diff --git a/notebooks/jupyter/AttnWSiamese_AttnWeights.ipynb b/notebooks/jupyter/AttnWSiamese_AttnWeights.ipynb deleted file mode 100644 index ea3d984..0000000 --- a/notebooks/jupyter/AttnWSiamese_AttnWeights.ipynb +++ /dev/null @@ -1,5291 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 172, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "metadata": {}, - "outputs": [], - "source": [ - "import ddi\n", - "import sys" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import datetime\n", - "import seaborn as sns\n", - "from ddi.dataset import *" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [], - "source": [ - "from ddi.utilities import *\n", - "from ddi.run_workflow import *" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "metadata": {}, - "outputs": [], - "source": [ - "rawdata_dir = '../data/raw/'\n", - "processed_dir = '../data/processed/'\n", - "up_dir = '..'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Preparing dataset " - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "metadata": {}, - "outputs": [], - "source": [ - "DSdataset_name = 'DS3' # or DS2, DS3\n", - "\n", - "# For DS3:\n", - "# interact_matfname_DS3 = 'NCRDInteractionMat'\n", - "interact_matfname_DS3 = 'CRDInteractionMat'" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_configs = {'DS1':{'DSdataset_name':'DS1', \n", - " 'fname_suffix':\"_Jacarrd_sim.csv\",\n", - " 'similarity_types':['enzyme',\n", - " 'indication',\n", - " 'offsideeffect',\n", - " 'pathway',\n", - " 'sideeffect',\n", - " 'target',\n", - " 'transporter',\n", - " 'chem'],\n", - " 'interact_matfname':'drug_drug_matrix',\n", - " 'exp_iden':'simtypeall',\n", - " 'kernel_option':'sqeuclidean',\n", - " 'data_fname':'data_v1',\n", - " 'ddi_interaction_labels_pth':os.path.join(up_dir, rawdata_dir, 'DS1', 'drug_drug_matrix.csv')}, \n", - " 'DS2':{'DSdataset_name':'DS2',\n", - " 'fname_suffix':'.csv',\n", - " 'similarity_types':['simMatrix'],\n", - " 'interact_matfname':'ddiMatrix',\n", - " 'exp_iden':'simtypeall',\n", - " 'kernel_option':'correlation',\n", - " 'ddi_interaction_labels_pth':os.path.join(up_dir, rawdata_dir, 'DS2', 'ddiMatrix.csv'),\n", - " 'data_fname':'data_v1'}, \n", - " 'DS3':{'DSdataset_name':'DS3',\n", - " 'fname_suffix':\"Mat.csv\",\n", - " 'similarity_types':['ATCSimilarity',\n", - " 'chemicalSimilarity',\n", - " 'distSimilarity',\n", - " 'GOSimilarity',\n", - " 'ligandSimilarity',\n", - " 'seqSimilarity',\n", - " 'SideEffectSimilarity'],\n", - " 'interact_matfname':['NCRDInteractionMat', 'CRDInteractionMat'],\n", - " 'exp_iden':['simtypeall_NCRDInteractionMat', 'simtypeall_CRDInteractionMat'],\n", - " 'kernel_option':'sqeuclidean',\n", - " 'ddi_interaction_labels_pth':[os.path.join(up_dir, rawdata_dir, 'DS3', 'NCRDInteractionMat.csv'), os.path.join(up_dir, rawdata_dir, 'DS3', 'CRDInteractionMat.csv')],\n", - " 'data_fname':'data_v1'}}\n", - "\n", - "dict_interact_matfname = {'NCRDInteractionMat': 0, 'CRDInteractionMat':1}" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": {}, - "outputs": [], - "source": [ - "ds_config = dataset_configs[DSdataset_name]\n", - "\n", - "fname_suffix = ds_config[\"fname_suffix\"]\n", - "similarity_types = ds_config[\"similarity_types\"]\n", - "kernel_option = ds_config[\"kernel_option\"]\n", - "data_fname = ds_config[\"data_fname\"]\n", - "interact_matfname = ds_config[\"interact_matfname\"]\n", - "exp_iden = ds_config[\"exp_iden\"]\n", - "ddi_interaction_labels_pth = ds_config[\"ddi_interaction_labels_pth\"]\n", - "\n", - "if DSdataset_name == 'DS3':\n", - " int_interact_matfname = dict_interact_matfname[interact_matfname_DS3]\n", - " interact_matfname = interact_matfname[int_interact_matfname]\n", - " exp_iden = exp_iden[int_interact_matfname]\n", - " ddi_interaction_labels_pth = ddi_interaction_labels_pth[int_interact_matfname]" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [], - "source": [ - "similarity_types_GIP = similarity_types.copy()\n", - "similarity_types_GIP.append(\"GIP\")" - ] - }, - { - "cell_type": "code", - "execution_count": 184, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "807" - ] - }, - "execution_count": 184, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "num_drugs = get_num_drugs(ddi_interaction_labels_pth, DSdataset_name)\n", - "num_drugs" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "metadata": {}, - "outputs": [], - "source": [ - "interaction_mat = get_interaction_mat(ddi_interaction_labels_pth, DSdataset_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "metadata": {}, - "outputs": [], - "source": [ - "sid_ddipairs_map = construct_sampleid_ddipairs(interaction_mat)" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path_current_dir ../data/processed/DS3/experiments\n" - ] - }, - { - "data": { - "text/plain": [ - "'../data/processed/DS3/experiments/simtypeall_CRDInteractionMat/exp_2020-10-19_16-36-14'" - ] - }, - "execution_count": 188, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp_dir = create_directory(exp_iden, os.path.join(processed_dir, DSdataset_name, 'experiments'))\n", - "exp_folder = \"\" # e.g. \"exp_2020-10-19_16-36-14\"\n", - "tr_val_dir = os.path.join(exp_dir, exp_folder)\n", - "tr_val_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 189, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "num_folds = len(os.listdir(os.path.join(up_dir, tr_val_dir + '/train_val/')))\n", - "num_folds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get attention weights" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "attn_dict = {'fold_'+str(fold):ReaderWriter.read_data(os.path.join(up_dir, tr_val_dir + '/test/fold_' + str(fold), 'sampleid_fattnw_map_test.pkl')) for fold in range(5)}" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [], - "source": [ - "def merge_dicts(*dict_args):\n", - " \"\"\"\n", - " Given any number of dictionaries, shallow copy and merge into a new dict,\n", - " precedence goes to key value pairs in latter dictionaries.