{ "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_protein_logo, plot_protein_importance_scores\n", "\n", "from ppi_utils import load_ppi_data, load_ppi_predictor, animate_ppi_example\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Load PPI data and predictor\n", "\n", "seq_length = 81\n", "\n", "residue_map = {'D': 0, 'E': 1, 'V': 2, 'K': 3, 'R': 4, 'L': 5, 'S': 6, 'T': 7, 'N': 8, 'H': 9, 'A': 10, 'I': 11, 'G': 12, 'P': 13, 'Q': 14, 'Y': 15, 'W': 16, 'M': 17, 'F': 18, '#': 19}\n", "\n", "encoder = OneHotEncoder(seq_length, residue_map)\n", "\n", "train_data_path = 'coiled_coil_binders_big_set_train.csv'\n", "test_data_path = 'coiled_coil_binders_big_set_test.csv'\n", "\n", "x_1_train, x_2_train, l_1_train, l_2_train, y_train, x_1_test, x_2_test, l_1_test, l_2_test, y_test = load_ppi_data(train_data_path, test_data_path, encoder)\n", "\n", "predictor_path = 'saved_models/ppi_rnn_baker_big_set_5x_negatives_classifier_symmetric_drop_25_5x_negatives_balanced_partitioned_data_epoch_10.h5'\n", "\n", "predictor = load_ppi_predictor(predictor_path)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Define sequence template and background\n", "\n", "#Define sequence templates\n", "\n", "sequence_templates = [\n", " '$' * i + '#' * (seq_length - i)\n", " for i in range(seq_length+1)\n", "]\n", "\n", "onehot_templates = [encoder(sequence_template)[None, ...] for sequence_template in sequence_templates]\n", "\n", "sequence_masks = [\n", " np.array([1 if sequence_templates[i][j] == '$' else 0 for j in range(len(sequence_templates[i]))])\n", " for i in range(seq_length+1)\n", "]\n", "\n", "#Calculate background distributions\n", "\n", "pseudo_count = 0.1\n", "\n", "x_means = []\n", "for i in range(seq_length + 1) :\n", " x_train_len = x_1_train[np.ravel(l_1_train) == i, ...]\n", " \n", " if x_train_len.shape[0] > 0 :\n", " x_mean_len = (np.sum(x_train_len, axis=(0, 1)) + pseudo_count) / (np.sum(x_train_len, axis=(0, 1, 3)).reshape(-1, 1) + 20. * pseudo_count)\n", " x_means.append(x_mean_len)\n", " else :\n", " x_means.append(np.ones((x_1_train.shape[2], x_1_train.shape[3])))\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Visualize a few background sequence distributions\n", "\n", "visualize_len = 67\n", "\n", "plot_protein_logo(residue_map, np.copy(x_means[visualize_len]), sequence_template=sequence_templates[visualize_len], figsize=(12, 1), logo_height=1.0, plot_start=0, plot_end=81)\n", "\n", "visualize_len = 72\n", "\n", "plot_protein_logo(residue_map, np.copy(x_means[visualize_len]), sequence_template=sequence_templates[visualize_len], figsize=(12, 1), logo_height=1.0, plot_start=0, plot_end=81)\n", "\n", "visualize_len = 81\n", "\n", "plot_protein_logo(residue_map, np.copy(x_means[visualize_len]), sequence_template=sequence_templates[visualize_len], figsize=(12, 1), logo_height=1.0, plot_start=0, plot_end=81)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Length = 67] Mean KL Div against background (bits) = 1.8338768041146942\n", "[Length = 68] Mean KL Div against background (bits) = 2.5460477414577154\n", "[Length = 69] Mean KL Div against background (bits) = 2.6963265731207375\n", "[Length = 70] Mean KL Div against background (bits) = 2.614518136782641\n", "[Length = 71] Mean KL Div against background (bits) = 2.651062021646643\n", "[Length = 72] Mean KL Div against background (bits) = 2.692262963282262\n", "[Length = 73] Mean KL Div against background (bits) = 2.664524444653306\n", "[Length = 74] Mean KL Div against background (bits) = 2.734017249859557\n", "[Length = 75] Mean KL Div against background (bits) = 2.6117448338622578\n", "[Length = 76] Mean KL Div against background (bits) = 2.6657638687763283\n", "[Length = 77] Mean KL Div against background (bits) = 2.6718047213385736\n", "[Length = 78] Mean KL Div against background (bits) = 2.641879650352336\n", "[Length = 79] Mean KL Div against background (bits) = 2.6207788735288795\n", "[Length = 80] Mean KL Div against background (bits) = 2.6820669050537473\n", "[Length = 81] Mean KL Div against background (bits) = 2.534693515341062\n" ] } ], "source": [ "#Calculate mean training set kl-divergence against background\n", "\n", "mean_kl_divs = []\n", "\n", "for i in range(seq_length + 1) :\n", " x_train_len = x_1_train[np.ravel(l_1_train) == i, ...]