From e193424ada2bc158723b336e7baf426e9367fc18 Mon Sep 17 00:00:00 2001 From: louwenjjr Date: Wed, 28 Oct 2020 17:06:41 +0100 Subject: [PATCH] more extensive result for transmission --- .../3-predicting-more-labels-checkpoint.ipynb | 7100 ++++++++++++++++- 4-networks.ipynb | 6661 +++++++--------- 2 files changed, 9518 insertions(+), 4243 deletions(-) diff --git a/.ipynb_checkpoints/3-predicting-more-labels-checkpoint.ipynb b/.ipynb_checkpoints/3-predicting-more-labels-checkpoint.ipynb index 71488a9..6585e2b 100644 --- a/.ipynb_checkpoints/3-predicting-more-labels-checkpoint.ipynb +++ b/.ipynb_checkpoints/3-predicting-more-labels-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -12,6 +12,7 @@ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", + "import pickle\n", "\n", "ROOT = os.path.dirname(os.getcwd())\n", "#path_data = os.path.join(ROOT, 'data')\n", @@ -30,14 +31,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_prep_old_and_unique_found_matches_s2v.pickle\n" + "C:\\Users\\joris\\Documents\\eScience_data\\data\\old_and_unique_found_matches_s2v.pickle\n", + "C:\\Users\\joris\\Documents\\eScience_data\\data\\old_and_unique_documents_library_s2v.pickle\n", + "C:\\Users\\joris\\Documents\\eScience_data\\data\\old_and_unique_documents_query_s2v.pickle\n" ] } ], @@ -45,38 +48,34 @@ "#loading training set\n", "import pickle\n", "\n", - "#if this file already exists these data files are no longer needed to load\n", - "outfile_check = os.path.join(path_data, 'nn_prep_old_and_unique_found_matches_s2v.pickle')\n", - "print(outfile_check)\n", - "if not os.path.exists(outfile_check):\n", - " outfile = os.path.join(path_data, 'old_and_unique_found_matches_s2v.pickle')\n", - " print(outfile)\n", - " if os.path.exists(outfile):\n", - " with open(outfile, 'rb') as inf:\n", - " old_and_unique_found_matches_s2v = pickle.load(inf)\n", - " else:\n", - " print('error')\n", + "outfile = os.path.join(path_data, 'old_and_unique_found_matches_s2v.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " old_and_unique_found_matches_s2v = pickle.load(inf)\n", + "else:\n", + " print('error')\n", "\n", - " outfile = os.path.join(path_data, 'old_and_unique_documents_library_s2v.pickle')\n", - " print(outfile)\n", - " if os.path.exists(outfile):\n", - " with open(outfile, 'rb') as inf:\n", - " old_and_unique_documents_library_s2v = pickle.load(inf)\n", - " else:\n", - " print('error')\n", + "outfile = os.path.join(path_data, 'old_and_unique_documents_library_s2v.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " old_and_unique_documents_library_s2v = pickle.load(inf)\n", + "else:\n", + " print('error')\n", "\n", - " outfile = os.path.join(path_data, 'old_and_unique_documents_query_s2v.pickle')\n", - " print(outfile)\n", - " if os.path.exists(outfile):\n", - " with open(outfile, 'rb') as inf:\n", - " old_and_unique_documents_query_s2v = pickle.load(inf)\n", - " else:\n", - " print('error')" + "outfile = os.path.join(path_data, 'old_and_unique_documents_query_s2v.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " old_and_unique_documents_query_s2v = pickle.load(inf)\n", + "else:\n", + " print('error')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -907,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ @@ -916,7 +915,7 @@ " 'Mass_sim':X_tanimoto_top20_test.mass_sim})\n", "precisions_s2v = []\n", "recalls_s2v = []\n", - "m_thres = 0.98\n", + "m_thres = 0.000705\n", "s2v_thresholds = np.arange(0, 1, 0.05)\n", "for t in s2v_thresholds:\n", " true_pos = np.sum((df_s2v.Actual >= tanimoto_thres) & (df_s2v.Predicted >= t) & (df_s2v.Mass_sim >= m_thres))\n", @@ -932,7 +931,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 204, "metadata": { "scrolled": false }, @@ -943,20 +942,18 @@ "Text(0.5, 1.0, 'Precision vs Recall\\nof s2v_score and predicted tanimoto predicting a match >0.6\\nusing different cutoffs over whole test set')" ] }, - "execution_count": 153, + "execution_count": 204, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -973,7 +970,7 @@ " plt.annotate(\">{:.2}\".format(threshold),\n", " (recalls_s2v[i], precisions_s2v[i]),\n", " textcoords=\"offset points\", xytext=(-22, -15), size = 8)\n", - "plt.legend()\n", + "plt.legend(loc='lower left')\n", "plt.xlabel(\"Recall\")\n", "plt.ylabel(\"Precision\")\n", "plt.ylim(0.35,1)\n", @@ -1827,7 +1824,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 166, "metadata": {}, "outputs": [], "source": [ @@ -1837,7 +1834,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 167, "metadata": {}, "outputs": [], "source": [ @@ -2116,121 +2113,20 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 168, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train on 40000 samples\n", - "Epoch 1/50\n", - "40000/40000 [==============================] - 6s 141us/sample - loss: 0.9358 - accuracy: 0.6217\n", - "Epoch 2/50\n", - "40000/40000 [==============================] - 5s 119us/sample - loss: 0.8324 - accuracy: 0.6731\n", - "Epoch 3/50\n", - "40000/40000 [==============================] - 5s 117us/sample - loss: 0.8090 - accuracy: 0.6870\n", - "Epoch 4/50\n", - "40000/40000 [==============================] - 5s 119us/sample - loss: 0.8005 - accuracy: 0.6906\n", - "Epoch 5/50\n", - "40000/40000 [==============================] - 5s 119us/sample - loss: 0.7934 - accuracy: 0.6904\n", - "Epoch 6/50\n", - "40000/40000 [==============================] - 5s 119us/sample - loss: 0.7881 - accuracy: 0.6909\n", - "Epoch 7/50\n", - "40000/40000 [==============================] - 5s 119us/sample - loss: 0.7848 - accuracy: 0.6915\n", - "Epoch 8/50\n", - "40000/40000 [==============================] - 5s 120us/sample - loss: 0.7809 - accuracy: 0.6920\n", - "Epoch 9/50\n", - "40000/40000 [==============================] - 5s 120us/sample - loss: 0.7796 - accuracy: 0.6924\n", - "Epoch 10/50\n", - "40000/40000 [==============================] - 5s 136us/sample - loss: 0.7777 - accuracy: 0.6923\n", - "Epoch 11/50\n", - "40000/40000 [==============================] - 5s 121us/sample - loss: 0.7765 - accuracy: 0.6938\n", - "Epoch 12/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7741 - accuracy: 0.6933\n", - "Epoch 13/50\n", - "40000/40000 [==============================] - 5s 123us/sample - loss: 0.7733 - accuracy: 0.6945\n", - "Epoch 14/50\n", - "40000/40000 [==============================] - 5s 124us/sample - loss: 0.7711 - accuracy: 0.6946\n", - "Epoch 15/50\n", - "40000/40000 [==============================] - 5s 121us/sample - loss: 0.7692 - accuracy: 0.6959\n", - "Epoch 16/50\n", - "40000/40000 [==============================] - 5s 121us/sample - loss: 0.7678 - accuracy: 0.6970\n", - "Epoch 17/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7670 - accuracy: 0.6992\n", - "Epoch 18/50\n", - "40000/40000 [==============================] - 5s 121us/sample - loss: 0.7653 - accuracy: 0.6990\n", - "Epoch 19/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7647 - accuracy: 0.6982\n", - "Epoch 20/50\n", - "40000/40000 [==============================] - 5s 123us/sample - loss: 0.7629 - accuracy: 0.7001\n", - "Epoch 21/50\n", - "40000/40000 [==============================] - 5s 121us/sample - loss: 0.7616 - accuracy: 0.7003\n", - "Epoch 22/50\n", - "40000/40000 [==============================] - 5s 130us/sample - loss: 0.7619 - accuracy: 0.7017\n", - "Epoch 23/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7612 - accuracy: 0.7014\n", - "Epoch 24/50\n", - "40000/40000 [==============================] - 5s 132us/sample - loss: 0.7594 - accuracy: 0.7021\n", - "Epoch 25/50\n", - "40000/40000 [==============================] - 5s 123us/sample - loss: 0.7582 - accuracy: 0.7018\n", - "Epoch 26/50\n", - "40000/40000 [==============================] - 5s 127us/sample - loss: 0.7580 - accuracy: 0.7020\n", - "Epoch 27/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7574 - accuracy: 0.7020\n", - "Epoch 28/50\n", - "40000/40000 [==============================] - 5s 123us/sample - loss: 0.7564 - accuracy: 0.7028\n", - "Epoch 29/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7561 - accuracy: 0.7050\n", - "Epoch 30/50\n", - "40000/40000 [==============================] - 5s 123us/sample - loss: 0.7551 - accuracy: 0.7030\n", - "Epoch 31/50\n", - "40000/40000 [==============================] - 5s 123us/sample - loss: 0.7551 - accuracy: 0.7027\n", - "Epoch 32/50\n", - "40000/40000 [==============================] - 5s 125us/sample - loss: 0.7534 - accuracy: 0.7029\n", - "Epoch 33/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7530 - accuracy: 0.7035\n", - "Epoch 34/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7529 - accuracy: 0.7054\n", - "Epoch 35/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7528 - accuracy: 0.7040\n", - "Epoch 36/50\n", - "40000/40000 [==============================] - 5s 132us/sample - loss: 0.7525 - accuracy: 0.7064\n", - "Epoch 37/50\n", - "40000/40000 [==============================] - 5s 123us/sample - loss: 0.7520 - accuracy: 0.7048\n", - "Epoch 38/50\n", - "40000/40000 [==============================] - 5s 129us/sample - loss: 0.7516 - accuracy: 0.7039\n", - "Epoch 39/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7507 - accuracy: 0.7074\n", - "Epoch 40/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7500 - accuracy: 0.7045\n", - "Epoch 41/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7501 - accuracy: 0.7060\n", - "Epoch 42/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7502 - accuracy: 0.7051\n", - "Epoch 43/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7488 - accuracy: 0.7061\n", - "Epoch 44/50\n", - "40000/40000 [==============================] - 5s 125us/sample - loss: 0.7493 - accuracy: 0.7056\n", - "Epoch 45/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7470 - accuracy: 0.7060\n", - "Epoch 46/50\n", - "40000/40000 [==============================] - 5s 122us/sample - loss: 0.7483 - accuracy: 0.7057\n", - "Epoch 47/50\n", - "40000/40000 [==============================] - 5s 124us/sample - loss: 0.7469 - accuracy: 0.7063\n", - "Epoch 48/50\n", - "40000/40000 [==============================] - 5s 125us/sample - loss: 0.7471 - accuracy: 0.7067\n", - "Epoch 49/50\n", - "40000/40000 [==============================] - 5s 124us/sample - loss: 0.7472 - accuracy: 0.7069\n", - "Epoch 50/50\n", - "40000/40000 [==============================] - 5s 123us/sample - loss: 0.7471 - accuracy: 0.7070\n", + "\n", + "Loading existing model\n", "Training loss: 0.7471\n", "\n", - "40000/40000 [==============================] - 2s 40us/sample - loss: 0.7503 - accuracy: 0.7036\n", - "Test accuracy: 70.36\n", - "Test loss: 0.7503\n", - "Saving model at: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_top20_bins_1\n", - "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_top20_bins_1\\assets\n" + "40000/40000 [==============================] - 2s 61us/sample - loss: 0.8435 - accuracy: 0.6981\n", + "Test accuracy: 69.81\n", + "Test loss: 0.8435\n" ] } ], @@ -2827,7 +2723,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 169, "metadata": {}, "outputs": [], "source": [ @@ -2886,7 +2782,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 170, "metadata": {}, "outputs": [ { @@ -3943,7 +3839,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 171, "metadata": {}, "outputs": [ { @@ -3951,114 +3847,13 @@ "output_type": "stream", "text": [ "New shape: (98876, 7)\n", - "Train on 99764 samples\n", - "Epoch 1/50\n", - "99764/99764 [==============================] - 23s 228us/sample - loss: 1.1148 - accuracy: 0.5084\n", - "Epoch 2/50\n", - "99764/99764 [==============================] - 18s 185us/sample - loss: 1.0719 - accuracy: 0.5329\n", - "Epoch 3/50\n", - "99764/99764 [==============================] - 18s 183us/sample - loss: 1.0617 - accuracy: 0.5361\n", - "Epoch 4/50\n", - "99764/99764 [==============================] - ETA: 0s - loss: 1.0513 - accuracy: 0.53 - 19s 186us/sample - loss: 1.0513 - accuracy: 0.5394\n", - "Epoch 5/50\n", - "99764/99764 [==============================] - 26s 256us/sample - loss: 1.0443 - accuracy: 0.5434\n", - "Epoch 6/50\n", - "99764/99764 [==============================] - 20s 199us/sample - loss: 1.0389 - accuracy: 0.5449\n", - "Epoch 7/50\n", - "99764/99764 [==============================] - 24s 238us/sample - loss: 1.0344 - accuracy: 0.5487\n", - "Epoch 8/50\n", - "99764/99764 [==============================] - 19s 186us/sample - loss: 1.0289 - accuracy: 0.5516\n", - "Epoch 9/50\n", - "99764/99764 [==============================] - 19s 191us/sample - loss: 1.0269 - accuracy: 0.5514\n", - "Epoch 10/50\n", - "99764/99764 [==============================] - 18s 183us/sample - loss: 1.0234 - accuracy: 0.5513\n", - "Epoch 11/50\n", - "99764/99764 [==============================] - 19s 187us/sample - loss: 1.0216 - accuracy: 0.5528\n", - "Epoch 12/50\n", - "99764/99764 [==============================] - 19s 186us/sample - loss: 1.0188 - accuracy: 0.5540\n", - "Epoch 13/50\n", - "99764/99764 [==============================] - 18s 182us/sample - loss: 1.0165 - accuracy: 0.5544\n", - "Epoch 14/50\n", - "99764/99764 [==============================] - 19s 192us/sample - loss: 1.0148 - accuracy: 0.5540\n", - "Epoch 15/50\n", - "99764/99764 [==============================] - 20s 199us/sample - loss: 1.0134 - accuracy: 0.5546\n", - "Epoch 16/50\n", - "99764/99764 [==============================] - 19s 193us/sample - loss: 1.0113 - accuracy: 0.5561\n", - "Epoch 17/50\n", - "99764/99764 [==============================] - 17s 175us/sample - loss: 1.0093 - accuracy: 0.5580\n", - "Epoch 18/50\n", - "99764/99764 [==============================] - 18s 176us/sample - loss: 1.0075 - accuracy: 0.5585\n", - "Epoch 19/50\n", - "99764/99764 [==============================] - 17s 169us/sample - loss: 1.0057 - accuracy: 0.5600\n", - "Epoch 20/50\n", - "99764/99764 [==============================] - 17s 165us/sample - loss: 1.0043 - accuracy: 0.5602\n", - "Epoch 21/50\n", - "99764/99764 [==============================] - 17s 175us/sample - loss: 1.0026 - accuracy: 0.5616\n", - "Epoch 22/50\n", - "99764/99764 [==============================] - 17s 167us/sample - loss: 1.0009 - accuracy: 0.5628\n", - "Epoch 23/50\n", - "99764/99764 [==============================] - 19s 187us/sample - loss: 0.9988 - accuracy: 0.5646\n", - "Epoch 24/50\n", - "99764/99764 [==============================] - 20s 198us/sample - loss: 0.9977 - accuracy: 0.5639\n", - "Epoch 25/50\n", - "99764/99764 [==============================] - 19s 187us/sample - loss: 0.9958 - accuracy: 0.5626\n", - "Epoch 26/50\n", - "99764/99764 [==============================] - 18s 184us/sample - loss: 0.9951 - accuracy: 0.5645\n", - "Epoch 27/50\n", - "99764/99764 [==============================] - 15s 155us/sample - loss: 0.9935 - accuracy: 0.5658\n", - "Epoch 28/50\n", - "99764/99764 [==============================] - 15s 152us/sample - loss: 0.9925 - accuracy: 0.5662\n", - "Epoch 29/50\n", - "99764/99764 [==============================] - 16s 156us/sample - loss: 0.9915 - accuracy: 0.5668\n", - "Epoch 30/50\n", - "99764/99764 [==============================] - 16s 164us/sample - loss: 0.9893 - accuracy: 0.5666\n", - "Epoch 31/50\n", - "99764/99764 [==============================] - 17s 170us/sample - loss: 0.9885 - accuracy: 0.5684\n", - "Epoch 32/50\n", - "99764/99764 [==============================] - 19s 192us/sample - loss: 0.9875 - accuracy: 0.5681\n", - "Epoch 33/50\n", - "99764/99764 [==============================] - 16s 159us/sample - loss: 0.9874 - accuracy: 0.5701\n", - "Epoch 34/50\n", - "99764/99764 [==============================] - 15s 151us/sample - loss: 0.9861 - accuracy: 0.5702\n", - "Epoch 35/50\n", - "99764/99764 [==============================] - 15s 154us/sample - loss: 0.9854 - accuracy: 0.5703\n", - "Epoch 36/50\n", - "99764/99764 [==============================] - 15s 154us/sample - loss: 0.9847 - accuracy: 0.5705\n", - "Epoch 37/50\n", - "99764/99764 [==============================] - 16s 162us/sample - loss: 0.9841 - accuracy: 0.5709\n", - "Epoch 38/50\n", - "99764/99764 [==============================] - 18s 178us/sample - loss: 0.9834 - accuracy: 0.5710\n", - "Epoch 39/50\n", - "99764/99764 [==============================] - 17s 175us/sample - loss: 0.9825 - accuracy: 0.5735\n", - "Epoch 40/50\n", - "99764/99764 [==============================] - 17s 173us/sample - loss: 0.9814 - accuracy: 0.5732\n", - "Epoch 41/50\n", - "99764/99764 [==============================] - 18s 179us/sample - loss: 0.9810 - accuracy: 0.5731\n", - "Epoch 42/50\n", - "99764/99764 [==============================] - 15s 151us/sample - loss: 0.9802 - accuracy: 0.5730\n", - "Epoch 43/50\n", - "99764/99764 [==============================] - 17s 167us/sample - loss: 0.9802 - accuracy: 0.5724\n", - "Epoch 44/50\n", - "99764/99764 [==============================] - 25s 251us/sample - loss: 0.9795 - accuracy: 0.5737\n", - "Epoch 45/50\n", - "99764/99764 [==============================] - 18s 185us/sample - loss: 0.9794 - accuracy: 0.5727\n", - "Epoch 46/50\n", - "99764/99764 [==============================] - 24s 245us/sample - loss: 0.9775 - accuracy: 0.5748\n", - "Epoch 47/50\n", - "99764/99764 [==============================] - 22s 218us/sample - loss: 0.9780 - accuracy: 0.5735\n", - "Epoch 48/50\n", - "99764/99764 [==============================] - 18s 183us/sample - loss: 0.9775 - accuracy: 0.5738\n", - "Epoch 49/50\n", - "99764/99764 [==============================] - 19s 195us/sample - loss: 0.9773 - accuracy: 0.5742\n", - "Epoch 50/50\n", - "99764/99764 [==============================] - 17s 169us/sample - loss: 0.9764 - accuracy: 0.5750\n", + "\n", + "Loading existing model\n", "Training loss: 0.9764\n", "\n", - "98876/98876 [==============================] - 5s 46us/sample - loss: 1.0281 - accuracy: 0.5599\n", + "98876/98876 [==============================] - 4s 43us/sample - loss: 1.0281 - accuracy: 0.5599\n", "Test accuracy: 55.99\n", - "Test loss: 1.0281\n", - "Saving model at: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_top20_bins_oversampled\n", - "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_2000_queries_top20_bins_oversampled\\assets\n" + "Test loss: 1.0281\n" ] } ], @@ -4076,7 +3871,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 172, "metadata": {}, "outputs": [], "source": [ @@ -4350,7 +4145,7 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 205, "metadata": {}, "outputs": [], "source": [ @@ -4362,8 +4157,8 @@ "ideal_case_top20 = []\n", "test_bins_nn_top20_overs = []\n", "\n", - "pred_thresh = 0.8\n", - "mass_sim_thresh = 0.95\n", + "pred_thresh = 0.6\n", + "mass_sim_thresh = 0.000705\n", "restr_nn = False # do mass+spec2vec restricitons on nn models\n", "highest_pred = False # take highest prediction instead of first prediction over threshold\n", "highest_class = True # take highest predicted class instead of first prediction over threshold\n", @@ -4473,12 +4268,12 @@ }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 206, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -4488,16 +4283,19 @@ } ], "source": [ + "from math import log\n", "plt.style.use('ggplot')\n", + "da_diff = log(mass_sim_thresh, 0.93)\n", + "\n", "if restr_nn:\n", - " ylabels = ['mass + mod cosine + min match 6',\n", - " 'mass + Spec2Vec',\n", - " 'mass + Spec2Vec + NN tanimoto >= {}'.format(pred_thresh),\n", + " ylabels = ['mass <= {:.1f}Da + mod cosine + min match 6'.format(da_diff),\n", + " 'mass <= {:.1f}Da + Spec2Vec'.format(da_diff),\n", + " 'mass <= {:.1f}Da + Spec2Vec + NN tanimoto >= {}'.format(da_diff, pred_thresh),\n", " 'ideal case Spec2Vec>0.4',\n", - " 'mass + Spec2Vec + NN bins exclude {}'.format(', '.join(bins_exclude))]\n", + " 'mass + Spec2Vec + NN bins exclude {}'.format(', '.join(map(str,bins_exclude)))]\n", "else:\n", - " ylabels = ['mass + mod cosine + min match 6',\n", - " 'mass + Spec2Vec',\n", + " ylabels = ['mass <= {:.1f}Da + mod cosine + min match 6'.format(da_diff),\n", + " 'mass <= {:.1f}Da + Spec2Vec'.format(da_diff),\n", " 'NN tanimoto >= {}'.format(pred_thresh),\n", " 'ideal case Spec2Vec>0.4',\n", " 'NN bins exclude {}'.format(', '.join(map(str,bins_exclude)))]\n", @@ -4537,7 +4335,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PCA?" + "## PCA" ] }, { @@ -4753,6 +4551,6772 @@ "ax.legend(targets)\n", "ax.grid()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Include more s2v scores in deep learning ensemble model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load models" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sys.path.insert(0, \"C:\\\\Users\\\\joris\\\\Documents\\\\eScience_data\\\\spec2vec_gnps_data_analysis\\\\custom_functions\")\n", + "from library_search import library_matching\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\joris\\Documents\\eScience_data\\data\\gnps_positive_ionmode_cleaned_by_matchms_and_lookups.pickle\n", + "number of spectra: 112956\n" + ] + } + ], + "source": [ + "from matchms.importing import load_from_json\n", + "outfile = os.path.join(path_data, 'gnps_positive_ionmode_cleaned_by_matchms_and_lookups.