkOQHYtpGiGh7zenIV4i2dS3nz1dtDPN3FikiJn1LuONGlSJYQoo8lPMY0zv6OXIWc1GqP6e8I1CG2Q2ETn3d2GZHleKVG9zj1A5Pfyngm60qxiVLBoZjR6NeDDPBBz3ufpOfdqza+qcDOXfErjIxZNTR7k6vlA3jZ3rOtlKrOtJTiDcCBNfz29ISe7IbHmG2J0Dr4MmMbMW2at5OrECVs0+0bOLmx6f4xgAaGacBk2g5XWRfYLpDaDqv5gP1CEYt+Qu9aa56h7M1aD0d7KLyMZpYVW5bvcQ2mxUC6l3ErVpYmv4k4vs2D0HY5y3O/DWxeWWCKpWEbyKEyOYrexrXqRtUm9FK2tQ0H8YUv/GPgEuzw+rgYg+b4vArw/kD6UB3ylZblCuEVKpPh0Ul4jBnfuzr+zbtN/FyyHMdg+nisTD7gmZDvDV4HhgL2a+SU6Rcob8pbG/+mchs2v33sFwodcmLg3wvlBfGy3Nfj1c0YS4zQD9rokNAwkWh04UHeVCk+Yt5Qio2o7rq26U4M9EzZdIcpVVEEhwNb1tBMAY5rYqQ1djA7wDdML19h+J5BOQOIzW9V4EjzRmH02KmSYmHz+7BS5ZmWUuwF7Dt6I9NPU05/p4BvBRgVNq/VqaZ1C6Hbllzt63rRwpL28J1EHhTXbNN90WG0Hkd1WdCm+6o5eBdt7VTKydwfoZxTdwLwVfOPKMVKwPfpLw2ez6N4FLkK8UzadJ8oJpOjUIqDgPe2LFcIr9cAnwmgW9Lta3elyl4YpdgB+Gfj1nTCN6rCWMq2rup7Q+ATAfQ23Z7kKsQz1VU5TyBXUwLobbr/Z3nrAQ6lm+Vjm9dm5OpA14sNdB8kV81e5Crd4eRqJIJuVmBuaZvus0pV0usdAWwW8Lfa8tqaXGURdNOBz5s3lGJN4DsNdEeQq5BJp013UuBZDzbdF5TC9lofgztNXFvYvN5JrvaMoFsH6SujKOyxb0aXrJ7fwUqNZnDq8dsfeF9H/GoRYyDfSjlcAuCUnntdKbYELqGcxgfgJut6I+BscrVc4a3Y1cPPppsOnKcUKyjFBKU4hOps9QkcHZlcfYxcqWIg8A3kNr89gc8XyzGrAedSjXd8jKr3+Wil2B0k1AG4jO52ZdplnAT8RCnWKMr5AeD0ALo9ESUzrvAKu7z6D1FecgcxmD5cyHIK7hnz7x33TidXEqMlA49r+cRUXHcidWv/e8zzfgxsmWwJfJ9cTSgmD7sE0q0P/EgpllOKiUrxBeALAXTbIJOwcUoxGfhvZKVhFEVs1u8suoOU4hNFfa8FXAi8yXrHXMr+A25Zmh7tniztMm4AXECuViz66m64YdOtjLTLKcX3HQCc7KFtiz8DC6x73yBXUrZcrUY1XzBUT3BzyeQvnvdjYMtkHHCOUmxQ1N3OyJhijsU2z7McZfyB9c4GDrpvO+guM56vhlIrAbdTTdL/HXK1E0AxOXLFVtrfNh44n1xtWNC9AcoTpAJmn73JUcZZuE/MtPm9EfgxuZrUUoeshuiQFYul4U8BJ3lo2+IWqKRgPIVcyaQoV2tSdub4yjgR+Cm5el1B9ybgbwLopgMXjcaD5+o9uPOW23SbAGeRq4ktZbkmcG4RUjlBKQ4FjvfQtsX/Atq6dxq5kn4t4SSuZf2HKWdRAjhOKTGqlOL1wOVUswHZ37YW8DNytUqh694PTu+pTff3wBkROmRd4GxDh3wey9HWB2z9AXAmuRIPuqwmu0Li7qOaivQrSvEuGHWGXg6dbd6zZTIZ0SFTCx3yEeC/OuLViNbpyLTmOaW4jnK82fLA75RiPv4YuNnIzNLEzoiCfg4qM6cernDceytiBL+C69jETGtyNRv4uPXkVGTJe2Xcg0avnPbsZBZwGOIZrkwqityLs4GPWo8uaJBJLG5CNi+aHvgNkNiyBfgPoJgNvNu6dwRwMNIWqh59keUVwIesJz8AvoZ456vhK5l+jFzdQrnTLQ9cR64eQgZW12BjKvnZaP3FyhtKbQnSQenfgLmCqrdsX6QNvIQoUx+djXcj7XIceI+fvgKRt4lPI/KdjD/DwhVUwzH+E/FSr4693FQ9