\n", - " \"\"\"\n", - " result = {}\n", - " for dictionary in dict_args:\n", - " result.update(dictionary)\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "attn_dict_Xa = merge_dicts(*[attn_dict['fold_' + str(fold)][\"X_a\"] for fold in range(num_folds)])\n", - "attn_dict_Xb = merge_dicts(*[attn_dict['fold_' + str(fold)][\"X_b\"] for fold in range(num_folds)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Split per prediction case" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['TP', 'FP', 'TN', 'FN']" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prediction_cases = [['TP', 1, 1], ['FP', 0, 1], ['TN', 0, 0], ['FN', 1, 0]]\n", - "sum(prediction_cases, [])[::3]" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'TP': [1,\n", - " 6,\n", - " 11,\n", - " 13,\n", - " 14,\n", - " 15,\n", - " 16,\n", - " 17,\n", - " 24,\n", - " 27,\n", - " 28,\n", - " 30,\n", - " 34,\n", - " 37,\n", - " 38,\n", - " 41,\n", - " 43,\n", - " 44,\n", - " 45,\n", - " 46,\n", - " 48,\n", - " 49,\n", - " 50,\n", - " 55,\n", - " 57,\n", - " 61,\n", - " 62,\n", - " 63,\n", - " 64,\n", - " 65,\n", - " 72,\n", - " 77,\n", - " 79,\n", - " 80,\n", - " 84,\n", - " 86,\n", - " 88,\n", - " 94,\n", - " 98,\n", - " 99,\n", - " 100,\n", - " 102,\n", - " 106,\n", - " 108,\n", - " 111,\n", - " 116,\n", - " 119,\n", - " 120,\n", - " 122,\n", - " 123,\n", - " 124,\n", - 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" 1770,\n", - " 1771,\n", - " 1772,\n", - " 1776,\n", - " 1781,\n", - " 1783,\n", - " 1786,\n", - " 1796,\n", - " 1798,\n", - " 1801,\n", - " 1804,\n", - " 1808,\n", - " 1811,\n", - " 1812,\n", - " 1815,\n", - " 1816,\n", - " 1824,\n", - " 1825,\n", - " 1830,\n", - " 1833,\n", - " 1836,\n", - " 1840,\n", - " 1841,\n", - " 1843,\n", - " 1845,\n", - " 1848,\n", - " 1849,\n", - " 1856,\n", - " 1857,\n", - " 1859,\n", - " 1862,\n", - " 1864,\n", - " 1871,\n", - " 1873,\n", - " 1876,\n", - " 1877,\n", - " 1879,\n", - " 1884,\n", - " 1886,\n", - " 1890,\n", - " 1891,\n", - " 1893,\n", - " 1894,\n", - " 1897,\n", - " 1899,\n", - " 1903,\n", - " 1904,\n", - " 1909,\n", - " 1910,\n", - " 1911,\n", - " 1919,\n", - " 1920,\n", - " 1926,\n", - " 1927,\n", - " 1929,\n", - " 1931,\n", - " 1932,\n", - " 1934,\n", - " 1935,\n", - " 1936,\n", - " 1938,\n", - " 1942,\n", - " 1951,\n", - " 1954,\n", - " 1966,\n", - " 1967,\n", - " 1969,\n", - " 1970,\n", - " 1971,\n", - " 1976,\n", - " 1978,\n", - " 1980,\n", - " 1983,\n", - " 1990,\n", - " 1998,\n", - " 2002,\n", - " 2004,\n", - " 2007,\n", - " 2009,\n", - " 2010,\n", - " 2015,\n", - " 2017,\n", - " 2019,\n", - " 2021,\n", - " 2023,\n", - " 2024,\n", - " 2025,\n", - " 2031,\n", - " 2033,\n", - " 2034,\n", - " 2036,\n", - " 2042,\n", - " 2043,\n", - " 2044,\n", - " 2045,\n", - " 2048,\n", - " 2050,\n", - " 2051,\n", - " 2054,\n", - " 2067,\n", - " 2069,\n", - " 2070,\n", - " 2071,\n", - " 2073,\n", - " 2074,\n", - " 2075,\n", - " 2077,\n", - " 2078,\n", - " 2081,\n", - " 2082,\n", - " 2087,\n", - " 2089,\n", - " 2092,\n", - " 2093,\n", - " 2095,\n", - " 2097,\n", - " 2102,\n", - " 2103,\n", - " 2110,\n", - " 2112,\n", - " 2113,\n", - " 2115,\n", - " 2116,\n", - " 2123,\n", - " 2128,\n", - " 2132,\n", - " 2133,\n", - " 2134,\n", - " 2135,\n", - " 2136,\n", - " 2140,\n", - " 2143,\n", - " 2147,\n", - " 2148,\n", - " 2155,\n", - " 2157,\n", - " 2158,\n", - " 2159,\n", - " 2162,\n", - " 2163,\n", - " 2168,\n", - " 2171,\n", - " 2172,\n", - " 2174,\n", - 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], - "source": [ - "case_IDs = {case[0]:predictionsTest[(predictionsTest.true_class == case[1]) & (predictionsTest.pred_class == case[2])].id.tolist() for case in prediction_cases}\n", - "case_IDs" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'TP': tensor([[0.1164, 0.0950, 0.1385, ..., 0.1228, 0.1156, 0.0788],\n", - " [0.0820, 0.0856, 0.1178, ..., 0.0944, 0.2222, 0.0689],\n", - " [0.0972, 0.0753, 0.1134, ..., 0.0702, 0.2020, 0.0388],\n", - " ...,\n", - " [0.1226, 0.0654, 0.2114, ..., 0.0832, 0.1700, 0.0597],\n", - " [0.0959, 0.0955, 0.1277, ..., 0.0887, 0.1311, 0.0839],\n", - " [0.0791, 0.0486, 0.0726, ..., 0.0340, 0.0596, 0.5226]]),\n", - " 'FP': tensor([[0.0779, 0.0955, 0.0621, ..., 0.0673, 0.0561, 0.3945],\n", - " [0.0499, 0.0554, 0.0545, ..., 0.0700, 0.0500, 0.5389],\n", - " [0.0625, 0.0700, 0.2449, ..., 0.0741, 0.0708, 0.0517],\n", - " ...,\n", - " [0.0756, 0.1037, 0.1303, ..., 0.1056, 0.1499, 0.0596],\n", - " [0.1219, 0.1571, 0.0449, ..., 0.1205, 0.0453, 0.0742],\n", - " [0.0727, 0.1265, 0.2372, ..., 0.0190, 0.2483, 0.0206]]),\n", - " 'TN': tensor([[0.0883, 0.0684, 0.0816, ..., 0.1269, 0.2002, 0.0386],\n", - " [0.0920, 0.1812, 0.1867, ..., 0.1946, 0.0833, 0.0460],\n", - " [0.1981, 0.0640, 0.1860, ..., 0.0630, 0.1467, 0.0369],\n", - " ...,\n", - " [0.0086, 0.0777, 0.3371, ..., 0.0085, 0.3527, 0.0088],\n", - " [0.0570, 0.0607, 0.0924, ..., 0.0531, 0.2994, 0.0533],\n", - " [0.0154, 0.0302, 0.0081, ..., 0.0172, 0.0060, 0.8833]]),\n", - " 'FN': tensor([[0.0505, 0.0553, 0.0551, ..., 0.0499, 0.0497, 0.5565],\n", - " [0.0629, 0.0595, 0.5390, ..., 0.0669, 0.0529, 0.0470],\n", - " [0.0552, 0.0665, 0.0540, ..., 0.0543, 0.0501, 0.5222],\n", - " ...,\n", - " [0.1535, 0.0896, 0.1478, ..., 0.1070, 0.1512, 0.0709],\n", - " [0.1723, 0.0832, 0.0469, ..., 0.2077, 0.0448, 0.1741],\n", - " [0.0604, 0.0906, 0.1552, ..., 0.1020, 0.1780, 0.0670]])}" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# (t)ensor of drug (a) and drug (b) attention weights\n", - "t_ab_perCase = {case:torch.stack([torch.mean(torch.stack([attn_dict_Xa[i], attn_dict_Xb[i]]),0) for i in case_IDs[case]]) for case in list(case_IDs)}\n", - "t_ab_perCase" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([74940, 9])" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# (t)ensor of drug (a) and drug (b) attention weights\n", - "t_ab = torch.stack([torch.mean(torch.stack([attn_dict_Xa[i], attn_dict_Xb[i]]),0) for i in list(predictionsTest[\"id\"])])\n", - "t_ab.