\n", " \n", " if x_train_len.shape[0] > 0 :\n", " x_train_clipped_len = np.clip(np.copy(x_train_len[:, 0, :, :]), 1e-8, 1. - 1e-8)\n", "\n", " kl_divs = np.sum(x_train_clipped_len * np.log(x_train_clipped_len / np.tile(np.expand_dims(x_means[i], axis=0), (x_train_clipped_len.shape[0], 1, 1))), axis=-1) / np.log(2.0)\n", "\n", " x_mean_kl_divs = np.sum(kl_divs * sequence_masks[i], axis=-1) / np.sum(sequence_masks[i])\n", " x_mean_kl_div = np.mean(x_mean_kl_divs)\n", "\n", " mean_kl_divs.append(x_mean_kl_div)\n", " \n", " print(\"[Length = \" + str(i) + \"] Mean KL Div against background (bits) = \" + str(x_mean_kl_div))\n", " else :\n", " mean_kl_divs.append(0)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "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", " n_inputs=2,\n", " multi_input_mode='siamese',\n", " scrambler_mode='occlusion',\n", " input_size_x=1,\n", " input_size_y=81,\n", " n_out_channels=20,\n", " input_templates=onehot_templates,\n", " input_backgrounds=x_means,\n", " batch_size=32,\n", " n_samples=32,\n", " sample_mode='gumbel',\n", " zeropad_input=True,\n", " mask_dropout=False,\n", " network_config=network_config\n", ")\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 40000 samples, validate on 4000 samples\n", "Epoch 1/10\n", "40000/40000 [==============================] - 679s 17ms/step - loss: 2.1750 - nll_loss: 1.9488 - entropy_loss: 0.2262 - val_loss: 1.8926 - val_nll_loss: 1.7581 - val_entropy_loss: 0.1345\n", "Epoch 2/10\n", "40000/40000 [==============================] - 611s 15ms/step - loss: 1.8592 - nll_loss: 1.6930 - entropy_loss: 0.1662 - val_loss: 1.7187 - val_nll_loss: 1.5377 - val_entropy_loss: 0.1810\n", "Epoch 3/10\n", "40000/40000 [==============================] - 612s 15ms/step - loss: 1.7481 - nll_loss: 1.5879 - entropy_loss: 0.1602 - val_loss: 1.6377 - val_nll_loss: 1.4842 - val_entropy_loss: 0.1536\n", "Epoch 4/10\n", "40000/40000 [==============================] - 610s 15ms/step - loss: 1.6883 - nll_loss: 1.5327 - entropy_loss: 0.1556 - val_loss: 1.6318 - val_nll_loss: 1.5114 - val_entropy_loss: 0.1205\n", "Epoch 5/10\n", "40000/40000 [==============================] - 621s 16ms/step - loss: 1.6587 - nll_loss: 1.5055 - entropy_loss: 0.1531 - val_loss: 1.5849 - val_nll_loss: 1.4276 - val_entropy_loss: 0.1573\n", "Epoch 6/10\n", "40000/40000 [==============================] - 641s 16ms/step - loss: 1.6398 - nll_loss: 1.4828 - entropy_loss: 0.1571 - val_loss: 1.5720 - val_nll_loss: 1.4169 - val_entropy_loss: 0.1551\n", "Epoch 7/10\n", "40000/40000 [==============================] - 688s 17ms/step - loss: 1.6282 - nll_loss: 1.4749 - entropy_loss: 0.1532 - val_loss: 1.5623 - val_nll_loss: 1.3944 - val_entropy_loss: 0.1679\n", "Epoch 8/10\n", "40000/40000 [==============================] - 680s 17ms/step - loss: 1.6107 - nll_loss: 1.4535 - entropy_loss: 0.1571 - val_loss: 1.5501 - val_nll_loss: 1.3986 - val_entropy_loss: 0.1515\n", "Epoch 9/10\n", "40000/40000 [==============================] - 583s 15ms/step - loss: 1.6049 - nll_loss: 1.4478 - entropy_loss: 0.1571 - val_loss: 1.5508 - val_nll_loss: 1.3971 - val_entropy_loss: 0.1536\n", "Epoch 10/10\n", "40000/40000 [==============================] - 353s 9ms/step - loss: 1.5912 - nll_loss: 1.4306 - entropy_loss: 0.1606 - val_loss: 1.5356 - val_nll_loss: 1.3703 - val_entropy_loss: 0.1653\n" ] } ], "source": [ "#Train scrambler\n", "\n", "n_epochs = 10\n", "\n", "train_history = scrambler.train(\n", " predictor,\n", " [x_1_train, x_2_train],\n", " y_train,\n", " [x_1_test, x_2_test],\n", " y_test,\n", " n_epochs,\n", " group_train=[l_1_train, l_2_train],\n", " group_test=[l_1_test, l_2_test],\n", " monitor_test_indices=np.arange(32).tolist(),\n", " monitor_batch_freq_dict={0 : 1, 100 : 5, 1250 : 10},\n", " nll_mode='reconstruction',\n", " predictor_task='classification',\n", " reference='label',\n", " entropy_mode='target',\n", " entropy_bits=2.4,\n", " entropy_weight=1.