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " spectrums = pickle.load(inf)\n", + "else:\n", + " filename = os.path.join(path_data,'gnps_positive_ionmode_cleaned_by_matchms_and_lookups.json')\n", + " spectrums = load_from_json(filename)\n", + " with open(outfile, 'wb') as outf:\n", + " pickle.dump(spectrums, outf)\n", + "\n", + "print(\"number of spectra:\", len(spectrums))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "95320 remaining spectra.\n" + ] + } + ], + "source": [ + "from matchms.filtering import normalize_intensities\n", + "from matchms.filtering import require_minimum_number_of_peaks\n", + "from matchms.filtering import select_by_mz\n", + "from matchms.filtering import select_by_relative_intensity\n", + "from matchms.filtering import reduce_to_number_of_peaks\n", + "from matchms.filtering import add_losses\n", + "\n", + "def post_process_s2v(s):\n", + " s = normalize_intensities(s)\n", + " s = select_by_mz(s, mz_from=0, mz_to=1000)\n", + " s = require_minimum_number_of_peaks(s, n_required=10)\n", + " s = reduce_to_number_of_peaks(s, n_required=10, ratio_desired=0.5)\n", + " if s is None:\n", + " return None\n", + " s_remove_low_peaks = select_by_relative_intensity(s, intensity_from=0.001)\n", + " if len(s_remove_low_peaks.peaks) >= 10:\n", + " s = s_remove_low_peaks\n", + " \n", + " s = add_losses(s, loss_mz_from=5.0, loss_mz_to=200.0)\n", + " return s\n", + "\n", + "# apply post processing steps to the data\n", + "spectrums_s2v = [post_process_s2v(s) for s in spectrums]\n", + "\n", + "# omit spectrums that didn't qualify for analysis\n", + "spectrums_s2v = [s for s in spectrums_s2v if s is not None]\n", + "\n", + "print(\"{} remaining spectra.\".format(len(spectrums_s2v)))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "del(spectrums)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4000" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "old_unique_spec_ids = [spec._obj.get(\"spectrumid\") for spec in old_and_unique_documents_query_s2v]\n", + "new_unique2_spec_ids = [spec._obj.get(\"spectrumid\") for spec in new_and_unique2_documents_query_s2v]\n", + "used_spec_ids = set(old_unique_spec_ids + new_unique2_spec_ids)\n", + "len(used_spec_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(False, True)" + ] + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spectrums_s2v[0].get(\"spectrumid\") in used_spec_ids, 'CCMSLIB00003128874' in used_spec_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from spec2vec import SpectrumDocument\n", + "\n", + "old_and_unique_documents_library_s2v_1dec = []\n", + "old_and_unique_documents_query_s2v_1dec = []\n", + "old_and_unique_documents_library_s2v_2dec = []\n", + "old_and_unique_documents_query_s2v_2dec = []\n", + "old_and_unique_documents_library_s2v_3dec = []\n", + "old_and_unique_documents_query_s2v_3dec = []\n", + "\n", + "new_and_unique2_documents_library_s2v_1dec = []\n", + "new_and_unique2_documents_query_s2v_1dec = []\n", + "new_and_unique2_documents_library_s2v_2dec = []\n", + "new_and_unique2_documents_query_s2v_2dec = []\n", + "new_and_unique2_documents_library_s2v_3dec = []\n", + "new_and_unique2_documents_query_s2v_3dec = []\n", + "\n", + "for spec in spectrums_s2v:\n", + " spec_id = spec.get(\"spectrumid\")\n", + "\n", + " if spec_id in old_unique_spec_ids:\n", + " old_and_unique_documents_query_s2v_1dec.append(SpectrumDocument(spec, n_decimals=1))\n", + " old_and_unique_documents_query_s2v_2dec.append(SpectrumDocument(spec, n_decimals=2))\n", + " old_and_unique_documents_query_s2v_3dec.append(SpectrumDocument(spec, n_decimals=3))\n", + " else:\n", + " old_and_unique_documents_library_s2v_1dec.append(SpectrumDocument(spec, n_decimals=1))\n", + " old_and_unique_documents_library_s2v_2dec.append(SpectrumDocument(spec, n_decimals=2))\n", + " old_and_unique_documents_library_s2v_3dec.append(SpectrumDocument(spec, n_decimals=3))\n", + " \n", + " if spec_id in new_unique2_spec_ids:\n", + " new_and_unique2_documents_query_s2v_1dec.append(SpectrumDocument(spec, n_decimals=1))\n", + " new_and_unique2_documents_query_s2v_2dec.append(SpectrumDocument(spec, n_decimals=2))\n", + " new_and_unique2_documents_query_s2v_3dec.append(SpectrumDocument(spec, n_decimals=3))\n", + " else:\n", + " new_and_unique2_documents_library_s2v_1dec.append(SpectrumDocument(spec, n_decimals=1))\n", + " new_and_unique2_documents_library_s2v_2dec.append(SpectrumDocument(spec, n_decimals=2))\n", + " new_and_unique2_documents_library_s2v_3dec.append(SpectrumDocument(spec, n_decimals=3))" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "ename": "MemoryError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_list_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0moutf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mold_and_unique_documents_library_s2v_1dec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mMemoryError\u001b[0m: " + ] + } + ], + "source": [ + "out_list = [old_and_unique_documents_library_s2v_1dec,\n", + " old_and_unique_documents_query_s2v_1dec,\n", + " old_and_unique_documents_library_s2v_2dec,\n", + " old_and_unique_documents_query_s2v_2dec,\n", + " old_and_unique_documents_library_s2v_3dec,\n", + " old_and_unique_documents_query_s2v_3dec,\n", + " new_and_unique2_documents_library_s2v_1dec,\n", + " new_and_unique2_documents_query_s2v_1dec,\n", + " new_and_unique2_documents_library_s2v_2dec,\n", + " new_and_unique2_documents_query_s2v_2dec,\n", + " new_and_unique2_documents_library_s2v_3dec,\n", + " new_and_unique2_documents_query_s2v_3dec]\n", + "out_list_file = os.path.join(path_data, 'old_library_1dec')\n", + "\n", + "with open(out_list_file, 'wb') as outf:\n", + " pickle.dump(old_and_unique_documents_library_s2v_1dec, outf)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Word2Vec(vocab=11787, size=300, alpha=0.025)\n", + "C:\\Users\\joris\\Documents\\eScience_data\\data\\old_and_unique_found_matches_s2v_1dec.pickle\n", + "Pre-selection includes spec2vec top 200.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.96%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "C:\\Users\\joris\\Documents\\eScience_data\\data\\new_and_unique2_found_matches_s2v_1dec.pickle\n", + "Pre-selection includes spec2vec top 200.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.96%.\n" + ] + } + ], + "source": [ + "#load models\n", + "path_models = os.path.join(path_data, \"trained_models\")\n", + "model_file1 = os.path.join(path_models, \"spec2vec_library_testing_4000removed_1dec.model\")\n", + "model1 = gensim.models.Word2Vec.load(model_file1)\n", + "print(model1)\n", + "\n", + "import pickle\n", + "outfile = os.path.join(path_data, 'old_and_unique_found_matches_s2v_1dec.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " old_and_unique_found_matches_s2v_1dec = pickle.load(inf)\n", + "else:\n", + " old_and_unique_found_matches_s2v_1dec = library_matching(old_and_unique_documents_query_s2v_1dec,\n", + " old_and_unique_documents_library_s2v_1dec,\n", + " model1,\n", + " presearch_based_on=[\"spec2vec-top200\"],\n", + " ignore_non_annotated=True,\n", + " intensity_weighting_power=0.5,\n", + " allowed_missing_percentage=100,\n", + " cosine_tol=0.005,\n", + " mass_tolerance=1.0)\n", + " with open(outfile, 'wb') as outf:\n", + " pickle.dump(old_and_unique_found_matches_s2v_1dec, outf)\n", + "\n", + "outfile = os.path.join(path_data, 'new_and_unique2_found_matches_s2v_1dec.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " new_and_unique2_found_matches_s2v_1dec = pickle.load(inf)\n", + "else:\n", + " new_and_unique2_found_matches_s2v_1dec = library_matching(new_and_unique2_documents_query_s2v_1dec,\n", + " new_and_unique2_documents_library_s2v_1dec,\n", + " model1,\n", + " presearch_based_on=[\"spec2vec-top200\"],\n", + " ignore_non_annotated=True,\n", + " intensity_weighting_power=0.5,\n", + " allowed_missing_percentage=100,\n", + " cosine_tol=0.005,\n", + " mass_tolerance=1.0)\n", + " with open(outfile, 'wb') as outf:\n", + " pickle.dump(new_and_unique2_found_matches_s2v_1dec, outf)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Word2Vec(vocab=115877, size=300, alpha=0.025)\n", + "C:\\Users\\joris\\Documents\\eScience_data\\data\\old_and_unique_found_matches_s2v_2dec.pickle\n", + "Pre-selection includes spec2vec top 200.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 8.16%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 21.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "C:\\Users\\joris\\Documents\\eScience_data\\data\\new_and_unique2_found_matches_s2v_2dec.pickle\n", + "Pre-selection includes spec2vec top 200.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 8.16%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 21.90%.\n" + ] + } + ], + "source": [ + "#load models\n", + "path_models = os.path.join(path_data, \"trained_models\")\n", + "model_file2 = os.path.join(path_models, \"spec2vec_library_testing_4000removed_2dec.model\")\n", + "model2 = gensim.models.Word2Vec.load(model_file2)\n", + "print(model2)\n", + "\n", + "import pickle\n", + "outfile = os.path.join(path_data, 'old_and_unique_found_matches_s2v_2dec.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " old_and_unique_found_matches_s2v_2dec = pickle.load(inf)\n", + "else:\n", + " old_and_unique_found_matches_s2v_2dec = library_matching(old_and_unique_documents_query_s2v_2dec,\n", + " old_and_unique_documents_library_s2v_2dec,\n", + " model2,\n", + " presearch_based_on=[\"spec2vec-top200\"],\n", + " ignore_non_annotated=True,\n", + " intensity_weighting_power=0.5,\n", + " allowed_missing_percentage=100,\n", + " cosine_tol=0.005,\n", + " mass_tolerance=1.0)\n", + " with open(outfile, 'wb') as outf:\n", + " pickle.dump(old_and_unique_found_matches_s2v_2dec, outf)\n", + "\n", + "outfile = os.path.join(path_data, 'new_and_unique2_found_matches_s2v_2dec.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " new_and_unique2_found_matches_s2v_2dec = pickle.load(inf)\n", + "else:\n", + " new_and_unique2_found_matches_s2v_2dec = library_matching(new_and_unique2_documents_query_s2v_2dec,\n", + " new_and_unique2_documents_library_s2v_2dec,\n", + " model2,\n", + " presearch_based_on=[\"spec2vec-top200\"],\n", + " ignore_non_annotated=True,\n", + " intensity_weighting_power=0.5,\n", + " allowed_missing_percentage=100,\n", + " cosine_tol=0.005,\n", + " mass_tolerance=1.0)\n", + " with open(outfile, 'wb') as outf:\n", + " pickle.dump(new_and_unique2_found_matches_s2v_2dec, outf)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Word2Vec(vocab=987631, size=300, alpha=0.025)\n", + "C:\\Users\\joris\\Documents\\eScience_data\\data\\old_and_unique_found_matches_s2v_3dec.pickle\n", + "Pre-selection includes spec2vec top 200.\n", + "Found 21 word(s) missing in the model. Weighted fraction not covered is 8.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 15.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.49%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 63 word(s) missing in the model. Weighted fraction not covered is 11.47%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.83%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 36.55%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 10.45%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 60 word(s) missing in the model. Weighted fraction not covered is 14.40%.\n", + "Found 98 word(s) missing in the model. Weighted fraction not covered is 22.36%.\n", + "Found 60 word(s) missing in the model. Weighted fraction not covered is 10.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 2.71%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 13.02%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 80 word(s) missing in the model. Weighted fraction not covered is 16.49%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.80%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.63%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 86 word(s) missing in the model. Weighted fraction not covered is 9.06%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 6.22%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.55%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 6.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 3.52%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.66%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 24 word(s) missing in the model. Weighted fraction not covered is 5.20%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.16%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 4.09%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 2.99%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 2.47%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.66%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 5.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 5.26%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 39 word(s) missing in the model. Weighted fraction not covered is 11.51%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 15.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 45 word(s) missing in the model. Weighted fraction not covered is 8.49%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 6.05%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 7.54%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 5.49%.\n", + "Found 36 word(s) missing in the model. Weighted fraction not covered is 10.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.43%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 7.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.22%.\n", + "Found 49 word(s) missing in the model. Weighted fraction not covered is 5.24%.\n", + "Found 38 word(s) missing in the model. Weighted fraction not covered is 5.46%.\n", + "Found 46 word(s) missing in the model. Weighted fraction not covered is 5.61%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 6.35%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.43%.\n", + "Found 34 word(s) missing in the model. Weighted fraction not covered is 12.29%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.77%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.64%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.38%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 2.82%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 10.25%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.11%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.71%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.45%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.11%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 4.93%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 16.22%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 6.98%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 48 word(s) missing in the model. Weighted fraction not covered is 8.68%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.87%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.12%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.87%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 14.73%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.98%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.14%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.16%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.26%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.69%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 18.21%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.43%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.31%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.94%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.83%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.11%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.57%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.13%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.29%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.09%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.58%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.11%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 39 word(s) missing in the model. Weighted fraction not covered is 6.36%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.74%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.49%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.79%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.88%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.88%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.68%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.84%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.90%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.08%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.98%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.70%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 5.18%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.78%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 3.00%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.34%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.43%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 4.13%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.08%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.06%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.36%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.56%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.98%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.81%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.04%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.98%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.70%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.15%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.59%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.10%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.80%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.20%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 6.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.60%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 3.70%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.11%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.04%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 9.12%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 5.37%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 21 word(s) missing in the model. Weighted fraction not covered is 6.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.69%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 26 word(s) missing in the model. Weighted fraction not covered is 5.96%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 22 word(s) missing in the model. Weighted fraction not covered is 5.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 10.67%.\n", + "Found 25 word(s) missing in the model. Weighted fraction not covered is 3.60%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 26.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.39%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.07%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 4.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 17.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.62%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.89%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 6.96%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.39%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.76%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.55%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.89%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.77%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.49%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.94%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 4 word(s) missing in the model. Weighted fraction not covered is 4.49%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 4.90%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.04%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.86%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.08%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 2.05%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 5.47%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.25%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.07%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.57%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.64%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.08%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.91%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.76%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 3.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 9.03%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.43%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.31%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.43%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.57%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.25%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.19%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 3.03%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.36%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 4.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.87%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 7.15%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.55%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.14%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.69%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.99%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.81%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.84%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.01%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 4.90%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 5.24%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.63%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.93%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.96%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.78%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.98%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.88%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.21%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.35%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 8.68%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.63%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 5.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.80%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 27 word(s) missing in the model. Weighted fraction not covered is 7.19%.\n", + "Found 26 word(s) missing in the model. Weighted fraction not covered is 5.16%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 10.71%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 5.84%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.28%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 8.45%.\n", + "Found 38 word(s) missing in the model. Weighted fraction not covered is 4.81%.\n", + "Found 43 word(s) missing in the model. Weighted fraction not covered is 6.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.95%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 6.95%.\n", + "Found 53 word(s) missing in the model. Weighted fraction not covered is 9.72%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 7.15%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.71%.\n", + "Found 59 word(s) missing in the model. Weighted fraction not covered is 8.95%.\n", + "Found 32 word(s) missing in the model. Weighted fraction not covered is 4.73%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.59%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.98%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 10.63%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 7.10%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.34%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.66%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 9.04%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.69%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 2.32%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 8.33%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 5.31%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.20%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 5.02%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.40%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 4.23%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 9.99%.\n", + "Found 26 word(s) missing in the model. Weighted fraction not covered is 4.04%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.04%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 4.63%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 4.57%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.43%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.17%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 12.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.46%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 10.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 6.76%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 6.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.42%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 8.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.63%.\n", + "Found 28 word(s) missing in the model. Weighted fraction not covered is 4.49%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.69%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 4.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.48%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 8.78%.\n", + "Found 72 word(s) missing in the model. Weighted fraction not covered is 7.43%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 6.43%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 9.60%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.00%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.21%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.01%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.69%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 8.71%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 5.52%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.86%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.62%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.91%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.37%.\n", + "Found 37 word(s) missing in the model. Weighted fraction not covered is 9.93%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 13.43%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.88%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.13%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.95%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 10.38%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 10.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.98%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 7.65%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 11.31%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.70%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.89%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.22%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 6.13%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 11.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.33%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.98%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 11.94%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 12.56%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.30%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.36%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.95%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.09%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.78%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.76%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 4.06%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.74%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.16%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 2.57%.\n", + "Found 66 word(s) missing in the model. Weighted fraction not covered is 10.75%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.29%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.22%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.56%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 3.56%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.87%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 9.29%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.87%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 7.67%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.89%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 14.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.73%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 12.71%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.31%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.83%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.92%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.07%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.11%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 4.10%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.67%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.91%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.50%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.15%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 13.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.01%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 2.63%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.07%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 4.09%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.83%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 1.26%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 7.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.09%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.88%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 5.25%.\n", + "Found 88 word(s) missing in the model. Weighted fraction not covered is 17.85%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 28 word(s) missing in the model. Weighted fraction not covered is 10.70%.\n", + "Found 52 word(s) missing in the model. Weighted fraction not covered is 14.96%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 133 word(s) missing in the model. Weighted fraction not covered is 20.49%.\n", + "Found 117 word(s) missing in the model. Weighted fraction not covered is 17.79%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.99%.\n", + "Found 112 word(s) missing in the model. Weighted fraction not covered is 19.56%.\n", + "Found 54 word(s) missing in the model. Weighted fraction not covered is 13.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.71%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 29 word(s) missing in the model. Weighted fraction not covered is 9.61%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 8.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 28.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.35%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 28.33%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.13%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 7.66%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 18.87%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.51%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 17.10%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 11.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 16.73%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 8.28%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 22.89%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 30 word(s) missing in the model. Weighted fraction not covered is 11.67%.\n", + "Found 49 word(s) missing in the model. Weighted fraction not covered is 13.16%.\n", + "Found 55 word(s) missing in the model. Weighted fraction not covered is 28.02%.\n", + "Found 66 word(s) missing in the model. Weighted fraction not covered is 23.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.04%.\n", + "Found 24 word(s) missing in the model. Weighted fraction not covered is 4.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 25 word(s) missing in the model. Weighted fraction not covered is 9.36%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.97%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 2.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.07%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.90%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 10.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.98%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.00%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 6.07%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 7.65%.\n", + "Found 98 word(s) missing in the model. Weighted fraction not covered is 19.12%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.98%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 22 word(s) missing in the model. Weighted fraction not covered is 4.62%.\n", + "Found 62 word(s) missing in the model. Weighted fraction not covered is 12.16%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.61%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 21.32%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 11.65%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 35 word(s) missing in the model. Weighted fraction not covered is 24.36%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.07%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 2.68%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 4.50%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.27%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 5.83%.\n", + "Found 29 word(s) missing in the model. Weighted fraction not covered is 21.31%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 7.37%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.89%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 8.52%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.26%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 11.44%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.03%.\n", + "Found 40 word(s) missing in the model. Weighted fraction not covered is 33.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 46 word(s) missing in the model. Weighted fraction not covered is 9.48%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 33 word(s) missing in the model. Weighted fraction not covered is 6.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 9.91%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 4.49%.\n", + "Found 26 word(s) missing in the model. Weighted fraction not covered is 6.40%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 17.94%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 54 word(s) missing in the model. Weighted fraction not covered is 6.52%.\n", + "Found 72 word(s) missing in the model. Weighted fraction not covered is 8.17%.\n", + "Found 74 word(s) missing in the model. Weighted fraction not covered is 7.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.89%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.35%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.08%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 24 word(s) missing in the model. Weighted fraction not covered is 3.73%.\n", + "Found 29 word(s) missing in the model. Weighted fraction not covered is 5.89%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 3.93%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.32%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.03%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 7.08%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 7.40%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.54%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 5.34%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.84%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 10.50%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.30%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.76%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 6.63%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.59%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.32%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.10%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.93%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.25%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.47%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.45%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.83%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 2.74%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.71%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 6.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.56%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.80%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 3.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.50%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 3.02%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.80%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.02%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.22%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.92%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.57%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.73%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 5.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.43%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.60%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.80%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.87%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.87%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.47%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 4.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.78%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.47%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.74%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.93%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.15%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.36%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.08%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.08%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.59%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.47%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 4.16%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 6.54%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.88%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.60%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 3.85%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.94%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.95%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 6.81%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 1.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 89 word(s) missing in the model. Weighted fraction not covered is 13.61%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 3.36%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.89%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.26%.\n", + "Found 31 word(s) missing in the model. Weighted fraction not covered is 3.90%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 16.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 7.10%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.84%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 6.81%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 1.58%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.98%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.74%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 6.00%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.95%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 9.37%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 13 word(s) missing in the model. Weighted fraction not covered is 2.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.22%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 1.92%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 5.36%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 7.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.22%.\n", + "Found 34 word(s) missing in the model. Weighted fraction not covered is 13.30%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 7.04%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 14.06%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 6.58%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 5.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 5.44%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.19%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.47%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 5.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 4.93%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.68%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.57%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 8.66%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.25%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 4.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.99%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 6.10%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.20%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.93%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 4.60%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.06%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.94%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.92%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.26%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.96%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.73%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 3.81%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 4.11%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.44%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.08%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.84%.\n", + "Found 63 word(s) missing in the model. Weighted fraction not covered is 13.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.15%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.00%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.76%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.90%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 4.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.55%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.00%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.36%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 4.06%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.79%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.45%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 8.66%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.80%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.88%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 19.17%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 11.08%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 2.93%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 1.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.71%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 6.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.70%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.31%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.47%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.11%.\n", + "Found 27 word(s) missing in the model. Weighted fraction not covered is 3.40%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 6.12%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 5.08%.\n", + "Found 36 word(s) missing in the model. Weighted fraction not covered is 7.85%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 9.41%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 7.19%.\n", + "Found 41 word(s) missing in the model. Weighted fraction not covered is 6.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 3.23%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 4.04%.\n", + "Found 67 word(s) missing in the model. Weighted fraction not covered is 7.44%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 10.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.47%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.00%.\n", + "Found 24 word(s) missing in the model. Weighted fraction not covered is 7.63%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 3.99%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 7.08%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.07%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.48%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.37%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.50%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.62%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 10.99%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.96%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.59%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.81%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 22.61%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.08%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 11.30%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 8.82%.\n", + "Found 35 word(s) missing in the model. Weighted fraction not covered is 13.42%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 5.76%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 13.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 5.38%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 2.06%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 11.47%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.31%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 16.59%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 10.02%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.83%.\n", + "Found 62 word(s) missing in the model. Weighted fraction not covered is 8.79%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 8.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 14.02%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.37%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.57%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.63%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.91%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.73%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.58%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.80%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 13.57%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 5.77%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 15.79%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.81%.\n", + "Found 32 word(s) missing in the model. Weighted fraction not covered is 12.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.50%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 5.61%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.07%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.92%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.91%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.73%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.98%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 6.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 7.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 15.83%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 47 word(s) missing in the model. Weighted fraction not covered is 9.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.35%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.93%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.44%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 3.51%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 4.33%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 2.79%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 2.86%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.44%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.35%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.75%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.26%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.49%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.90%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 3.88%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.08%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.88%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 3.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 9.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 14.65%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 10.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 9.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.09%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 3.99%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.05%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.80%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.71%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.46%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.43%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.41%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.09%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.47%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 4.58%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.00%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.09%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.03%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 21 word(s) missing in the model. Weighted fraction not covered is 3.07%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.62%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 46 word(s) missing in the model. Weighted fraction not covered is 11.99%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.58%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.35%.\n", + "Found 30 word(s) missing in the model. Weighted fraction not covered is 9.72%.\n", + "Found 90 word(s) missing in the model. Weighted fraction not covered is 17.25%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 2.28%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 4.02%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.26%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 11.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.39%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.85%.\n" + ] + } + ], + "source": [ + "#load models\n", + "path_models = os.path.join(path_data, \"trained_models\")\n", + "model_file3 = os.path.join(path_models, \"spec2vec_library_testing_4000removed_3dec.model\")\n", + "model3 = gensim.models.Word2Vec.load(model_file3)\n", + "print(model3)\n", + "\n", + "import pickle\n", + "outfile = os.path.join(path_data, 'old_and_unique_found_matches_s2v_3dec.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " old_and_unique_found_matches_s2v_3dec = pickle.load(inf)\n", + "else:\n", + " old_and_unique_found_matches_s2v_3dec = library_matching(old_and_unique_documents_query_s2v_3dec,\n", + " old_and_unique_documents_library_s2v_3dec,\n", + " model3,\n", + " presearch_based_on=[\"spec2vec-top200\"],\n", + " ignore_non_annotated=True,\n", + " intensity_weighting_power=0.5,\n", + " allowed_missing_percentage=100,\n", + " cosine_tol=0.005,\n", + " mass_tolerance=1.0)\n", + " with open(outfile, 'wb') as outf:\n", + " pickle.dump(old_and_unique_found_matches_s2v_3dec, outf)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\joris\\Documents\\eScience_data\\data\\new_and_unique2_found_matches_s2v_3dec.pickle\n", + "Pre-selection includes spec2vec top 200.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 7.66%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 18.87%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.51%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 17.10%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 11.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 16.73%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 8.28%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 22.89%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 30 word(s) missing in the model. Weighted fraction not covered is 11.67%.\n", + "Found 49 word(s) missing in the model. Weighted fraction not covered is 13.16%.\n", + "Found 55 word(s) missing in the model. Weighted fraction not covered is 28.02%.\n", + "Found 66 word(s) missing in the model. Weighted fraction not covered is 23.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.04%.\n", + "Found 24 word(s) missing in the model. Weighted fraction not covered is 4.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 25 word(s) missing in the model. Weighted fraction not covered is 9.36%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.97%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 2.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.07%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.90%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 10.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.98%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.00%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 6.07%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 7.65%.\n", + "Found 98 word(s) missing in the model. Weighted fraction not covered is 19.12%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.98%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 22 word(s) missing in the model. Weighted fraction not covered is 4.62%.\n", + "Found 62 word(s) missing in the model. Weighted fraction not covered is 12.16%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.61%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 21.32%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 11.65%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 35 word(s) missing in the model. Weighted fraction not covered is 24.36%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.07%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 2.68%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 4.50%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.27%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 5.83%.\n", + "Found 29 word(s) missing in the model. Weighted fraction not covered is 21.31%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 7.37%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.89%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 8.52%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.26%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 11.44%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.03%.\n", + "Found 40 word(s) missing in the model. Weighted fraction not covered is 33.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 46 word(s) missing in the model. Weighted fraction not covered is 9.48%.\n", + "Found 33 word(s) missing in the model. Weighted fraction not covered is 6.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 9.91%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 4.49%.\n", + "Found 26 word(s) missing in the model. Weighted fraction not covered is 6.40%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 17.94%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 54 word(s) missing in the model. Weighted fraction not covered is 6.52%.\n", + "Found 72 word(s) missing in the model. Weighted fraction not covered is 8.17%.\n", + "Found 74 word(s) missing in the model. Weighted fraction not covered is 7.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.31%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.89%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.35%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.08%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 24 word(s) missing in the model. Weighted fraction not covered is 3.73%.\n", + "Found 29 word(s) missing in the model. Weighted fraction not covered is 5.89%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 3.93%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.32%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.03%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 7.08%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 7.40%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.54%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 5.34%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.84%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 10.50%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.30%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.76%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 6.63%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.59%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.32%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.10%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.93%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.25%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.47%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.45%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.83%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 2.74%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.71%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 6.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.56%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.80%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 3.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.50%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 3.02%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.80%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.02%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.22%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.92%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.57%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.73%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 5.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.43%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.60%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.80%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.87%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.87%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.47%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 4.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.78%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.47%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.74%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.93%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.15%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.36%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.08%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.08%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.59%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.47%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 4.16%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 6.54%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.88%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.60%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 3.85%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.94%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.95%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 6.81%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 1.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 89 word(s) missing in the model. Weighted fraction not covered is 13.61%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 3.36%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.89%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.26%.\n", + "Found 31 word(s) missing in the model. Weighted fraction not covered is 3.90%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 16.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 7.10%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.84%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 6.81%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 1.58%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.98%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.74%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 6.00%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.95%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 9.37%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 2.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.22%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 1.92%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 5.36%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 7.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.22%.\n", + "Found 34 word(s) missing in the model. Weighted fraction not covered is 13.30%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 7.04%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 14.06%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 6.58%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 5.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 5.44%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.19%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.47%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 5.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 4.93%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.68%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.57%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 8.66%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.25%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 4.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.99%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 6.10%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.20%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.93%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 4.60%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.06%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.94%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.92%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.26%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.96%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.73%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 3.81%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 4.11%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.44%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.08%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.84%.\n", + "Found 63 word(s) missing in the model. Weighted fraction not covered is 13.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.15%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.00%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.76%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.90%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 4.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.55%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.00%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.36%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 4.06%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.79%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.45%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 8.66%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.80%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.88%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 19.17%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 11.08%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 2.93%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 1.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.71%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 6.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.70%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.31%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.47%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.11%.\n", + "Found 27 word(s) missing in the model. Weighted fraction not covered is 3.40%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 6.12%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 5.08%.\n", + "Found 36 word(s) missing in the model. Weighted fraction not covered is 7.85%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 9.41%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 7.19%.\n", + "Found 41 word(s) missing in the model. Weighted fraction not covered is 6.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 3.23%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 4.04%.\n", + "Found 67 word(s) missing in the model. Weighted fraction not covered is 7.44%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 10.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.47%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.00%.\n", + "Found 24 word(s) missing in the model. Weighted fraction not covered is 7.63%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 3.99%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 7.08%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.07%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.48%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.37%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.50%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.62%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 10.99%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.96%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.59%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.81%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 22.61%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.08%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 11.30%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 8.82%.\n", + "Found 35 word(s) missing in the model. Weighted fraction not covered is 13.42%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 5.76%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 13.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 5.38%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 2.06%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 11.47%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.31%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 16.59%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 10.02%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.83%.\n", + "Found 62 word(s) missing in the model. Weighted fraction not covered is 8.79%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 8.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 14.02%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.37%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.57%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.63%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.91%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.73%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.58%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.80%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 13.57%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 5.77%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5 word(s) missing in the model. Weighted fraction not covered is 15.79%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.81%.\n", + "Found 32 word(s) missing in the model. Weighted fraction not covered is 12.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.50%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 5.61%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.07%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.92%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.91%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.73%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.98%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 6.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 7.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 15.83%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 47 word(s) missing in the model. Weighted fraction not covered is 9.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.35%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.93%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.44%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 3.51%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 4.33%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 2.79%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 2.86%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.44%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.35%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.75%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.26%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.49%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.90%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 3.88%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.08%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.88%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 3.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 9.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 14.65%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 10.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 9.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.09%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 3.99%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.05%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.80%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.71%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.46%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.43%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.41%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 0.68%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.09%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.47%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 4.58%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.00%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.09%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.03%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 21 word(s) missing in the model. Weighted fraction not covered is 3.07%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 46 word(s) missing in the model. Weighted fraction not covered is 11.99%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.58%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.35%.\n", + "Found 30 word(s) missing in the model. Weighted fraction not covered is 9.72%.\n", + "Found 90 word(s) missing in the model. Weighted fraction not covered is 17.25%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 2.28%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 4.02%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.26%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 11.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.39%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.85%.\n", + "Found 21 word(s) missing in the model. Weighted fraction not covered is 8.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 15.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.49%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 63 word(s) missing in the model. Weighted fraction not covered is 11.47%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.83%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 36.55%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 10.45%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 60 word(s) missing in the model. Weighted fraction not covered is 14.40%.\n", + "Found 98 word(s) missing in the model. Weighted fraction not covered is 22.36%.\n", + "Found 60 word(s) missing in the model. Weighted fraction not covered is 10.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 2.71%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 13.02%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 80 word(s) missing in the model. Weighted fraction not covered is 16.49%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.80%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.63%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 86 word(s) missing in the model. Weighted fraction not covered is 9.06%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 6.22%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.55%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 6.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 3.52%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.66%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 24 word(s) missing in the model. Weighted fraction not covered is 5.20%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.01%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.16%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 4.09%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 2.99%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 2.47%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.66%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 5.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 5.26%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 39 word(s) missing in the model. Weighted fraction not covered is 11.51%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 15.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 45 word(s) missing in the model. Weighted fraction not covered is 8.49%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 6.05%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 7.54%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 5.49%.\n", + "Found 36 word(s) missing in the model. Weighted fraction not covered is 10.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.43%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7 word(s) missing in the model. Weighted fraction not covered is 7.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.22%.\n", + "Found 49 word(s) missing in the model. Weighted fraction not covered is 5.24%.\n", + "Found 38 word(s) missing in the model. Weighted fraction not covered is 5.46%.\n", + "Found 46 word(s) missing in the model. Weighted fraction not covered is 5.61%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 6.35%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.43%.\n", + "Found 34 word(s) missing in the model. Weighted fraction not covered is 12.29%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.77%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.64%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.38%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 2.82%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 10.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.11%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.26%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.71%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.45%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.11%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 4.93%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 16.22%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 6.98%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 48 word(s) missing in the model. Weighted fraction not covered is 8.68%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.87%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.12%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.87%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 14.73%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.98%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.14%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.16%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.26%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.69%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 18.21%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.43%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.31%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.94%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.83%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.11%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.57%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.13%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.29%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.26%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.09%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.58%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.11%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 39 word(s) missing in the model. Weighted fraction not covered is 6.36%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.74%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.49%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.79%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.88%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.88%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.68%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.84%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.90%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.50%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.08%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.02%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.98%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.70%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 5.18%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.78%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 3.00%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.34%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.43%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 4.13%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.52%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 3.08%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.06%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.37%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.86%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.36%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.56%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.98%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.82%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.81%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.04%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.49%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.98%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.70%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.15%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.46%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.35%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.59%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.83%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.10%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.80%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.20%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 6.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.60%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 3.70%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.11%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.09%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.76%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.56%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.49%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.04%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 9.12%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.04%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 5.37%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.17%.\n", + "Found 21 word(s) missing in the model. Weighted fraction not covered is 6.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.31%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.69%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 26 word(s) missing in the model. Weighted fraction not covered is 5.96%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 22 word(s) missing in the model. Weighted fraction not covered is 5.35%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 10.67%.\n", + "Found 25 word(s) missing in the model. Weighted fraction not covered is 3.60%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 26.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.39%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.07%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 4.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 17.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.62%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.89%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 6.96%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.39%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.76%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.63%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.55%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.89%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.97%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.60%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.77%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.45%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.49%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.94%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 4.49%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 4.90%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.04%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.86%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.08%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 2.05%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 19 word(s) missing in the model. Weighted fraction not covered is 5.47%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.65%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.96%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.38%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.25%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.07%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.57%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.64%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.08%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.91%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.72%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.76%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.85%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.03%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 3.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 9.03%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 2.43%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.31%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.43%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.33%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.06%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 2.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.40%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.57%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.25%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.19%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 3.03%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.36%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.82%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 4.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.87%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 7.15%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.45%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.55%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.14%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.39%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.90%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.69%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.99%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.81%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.48%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.84%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.82%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.01%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 4.90%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 5.24%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.63%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.91%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.93%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.47%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.62%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.96%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.78%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.54%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.98%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.52%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.32%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.88%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.95%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.43%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.21%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.99%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.16%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.35%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 8.68%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.63%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 5.14%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.80%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.48%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 27 word(s) missing in the model. Weighted fraction not covered is 7.19%.\n", + "Found 26 word(s) missing in the model. Weighted fraction not covered is 5.16%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 10.71%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 5.84%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.28%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 8.45%.\n", + "Found 38 word(s) missing in the model. Weighted fraction not covered is 4.81%.\n", + "Found 43 word(s) missing in the model. Weighted fraction not covered is 6.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.95%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.22%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 6.95%.\n", + "Found 53 word(s) missing in the model. Weighted fraction not covered is 9.72%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 7.15%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 3.71%.\n", + "Found 59 word(s) missing in the model. Weighted fraction not covered is 8.95%.\n", + "Found 32 word(s) missing in the model. Weighted fraction not covered is 4.73%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.59%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.98%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.32%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 10.63%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 7.10%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.34%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.79%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.66%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 9.04%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.69%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 2.32%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 8.33%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 5.31%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 5.64%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.20%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 5.02%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.40%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 4.23%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 9.99%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 26 word(s) missing in the model. Weighted fraction not covered is 4.04%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.04%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 4.63%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 4.57%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.43%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.17%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 12.51%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 6.46%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 10.45%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 6.76%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 6.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.42%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 8.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 3.63%.\n", + "Found 28 word(s) missing in the model. Weighted fraction not covered is 4.49%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 3.69%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 4.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.48%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 8.78%.\n", + "Found 72 word(s) missing in the model. Weighted fraction not covered is 7.43%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 6.43%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 9.60%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.00%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.21%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.31%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.01%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.69%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 8.71%.\n", + "Found 23 word(s) missing in the model. Weighted fraction not covered is 5.52%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.90%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.57%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.86%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.62%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.91%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 4.37%.\n", + "Found 37 word(s) missing in the model. Weighted fraction not covered is 9.93%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 13.43%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.88%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.13%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.95%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 3.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 17 word(s) missing in the model. Weighted fraction not covered is 10.38%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 10.69%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.61%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.98%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.65%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 3.67%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 7.65%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 11.31%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.78%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.96%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.70%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.89%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.20%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 2.22%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 14 word(s) missing in the model. Weighted fraction not covered is 6.13%.\n", + "Found 18 word(s) missing in the model. Weighted fraction not covered is 11.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.55%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 4.33%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.98%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 11.94%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 12.56%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.07%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.10%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.79%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.00%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.33%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.87%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.19%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.30%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.36%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 2.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.40%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.95%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.09%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.92%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.78%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.76%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 4.06%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 2.74%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.16%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 2.57%.\n", + "Found 66 word(s) missing in the model. Weighted fraction not covered is 10.75%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 2.29%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 2.22%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.56%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 17 word(s) missing in the model. Weighted fraction not covered is 3.56%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 3.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.11%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.44%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 4.87%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.18%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.29%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 9.29%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 2.87%.\n", + "Found 10 word(s) missing in the model. Weighted fraction not covered is 7.67%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 4.89%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 14.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.73%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 12.71%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 3.31%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.83%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.36%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.53%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.46%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.97%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.92%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 4.07%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.58%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.11%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 16 word(s) missing in the model. Weighted fraction not covered is 4.10%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.60%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 2.67%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.17%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.53%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.19%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.24%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.34%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.23%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.91%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 3.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.75%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.08%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 1.56%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.13%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.41%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.50%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.71%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.25%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.15%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.29%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.15%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 13.28%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.23%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 3.39%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.30%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 2.01%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.13%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.16%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.27%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.21%.\n", + "Found 11 word(s) missing in the model. Weighted fraction not covered is 1.34%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.61%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.10%.\n", + "Found 13 word(s) missing in the model. Weighted fraction not covered is 2.63%.\n", + "Found 9 word(s) missing in the model. Weighted fraction not covered is 2.52%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.07%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.67%.\n", + "Found 15 word(s) missing in the model. Weighted fraction not covered is 4.09%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.72%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.74%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.51%.\n", + "Found 8 word(s) missing in the model. Weighted fraction not covered is 1.83%.\n", + "Found 12 word(s) missing in the model. Weighted fraction not covered is 1.26%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 7 word(s) missing in the model. Weighted fraction not covered is 0.84%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.20%.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.18%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.42%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.39%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.62%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 7.24%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.38%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.44%.\n", + "Found 6 word(s) missing in the model. Weighted fraction not covered is 1.75%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.50%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.51%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 0.69%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.05%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.09%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 0.88%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.73%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.12%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.37%.\n", + "Found 20 word(s) missing in the model. Weighted fraction not covered is 5.25%.\n", + "Found 88 word(s) missing in the model. Weighted fraction not covered is 17.85%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.01%.\n", + "Found 28 word(s) missing in the model. Weighted fraction not covered is 10.70%.\n", + "Found 52 word(s) missing in the model. Weighted fraction not covered is 14.96%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.77%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.81%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.70%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.66%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 0.54%.\n", + "Found 133 word(s) missing in the model. Weighted fraction not covered is 20.49%.\n", + "Found 117 word(s) missing in the model. Weighted fraction not covered is 17.79%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.59%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.64%.\n", + "Found 5 word(s) missing in the model. Weighted fraction not covered is 1.99%.\n", + "Found 112 word(s) missing in the model. Weighted fraction not covered is 19.56%.\n", + "Found 54 word(s) missing in the model. Weighted fraction not covered is 13.50%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 0.28%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.72%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 1.71%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 0.93%.\n", + "Found 3 word(s) missing in the model. Weighted fraction not covered is 1.27%.\n", + "Found 29 word(s) missing in the model. Weighted fraction not covered is 9.61%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 2.23%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 8.16%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 28.20%.\n", + "Found 2 word(s) missing in the model. Weighted fraction not covered is 3.35%.\n", + "Found 4 word(s) missing in the model. Weighted fraction not covered is 28.33%.\n", + "Found 1 word(s) missing in the model. Weighted fraction not covered is 3.13%.\n" + ] + } + ], + "source": [ + "#largest fraction missing is 36.55%.\n", + "outfile = os.path.join(path_data, 'new_and_unique2_found_matches_s2v_3dec.pickle')\n", + "print(outfile)\n", + "if os.path.exists(outfile):\n", + " with open(outfile, 'rb') as inf:\n", + " new_and_unique2_found_matches_s2v_3dec = pickle.load(inf)\n", + "else:\n", + " new_and_unique2_found_matches_s2v_3dec = library_matching(new_and_unique2_documents_query_s2v_3dec,\n", + " new_and_unique2_documents_library_s2v_3dec,\n", + " model3,\n", + " presearch_based_on=[\"spec2vec-top200\"],\n", + " ignore_non_annotated=True,\n", + " intensity_weighting_power=0.5,\n", + " allowed_missing_percentage=100,\n", + " cosine_tol=0.005,\n", + " mass_tolerance=1.0)\n", + " with open(outfile, 'wb') as outf:\n", + " pickle.dump(new_and_unique2_found_matches_s2v_3dec, outf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combining the three models" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9679 False True\n", + "723 False False\n", + "980 False False\n", + "944 False False\n", + "982 False False\n", + "315 False True\n", + "87232 False False\n", + "26326 False True\n", + "943 False True\n", + "949 False False\n", + "950 False False\n", + "1715 False False\n", + "1717 False False\n", + "799 False False\n", + "805 False False\n", + "945 False False\n", + "981 False False\n", + "9678 False False\n", + "93289 False False\n", + "500 False False\n", + "84660 False False\n", + "26464 False True\n", + "1641 False False\n", + "10631 False False\n", + "6782 False False\n", + "802 False False\n", + "946 False False\n", + "12298 False False\n", + "800 False False\n", + "13472 False False\n", + "968 False False\n", + "89242 False False\n", + "84659 False False\n", + "88952 False False\n", + "7791 False False\n", + "1716 False False\n", + "4292 False False\n", + "970 False False\n", + "88973 False False\n", + "89241 False False\n" + ] + } + ], + "source": [ + "ID = 3\n", + "names0_2dec = list(old_and_unique_found_matches_s2v_2dec[ID].sort_values('s2v_score', ascending=False).index)\n", + "names0_3dec = list(old_and_unique_found_matches_s2v_3dec[ID].sort_values('s2v_score', ascending=False).index)\n", + "names0_1dec = list(old_and_unique_found_matches_s2v_1dec[ID].sort_values('s2v_score', ascending=False).index)\n", + "\n", + "for name in names0_1dec:\n", + " print(name, name in names0_2dec, name in names0_3dec)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13 6\n", + "5 1\n", + "8 17\n", + "15 12\n", + "6 9\n", + "15 20\n", + "0 2\n", + "5 0\n", + "9 6\n", + "10 9\n", + "17 20\n", + "16 20\n", + "0 0\n", + "5 3\n", + "10 14\n", + "11 20\n", + "14 20\n", + "20 7\n", + "20 0\n", + "10 1\n", + "11 6\n", + "20 0\n", + "19 1\n", + "0 2\n", + "20 20\n", + "19 1\n", + "20 10\n", + "2 6\n", + "6 0\n", + "7 4\n", + "8 8\n", + "6 7\n", + "12 3\n", + "5 2\n", + "12 0\n", + "5 3\n", + "16 19\n", + "17 8\n", + "17 1\n", + "0 2\n", + "20 18\n", + "0 1\n", + "7 5\n", + "2 14\n", + "10 11\n", + "11 9\n", + "2 2\n", + "20 20\n", + "20 20\n", + "20 20\n", + "1 3\n", + "1 2\n", + "16 4\n", + "5 2\n", + "18 13\n", + "17 19\n", + "20 20\n", + "11 2\n", + "20 20\n", + "16 19\n", + "8 7\n", + "17 17\n", + "12 10\n", + "8 5\n", + "15 9\n", + "15 6\n", + "3 6\n", + "20 3\n", + "14 8\n", + "20 9\n", + "15 14\n", + "11 10\n", + "10 17\n", + "13 10\n", + "4 8\n", + "17 19\n", + "15 19\n", + "12 8\n", + "17 17\n", + "16 19\n", + "8 2\n", + "6 11\n", + "19 15\n", + "20 11\n", + "20 7\n", + "6 1\n", + "7 1\n", + "18 17\n", + "20 17\n", + "18 6\n", + "17 18\n", + "1 3\n", + "15 9\n", + "8 12\n", + "14 13\n", + "19 18\n", + "20 19\n", + "20 15\n", + "11 1\n", + "12 20\n", + "16 20\n", + "18 20\n", + "16 14\n", + "12 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range(len(old_and_unique_found_matches_s2v_3dec)):\n", + " names_1 = list(old_and_unique_found_matches_s2v_1dec[ID].sort_values('s2v_score', ascending=False).index)\n", + " names_2 = list(old_and_unique_found_matches_s2v_2dec[ID].sort_values('s2v_score', ascending=False).index)\n", + " names_3 = list(old_and_unique_found_matches_s2v_3dec[ID].sort_values('s2v_score', ascending=False).index)\n", + " num = len([names_2[i] for i in range(20) if names_2[i] in names_1])\n", + " num2 = len([names_2[i] for i in range(20) if names_2[i] in names_3])\n", + " print(num, num2)\n", + " nums.append(num)\n", + " nums2.append(num2)\n", + "print(np.mean(nums), np.mean(nums2))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(313, 5866, 82675)\n", + "(35, 5875, 74441)\n", + "(60962, 5867, 74440)\n", + "(33, 6886, 71084)\n", + "(60944, 58626, 71238)\n", + "(61008, 58635, 70790)\n", + "(60966, 58709, 77024)\n", + "(67950, 58699, 71085)\n", + "(70487, 58700, 59794)\n", + "(61016, 58069, 59795)\n", + "(60985, 58688, 70791)\n", + "(60956, 57949, 59797)\n", + "(90721, 18832, 67680)\n", + "(68517, 18711, 90338)\n", + "(90720, 24043, 90339)\n", + "(43846, 67387, 71237)\n", + "(84468, 23129, 77560)\n", + "(70488, 1895, 77023)\n", + "(61030, 20997, 60768)\n", + "(40440, 66701, 61247)\n", + "(57938, 21826, 66154)\n", + "(63583, 24252, 66783)\n", + "(50352, 59839, 59798)\n", + "(79606, 59826, 18438)\n", + "(1501, 18723, 66774)\n", + "(61021, 4760, 67503)\n", + "(63995, 21965, 60011)\n", + "(16580, 66597, 60002)\n", + "(67875, 24050, 19968)\n", + "(70370, 5954, 66109)\n", + "(67557, 88690, 82674)\n", + "(67668, 22829, 66177)\n", + "(66645, 21854, 83688)\n", + "(12289, 15252, 66850)\n", + "(68015, 18399, 71239)\n", + "(87675, 66460, 19612)\n", + "(88658, 24358, 90337)\n", + "(87674, 4858, 82449)\n", + "(79639, 57365, 66568)\n", + "(1827, 23090, 77559)\n" + ] + } + ], + "source": [ + "for tup in zip(names0_1dec, names0_2dec, names0_3dec):\n", + " print(tup)" + ] } ], "metadata": { diff --git a/4-networks.ipynb b/4-networks.ipynb index 2914f55..776e2f2 100644 --- a/4-networks.ipynb +++ b/4-networks.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -1015,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -1670,7 +1670,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1698,7 +1698,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -1782,10 +1782,11 @@ }, { "cell_type": "code", - "execution_count": 349, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ + "from matplotlib import cm\n", "def plot_graph(G, attribute_key = 's2v_score', cutoff = 0.4, tan_cutoff = 0.6, node_labels = False,\n", " save = False):\n", " '''Plot graph G with special edges\n", @@ -2279,7 +2280,7 @@ }, { "cell_type": "code", - "execution_count": 373, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2291,3885 +2292,37 @@ }, { "data": { - "image/svg+xml": [ - "\r\n", - "\r\n", - "\r\n", - "\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 2020-10-19T16:58:48.124578\r\n", - " image/svg+xml\r\n", - " \r\n", - " \r\n", - " Matplotlib v3.3.0, https://matplotlib.org/\r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\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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" ] @@ -6599,12 +2752,12 @@ }, { "cell_type": "code", - "execution_count": 299, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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SRuG46mkx0NHM2ppZDDACmJF3gJnFAv8ERrr7ujzza5hZre8eA5cBq8JQk4iUkOnfpnDdhC+pYMZ79wxQSJRBIR9RuHuWmY0G5gAVgSnuvtrM7g4unwD8DmgAvBi8322WuweAJsD04LxKwJvuPjvUmkSk+GXnOONnJzBx4Ub6ta3PSzf3oYGa+pVJ5l76TvcHAgGPj9dHLkQiJTUtk/unfcvCdXsYdU5rfntlNypXVFO/aGdmS4L/pBeJPpktIkWyftdh7pwaz7aDx3js6h7c2E9XIZZ1CgoRKbR5a3bxwNvLqFq5Im/d2Z9AGzX1Kw8UFCJySu7O8x8n8dS8dZzRvA4vj+xLczX1KzcUFCJyUkfTs3jk3eXMXLmTH/duzrhrelK1svo1lScKChE5oeT9adw5NZ51uw7zq6FduPP8dgSvUpRyREEhIgVatGEv972xlOwc5++39ePCTo0iXZJEiIJCRP6Lu/Pqos08+uFa2jaswaRRAdo2rBHpsiSCFBQi8h/pWdn89l+reCc+hYFdG/P0Db2ppaZ+5Z6CQkQA2H3oOHe/voSlWw/y00s68MDATlRQUz9BQSEiwLLkg9z1WjyHjmXx4s19GNqjWaRLkiiioBAp595bksIvp6+kca0qvHfPALo1rx3pkiTKKChEyqms7Bwem5XA3z7fxDntGvDCzX2oXyMm0mVJFFJQiJRDB9MyGP3mt3yetJefDGjDr6/oqqZ+ckIKCpFyJnFnblO/nanHefyanlx/VqtTryTlmoJCpByZvWonD72zjBpVKvFWXH/6tq4X6ZKkFFBQiJQDOTnOcx8n8fS8dfRqWYeXRwZoWqdqpMuSUkJBIVLGHUnP4uF3ljFn9S6u7tOCPw/voaZ+UiQKCpEybOu+3KZ+63cf5rdXduP2c9uoqZ8UWVguczCzwWaWaGZJZja2gOVmZn8NLl9hZn0Ku66InJ4vkvbyoxc+Z+eh47x6ez/uOK+tQkJOS8hBYWYVgReAIUA34EYz65Zv2BCgY/ArDnipCOuKSBG4O3/7fBOjpnxD41pVmDH6XM7vqM6vcvrCceqpH5Dk7hsBzGwaMAxYk2fMMGCquzvwlZnVNbNmQJtCrCsihbTr0HH+PHMt7y/bzmXdmvDUDb2pWUVnmCU04fgNagEk55lOAc4uxJgWhVwXADOLI/dohNhY3cxdJK9DxzOZ+OlGJn++kewc58GBnbj/kg5q6idhEY6gKOg30Qs5pjDr5s50nwhMBAgEAgWOESlv0rOyeeOrrTz38XoOpGXyo17NefiyTrRuoPtHSPiEIyhSgLwf7WwJbC/kmJhCrCsi+eTkOB+s2M6TcxNJ3n+Mczs0YOzgrvRoWSfSpUkZFI6gWAx0NLO2wDZgBHBTvjEzgNHB9yDOBlLdfYeZ7SnEuiKSx2fr9zBuVgKrtx+iW7PaTL29B+d3bKgrmqTYhBwU7p5lZqOBOUBFYIq7rzazu4PLJwAzgaFAEpAG3HaydUOtSaQsWrUtlfGzE/hs/V5a1qvGMzf05ke9mut9CCl2lnshUukSCAQ8Pj4+0mWIlIjk/Wk8OTeR95dtp171yoy+pCO39I+lSiV9ulqKxsyWuHugqOvpujmRKLXvSDrPf5LE619toWIF476L23PXhe2prXtYSwlTUIhEmbSMLKZ8vokJn24kLSOL6wOteGBgJzXxk4hRUIhEiazsHN6JT+GZeevYfTidQd2aMGZwZzo0rhXp0qScU1CIRJi7M2f1Lh6fk8DGPUfp27oeL97ch0Cb+pEuTQRQUIhE1OLN+3ls5lqWbj1I+0Y1mDiyL4O6NdGlrhJVFBQiEbB+12HGz05k3tpdNKldhXFX9+Davi2ppPtWSxRSUIiUoB2px3jmo/X8Y0kyNWIq8cjlnbn93LZUi9GlrhK9FBQiJSD1WCYTPt3AlM83kePOTwa0ZfQlHahfIybSpYmckoJCpBilZ2Xz2pdbeP6TJA6mZfLj3s15+LLOtKpfPdKliRSagkKkGOTkOP9ato2/zF3HtoPHOL9jQ8YM7sIZLdS0T0ofBYVIGLk7n67bw/jZiazdcYgzWtRm/DU9Oa9jw0iXJnLaFBQiYbIi5SDjZiWwaMM+WtWvxrMjenNVTzXtk9JPQSESoi37jvLEnET+vWIH9WvE8PurunHz2a2JqaRLXaVsUFCInKa9R9J5bv563vh6K5UrVuD+SzoQd0E7aqlpn5QxCgqRIjqansXkzzYxceEGjmflcMNZrXjg0o40rq2mfVI2KShECikzO4dpi5N5dt569h5JZ3D3pjwyuDPtG9WMdGkixUpBIXIK7s6sVTt5Yk4im/Ye5aw29Xh5ZF/6tq4X6dJESoSCQuQkvt64j8dmJbAs+SAdG9dk8qgAl3ZtrKZ9Uq6EFBRmVh94G2gDbAaud/cD+ca0AqYCTYEcYKK7Pxtc9gfgTmBPcPiv3H1mKDWJhEPizsOMn53Axwm7aVq7Ko9f05Nr+rakoi51lXIo1COKscB8dx9nZmOD02PyjckCHnb3pWZWC1hiZh+5+5rg8qfd/ckQ6xAJi+0Hj/HUR+t4b2kKNatUYszgLtx2bhuqVlbTPim/Qg2KYcBFwcevAgvIFxTuvgPYEXx82MzWAi2ANYhEidS0TF5ckMTfF20Gh/85ry33XdyButXVtE8k1KBoEgwC3H2HmTU+2WAzawOcCXydZ/ZoMxsFxJN75HHgBOvGAXEAsbGxIZYtkut4ZjavLtrMC58kcTg9i+FntuChQZ1oWU9N+0S+c8qgMLN55L6/kN+vi7IhM6sJvAc84O6HgrNfAh4FPPj9L8DtBa3v7hOBiQCBQMCLsm2R/LJznOnfbuOpuYlsTz3ORZ0b8YvLu9Ctee1IlyYSdU4ZFO4+8ETLzGyXmTULHk00A3afYFxlckPiDXf/Z57n3pVnzCTg30UpXqSo3J0FiXsYPzuBhJ2H6dmyDk9e34sB7dW0T+REQj31NAO4FRgX/P5+/gGWex3h34C17v5UvmXNvjt1BQwHVoVYj8gJLUs+yGMz1/L1pv20blCd5286kyt6NNOlriKnEGpQjAPeMbM7gK3AdQBm1hyY7O5DgXOBkcBKM1sWXO+7y2AfN7Pe5J562gzcFWI9Ij+wae9RnpiTwMyVO2lQI4b/HdadEWfFqmmfSCGFFBTuvg+4tID524GhwcefAwX+y+buI0PZvsjJ7DmczrPz1zHtm2RiKlXgZ5d25M4L2lGzij5nKlIU+ouRMudIehYTF25k8mcbycjK4cZ+sdx/aQca11LTPpHToaCQMiMjK4dpi7fy1/nr2Xskgyt6NOPnl3embcMakS5NpFRTUEip5+58uHIHT8xJZMu+NM5uW5/Jt3ald6u6kS5NpExQUEiptmjDXsbNSmBFSiqdm9Ti7z85i4s6N9KVTCJhpKCQUmntjkOMm5XAp+v20LxOVZ68rhfDz2yhpn0ixUBBIaVKyoE0npq7junLtlG7amV+NbQLo85R0z6R4qSgkFLhwNEMXlyQxKuLtoBB3AXtuPfCDtSprvtTixQ3BYVEteOZ2fz9i828uCCJI+lZXNunJQ8O6kTzutUiXZpIuaGgkKiUneO8tySFpz5ax85Dx7m0S2N+MbgLnZvWinRpIuWOgkKiirszf+1uxs9OYP3uI/RuVZdnRvSmf7sGkS5NpNxSUEjUWLLlAONnJfDN5v20bViDl27uw+AzmupSV5EIU1BIxG3Yc4QnZicye/VOGtaswv/9+AxuOKsVlSuqaZ9INFBQSMTsPnScZ+av5+3FyVStVIGHBnXijvPaUkNN+0Siiv4ipcQdPp4ZbNq3iczsHEb2b83oSzrQsGaVSJcmIgVQUEiJycjK4Y2vt/Dcx0nsP5rBlT2b8cjlnWndQE37RKKZgkKKXU6O88GK7Tw5N5Hk/ccY0L4BY4d0oWfLupEuTUQKQUEhxerz9XsZN3stq7Ydomuz2rx6ew8u6NhQVzKJlCIKCikWq7alMn52Ap+t30uLutV4+oZeDOvVggpq2idS6oQUFGZWH3gbaEPuPa+vd/cDBYzbDBwGsoEsdw8UZX0pPZL3p/GXuYn8a9l26lavzG+u6Mot/VuraZ9IKRbqEcVYYL67jzOzscHpMScYe7G77w1hfYli+49m8PzHSbz+1RbM4J6L2nP3he2pU01N+0RKu1CDYhhwUfDxq8ACivZCH+r6EmHHMrKZ8sUmJizYwNGMLK7r24oHB3WiaR3dn1qkrAg1KJq4+w4Ad99hZo1PMM6BuWbmwMvuPrGI62NmcUAcQGxsbIhlS6iysnP4x5IUnv5oHbsPpzOwaxPGDO5MxyZq2idS1pwyKMxsHtC0gEW/LsJ2znX37cEg+MjMEtx9YRHWJxguEwECgYAXZV0JH3dn7ppdPD47gQ17jtInti4v3NyHs9rUj3RpIlJMThkU7j7wRMvMbJeZNQseDTQDdp/gObYHv+82s+lAP2AhUKj1JTrEb97PY7MSWLLlAO0a1eDlkX25rFsTXeoqUsaFeuppBnArMC74/f38A8ysBlDB3Q8HH18G/G9h15fIS9p9mPGzE/lozS4a16rCn4f34PpASyqpaZ9IuRBqUIwD3jGzO4CtwHUAZtYcmOzuQ4EmwPTgf52VgDfdffbJ1pfosDP1OM/MW8c78clUj6nEzy/rxO3ntaV6jD5+I1KehPQX7+77gEsLmL8dGBp8vBHoVZT1JbIOHc9kwoINTPliE9k5zq0D2nD/JR2pXyMm0qWJSAToX0P5j/SsbF77cgvPf5LEwbRMhvVuzs8v60yr+tUjXZqIRJCCQsjJcd5fvo0n56xj28FjnN+xIWMGd+GMFnUiXZqIRAEFRTnm7ixcv5dxsxJYu+MQ3ZvXZtw1PTi/Y6NIlyYiUURBUU6tTEll3Oy1fJG0j5b1qvHsiN5c1bO5mvaJyA8oKMqZrfvSeGJuIh8s30696pX53ZXduLl/LFUqqWmfiBRMQVFO7DuSznMfJ/HG11uoWMEYfXEH4i5sR+2qatonIienoCjj0jKymPzZJiYu3MixzGyuD7TigYEdaVJbTftEpHAUFGVUZnYOby9O5tn569lzOJ3Luzfhkcu70KFxzUiXJiKljIKijHF3Zq/ayRNzEtm49yiB1vWYcEsf+rZW0z4ROT0KijLkm037eWzWWr7depAOjWsyaVSAgV0bq2mfiIREQVEGrNt1mPGzEpifsJsmtasw/poeXNNHTftEJDwUFKXYjtRjPDV3He8tTaFGlUr8YnBnbhvQlmoxutRVRMJHQVEKpaZl8uKnSbzyxWbc4fZz23LfxR2op6Z9IlIMFBSlyPHMbKZ+uZkXPtnAoeOZDO/dggcHdVLTPhEpVgqKUiA7x/nXt9t46qPcpn0XdmrEmMFd6Na8dqRLE5FyQEERxdydBev2MH5WAgk7D9OjRR2euLYnAzo0jHRpIlKOKCii1PLkgzw2ay1fbdxPbP3qPHfjmVzRo5ma9olIiVNQRJnNe4/yxJxEPly5gwY1Yvjjj7pzY79YYirpUlcRiYyQgsLM6gNvA22AzcD17n4g35jOwTHfaQf8zt2fMbM/AHcCe4LLfuXuM0OpqbTaczidv85fz1vfbCWmUgV+emlH7jy/LbXUtE9EIizUI4qxwHx3H2dmY4PTY/IOcPdEoDeAmVUEtgHT8wx52t2fDLGOUutIehaTP9vIpIUbOZ6Vw439WvHTSzvSuJaa9olIdAg1KIYBFwUfvwosIF9Q5HMpsMHdt4S43VIvMzuHad9s5dn569l7JIOhPZry88s6066RmvaJSHQJNSiauPsOAHffYWaNTzF+BPBWvnmjzWwUEA88nP/UVVnj7sxcuZMn5iSweV8a/drWZ9KoLpwZWy/SpYmIFOiUQWFm84CmBSz6dVE2ZGYxwI+AX+aZ/RLwKODB738Bbj/B+nFAHEBsbGxRNh01vtywj3Gz1rI8JZXOTWox5ScBLu6spn0iEt1OGRTuPvBEy8xsl5k1Cx5NNAN2n+SphgBL3X1Xnuf+z2MzmwT8+yR1TAQmAgQCAT9V3dFk7Y5DjJ+dwILEPTSrU5Unru3J1X1aUlGXuopIKRDqqacZwK3AuOD3908y9kbynXb6LmSCk8OBVSHWE1W2Hcxt2vfPb1OoVaUSvxzShVsHtKFqZTXtE5HSI9SgGAe8Y2Z3AFuB6wDMrDkw2d2HBqerA4OAu/Kt/7iZ9Sb31NPmApaXSgfTMnhxwQZeWbQZgLjz23HPRe2pW11N+0Sk9AkpKNx9H7lXMuWfvx0Ymmc6DWhQwLiRoWw/2hzPzOaVRZt58ZMkDqdncU2fljw4qBMt6laLdGkiIqdNn8wOg+wc572lKTz90Tp2pB7n4s6NGDOkC12aqmmfiJR+CooQuDsfJ+xm/OwE1u06Qq9WdXnq+t6c0/4HB08iIqWWguI0Ld16gHGzEvhm037aNqzBizf3YcgZTXWpq4iUOQqKItqw5whPzklk1qqdNKxZhUd/fAYjzmpFZd2fWkTKKAVFIe0+dJxn569n2uJkqlaqwIMDO/E/57elRhXtQhEp2/QqdwqHj2cyaeFGJn22iczsHG4+O5b7L+lIo1pVIl2aiEiJUFCcQEZWDm9+vYXnPk5i39EMrujZjEcu60ybhjUiXZqISIlSUOSTk+P8e+UOnpyTyNb9aZzTrgFjh3ShV6u6kS5NRCQiFBR5fJG0l3GzEli5LZUuTWvxym1ncWGnRrqSSUTKNQUFsHp7KuNnJ7Jw3R5a1K3GU9f3YljvFmraJyJCOQ+K5P1pPPXROv61bBu1q1bm10O7MvKc1mraJyKSR7kMigNHM3j+kyRe+3ILZnDXBe2556L21Kmm+1OLiORXroLiWEY2U77YxIQFGziakcW1fXOb9jWro6Z9IiInUi6CIis7h3eXpPD0vHXsOpTOwK6N+cXgLnRqUivSpYmIRL0yHRTuzry1u3l8dgLrdx/hzNi6PHdjH/q1rR/p0kRESo0yGxRLtuxn3KwEFm8+QLuGNZhwSx8u766mfSIiRVXmgiJp9xEen53A3DW7aFSrCn8afgY3BFpRSU37REROS5kJil2HjvPMvPW8E59MtcoVeXhQJ+44vy3VY8rMjygiEhGl/lX00PFMJn66kcmfbyQ7xxnZvzX3X9KBBjXVtE9EJBxCCgozuw74A9AV6Ofu8ScYNxh4FqgITHb3ccH59YG3gTbAZuB6dz9QmG2nZ2Xzxldbee7j9RxIy+RHvZrz88s6E9ugeig/koiI5BPqEcUq4Grg5RMNMLOKwAvAICAFWGxmM9x9DTAWmO/u48xsbHB6zKk2ejAtk4FPfUry/mOc16EhY4d04YwWdUL8UUREpCAhBYW7rwVOdSVRPyDJ3TcGx04DhgFrgt8vCo57FVhAIYIi+UAanapUZurtPbigU6PTLV9ERAqhJN6jaAEk55lOAc4OPm7i7jsA3H2HmTU+0ZOYWRwQF5xMn/XABatmPVAM1ZY+DYG9kS4iSmhffE/74nvaF9/rfDornTIozGwe0LSARb929/cLsY2CDje8EOv99wruE4GJwZri3T1Q1Ocoi7Qvvqd98T3ti+9pX3zPzAp8H/lUThkU7j7wdJ44jxSgVZ7plsD24ONdZtYseDTRDNgd4rZERCTMSuJTaIuBjmbW1sxigBHAjOCyGcCtwce3AoU5QhERkRIUUlCY2XAzSwHOAT40sznB+c3NbCaAu2cBo4E5wFrgHXdfHXyKccAgM1tP7lVR4wq56Ymh1F3GaF98T/vie9oX39O++N5p7QtzL/LbBSIiUo6oAZKIiJyUgkJERE4qqoPCzAabWaKZJQU/uZ1/uZnZX4PLV5hZn0jUWRIKsS9uDu6DFWa2yMx6RaLO4naq/ZBn3Flmlm1m15ZkfSWpMPvCzC4ys2VmttrMPi3pGktKIf4+6pjZB2a2PLgvbotEnSXBzKaY2W4zW3WC5UV/3XT3qPwity/UBqAdEAMsB7rlGzMUmEXuZzX6A19Huu4I7osBQL3g4yFlcV8UZj/kGfcxMBO4NtJ1R/B3oi65HRBig9ONI113BPfFr4DxwceNgP1ATKRrL6b9cQHQB1h1guVFft2M5iOK/7T+cPcM4LvWH3kNA6Z6rq+AusHPY5Q1p9wX7r7Iv2+o+BW5n1cpawrzOwFwP/AeZftzOYXZFzcB/3T3rQDuXlb3R2H2hQO1LLffUE1ygyKrZMssGe6+kNyf70SK/LoZzUFRUOuPFqcxpiwo6s95B7n/MZQ1p9wPZtYCGA5MKMG6IqEwvxOdgHpmtsDMlpjZqBKrrmQVZl88T26X6+3ASuBn7p5TMuVFnSK/bkbz/SgK0/ojLO1BSoFC/5xmdjG5QXFesVYUGYXZD88AY9w9u4zf9rYw+6IS0Be4FKgGfGlmX7n7uuIuroQVZl9cDiwDLgHaAx+Z2WfufqiYa4tGRX7djOagOFnrj6KMKQsK9XOaWU9gMjDE3feVUG0lqTD7IQBMC4ZEQ2ComWW5+79KpMKSU9i/j73ufhQ4amYLgV5AWQuKwuyL24BxnnuSPsnMNgFdgG9KpsSoUuTXzWg+9XSy1h/fmQGMCr6L3x9I9WA32jLmlPvCzGKBfwIjy+B/jN855X5w97bu3sbd2wDvAveWwZCAwv19vA+cb2aVzKw6uV2b15ZwnSWhMPtiK7lHVphZE3K7qG4s0SqjR5FfN6P2iMLds8zsu9YfFYEp7r7azO4OLp9A7lUtQ4EkII3c/xrKnELui98BDYAXg/9NZ3kZ65hZyP1QLhRmX7j7WjObDawAcsi9u2SBl0yWZoX8vXgUeMXMVpJ76mWMu5fJ1uNm9ha59/lpGGyx9HugMpz+66ZaeIiIyElF86knERGJAgoKERE5KQWFiIiclIJCREROSkEhIiInpaAQEZGTUlCIiMhJ/T9LnfwmzpI3lgAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -6630,27 +2783,49 @@ "source": [ "cutoff = 0.6\n", "ys = [-1, 0, 1]\n", - "xs = [0, cutoff, 1]\n", + "xs = [0.2, cutoff, 1]\n", "plt.plot(xs, ys)\n", "plt.ylim(-1,1)\n", "plt.xlim(0,1)\n", + "plt.title('tanimoto')\n", "plt.show()\n", "cutoff = 0.4\n", "ys = [-1, 0, 1]\n", - "xs = [0, cutoff, 1]\n", + "xs = [0.2, cutoff, 1]\n", "plt.plot(xs, ys)\n", "plt.ylim(-1,1)\n", "plt.xlim(0,1)\n", + "plt.title('s2v')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 369, + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.75" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(0.3 - 0.6) / (0.6 - 0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "def tr_tan(v, n, graph, cutoff = 0.6):\n", + "def tr_tan(v, n, graph, cutoff = 0.6, low_cutoff = 0.2):\n", " '''Tanimoto 'transmission' transforming tanimoto_score(v,n) between -1, 1\n", " \n", " v: hashable, starting point node name, probably str or int\n", @@ -6658,15 +2833,19 @@ " graph: networkx graph\n", " cutoff: float, determines 0, above and below values will be\n", " transformed to 1 - 0, and -1 - 0, respectively\n", + " low_cutoff: float, determines -1 transmission value. values between\n", + " cutoff and low_cutoff will be transformed to -1 - 0.\n", " '''\n", " tan = graph[v][n]['tanimoto']\n", " if tan >= cutoff:\n", " transformed = (tan - cutoff) / (1 - cutoff) #between 0 - 1\n", + " elif tan < low_cutoff:\n", + " transformed = 0\n", " else:\n", - " transformed = (tan - cutoff) / cutoff #between -1 - 0\n", + " transformed = (tan - cutoff) / (cutoff - low_cutoff) #between -1 - 0\n", " return transformed\n", "\n", - "def tr_s2v(n, q, graph, cutoff = 0.4):\n", + "def tr_s2v(n, q, graph, cutoff = 0.4, low_cutoff = 0.2):\n", " '''\n", " Transforms s2v score of given node to query between -1, 1\n", " \n", @@ -6679,8 +2858,10 @@ " s2v = graph[n][q]['s2v_score']\n", " if s2v >= cutoff:\n", " transformed = (s2v - cutoff) / (1 - cutoff)\n", + " elif s2v < low_cutoff:\n", + " transformed = 0\n", " else:\n", - " transformed = (s2v - cutoff) / cutoff\n", + " transformed = (s2v - cutoff) / (cutoff - low_cutoff) #between -1 - 0\n", " return transformed\n", "\n", "def tr_node(v, graph, tan_cutoff = 0.6, s2v_cutoff = 0.4):\n", @@ -6706,33 +2887,33 @@ }, { "cell_type": "code", - "execution_count": 372, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "66196 2.5132160105256416\n", - "65988 2.8777340704972194\n", - "67788 2.9149714780258926\n", - "66043 2.9149714780258926\n", - "7167 0.9733207546840394\n", - "17281 1.165371175789209\n", - "66339 3.288933712543805\n", - "88004 0.8482064548982047\n", - "87824 0.9310438848090519\n", - "87573 -0.1808034641637389\n", - "88097 0.8924952369513416\n", - "87572 -0.12257992442438778\n", - "16614 0.9365557700151876\n", - "20922 0.22051909239099773\n", - "17141 0.25042704337374044\n", - "7322 0.2591685244378703\n", - "87571 -0.02852674618453152\n", - "7219 0.5035949502557986\n", - "87597 0.7087962983370932\n", - "90460 0.7720353747281663\n" + "66196 4.284089372785733\n", + "65988 4.648607432757311\n", + "67788 4.6858448402859825\n", + "66043 4.6858448402859825\n", + "7167 2.448920671478452\n", + "17281 1.630261184861902\n", + "66339 5.102699796856363\n", + "88004 0.6316408246046001\n", + "87824 0.0\n", + "87573 -0.33284291574802327\n", + "88097 -0.4696746921897297\n", + "87572 -0.21639583626932146\n", + "16614 0.7691812648054384\n", + "20922 0.4342188539814268\n", + "17141 0.49403475594691204\n", + "7322 0.5115177180751717\n", + "87571 -0.028289479789608718\n", + "7219 1.448553703161007\n", + "87597 0.32664882085543123\n", + "90460 2.220994348004344\n" ] }, { @@ -6747,7 +2928,7 @@ " \r\n", " \r\n", " \r\n", - " 2020-10-19T16:58:24.510334\r\n", + " 2020-10-28T10:59:49.050775\r\n", " image/svg+xml\r\n", " \r\n", " \r\n", @@ -6771,112 +2952,112 @@ " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", @@ -6892,23 +3073,23 @@ "C -8.660254 2.296726 -7.747755 4.499694 -6.123724 6.123724 \r\n", "C -4.499694 7.747755 -2.296726 8.660254 0 8.660254 \r\n", "z\r\n", - "\" id=\"m8f355954ba\" style=\"stroke:#1f78b4;\"/>\r\n", + "\" id=\"m2489fe80c6\" style=\"stroke:#1f78b4;\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -6923,14 +3104,14 @@ "C -8.660254 2.296726 -7.747755 4.499694 -6.123724 6.123724 \r\n", "C -4.499694 7.747755 -2.296726 8.660254 0 8.660254 \r\n", "z\r\n", - "\" id=\"mc04ddb1816\" style=\"stroke:#67000d;\"/>\r\n", + "\" id=\"mabf696a1e9\" style=\"stroke:#67000d;\"/>\r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7017,7 +3198,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7094,7 +3275,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7117,7 +3298,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7201,7 +3382,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7213,7 +3394,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7225,7 +3406,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7263,7 +3444,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7275,7 +3456,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7287,7 +3468,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7299,7 +3480,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7310,7 +3491,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7434,7 +3615,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7445,7 +3626,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7459,7 +3640,7 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -7483,6 +3664,2536 @@ "set_matplotlib_formats('svg')\n", "plot_graph(test_G, tan_cutoff = 0.6, attribute_key = 's2v_score', cutoff = 0.4, node_labels = True)" ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n", + "10\n", + "11\n", + "12\n", + "13\n", + "14\n", + "15\n", + "16\n", + "17\n", + "18\n", + "19\n", + "20\n", + "21\n", + "22\n", + "23\n", + "24\n", + "25\n", + "26\n", + "27\n", + "28\n", + "29\n", + "30\n", + "31\n", + "32\n", + "33\n", + "34\n", + "35\n", + "36\n", + "37\n", + "38\n", + "39\n", + "40\n", + "41\n", + "42\n", + "43\n", + "44\n", + "45\n", + "46\n", + "47\n", + "48\n", + "49\n", + "50\n", + "51\n", + "52\n", + "53\n", + 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+ " current.index)\n", + " query_node_test_graph = [node for node in test_graph.nodes if isinstance(node, str) and 'query' in node][0]\n", + " nodes_test_graph = [node for node in test_graph.nodes if not node == query_node_test_graph]\n", + " transms = []\n", + " for node_v in nodes_test_graph:\n", + " transms.append(tr_node(node_v, test_graph))\n", + " current['transmission'] = transms\n", + " nn_tested_tanimoto_top20_new_and_unique2_found_matches_s2v_transmission.append(current)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cosine_scorecosine_matchesmod_cosine_scoremod_cosine_matchess2v_scorelabelsimilarityparent_massmass_simsim_predictionsguess_within_threshtransmission
888990.9615840.3982990.9615840.3982990.59404100.8013180.0257919.991078e-010.814690[True]1.076349
106890.9647110.4404180.9647110.4404180.53809000.6727410.0499453.901854e-320.446978[False]0.567731
889010.7129050.5498960.7129050.5498960.53224200.8013180.0257919.991078e-010.826895[True]1.179347
211370.0294310.8247770.7239560.9365620.53001100.9904260.0268354.386286e-020.750216[False]1.133517
105330.9210190.6868680.9210190.6868680.52552100.6595140.0518831.175322e-340.527571[False]0.509053
100550.0111600.7481300.6095380.8484500.51888400.8080000.0245992.815378e-020.680828[False]1.039611
93760.0423370.7821580.5213290.8780990.50897600.9112660.0256436.424324e-010.514842[False]1.237986
726740.0074710.7481300.7322350.9365620.49743400.3149250.0245992.815378e-020.720831[False]-1.918905
109080.0326870.9266520.0337260.9211320.49633200.8869570.0268354.386286e-020.509413[False]1.406766
92010.0279400.9365620.6634940.9365620.48988800.8728320.0268354.386286e-020.730985[False]1.409567
105300.0313740.9151950.0315450.9211320.48163500.6509090.0268354.386286e-020.505080[False]0.106363
89310.9575670.3982990.9575670.3982990.48120400.6511630.0597856.238483e-450.436374[False]0.542914
87890.0095220.8484500.0119670.8780990.48108300.8320000.0278801.922234e-030.329643[False]1.119951
112480.0186680.8780990.0205670.8689240.47998600.8696480.0268354.386286e-020.461506[False]1.446120
224270.0057570.7087870.0057670.7087870.47705500.9008020.0245982.814122e-020.389515[False]1.277422
93900.0041520.7087870.4513990.8866330.47027300.8894470.0235541.233803e-030.498270[False]1.205014
746740.5478690.6868680.5478690.6868680.46728700.6595140.0518831.175322e-340.496322[False]0.606111
100100.4123960.7657610.4125930.7821580.46660200.8013180.0257919.991078e-010.697837[False]1.288749
726750.0141570.7821580.1648400.9317870.46333100.3149250.0245992.815378e-020.555072[False]-1.862066
636340.1364730.5814040.9412060.6632990.45916500.8807730.0213181.524310e-060.474313[False]0.725078
\n", + "
" + ], + "text/plain": [ + " cosine_score cosine_matches mod_cosine_score mod_cosine_matches \\\n", + "88899 0.961584 0.398299 0.961584 0.398299 \n", + "10689 0.964711 0.440418 0.964711 0.440418 \n", + "88901 0.712905 0.549896 0.712905 0.549896 \n", + "21137 0.029431 0.824777 0.723956 0.936562 \n", + "10533 0.921019 0.686868 0.921019 0.686868 \n", + "10055 0.011160 0.748130 0.609538 0.848450 \n", + "9376 0.042337 0.782158 0.521329 0.878099 \n", + "72674 0.007471 0.748130 0.732235 0.936562 \n", + "10908 0.032687 0.926652 0.033726 0.921132 \n", + "9201 0.027940 0.936562 0.663494 0.936562 \n", + "10530 0.031374 0.915195 0.031545 0.921132 \n", + "8931 0.957567 0.398299 0.957567 0.398299 \n", + "8789 0.009522 0.848450 0.011967 0.878099 \n", + "11248 0.018668 0.878099 0.020567 0.868924 \n", + "22427 0.005757 0.708787 0.005767 0.708787 \n", + "9390 0.004152 0.708787 0.451399 0.886633 \n", + "74674 0.547869 0.686868 0.547869 0.686868 \n", + "10010 0.412396 0.765761 0.412593 0.782158 \n", + "72675 0.014157 0.782158 0.164840 0.931787 \n", + "63634 0.136473 0.581404 0.941206 0.663299 \n", + "\n", + " s2v_score label similarity parent_mass mass_sim \\\n", + "88899 0.594041 0 0.801318 0.025791 9.991078e-01 \n", + "10689 0.538090 0 0.672741 0.049945 3.901854e-32 \n", + "88901 0.532242 0 0.801318 0.025791 9.991078e-01 \n", + "21137 0.530011 0 0.990426 0.026835 4.386286e-02 \n", + "10533 0.525521 0 0.659514 0.051883 1.175322e-34 \n", + "10055 0.518884 0 0.808000 0.024599 2.815378e-02 \n", + "9376 0.508976 0 0.911266 0.025643 6.424324e-01 \n", + "72674 0.497434 0 0.314925 0.024599 2.815378e-02 \n", + "10908 0.496332 0 0.886957 0.026835 4.386286e-02 \n", + "9201 0.489888 0 0.872832 0.026835 4.386286e-02 \n", + "10530 0.481635 0 0.650909 0.026835 4.386286e-02 \n", + "8931 0.481204 0 0.651163 0.059785 6.238483e-45 \n", + "8789 0.481083 0 0.832000 0.027880 1.922234e-03 \n", + "11248 0.479986 0 0.869648 0.026835 4.386286e-02 \n", + "22427 0.477055 0 0.900802 0.024598 2.814122e-02 \n", + "9390 0.470273 0 0.889447 0.023554 1.233803e-03 \n", + "74674 0.467287 0 0.659514 0.051883 1.175322e-34 \n", + "10010 0.466602 0 0.801318 0.025791 9.991078e-01 \n", + "72675 0.463331 0 0.314925 0.024599 2.815378e-02 \n", + "63634 0.459165 0 0.880773 0.021318 1.524310e-06 \n", + "\n", + " sim_predictions guess_within_thresh transmission \n", + "88899 0.814690 [True] 1.076349 \n", + "10689 0.446978 [False] 0.567731 \n", + "88901 0.826895 [True] 1.179347 \n", + "21137 0.750216 [False] 1.133517 \n", + "10533 0.527571 [False] 0.509053 \n", + "10055 0.680828 [False] 1.039611 \n", + "9376 0.514842 [False] 1.237986 \n", + "72674 0.720831 [False] -1.918905 \n", + "10908 0.509413 [False] 1.406766 \n", + "9201 0.730985 [False] 1.409567 \n", + "10530 0.505080 [False] 0.106363 \n", + "8931 0.436374 [False] 0.542914 \n", + "8789 0.329643 [False] 1.119951 \n", + "11248 0.461506 [False] 1.446120 \n", + "22427 0.389515 [False] 1.277422 \n", + "9390 0.498270 [False] 1.205014 \n", + "74674 0.496322 [False] 0.606111 \n", + "10010 0.697837 [False] 1.288749 \n", + "72675 0.555072 [False] -1.862066 \n", + "63634 0.474313 [False] 0.725078 " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nn_tested_tanimoto_top20_new_and_unique2_found_matches_s2v_transmission[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "tans = []\n", + "transms = []\n", + "for res in nn_tested_tanimoto_top20_new_and_unique2_found_matches_s2v_transmission:\n", + " tans.extend(res['similarity'])\n", + " transms.extend(res['transmission'])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "g = sns.displot(x=tans, y=transms, kind=\"kde\", rug=True)" + ] } ], "metadata": {