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.09918541461229324,\n", - " 0.09920194000005722,\n", - " 0.13910947740077972,\n", - " 0.11701689660549164,\n", - " 0.14935298264026642,\n", - " 0.07622390240430832,\n", - " 0.08381940424442291,\n", - " 0.14151282608509064,\n", - " 0.0945785865187645]" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Mean values per Similarity type\n", - "pymean_ab = torch.mean(t_ab, 0).tolist()\n", - "pymean_ab" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot weight distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " similarities absolute ranked\n", - "0 enzyme 0.099185 6.0\n", - "1 indication 0.099202 5.0\n", - "2 offsideeffect 0.139109 2.0\n", - "3 pathway 0.117017 4.0\n", - "4 sideeffect 0.149353 1.0\n", - "5 target 0.076224 8.0\n", - "6 transporter 0.083819 7.0\n", - "7 chem 0.141513 3.0\n", - "8 GIP 0.094579 9.0" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_attn_scores = pd.DataFrame({'similarities':similarity_types_GIP, 'absolute':pymean_ab, 'ranked':totalRank_tp_tn})\n", - "df_attn_scores" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate fake example data" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.3573, 0.4802, 0.1297, 0.1223, 0.0649, 0.4995, 0.0510, 0.1600, 0.4840]])" - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t_fake_first = (torch.randint(0, 5000, (1, len_sim))/10000.0)\n", - "t_fake_first" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.2094, 0.2623, 0.1871, 0.1536, 0.2213, 0.3035, 0.1774, 0.0244, 0.0557],\n", - " [0.0509, 0.2252, 0.3365, 0.3433, 0.2322, 0.0703, 0.0586, 0.0600, 0.3445],\n", - " [0.0924, 0.1862, 0.2709, 0.3149, 0.2308, 0.1444, 0.3403, 0.2280, 0.1780],\n", - " [0.0811, 0.2086, 0.3377, 0.1930, 0.3015, 0.0150, 0.0401, 0.3407, 0.0570]])" - ] - }, - "execution_count": 146, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t_fake_rest = (torch.randint(0, 3500, (4, len_sim))/10000.0)\n", - "t_fake_rest" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.3573, 0.4802, 0.1297, 0.1223, 0.0649, 0.4995, 0.0510, 0.1600, 0.4840],\n", - " [0.5667, 0.7425, 0.3168, 0.2759, 0.2862, 0.8030, 0.2284, 0.1844, 0.5397],\n", - " [0.4082, 0.7054, 0.4662, 0.4656, 0.2971, 0.5698, 0.1096, 0.2200, 0.8285],\n", - " [0.4497, 0.6664, 0.4006, 0.4372, 0.2957, 0.6439, 0.3913, 0.3880, 0.6620],\n", - " [0.4384, 0.6888, 0.4674, 0.3153, 0.3664, 0.5145, 0.0911, 0.5007, 0.5410]])" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t_fake = torch.cat((t_fake_first, (t_fake_rest + t_fake_first)))\n", - "t_fake" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.1223, 0.1297, 0.4840, 0.3573, 0.4802, 0.0649, 0.1600, 0.4995, 0.0510],\n", - " [0.2759, 0.3168, 0.5397, 0.5667, 0.7425, 0.2862, 0.1844, 0.8030, 0.2284],\n", - " [0.4656, 0.4662, 0.8285, 0.4082, 0.7054, 0.2971, 0.2200, 0.5698, 0.1096],\n", - " [0.4372, 0.4006, 0.6620, 0.4497, 0.6664, 0.2957, 0.3880, 0.6439, 0.3913],\n", - " [0.3153, 0.4674, 0.5410, 0.4384, 0.6888, 0.3664, 0.5007, 0.5145, 0.0911]])" - ] - }, - "execution_count": 159, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t_fake_ord = torch.index_select(t_fake, 1, torch.LongTensor([0, 1, 2 , 3, 4,5 ,6,7,8]))\n", - "t_fake_ord" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[0.0951, 0.0959, 0.1366, 0.1203, 0.1361, 0.0898, 0.0988, 0.1387, 0.0886],\n", - " [0.0922, 0.0960, 0.1200, 0.1233, 0.1470, 0.0932, 0.0841, 0.1562, 0.0879],\n", - " [0.1100, 0.1101, 0.1581, 0.1039, 0.1398, 0.0929, 0.0860, 0.1221, 0.0771],\n", - " [0.1054, 0.1016, 0.1319, 0.1067, 0.1325, 0.0915, 0.1003, 0.1296, 0.1006],\n", - " [0.0973, 0.1133, 0.1219, 0.1101, 0.1414, 0.1024, 0.1171, 0.1188, 0.0778]])" - ] - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t_fake_softmax = nn.Softmax(dim=1)(t_fake_ord)\n", - "t_fake_softmax" - ] - }, - { - "cell_type": "code", - "execution_count": 161, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])" - ] - }, - "execution_count": 161, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "torch.sum(t_fake_softmax, dim=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[[7.0, 6.0, 2.0, 4.0, 3.0, 8.0, 5.0, 1.0, 9.0],\n", - " [7.0, 5.0, 4.0, 3.0, 2.0, 6.0, 9.0, 1.0, 8.0],\n", - " [5.0, 4.0, 1.0, 6.0, 2.0, 7.0, 8.0, 3.0, 9.0],\n", - " [5.0, 6.0, 2.0, 4.0, 1.0, 9.0, 8.0, 3.0, 7.0],\n", - " [8.0, 5.0, 2.0, 6.0, 1.0, 7.0, 4.0, 3.0, 9.0]]" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import rankdata\n", - "\n", - "p_fake = t_fake_softmax.tolist()\n", - "p_fake_ranked = [rankdata([-1 * i for i in x]).tolist() for x in p_fake]\n", - "p_fake_ranked" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[6.400000095367432,\n", - " 5.199999809265137,\n", - " 2.200000047683716,\n", - " 4.599999904632568,\n", - " 1.7999999523162842,\n", - " 7.400000095367432,\n", - " 6.800000190734863,\n", - " 2.200000047683716,\n", - " 8.399999618530273]" - ] - }, - "execution_count": 163, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "totalRankMean_fake = torch.mean(torch.tensor(p_fake_ranked),0).tolist()\n", - "totalRankMean_fake" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[6.0, 5.0, 2.5, 4.0, 1.0, 8.0, 7.0, 2.5, 9.0]" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "totalRank_fake = rankdata(totalRankMean_fake).tolist() \n", - "totalRank_fake" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[4, 2, 7, 3, 1, 0, 6, 5, 8]" - ] - }, - "execution_count": 165, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "totalRankOrder_fake = [i[0] for i in sorted(enumerate(totalRank_fake), key=lambda x:x[1])]\n", - "totalRankOrder_fake" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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similaritiesrankedabsolute
0enzyme6.00.099185
1indication5.00.099202
2offsideeffect2.50.139109
3pathway4.00.117017
4sideeffect1.00.149353
5target8.00.076224
6transporter7.00.083819
7chem2.50.141513
8GIP9.00.094579
\n", - "