\n", ")\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved scrambler model at saved_models/ppi_occlusion_scrambler_bits_24_epochs_10_label.h5 \n" ] } ], "source": [ "#Save scrambler checkpoint\n", "save_dir = 'saved_models'\n", "\n", "model_name = 'ppi_occlusion_scrambler_bits_24_epochs_10_label'\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.save_model(model_path)\n", "\n", "#Sub-select train history monitor (one example only) to save space\n", "for t in range(len(train_history['monitor_pwms'])) :\n", " for k in range(len(train_history['monitor_pwms'][t])) :\n", " train_history['monitor_pwms'][t][k] = train_history['monitor_pwms'][t][k][0:1, ...]\n", " train_history['monitor_importance_scores'][t][k] = train_history['monitor_importance_scores'][t][k][0:1, ...]\n", "\n", " train_history['monitor_nll_losses'][t] = train_history['monitor_nll_losses'][t][0:1, ...]\n", " train_history['monitor_entropy_losses'][t] = train_history['monitor_entropy_losses'][t][0:1, ...]\n", "\n", "pickle.dump({'train_history' : train_history}, open(save_dir + '/' + model_name + '_train_history.pickle', 'wb'))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded scrambler model from saved_models/ppi_occlusion_scrambler_bits_24_epochs_10_label.h5 \n" ] } ], "source": [ "#Load models\n", "save_dir = 'saved_models'\n", "\n", "model_name = 'ppi_occlusion_scrambler_bits_24_epochs_10_label'\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": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot training statistics\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(2 * 4, 3))\n", "\n", "n_epochs_actual = len(train_history['nll_loss'])\n", "\n", "ax1.plot(np.arange(1, n_epochs_actual + 1), train_history['nll_loss'], linewidth=3, color='green')\n", "ax1.plot(np.arange(1, n_epochs_actual + 1), train_history['val_nll_loss'], linewidth=3, color='orange')\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", "ax2.plot(np.arange(1, n_epochs_actual + 1), train_history['entropy_loss'], linewidth=3, color='green')\n", "ax2.plot(np.arange(1, n_epochs_actual + 1), train_history['val_entropy_loss'], linewidth=3, color='orange')\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": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4000/4000 [==============================] - 10s 3ms/step\n" ] } ], "source": [ "#Interpret the test set using the trained scrambler\n", "\n", "[pwm_1_test, pwm_2_test], [sample_1_test, sample_2_test], [importance_scores_1_test, importance_scores_2_test] = scrambler.interpret([x_1_test, x_2_test], group=[l_1_test, l_2_test])\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test pair 0:\n", " - Prediction (original) = 0.85\n", " - Predictions (scrambled) = [0.44, 0.01, 0.63, 0.59, 0.46, 0.79, 0.43, 0.4, 0.56, 0.64, 0.67, 0.19, 0.02, 0.65, 0.0, 0.15, 0.73, 0.53, 0.18, 0.17, 0.23, 0.32, 0.39, 0.02, 0.29, 0.36, 0.07, 0.57, 0.04, 0.17, 0.12, 0.26]\n", "Binder 1:\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Binder 2:\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Visualize a few reconstructed sequence patterns\n", "\n", "#Color by ground truth hbnet positions for test pair 0\n", "special_sequence_template_1 = (\"TAEELLEV$KK$DRV$KE$LRV$EEILKVVEVLTRGEVSSEVLKRVLRKLEELTDKLRRV$EE$RRVVEKLN\" + \"#\" * 81)[:81]\n", "special_sequence_template_2 = (\"DLEDLLRRLRRLVDE$RRLVEELERV$RRLEKAVRDNEDERELARL$RE$$DI$DK$DKLAREILEVLKRLLERTE\" + \"#\" * 81)[:81]\n", "\n", "score_quantile = 0.95\n", "\n", "plot_examples = [0]\n", "save_examples = []\n", "\n", "for test_ix