jOEyjyzfzpgh2ZOl69u2RgbKp/BnMvk1Mnk2v2EjxuJHQ7x9Ycj0y+TqKsrKRwEXkqt5SL25QoFMmdzokcnajO2g7ncCdg8yeTb/znTgXuTodFf7snl+Ga3/ZJXxdVBauVvfonsZOAytF1t0e1CeCK9Ppu8kV9dQHhvGA5eRq4eL8rra5R3I5NlsD1OBP5KruYhDwG6Xkygf6PETtK6GNii1G2MTvt53zaZqMOyItMsF+MfZXzruvQXZdObWIbHI9LPk6gbKMcQTgSuLcW8Gbr17M/Id5l6G1YG7Cln6Qm5cfXVjYH6RK9yXS3Y2otNM7Ir07+dpp4+3Qca9xXR5ZHKmnyBXv4fSvp1JwG/qdEihj6+g3D8UcEmDPnZ922aIQ+YJ/Ce7XYGElJrYG9gJmXi20SE7MGaQhmbmaUam55GrOyg7UVYAbiza19q4bZvFSvFLymF244ErBmTb/IZyLnGQifCjSLsM3QjcCWJ3mbsqFuqFdR3lTRY9rIi/M6I185CB2MYK1A9sdWWsW0L30a1OvbxiZBKF4pCIXzkeTaT+dDZfGVel/tzz2Z77M3AZx8106+AyjsWbZQ4m9oEjPXR5HPCVVL0UIG3LN7AB/IlyHF8Pk/Ebx3X8plGffswny+m4Y7HswxjayPI23Mnua9P8FSFK13noujOOx+CTyVq4jWMot5cwmaigeFwnio1NrnKOx9++6o6L7sFue7aB/FDFOHb/rR6Nb2xYG1+7lLhgH926uNulvWTdpl3eQNmz3MOK1IyzWvMosoJio0mHxMInk3XwOaUyvQgZG2wo6uLRMz0XGYts2IcE2biG6lHoIGNGnT5+ALjL8WhFujSOx1Any7oMBzH6+F6qK88gY0ndsce/opzVqIfJ1OuQu6muPIPUQXfG8Rh84+W61Ns2PrpB2DbPI0ayjeUYsnEM8Qbyha0pZHf85UPjB7+gumwYgjsoL7GG4ue4B5xBIUYmt+BXSHW4nOqyYQh+1vJ9WxG4DAMof0Nvg1IcMv0U4vlshcLwaft9aM0TuAeAJvyWanx8HUKMLPv+Biilik21F7UpnIHWMukDF7d6W6mVKU8gQ2SyPP0vH7aViVl3T6O1HUoCWr9AeYOpbSCHfJvJK7be2o5DMe1SaOQAosucbzcjZryMRSyv4dHJKao+w6d7fvGI5XUZbXWWTPja94NM/xW4qi1ZrA7pA7G8LsE9ARgUhimTWkQZFlpzG+6YliacFsMPiad1ed38kE1p57dlVDTa1uXUmqeonmU+SJyPHBEbDjF8vt+aU6bnE2cwXYvb2+CDHS4R4l1ajvCT5XyIbZdDoytSy9lxp3UIlaV5fzJjs/TYbzuLYU0UM/0X3CspPtjGWYhMoP8wnsuoxvHXwY6T9sF8Zscg++jmU95DIjSZvhO4PryIo7iYdhO3mHbZO8QG4tvlGQxPyd+KxM+2xU8pb6QOxem01Y+CfvTxsPBbZC9KK2jN48QZ10u9LugDVxPh/NOah4l3bsbgR8Q5NztHP4n8Z0bQXIx785aN+zA6hdbcjSjetvgyYYOi7Yn6LtXA9BAcT9xA1RpFyrBvRJD+G2GD8M8p58q0s5L4MCZLmZHPbEEXasD4vGCxyJHlribcjpGGRmt+R1svpuAsJDa1Lb6Ge4nZhRhP3Rhdpq9DVmGa8CjGZLlYzu5qI14IZga+52pfbb2sUSgmNyc0vujm5yuj/Wx9yiswbjoJuzDDM0xeM8OKV+rjLxKeHu1SqrJ0hSlBufzjGQsXuAy4MYDXHIzQPK25F/hhUCn7hYx7MePlc4RtFtSYnvRM/wk4O4DuGcqrVxfgDj2xcTeGo0Nr/gicE0DXP9rrEBONmasc/G5FJipNeIryhPIc3KEuNv6I2DcAaM3NDMsjLw6y9u1SMLPbwvihNfMRG2yJox8D+VLql4rvwl66lsZe3RRTxkJg72LZwsRM6j1T36vcEa/I6Q38foe1WUFr/kp97sFFODyxxcBxRg3dk3TrZf4W9d6bCyvPM/0kVoowBx4EPlTUV4/uVsSQrMO1VDM3nIOEdtThNDJ9OmXlqZEDZnzlM9GfgSx5QZt2Cz+LtMsXrPtfon4SVmmXhcHUlCrvVAfdo9Sn01nImBFgyuQl/O2kzhg8kvoJ3yJgPzJte0dnUb+6cXohg/6R6V/TvOx+eVGmJTUBA6nP+2qe/5oxIyQkThrs8JhyarhQOpPXbJo98hdSbYP/2cAP4EQyfZnFbz5a+zxFvolbiA55CfhAsYpoYib1nqmqDonHRbjj8U2cQ3Uy+e80rzYcQ6bteOVjqGaXsnEgmb539EoMJlemHRMvILK0+/PRDfy6lOV5NHvkz8DyxmrNH6h3rD2OTBJsHEXZOWRDAx8m02Pxw2E6ZAGiQ2wnRxO/LmV5FuLoqcMpZLokN625ifoV+UeIcxb58BXqHXlnau3c09YplNbhDk+l1FC8owkJCQkJCQkJCQldQ2sdtPG6lYGckJCQkJCQkJCQ8GpHPyEWCQkJCQkJCQkJCa86JAM5ISEhISEhISEhwUAykBMSEhISEhISEhIMJAM5ISEhISEhISEhwUAykBMSEhISEhISEhIMJAM5ISEhISEhISEhwUAykBMSEhISEhISEhIMJAM5ISEhISEhISEhwUAykBMSEhISEhISEhIMJAM5ISEhISEhISEhwcD/B7y6Zenl+dGkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sequence 4:\n",
      " - Prediction (original) = -1.2\n",
      " - Predictions (scrambled, pretrained) = [-0.7, -1.1, -0.7, -0.3, -1.6, -1.0, -1.7, -1.2, -0.6, -0.4, -1.4, -0.8, -1.1, -1.2, -0.8, -2.2, -1.0, -1.3, -1.1, -1.1, -1.5, -0.8, -1.0, -0.9, -1.1, -0.5, -1.8, -0.2, -0.2, -0.0, -0.7, -1.5]\n",
      " - Predictions (scrambled, finetuned) = [-0.3, -1.1, -1.2, -0.8, -1.4, -1.3, -0.9, -1.3, -0.6, 0.11, -0.9, -1.1, -1.3, -1.4, -1.5, -1.2, -0.7, -1.4, -1.2, -0.5, -1.1, -1.5, -1.4, -0.1, -0.5, -1.3, -1.6, -1.6, -1.2, -1.0, -0.4, -0.9]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize a few reconstructed sequence patterns\n",
    "\n",
    "plot_examples = np.arange(5).tolist()\n",
    "save_examples = []\n",
    "\n",
    "pretrained_importance_scores_test *= sequence_mask[None, None, :, None]\n",
    "finetuned_importance_scores_test *= sequence_mask[None, None, :, None]\n",
    "\n",
    "for test_ix in plot_examples :\n",
    "    \n",
    "    print(\"Test sequence \" + str(test_ix) + \":\")\n",
    "    \n",
    "    y_test_hat_ref = predictor.predict(x=[x_test[test_ix:test_ix+1, ...]], batch_size=1)[0, 0]\n",
    "    y_test_hat_pretrained = predictor.predict(x=[pretrained_sample_test[test_ix, ...]], batch_size=32)[:32, 0].tolist()\n",
    "    y_test_hat_finetuned = predictor.predict(x=[finetuned_sample_test[test_ix, ...]], batch_size=32)[:32, 0].tolist()\n",
    "    \n",
    "    print(\" - Prediction (original) = \" + str(round(y_test_hat_ref, 2))[:4])\n",
    "    print(\" - Predictions (scrambled, pretrained) = \" + str([float(str(round(y_test_hat_pretrained[i], 2))[:4]) for i in range(len(y_test_hat_pretrained))]))\n",
    "    print(\" - Predictions (scrambled, finetuned) = \" + str([float(str(round(y_test_hat_finetuned[i], 2))[:4]) for i in range(len(y_test_hat_finetuned))]))\n",
    "    \n",
    "    save_figs = False\n",
    "    if save_examples is not None and test_ix in save_examples :\n",
    "        save_figs = True\n",
    "    \n",
    "    plot_dna_logo(x_test[test_ix, 0, :, :], sequence_template=sequence_template, figsize=(10, 1), plot_start=0, plot_end=50, plot_sequence_template=True, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_orig_sequence\")\n",
    "    \n",
    "    plot_dna_logo(pretrained_pwm_test[test_ix, 0, :, :], sequence_template=sequence_template, figsize=(10, 1), plot_start=0, plot_end=50, plot_sequence_template=True, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scrambld_pwm_pretrained\")\n",
    "    plot_dna_importance_scores(pretrained_importance_scores_test[test_ix, 0, :, :].T, encoder.decode(x_test[test_ix, 0, :, :]), figsize=(10, 1), score_clip=None, sequence_template=sequence_template, plot_start=0, plot_end=50, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scores_pretrained\")\n",
    "    \n",
    "    plot_dna_logo(finetuned_pwm_test[test_ix, 0, :, :], sequence_template=sequence_template, figsize=(10, 1), plot_start=0, plot_end=50, plot_sequence_template=True, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scrambld_pwm_finetuned\")\n",
    "    plot_dna_importance_scores(finetuned_importance_scores_test[test_ix, 0, :, :].T, encoder.decode(x_test[test_ix, 0, :, :]), figsize=(10, 1), score_clip=None, sequence_template=sequence_template, plot_start=0, plot_end=50, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scores_finetuned\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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