" - ], - "text/plain": [ - " similarities ranked absolute\n", - "0 enzyme 6.0 0.099185\n", - "1 indication 5.0 0.099202\n", - "2 offsideeffect 2.5 0.139109\n", - "3 pathway 4.0 0.117017\n", - "4 sideeffect 1.0 0.149353\n", - "5 target 8.0 0.076224\n", - "6 transporter 7.0 0.083819\n", - "7 chem 2.5 0.141513\n", - "8 GIP 9.0 0.094579" - ] - }, - "execution_count": 166, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_attn_scores_fake = pd.DataFrame({'similarities':similarity_types_GIP, 'ranked':totalRank_fake, 'absolute':pymean_ab})\n", - "df_attn_scores_fake" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make final plot" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'enzyme': 'red',\n", - " 'pathway': 'red',\n", - " 'target': 'red',\n", - " 'transporter': 'red',\n", - " 'indication': 'blue',\n", - " 'offsideeffect': 'blue',\n", - " 'sideeffect': 'blue',\n", - " 'GIP': 'blue',\n", - " 'chem': 'green'}" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# DS1\n", - "ds1_biological = dict.fromkeys(['enzyme',\n", - " 'pathway',\n", - " 'target', 'transporter'], \"red\")\n", - "ds1_phenotypic = dict.fromkeys(['indication',\n", - " 'offsideeffect',\n", - " 'sideeffect', 'GIP'], \"blue\")\n", - "ds1_chemical = dict.fromkeys(['chem'], \"green\")\n", - "\n", - "# DS3\n", - "ds3_biological = dict.fromkeys(['ligandSimilarity', 'GOSimilarity', 'distSimilarity', 'seqSimilarity'], \"red\")\n", - "ds3_phenotypic = dict.fromkeys(['ATCSimilarity', 'SideEffectSimilarity', 'GIP'], \"blue\")\n", - "ds3_chemical = dict.fromkeys(['chemicalSimilarity'], \"green\")\n", - "\n", - "# ['', '', ]\n", - "\n", - "if (DSdataset_name == 'DS1'):\n", - " dict_sims_colors = {**ds1_biological, **ds1_phenotypic, **ds1_chemical}\n", - "if (DSdataset_name == 'DS3'):\n", - " dict_sims_colors = {**ds3_biological, **ds3_phenotypic, **ds3_chemical} \n", - " \n", - "dict_sims_colors" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot\n", - "import matplotlib.patches as mpatches\n", - "from matplotlib.colors import ListedColormap\n", - "\n", - "plt.close('all')\n", - "fig3 = plt.figure(constrained_layout=False)\n", - "gs = fig3.add_gridspec(5, 9, hspace=0.1, top=1.1)\n", - "\n", - "f3_ax0 = fig3.add_subplot(gs[0, :])\n", - "f3_ax1 = fig3.add_subplot(gs[1, :])\n", - "f3_ax2 = fig3.add_subplot(gs[2, :])\n", - "f3_ax3 = fig3.add_subplot(gs[3, :])\n", - "f3_ax4 = fig3.add_subplot(gs[4, :])\n", - "\n", - "red_patch = mpatches.Patch(color='red', label='Biological')\n", - "blue_patch = mpatches.Patch(color='blue', label='Phenotypic')\n", - "green_patch = mpatches.Patch(color='green', label='Chemical')\n", - "\n", - "fontsize = 15\n", - "font_scale = 1.33\n", - "\n", - "list1_9 = list(range(10))[1:]\n", - "\n", - "if (DSdataset_name == 'DS1'):\n", - " letters_space = 9.25\n", - "if (DSdataset_name == 'DS3'):\n", - " letters_space = 8.25\n", - "\n", - "\n", - "def make_ranked_plot(ax, title, xlabel, ylabel, dict_colors, fontsize=fontsize, ytickfont=fontsize):\n", - " ax.set_title(title, fontsize=fontsize)\n", - " ax.set_xlabel(xlabel, fontsize=int(fontsize/3))\n", - " ax.set_ylabel(ylabel, fontsize=int(fontsize/3))\n", - " ax.set_yticklabels(ax.yaxis.get_ticklabels(), fontsize=ytickfont)\n", - " return ax\n", - "\n", - "def make_absolute_plot(ax, title, xlabel, ylabel, dict_colors, fontsize=fontsize):\n", - " ax.set_title(title, fontsize=fontsize)\n", - " ax.set_xlabel(xlabel, fontsize=int(fontsize/3))\n", - " ax.set_ylabel(ylabel, fontsize=int(fontsize/3))\n", - " [t.set_color(dict_colors[t.get_text()]) for t in ax.xaxis.get_ticklabels()]\n", - " legend1 = pyplot.legend(handles=[blue_patch, red_patch, green_patch], \n", - " bbox_to_anchor=(-0.19, -0.2), \n", - " loc=2, fontsize='x-small',\n", - " facecolor='white', framealpha=0)\n", - " ax.add_artist(legend1)\n", - " return ax\n", - "\n", - "# Subplot A\n", - "sns.set(font_scale=1.0)\n", - "ax_abs_fake = make_ranked_plot(ax = sns.heatmap(p_fake, \n", - " xticklabels=[], \n", - " yticklabels=['DrugPair 1', 'DrugPair 2', 'DrugPair 3', '...', 'DrugPair K'], \n", - " robust=True, \n", - " annot=True,fmt=\".2\", \n", - " square=False,\n", - " cbar=False,\n", - " ax=f3_ax0\n", - " ), \n", - " title='Attention weights (example)', xlabel='', ylabel='', dict_colors=dict_sims_colors, ytickfont=12)\n", - "ax_abs_fake.text(letters_space, 3,'A', fontsize=25)\n", - "\n", - "# Subplot B\n", - "sns.set(font_scale=1.5)\n", - "ax_abs = make_ranked_plot(ax = sns.heatmap([df_attn_scores['absolute']], \n", - " xticklabels=[], \n", - " yticklabels=['Avg\\nWeight\\n(actual)'], \n", - " robust=True, \n", - " annot=True,fmt=\".2g\", \n", - " square=True,\n", - " cbar=False,\n", - " ax=f3_ax1\n", - " ),\n", - " title='', xlabel='', ylabel='', dict_colors=dict_sims_colors)\n", - "ax_abs.text(letters_space, 0.6,'B', fontsize=25)\n", - "\n", - "# Subplot C\n", - "sns.set(font_scale=1.0)\n", - "ax_rank_fake = make_ranked_plot(ax = sns.heatmap(p_fake_ranked, \n", - " xticklabels=[], \n", - " yticklabels=['DrugPair 1', 'DrugPair 2', 'DrugPair 3', '...', 'DrugPair K'], \n", - " robust=True, \n", - " annot=True,fmt=\".1g\", \n", - " square=False, \n", - " cbar=False,\n", - " cmap=sns.cm.rocket_r,\n", - " ax=f3_ax2\n", - " ), \n", - " title='Attention weights - Ranked (example)', xlabel='', ylabel='', dict_colors=dict_sims_colors, ytickfont=12)\n", - "ax_rank_fake.text(letters_space, 3,'C', fontsize=25)\n", - "\n", - "# Subplot D\n", - "sns.set(font_scale=1.5)\n", - "ax_rank_avg = make_ranked_plot(ax = sns.heatmap([p_ab_tp_tn_mean], \n", - " xticklabels=[], \n", - " yticklabels=['Avg of\\nranks\\n(actual)'], \n", - " robust=True, \n", - " annot=True,fmt='.1f', \n", - " square=True,\n", - " cbar=False,\n", - " cmap=sns.cm.rocket_r,\n", - " ax=f3_ax3\n", - " ),\n", - " title='', xlabel='', ylabel='', dict_colors=dict_sims_colors)\n", - "ax_rank_avg.text(letters_space, 0.6,'D', fontsize=25)\n", - "\n", - "# Subplot E\n", - "sns.set(font_scale=1.5)\n", - "ax_rank_of_sums = make_absolute_plot(ax = sns.heatmap([totalRank_tp_tn], \n", - " xticklabels=df_attn_scores['similarities'], \n", - " yticklabels=['Final\\nrank\\n(actual)'], \n", - " robust=True, \n", - " annot=True,fmt='.1g', \n", - " square=True,\n", - " cbar=False,\n", - " cmap=sns.cm.rocket_r,\n", - " ax=f3_ax4\n", - " ),\n", - " title='', xlabel='', ylabel='', dict_colors=dict_sims_colors)\n", - "ax_rank_of_sums.text(letters_space, 0.6,'E', fontsize=25)\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}