in plot_examples :\n", " \n", " pwm_1_curr = pwm_1_test[test_ix:test_ix+1, ...] * sequence_masks[l_1_test[test_ix, 0]][None, None, :, None]\n", " pwm_2_curr = pwm_2_test[test_ix:test_ix+1, ...] * sequence_masks[l_2_test[test_ix, 0]][None, None, :, None]\n", " \n", " importance_scores_1_curr = importance_scores_1_test[test_ix:test_ix+1, ...] * sequence_masks[l_1_test[test_ix, 0]][None, None, :, None]\n", " importance_scores_2_curr = importance_scores_2_test[test_ix:test_ix+1, ...] * sequence_masks[l_2_test[test_ix, 0]][None, None, :, None]\n", " \n", " print(\"Test pair \" + str(test_ix) + \":\")\n", " \n", " y_test_hat_ref = predictor.predict(x=[x_1_test[test_ix:test_ix+1, ...], x_2_test[test_ix:test_ix+1, ...]], batch_size=1)[0, 0]\n", " y_test_hat = predictor.predict(x=[sample_1_test[test_ix, ...], sample_2_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) = \" + str([float(str(round(y_test_hat[i], 2))[:4]) for i in range(len(y_test_hat))]))\n", " \n", " save_figs = False\n", " if save_examples is not None and test_ix in save_examples :\n", " save_figs = True\n", " \n", " sequence_template_1 = sequence_templates[l_1_test[test_ix, 0]]\n", " sequence_template_2 = sequence_templates[l_2_test[test_ix, 0]]\n", " if special_sequence_template_1 is not None :\n", " sequence_template_1 = special_sequence_template_1\n", " sequence_template_2 = special_sequence_template_2\n", " \n", " q_1 = np.quantile(importance_scores_1_curr[0, 0, :, :], q=score_quantile)\n", " q_2 = np.quantile(importance_scores_2_curr[0, 0, :, :], q=score_quantile)\n", " \n", " seq_1 = encoder.decode(x_1_test[test_ix, 0, :, :])[:l_1_test[test_ix, 0]]\n", " seq_2 = encoder.decode(x_2_test[test_ix, 0, :, :])[:l_2_test[test_ix, 0]]\n", " \n", " print(\"Binder 1:\")\n", "\n", " plot_protein_logo(residue_map, x_1_test[test_ix, 0, :, :], sequence_template=sequence_template_1.replace('#', '@'), color_reference=['red'], sequence_colors=np.zeros(81, dtype=np.int).tolist(), figsize=(12, 1), plot_start=0, plot_end=81, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_orig_sequence_binder_1\")\n", " plot_protein_logo(residue_map, pwm_1_curr[0, 0, :, :], sequence_template=sequence_template_1.replace('#', '@'), color_reference=['red'], sequence_colors=np.zeros(81, dtype=np.int).tolist(), figsize=(12, 1), plot_start=0, plot_end=81, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scrambld_pwm_binder_1\")\n", " plot_protein_importance_scores(importance_scores_1_curr[0, 0, :, :].T, seq_1, figsize=(12, 1), score_clip=q_1, sequence_template=sequence_template_1, single_color='red', fixed_sequence_template_scores=False, plot_start=0, plot_end=81, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scores_binder_1\")\n", "\n", " print(\"Binder 2:\")\n", "\n", " plot_protein_logo(residue_map, x_2_test[test_ix, 0, :, :], sequence_template=sequence_template_2.replace('#', '@'), color_reference=['red'], sequence_colors=np.zeros(81, dtype=np.int).tolist(), figsize=(12, 1), plot_start=0, plot_end=81, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_orig_sequence_binder_2\")\n", " plot_protein_logo(residue_map, pwm_2_curr[0, 0, :, :], sequence_template=sequence_template_2.replace('#', '@'), color_reference=['red'], sequence_colors=np.zeros(81, dtype=np.int).tolist(), figsize=(12, 1), plot_start=0, plot_end=81, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scrambld_pwm_binder_2\")\n", " plot_protein_importance_scores(importance_scores_2_curr[0, 0, :, :].T, seq_2, figsize=(12, 1), score_clip=q_2, sequence_template=sequence_template_2, single_color='red', fixed_sequence_template_scores=False, plot_start=0, plot_end=81, save_figs=save_figs, fig_name=model_name + \"_test_ix_\" + str(test_ix) + \"_scores_binder_2\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Environment (conda_tensorflow_p36)", "language": "python", "name": "conda_tensorflow_p36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }