From 0dc3c37ef800e065d97790999c314d34e56f0a94 Mon Sep 17 00:00:00 2001 From: Kexin Huang Date: Wed, 23 Sep 2020 09:10:27 -0700 Subject: [PATCH] add utils function to download HIV data and fix the tutorial 2 bugs --- DeepPurpose/dataset.py | 31 ++- DeepPurpose/utils.py | 21 +++ ...rial_2_Drug_Property_Pred_Assay_Data.ipynb | 177 ++++++++++-------- 3 files changed, 152 insertions(+), 77 deletions(-) diff --git a/DeepPurpose/dataset.py b/DeepPurpose/dataset.py index 784434f..500d358 100644 --- a/DeepPurpose/dataset.py +++ b/DeepPurpose/dataset.py @@ -2,7 +2,7 @@ import numpy as np import wget from zipfile import ZipFile -from DeepPurpose.utils import convert_y_unit +from DeepPurpose.utils import * import json import os @@ -393,6 +393,35 @@ def load_AID1706_SARS_CoV_3CL(path = './data', binary = True, threshold = 15, ba print('Done!') return np.array(X_drug), target, np.array(y) +def load_HIV(path = './data'): + download_unzip('HIV', path, 'hiv.csv') + + df = pd.read_csv(os.path.join(path,'hiv.csv')) + df = df.iloc[df['smiles'].drop_duplicates(keep = False).index.values] + + df = df[df["HIV_active"].notnull()].reset_index(drop = True) + y = df["HIV_active"].values + drugs = df.smiles.values + drugs_idx = np.array(list(range(len(drugs)))) + + return drugs, y, drugs_idx + +def load_AqSolDB(path = './data'): + + if os.path.exists(os.path.join(path,'curated-solubility-dataset.csv')): + print('Dataset already downloaded in the local system...', flush = True, file = sys.stderr) + else: + wget.download('https://dataverse.harvard.edu/api/access/datafile/3407241?format=original&gbrecs=true', path) + + df = pd.read_csv(os.path.join(path,'curated-solubility-dataset.csv')) + df = df.iloc[df['SMILES'].drop_duplicates(keep = False).index.values] + + y = df["Solubility"].values + drugs = df.SMILES.values + drugs_idx = df.Name.values + + return drugs, y, drugs_idx + def load_broad_repurposing_hub(path = './data'): url = 'https://deeppurpose.s3.amazonaws.com/broad.csv' if not os.path.exists(path): diff --git a/DeepPurpose/utils.py b/DeepPurpose/utils.py index 62283c8..0aafde4 100644 --- a/DeepPurpose/utils.py +++ b/DeepPurpose/utils.py @@ -19,6 +19,7 @@ import wget from zipfile import ZipFile import os +import sys # ESPF encoding vocab_path = './DeepPurpose/ESPF/drug_codes_chembl_freq_1500.txt' @@ -871,6 +872,26 @@ def load_dict(path): with open(path + '/config.pkl', 'rb') as f: return pickle.load(f) +URLs = { + 'HIV': 'https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/molnet_publish/hiv.zip' + } + + +def download_unzip(name, path, file_name): + if not os.path.exists(path): + os.mkdir(path) + + if os.path.exists(os.path.join(path, file_name)): + print('Dataset already downloaded in the local system...', flush = True, file = sys.stderr) + else: + print('Download zip file...', flush = True, file = sys.stderr) + url = URLs[name] + saved_path = wget.download(url, path) + + print('Extract zip file...', flush = True, file = sys.stderr) + with ZipFile(saved_path, 'r') as zip: + zip.extractall(path = path) + def download_pretrained_model(model_name, save_dir = './save_folder'): if model_name == 'DeepDTA_DAVIS': print('Beginning Downloading DeepDTA_DAVIS Model...') diff --git a/Tutorial_2_Drug_Property_Pred_Assay_Data.ipynb b/Tutorial_2_Drug_Property_Pred_Assay_Data.ipynb index 0f822b6..41bded2 100644 --- a/Tutorial_2_Drug_Property_Pred_Assay_Data.ipynb +++ b/Tutorial_2_Drug_Property_Pred_Assay_Data.ipynb @@ -22,17 +22,9 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "RDKit WARNING: [00:01:20] Enabling RDKit 2019.09.3 jupyter extensions\n" - ] - } - ], + "outputs": [], "source": [ - "from DeepPurpose import utils, models, dataset, property_pred\n", + "from DeepPurpose import utils, dataset, CompoundPred\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] @@ -88,7 +80,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "DeepPurpose also provides data loaders to ease preprocessing. For example, in this tutorial, we will use the AID1706 screening data for SARS-CoV 3CL Protease. We can use ```dataset.load_AID1706_SARS_CoV_3CL```. It will download, preprocess to the designated data format. It supports label log-scale transformation for easier regression and also allows label binarization given a customized threshold. In this case, we use the binary label." + "DeepPurpose also provides data loaders to ease preprocessing. For example, in this tutorial, we will use the HIV screening data. We can use ```dataset.load_HIV```. It will download, preprocess to the designated data format. " ] }, { @@ -96,20 +88,24 @@ "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Dataset already downloaded in the local system...\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Beginning Processing...\n", - "Default binary threshold for the binding affinity scores is 15, recommended by the investigator\n", - "Done!\n", - "Drug 1: COC1=CC=CC2=C1OC(=O)C(=C2)C(=O)NCC3=CC=CC=C3Br\n", + "Drug 1: CCC1=[O+][Cu-3]2([O+]=C(CC)C1)[O+]=C(CC)CC(CC)=[O+]2\n", "Score 1: 0\n" ] } ], "source": [ - "X_drugs, X_targets, y = dataset.load_AID1706_SARS_CoV_3CL(path = './data', binary = True, threshold = 15, balanced = True)\n", + "X_drugs, y, drugs_index = dataset.load_HIV(path = './data')\n", "print('Drug 1: ' + X_drugs[0])\n", "print('Score 1: ' + str(y[0]))" ] @@ -145,8 +141,8 @@ "metadata": {}, "outputs": [], "source": [ - "drug_encoding = 'MPNN'\n", - "#drug_encoding = 'Morgan'" + "#drug_encoding = 'MPNN'\n", + "drug_encoding = 'Morgan'" ] }, { @@ -162,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -170,10 +166,23 @@ "output_type": "stream", "text": [ "Drug Property Prediction Mode...\n", - "in total: 26640 drugs\n", + "in total: 41127 drugs\n", "encoding drug...\n", - "unique drugs: 13764\n", - "drug encoding finished...\n", + "unique drugs: 41127\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [09:01:53] WARNING: not removing hydrogen atom without neighbors\n", + "RDKit WARNING: [09:01:53] WARNING: not removing hydrogen atom without neighbors\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Done.\n" ] }, @@ -206,23 +215,23 @@ " \n", " \n", " 0\n", - " CC1=NC2=C(S1)N(C3=C2C=C(C=C3)Cl)CCN4CCOCC4\n", + " CCC1=[O+][Cu-3]2([O+]=C(CC)C1)[O+]=C(CC)CC(CC)...\n", " 0\n", - " [[[tensor(1.), tensor(0.), tensor(0.), tensor(...\n", + " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " SMILES Label \\\n", - "0 CC1=NC2=C(S1)N(C3=C2C=C(C=C3)Cl)CCN4CCOCC4 0 \n", + " SMILES Label \\\n", + "0 CCC1=[O+][Cu-3]2([O+]=C(CC)C1)[O+]=C(CC)CC(CC)... 0 \n", "\n", " drug_encoding \n", - "0 [[[tensor(1.), tensor(0.), tensor(0.), tensor(... " + "0 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... " ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -234,6 +243,27 @@ "train.head(1)" ] }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.unique(y)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -251,7 +281,7 @@ "source": [ "config = utils.generate_config(drug_encoding = drug_encoding, \n", " cls_hidden_dims = [1024,1024,512], \n", - " train_epoch = 5, \n", + " train_epoch = 3, \n", " LR = 0.001, \n", " batch_size = 128,\n", " hidden_dim_drug = 128,\n", @@ -269,22 +299,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model = property_pred.model_initialize(**config)\n", + "model = CompoundPred.model_initialize(**config)\n", "model" ] }, @@ -297,39 +327,36 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Let's use 1 GPU!\n", + "Let's use CPU/s!\n", "--- Data Preparation ---\n", "--- Go for Training ---\n", - "Training at Epoch 1 iteration 0 with loss 0.69342. Total time 0.0 hours\n", - "Training at Epoch 1 iteration 100 with loss 0.66905. Total time 0.00638 hours\n", - "Validation at Epoch 1 , AUROC: 0.73941 , AUPRC: 0.73430 , F1: 0.65\n", - "Training at Epoch 2 iteration 0 with loss 0.64731. Total time 0.01 hours\n", - "Training at Epoch 2 iteration 100 with loss 0.54385. Total time 0.01611 hours\n", - "Validation at Epoch 2 , AUROC: 0.83196 , AUPRC: 0.83827 , F1: 0.33049\n", - "Training at Epoch 3 iteration 0 with loss 0.64983. Total time 0.01972 hours\n", - "Training at Epoch 3 iteration 100 with loss 0.42492. Total time 0.02611 hours\n", - "Validation at Epoch 3 , AUROC: 0.87938 , AUPRC: 0.87592 , F1: 0.80997\n", - "Training at Epoch 4 iteration 0 with loss 0.36398. Total time 0.02972 hours\n", - "Training at Epoch 4 iteration 100 with loss 0.31953. Total time 0.03611 hours\n", - "Validation at Epoch 4 , AUROC: 0.91745 , AUPRC: 0.90816 , F1: 0.84622\n", - "Training at Epoch 5 iteration 0 with loss 0.33343. Total time 0.03972 hours\n", - "Training at Epoch 5 iteration 100 with loss 0.21458. Total time 0.04583 hours\n", - "Validation at Epoch 5 , AUROC: 0.95406 , AUPRC: 0.94003 , F1: 0.89561\n", + "Training at Epoch 1 iteration 0 with loss 0.69454. Total time 0.0 hours\n", + "Training at Epoch 1 iteration 100 with loss 0.11414. Total time 0.00138 hours\n", + "Training at Epoch 1 iteration 200 with loss 0.17790. Total time 0.0025 hours\n", + "Validation at Epoch 1 , AUROC: 0.78535 , AUPRC: 0.38694 , F1: 0.37810\n", + "Training at Epoch 2 iteration 0 with loss 0.11183. Total time 0.00333 hours\n", + "Training at Epoch 2 iteration 100 with loss 0.10962. Total time 0.00472 hours\n", + "Training at Epoch 2 iteration 200 with loss 0.11443. Total time 0.00611 hours\n", + "Validation at Epoch 2 , AUROC: 0.82523 , AUPRC: 0.47837 , F1: 0.46601\n", + "Training at Epoch 3 iteration 0 with loss 0.08445. Total time 0.00666 hours\n", + "Training at Epoch 3 iteration 100 with loss 0.14978. Total time 0.00805 hours\n", + "Training at Epoch 3 iteration 200 with loss 0.11269. Total time 0.00944 hours\n", + "Validation at Epoch 3 , AUROC: 0.84940 , AUPRC: 0.52009 , F1: 0.45989\n", "--- Go for Testing ---\n", - "Testing AUROC: 0.9550845321335961 , AUPRC: 0.9388383008711587 , F1: 0.8995714551891187\n", + "Testing AUROC: 0.7718187151198811 , AUPRC: 0.4290818621138395 , F1: 0.4144578313253012\n", "--- Training Finished ---\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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yPfZY9drWplxrAaCjo7rJGwCefBI4/fTSvntVAJRcTuPgjYdw96UFQl9fdZMvc/oJ+UQ4CPVE1sJhAoAXXPvrnWNu9gWwLxE9TESPEtFxpo6I6Gwi6iGino0bN6Y03Pj4Pc0HZRONctxNWM4nN1qt1NAA/OEP6n1PD7BtW/h9NO96V+WxoKf2OCqeoL5NwmHXruom3ywKOolwEOqJrIWDyWnTa6ZrArAPgCMBzARwAxGNrLiIeQEzdzBzx9ixYxMfaLX4Pc37Hffz8w/z/wfK4yq0GskvO6oWDtu2lQeN2RpL29uB++6za1sN7sn6wAPL4xF+5rJCPfWUeu3ri69WyqrGgwgHoZ7IWjisBzDJtT8RwAZDm7uZeQczPw9gJZSwqCuiPM0D/hOb7YSn4yq8aiQv2haweXO01YJm+nQVq5AmXv3/+vWleIQzzgC+/vXSOZ159QtfUJ5QURk5slyQhpUerQbxVhLqCr+kS94NwAEA7gDwGoCdAA50jl8F4DjLPpoArAawJ4AWAE8AeK+nzXEAFjrvx0CpodqC+s1L4j0vQRlcvfgl00s6e+hjj1WXjI+I+YgjkkvuZ9omT1ZjjXpdnAytXV3lv5dfBtkkeOUVSbwn5AtUm3iPiA4H8AiAfwHwM5SvOHYBOMdSEO0EcB6ApQD+BuA2Zl5ORFcS0QlOs6UAeoloBYAHAPw7M/ukb8s3tk/zQPSVRly0WslGXWWCuWSrSIvddot3XRyjt3tlFtWJICqiVhLqCVu10v+DmrTfC+CrnnN/BXCg7Q2ZeQkz78vMezFzl3PsUmZe7LxnZv4qM09l5n9l5lts+65notgNqkELh1Gjssns6kfQvZOommfLY4+V3kd1IoiqghLhINQTtsLhQADXO8sQr+b0NQD5swjXIVFWGnHRK4YRIyqTA2aJKXmeZvVq/4nWHT+RBPffX3ofxYnAmx127VoVpU3kLyhEOAj1hK1weAeA37PeOABvJjMcIW0WL1avzz2n1CVdXdml/ralr688gFAzbpyql5Ek//hH6X0U1Z5JBaUNzn4Bj2KQFuoJW+HwEIALnAhnjf5TPwvA7xIdlZAKxSLwzW+W9vUkNnp07cbkh2llcccd6tWvmNKIEdHv447s1qo9vboaP95ftReWh8lkq5CVg1BP2AqHS6BUS0847xnAbCJ6AMAhAK5IZ3hCkpiisPUkbGt/aGxUacJ33z3Zsdnw3e8qYWaaZJubgYsvjt4ns3KPHTNGCZ3OTmD4cHVu6VJ/1Z5NHiavAPETDmm6zwpCXKyEAzM/AeAIAK8A6IQKZjvPOf1hZl6ZzvCEJPF72t20qdIYPmeOfzW7efNKqTey5K67/G0Vn/60f+W3MHbsUIkAte3gjTfU8SDDuE2pVq8AMQkHk+0ii9oSghCGdRAcM/+VmY8BsBtU8NpwZj6KmR8LuVTICUEGV68xfN68YO+pvNkpDjywusR7brRtQAfYmfBmhzWxeXP5JG8SDmm7zwpCXCJHSDPzO8y8gZkD/E2EPBI1liIL76mkqDbxnokwl1r393GFQbHa21u+CnAbpLUqyS/vVNq1JQQhDKvs/kR0aUgTZuZvJTAeIUX0ZNbZqSafyZOVYMjzpG9LtSm7TfzmN8CHPmTXdto04LLLKo9v3QrMnau+Y7fwOvvsYHfetGtLCEIYtqVfLg84p5+HRDjUAbpgTrXkTa2UxsrhxhuBq64qP1YslgtX9/396O1V1+naE0CwYMgqEaAgBGFrkG7wbgDaAHwOwNMA9k5xjEIO+V3OnJerTdltwpsJ3mQ81oSpoDo77cbX2FiyOYhRWqglsbOyMvPrzPxTAP8DVd1NGCAUi3b1mYcMSX8smmpSdvvR1la+bzIea8LyOq1bZycc9GcQryWh1iSRslu7uQoDhLlzK6uzmZg4Mf2xaC67DDjyyGT7/NSnyveDjMRLl6pXv4SGkyeXCwcbtZzXa0niIYQsSUI4fBxA/kqxCanRa5kj9/nn0x2Hl6TVSs88Uz4ZB0WS//zn6tUkELUNwe2tZJtKQwskiYcQssY2ZfdNhq2biB4HMBfAjekOU6gXurtLyfFsVhdpc8kl5bEaZ5xRfr6jA9hjD/O1DzxQPhm78zB50QJzg6d01dixpfiQX/86+vi10VviIbJBVmclbL2VjkZlNtZ3AKwF8CMANYiXFWpFW5t59aB19EHBY1kzaJCK0dC880558r7ly+3rQOzYoSaNoBWK97PrdCXFYnQPJCIllCQeIhv06kwLYb06A/qHu3dk/KoA1dOW10pw/ZXububm5vKKac3NzHPmVB7PwzZnTmnsu3Zlf3+i6q/x6yPpSoFR/gZsqxzWC1lVY8wTqLYSnCC4KRTU07dbXXPzzcCSJXarBp27KSvmzy+pB6qNzxg1Kvo1HCNVt/caUx+1iofor/aPqMWe+jvEPn+5RBTJA4mZUy4e6U9HRwf39PTU6vaCQ0ND+ETY3l6Kyh4zxt64XS3t7Uq9VCwCp58ev5/Ro1WiwlrT1gZcc01t1B1+ai79Hdcr/fVzBUFEy5i5w3guQDjsQqWdwdgUKn1GzKrE1SPCIR8E6cbdtLaWktaFpZFICiJg0aLq70eUj6I9tZyw/B4CiOq7ZkWxCJx1FrBtW+mY/lvtrzaHIOEQpFY6CsoQHbbpdsIAZ/p0u3bay0ZnNtW1IYgqA8+SYvLk4CA2W8aNS2Y8YYSlA6+lqiNqOdV68f4pFIALLyztp1XHvW7wM0bU0yYG6XzgZ9DzM7hqHnpIHRs0yGzsrnZrbVX9RjEMNzSY+/n+95Mdm982Z07w+dGja2cQ7u5W34XpO47TLk8sXarGOW1arUeSDRCDtJAFUZ5m3U+Zgwap15071VOarsRWLd46FFEynba0lO9PmKD6OfbYZMYWxpIlwec3by43CLur2SX1hO731F8oAD/5Samd3xN2PcZm6NicJlsn/36M9VdARPtD1YveD8Bgz2lmVQhIGMBMnmxncyAq97LRwkHnFUrK4LtxY7maqqtLTaI2HlXecqpf+Yqa/JYtS2ZsYYQJWm+iP13NDkjGPz/M5/+004BZs9S+n+2jHr1/9N+gCAf7COl/A9AD4HgAxwIYBeDdAI6EysiaswTOQi2wKZ0JqKdd96TlfUpPqpaBjtR2E9eVVScaDMu+mhTVfgfVPqH7PfWffrr6DsePD+8jim0iL8jKoYStWukqAHcAeC+UIDiLmacAmAagEcC3bW9IRMcR0UoiWkVEFxnOf46INhLR4872edu+hdqiDcw6/sEvCV17e/m+XjlourqSqRex//7l6pXOzviT+0svKfXKySdXPy4bkvgOvE/oUYzDYU/3Ni7IUSsP5gG9qhThADuDNFRivelQwmQXgA+4zs0B8CfLfhoBPAe16miByug61dPmcwB+bNOf3sQgnU9sDZIbNpTOa+bMqTQgNzXFN0Yzx4tUrtXGXG7g936PgwaF9+GO7I1qHI7iXBD2N+AeT56N0czMxaIa64wZtR5JNiABg3QzgC3MvAvAJgBuh76VAPa37OdgAKuYeTUzbwdwC4ATLa8V6gzvSsLPcOldOQDAvHkqLsF97TE+Vi2/FQpQrl7JszrDjf48OiHgpZcCH/94eZvzzy+9b2urVM25n9CLRWD2bLOaaO5c82qiqwsY7LUsxsD9W+e9DjkgNgc3tsLhOQATnPdPAjiTiBqIqAHAGQBetuxnAoAXXPvrXf26OYWIniSi24lokmXfQg4pFNSksGuX/+Tgndj8rt13X3O7sCI/WkViaxOpNdpWogPKNm+u/IzuYLNhw1TwVoPz3+yuJnfuucqQ7Pcd9faa02AUCsDllyf6seoCUSuVsBUOv4IyPgPK/nA8gH8AeB3AZwD80LIfkxaVDfeawszvA3AffDK+EtHZRNRDRD0bvfUchbrCtHIw8cwz5uMNIX/FesVgWsnMmVPazwv687z6qnr94Q8rXVt//OPS+7VrgYULS0LWXU1u/vxogX/ulVZWxvc8oT+zCAf7GtKXM/PZzvv7ABwClar7RgDHM7NtmdD1ANwrgYkAyjLgM3MvM+sA9v8GcJDPmBYwcwczd4wdO9by9kIeue220vsgQ+kjj5iPB6Vs8LrNelcj8+apV7frapiwSZu331bjdhuFvWnF3SkeADWpe91vAbUiiIq+73UB/9W2CQjj3D8qSUZhi3Bw4WeMSGODiqtYDWBPlAzS7/W0Ged6fzKAR8P6FYN0/RLFUFqNcTeMvr7aG6HzsrW1hRukV62y+3537oz2O0QlySjs7m7mkSNVH7vtln/jeRIgwCBtO6nfAeAkAM027UP6mg7gWSg7Rqdz7EoAJzjvvwNguSM4HgDwL2F9inCoX6Lk0B82LPpEFyUXv/uaWk/Qtdqam5lbWsLbrFxZ2veju5t58uRSuzQm26RqMNRjqo8kSEI4rIByYX0NwHUADrG5LqtNhEP94ude6s69pDn66GgTHZH9P7fb5bKtLXyC9G7771+bydzmOzj22NL7IUOC29sI4FGjmJcvL+37fZ9ZTLZR/n6CGIiFfpg5UDjY2hymAvgAgG4AnwTwMBH9nYguIaJ3x9JnCQKiRdHutVe0vpntXCd1qghNb6+6tq1N6f5tjNVPPx1tbFnBDCxdqt4PGxZeEnXz5vA+33gDuPvu4DZBEdZJZmdNKgrbL+jPJh1Mf8Xa9MbMy5j5Aigj8icA/AXANwD8nYj+L6XxCf2cKFG0zOr1zDMro6xN2LQBzBPZjh1qMt21K3oa8VobtP14661k+mEGrr02uE1QhHWSleNMfz/NzUrIRTFQ+wkTonynGU8VvyWFzQbgo1BxC33V9FPtJmql+sa2HvFZZ6ml/oIFal9Hs+69t1m90NZmp8YIU01MmhRdnROmvqn3zf2dmbCx2ySlsglTCdqos4JSuvdn1RKSTNlNRHsR0WVE9CyA30DFLvwgSYElDCxsAuWA0spBP5lrdU9Hh4qm9j7h9/baPaGGqSaGDAn7BJWEqW8AFaxGlN+VRhC6QJMfNgGHSWVndf+9DBtWGZ9hk4SwUCj9fXlZt66+ihYlhW1W1lFEdA4RPQzlafQ1AI8COA7AJGb+eopjFAQApXgGLRT0pLprl/rnHjas8hqbiSFMteUXwV0tCxeqsbtTYdQDROU2GhM64DCINNKZ+NkIbASRnxpy9Gj1eU2R5P0Z22eWlwH8GMAWqMR4ezDzZ5n5XmdpIgipo//StHDQr/p43PoBQTmgikVgxYrqx+53XwA4us6K7E6Y4D9m9xN2kFBOKzurn/OAjSDq6qoMftMPDfVWtCgJbIXDN6FWCB9l5kXMnEFJeEEoJ0w4VOO5YlJtFYuqOJApAtu9SjFF00bJ4RSUODCPjBhhztWkvb7cT9hu9OccPz692symR1VvlLwfhQJw6KGl/ZEj1Tj9ik/luWhREti6sn6fmV9KezCC4EexCPzyl+r9176m9t1qJSD5+gGdnf5V49z5oM47D+juLl95zJ9vf596Ew5AqSiOG5PXlxstpP/wh2yzs7KlSzNQrlr65jeDy8vWS5bfuNShKUwYaOgn0i1b1L42NP+f40CtnxZtU4TbEvRk6C5209hYufI4/fTgvt1G7v4iHMKepPXvFJZFN2lsXZqB8s+lHz7qsWhREohwEHKPX0DVTTep93feWfIgsfV8siHoyXDMmNJ7k7dRWOBcvQsH0wQf9iSthcNdd6nfi0ip5IiS8wAyqfM2b7bv2yQc9ENHFmqxPCHCQcg9fk+k7qCuNDxIurrMdagBYMaM0vs4k7tbeNx/f/TrawmzeeVgWy/jsstK9gh3evEkfj+Th5SNS7M2pP/iF6Vj7t+oUFCGeAD44x/7v2AAUF0QXF42CYLr30RJhJd0wFJ3t0o0573PmDGV9/UGWgUFx40dW2q3xx7xg9FqsY0fX/753Z/bHZAWJ3gw7u/nDsiL2rcpDxTAPGtWebuJE9Xx55+PN8Y8gmoT7+V9E+HQv/H75zVtUROu2fCpT6m+GxuD7+2NxHVPSjr6e/bs8uNBkbl53bzj9fvcr78er/842AgHv78Nv4ePUaPK240fr44/91y8MeaRIOFgGwR3IhGd4dpvJ6JHiOgtp5SnIfxIEJLBZGj2yxb3zg8AACAASURBVHeUhgeJVi2FGVL9fN+1DaSrq7ywkValDB/u3ydRuX0jDzCX7/t9bm9BIhvSzGXk97fhp7Z8/fXyfe0VF1Rcqj8RJc7BXW7th1AJ+BYAOALA5ckOSxDK8Rqar7kmOw+SKFXBgjx2Ojsr02ps3Vo52brZtSs80jgPmD73rbdG74c5neCyoL8NP6ExenT5vggHM3sBeBIAiGgIVMGerzLzhQD+A6pimyBkRtJuq0FESZ+hJxr306/2xLExrJuoh5KVDQ2VT/zf+Ea8vpIOLgv72/AzpJ90Uvm+CAczgwHoZ55Docp9/q+zvxLA+ITHJQihJOm26kexCNxyi337rq7K+hBafeR9EtW8613BfdaDcOjrq/QIMtW0tmHy5GQT3YX9begHDW+CxUMOKd/XakURDuWsAXC48/5EAMuY+U1nf3cAb5ouEoR6Rk/yNgVwAGUHKRT84zIAsyps7tzgMeRNOPhlkk0i31BrKzB9evaJ7goF4OMfLz+mXVm1oNI2iF/9Kr1x5Alb4fATAJcTUQ+AcwHc6Dr3QagyooLQrwhLB+HlmmvUq59aZNMmsyosKGX37NnA739vP4YsGDfO/+m5GpXQ5Mnq+1iypDaJ7ry/g1aVaUGlufzy/p+RFYC9KyuAAoBrAXzWc/wnAGbZ9pPGJq6sQhpEcTFtaytdF7UesXaR9NsGDSrfb2io3h01yjZiRPn+e97DPHKk/2f0G3fYtm1b8Pce5KYc5Mpqy8yZ5dfdcEN1taVti1jVEiRR7IeZi8z8ZWb+qef4F5l5UXLiShDyga1bbEtLadUARM/F81JISkuvS6jfU3uUHEJRuPji8n0iYO+9zW2nTy+9/7pFlRd3FPK2beUJFb34/R4c4O0F2NsuvPd95JH49SFMGWrrrgaEn9RwbwD2BXCwa38IgO8A+BWA82z6SHOTlYOQBrbBd+ecY77W9qlxwoT4T/Xukpju6OQkt//8z/L9qVP9y6C6n7SjBveNGuV/LqjU586dpXbMwfcI6mfWLP/vNurKoZoVR5YggZXDjwGc6trvAnAhlJfS1UT0pYRklSDkBre7bBBHHGG+1taTyuYJ2w93Scyw6mxx8RrE33jDvwyq+0mbQ57ovXiDzjSNjcGuqFHuE2S78K4cvOVGNYMHh8fTxC08lSdshcP7ADwMAETUAOCzAL7BzAcB+DaAlP4sBaG26Em+u7tSVaRVIn7J+Ww5OaEoIZPxPCw7rA1XXFG+v2FD9X1GQZeBdeN2dd1rr9Jxm3rcfhO0bS3viy7yF1R6XH4Cq55qQNgKh5EAdAb7AwCMAnC7s/8ggHcnOyxByBemoLuxTs6Aal1N9QTul921mv6jPr2b8HuizwrvhOrV57sne5vP6zdBr15tN55p08zHTZ5NbuqtBoStcHgFgDZBfRTAc8z8grM/DIAhga8ZIjqOiFYS0Soiuiig3alExETUYdu3IKSJV1WkcyJVu3K480712tdX+aTf3Bz89D94cHX3rge8E2pUF2M3fhN0sQg89JBdH6ecYjYsB40rzQj+tLAVDosBfIeI/hPK1uDKeo5/BWAlc4moEcB1AI4HMBXATCKaami3G4DzAfzJcnyCUDOqebIvFsvTTDCXhEF7uxJAfqVKAeDSS+Pfu16JqrfXkc9jxvhP0J2d9hXqXnnF7HkUNK6urvoSDIC9cLgIwD0AjoUSFFe5zp2AUiqNMA4GsIqZVzPzdgC3QEVce/kWgO8BiBmALwjZUc3KwZSMj1kJhjVr/Ivba045Rb0S+WeqrXe8E3FUvf0nPqFef/xj/wk6qsAxGbaDxpWkG2uSqUWCsBIOzLyFmb/AzP/KzGcy8xbXuUOZ+eKg611MAPCCa3+9c+yfENEBACYx8z1BHRHR2UTUQ0Q9GzdutLy9ICSHfsKvZuUQ5tUSNhEOGlQaiylTrY2RNW/pObx4J+KgCn0mdNugFVgcQ7H3twuqhJdUhHeW8RORyoQS0Wgi+hgRzSKi6UTkk0rMvwvDsX+akBxPqKuhVFeBMPMCZu5g5o6xY8eGNReE1Khm5eA3KenjfgF1Gi0cGhrMrrf77x8+hiFD0gugSwr3RFwoAKedZn+tFn5BwiGOodibiVZ//34k4cbql7crjdQi1sKBiL4N4EWowLeFUGqmF4noWxHutx7AJNf+RABux7jdAOwP4EEiWgPgEACLxSgt5JlqnrzDoqlNXlKzZ5faTnUsdjt3KhUDoNRRGptJf/Pm8mvi1MROG68QPegg+2ttVg6FQvTf0ZSJtlDwT/GehBtrlvETtpXgLoCq29AN4CgA73FeuwH8BxGdb3m/vwDYh4j2JKIWADOgbBgAAGZ+k5nHMPMUZp4C4FEAJzBzj+0HEoSsqUY42NSlcHtJdXUBCxeWzrndTE0qBptaFLvvXr6fx5TU7rQcUbERDsWiErBRMT21T5hQ2S4pN9awlWai+IVOuzcAzwC42ufc1QCesenHaT8dwLMAngPQ6Ry7EkoIeNs+CKAjrE9JnyHUgn33VSkRnnoqu3v6pWXwS2HhTSZn2i65RPVtuj4vW2sr85w5pZQkQak2vNuxx5be6zrg7pQmUWqUmzadEFCnTDG1SSrpnmmsQSlBwkBA+gzbCf0dANN8zk0D8I5NP2ltIhyEWrDffuo/aMWK7O5pk6/I3cabL8i9NTWp14cfVn27JzLvBOSXSynLLWquJluBowVG3K2xUfUTJGCSzMjqzqFVbbbXIOFga3PohbIFmHgvStHTgjDgSCJFhS026gN3G+bw9t6gLpOq6/rro481aWw+SxS2bgXmz7ePb/Cjr0/1ExSYl6TB2K1yTKsCImBvkL4TwLccL6VmACCiJiKaCaUS+mU6wxOEfFIsAs89p94fc0x2qZiD3CWBSt22HqMJrWN/+eXKpH3eaPBZs+zGN2yYXbu8kJTACevHazDOKlahKvyWFO4Nyovo/wDsArADKp3GDmf/DwCG2fST1iZqJSFLktb7xrm/1r23tanNmxrcrfKIqibxw0ZNM3RoPNVMoRB8PqgAUC1VXbabO1V3En8/Yb+VfT/+aiVS58MhIgLwMQAfAjAawCYAvwfwG7btJCU6Ojq4p0ccmoRsmDLFnFxNRzXngWpUXX7/zWmqzw4/3D+30e67K9XXwoWVqpv99weefjrZsRAlr8Lq7i6pf5L4+9G/RbXjJKJlzGwMFYhSCY6Z+R5m/garaOlvMPOSWgsGQciavOfqz5uKgig8duLPf/Y/d9NNwLx55QFmo0ap1w9/uPrxuWlqAhbFqGvZ2gqceqr/ebddIOjvJ0/qpkgR0oIgZOxrHhGdXiEqQXYME7vvrib98ePD2y5apJ76g+7hV1gHAO69V02Up58ebYxxOOaY6AZeHZtywQV27f3+TkaPNqfGOPfcGgkMP30TlD2hz3Lb6ddPFpvYHIQsqbXNIYg4MQptbcoVM8w90n3N/ferY2vW2Ovbu7vj2UCCrpk2LZ4NwM/t9MILKz9r0DZyZOn7ufde/3Zu/P5+2trM13rtKu5rqwUBNoeg2M4rgVLeI0EQFPrJsrNTqQImT85PSuYoqq329pJnk3u1oZ9YAf/PpCOvbaLDtX5d9xV1BRDkavroo9H60ixYYB6HTUS5G3fUtV/pVC/e70H/Dn4eYeyZhd12l4aGFP/+/KRGPW2ychAEhe3KQUf1Bl3j9rBhLj/36KPq2CuvhN+rsbG8H78n5Cy3vj7z8csuq/ysQVtzc+lz3XqrfzsT3nPVRKbHXbkigSA4QRDqgLA4CI1b7x3HwB5l5dDXV64nN6UWj+sJpbPSRsUvj9Ly5dH62bFDPfkXi/YrBz9Mv53t95JGZlYRDoLQjzCl7fbS2FgeKBfHwK6T2dmmK3cnBDSNMW650yhCxa0y8hMOd98d3eC7bh1wxhnAl74U3M7rieTFm/K7vR045xx7Z4HEveX8lhT1tIlaSRDKCUomd+ih4W1Nagr3+WeeUce2brVXfXjVVN4+094OOqj0/s03/dulofYK+j38vhP37+P+Dv3GZ/p+w4ColQRhYBFU7N771GqTNtyLVie51UphsQxBT7a6znOaPP546X2Qkbs3hUxxQb+HG1O6dG8upWuuqWyTVEpwNyIcBKEfElXF4M2lFOb5YhIOO3eqZ1g/lVaQmmrbtiijjYdbIMSp3VANtr+HSWh5BYb3t7ER5nEQ4SAI/ZCgiZi5+v4PO0zp0E06/66uyuNhT7ZZFxjKWjjYBkiahIP3mPe7SiszqwgHQeiHmDxftEE2jnDwGmlffNG/sH2hAOiy7rZqqqyZNCm8jS0jR4a36ery96xyG6l/9rPK815BVm2KcVtEOAhCP8RkR/j85+P3Z3KTDHKf1Km7n3oq3ZoDcUlygn30UaCtLbhNoQB88YulfbfqzZ0u47zzKq/1jlWEgyAIVeG1Ixx+uDoeZ+VgyiIK+OvS9T1sXV297bIsoFQt27crI3FYzMehh6rX1lb/780UK+EVBt7VRVq5lkQ4CMIAIW6aZz/bAqCSxWncSeH0PYI8mNyTmndiPeccpWrJAy0twAc+4H9++3YliD/0oeB+fv979bp1a7TfwK1WKhYrVxd+6r1qycnXLwhCXuns9J/M3nqr9F7nZCoWS+39Jnhv9ljvE/Nhh9lFXwcxfnz0bLMmtm8HgsrF6PxKQUbnc88Fbrst2n21HcJ9XWdn5XeVRnQ0IMJBEAYMcVcOQW6Y3lTbW7cCc+cC69er/Q99yPxUG+b339kZnMbbD/cKZ999w6PFbQn6zg49VE3izz7r3+b66+3iJ9zqNW2HuPDC0rEsa4mIcBCEAUJcPX7UOhW9vSU9uZ9XU9hkFmeyGzu2cgLWdpeTT7YLtIsjSPQkHlSwyIb2dnMNbvdKIctaIiIcBGGAEXXlYHKLbW0N99DRmNQeYZNZnMnuM58B7rqrtP/IIyWh1NRkF9tgm7jQRLVeRGvWAG+8Edymq6tSyKURHQ2IcBAEIQS/9Bqm7Kp+eFcCYZPw5s3Rayswlxtrt21TNRLOPVcZxt21F/zwftawlCBJM25c8PlCAfjOd0r7LS0pxpD4JV2qp00S7wlCOF/+cnmStiQq13V3q76IoieF6+4OTnLX3KzOEzHvsUd4crsxY8zHiZgPO8wuQZ5pjH4J85LemJl/8IPK44MHl3+W8eNL+9VOfQhIvJf5RA7gOAArAawCcJHh/DkAngLwOICHAEwN61OEgyAE093N3NJSPumkUdo0agnVsAI3Wqj09IRPrt5ymu7N+9lthYP+THHKm8YRDitXlh9ra2P+9Kf9r2luru43zI1wANAI4DkA7wbQAuAJ7+QPYLjr/QkAfhvWrwgHQQjGttpbEnhXE0GTV9CErid8ZuanngqfXBsakpmg/T5T2isIZuYVK8qPLVzIPG5c8HXVCPkg4ZC1zeFgAKuYeTUzbwdwC4AT3Q2Y+R+u3aGA1LEWhGrJ0gUySoZXW8O0TcW3NJP32RRRSgKvUbupCXj55eBr+kucwwQAL7j21zvHyiCiLxHRcwC+B+B8U0dEdDYR9RBRz8aNG1MZrCD0F7J0gYxCkGHa7YUTtxxokmihxxEeV6OO2ysc5s4Fhg8Pv64/xDmYPK0rvmpmvo6Z9wLwDQDfNHXEzAuYuYOZO8bqFJCCIBjxc0dNwwUyCt4ncu0d5M3kGnWSdXtWmWIHsuKoo6K1v+ee8v3XXgO2bAm/rj/EOawH4E6WOxHAhoD2twA4KdURCcIAIE61tyzHpp/IdcEgrzpq8WL7/trbS2qtri67CmxRiJLHKEp+qGIRuPTSyuNh8RmpCXk/Y0QaG4AmAKsB7ImSQfq9njb7uN5/AgEGE72JQVoQ+i/d3cxDhtgZdVtayo2zYd5QtgZp91iiGKbDDO7esccxZKflrUQcRYGWAEQ0HcCPoDyXbmLmLiK60hnkYiK6BsA0ADsAvA7gPGZeHtRnR0cH9wRlxhIEoW6ZMsU/ZbiXtjalitE0NESzEYS1jTKWLCCqzhBPRMuYucN4LmvhkAYiHASh/xJlgvdOllEmc5uJNqqwyYJqxhMkHCR9hiAIuSaKsdXbtqvLXu9v067W3l0m3HU0kkSEgyAIucY2GV5LS6VhtlAARo2yu09fX/hEW01ivriEeWq562gkiQgHQRByjfa0CssCe9hhZu+rTZvs7xU20Xq9vrykUd706KPD26QRCCfCQRCE3FMoKENzd3cpJsI7ET/8sHlSj6oKCptotevtokXlx5ubgSVLyo8lUerU1pyadCCcCAdBEOoGPTG3t1caYrdvVxHFXuKogsImWm+ZU0ClBD/55PJjSaT0sE0AkbQ9RISDIAh1h9/k3dtbuXqwVUu5CZto/cqcvvOO/T2SJI1AOBEOgiDUHUGTt0kl5FZLhQkJm4k2jVxGcWlsTCfaXYSDIAh1R9DkHTRxFwrBuZZsJ9q4Kpzx4+3aRTFsX399OmlQRDgIglB3FAr+K4CwiTtIeOzaZTfRxrFjtLYCX/+6Xdtvfcu+X6+dIylEOAiCUJeYaljbqISChIftiiCqHaOtTbU/9VS79t805qIuR3tChSXmi4sIB0EQ6pK4mWa7upTbqRdTEF3Y/d15nAB/ddCwYap9lOyyYWhPqF/8Irk+3UhuJUEQBhzFInDWWcC2bWqfCDjnHGDevOh92dgHdN6mSZOA9euj3yOICRPi9ym5lQRBEAJgBhYujJ6Cwra9Vle9+GK0/m3YEFQRpwpEOAiCMODo7CytGjRxUlDYttfqqokTo/Vvw7hxyfcJiHAQBGEA4uexFDV+waZ9W1vJDhLFC8mW889Pvk9AhIMgCAMQP6+kqPELYe1bW5VXlWbWLLt+hw71t2W0talzY8eq/WOPteszKiIcBEEYcJjiFOKkoDC115O6yXvKnYgvyJA9eLDaTJx6qjJu33ST2hdXVkEQhITQbrBDhqj9MWPipaBwt9futIsWKQP3mjXB/QWtOnp7gbffNp+7/Xb12tSkXkU4CIIgJEihAJxwgnp/7bXVp6DYti1cILjp6opX/6G3V72KcBAEQUiJxkb12tcX73q3K+vee0dzhS0UVGxFVEaPVq8iHARBEFKiGuHgremwbl14uU73uSlTVPW6oESAJqZOVa8iHARBEFJCT7BxhIOppkNQrIRXmOiSpFGTVPzxj6ovEQ6CIAgpoVcOcSbYqLESfsJky5Zo9921SwmVe+9V+yIcBEEQEqaap++osRJJFgjaulXVcQD6kXAgouOIaCURrSKiiwznv0pEK4joSSK6n4jasx6jIAj9n2JRVYYD1FN91LxKUWMl/IRGW1t5/IPuZ7fdgu//8svqtV8IByJqBHAdgOMBTAUwk4imepo9BqCDmd8H4HYA38tyjIIg9H+0/n/zZrX/+uvhxmQvUVOG+wmTa65R2VqB8n7GjFHHvIJDo3Mq9QvhAOBgAKuYeTUzbwdwC4AT3Q2Y+QFm1pq5RwGkkKpKEISBTFRjsh+Fgopt2LUrPMYhSJgMH67a/PrXpX60ofp73zMLlSOPVO9nzlSeT1FXPmFkLRwmAHjBtb/eOebHWQB+k+qIBEEYcCSVeC8qfsJkxw71OnSoei0WgRecmfLqq4HZs8uFyuzZwJ13lvrVnk9JCoishYMpHtDoyEVEpwPoAPB9n/NnE1EPEfVs3LgxwSEKgtDfSSrxXlJo1dDQoSWVl3avffFFVWuiq6skVJYsqUyvEWflE0TWwmE9gEmu/YkAKkpVENE0AJ0ATmDmbd7zAMDMC5i5g5k7xur0hIIgCBYklXgvCYpF4Pnn1fsTTgDmzg1XeWWx8slaOPwFwD5EtCcRtQCYAaCsqioRHQDgJ1CC4dWMxycIwgAgbv3ppPGuEjZsKOVO8uKe+LNY+WQqHJh5J4DzACwF8DcAtzHzciK6koicFFj4PoBhAH5BRI8TUYIluQVBEBRRjMlpYTKM++Ge+LNY+TQl15UdzLwEwBLPsUtd76dlPSZBEIRaYKsG8k78WpB1dqo+Jk9W55MUcJkLB0EQBEExebLyNPLS1qYS8gVN/IVCuqsdSZ8hCIJQI4IC42qt8hLhIAiCUCPyYhg3IWolQRCEGpK2eigusnIQBEEQKhDhIAiCIFQgwkEQBEGoQISDIAiCUIEIB0EQBKEC4qjVrXMIEW0EYAglsWIMgNcSHE5a1MM462GMgIwzaephnPUwRiD7cbYzszFzab8QDtVARD3M3FHrcYRRD+OshzECMs6kqYdx1sMYgXyNU9RKgiAIQgUiHARBEIQKRDgAC2o9AEvqYZz1MEZAxpk09TDOehgjkKNxDnibgyAIglCJrBwEQRCECgascCCi44hoJRGtIqKLajyWm4joVSJ62nVsNBHdS0R/d15HOceJiP7LGfeTRHRghuOcREQPENHfiGg5Ec3N21iJaDAR/ZmInnDGeIVzfE8i+pMzxludMrUgokHO/irn/JS0x+gZbyMRPUZE9+R1nES0hoiecioz9jjHcvObu8Y5kohuJ6JnnL/RD+ZtnES0n/M96u0fRHRB3sYJAGDmAbcBaATwHIB3A2gB8ASAqTUczxEADgTwtOvY9wBc5Ly/CMB3nffTAfwGAAE4BMCfMhznOAAHOu93A/AsgKl5Gqtzr2HO+2YAf3LufRuAGc7x+QDmOO/PBTDfeT8DwK0Z//ZfBfAzAPc4+7kbJ4A1AMZ4juXmN3eNaSGAzzvvWwCMzOM4XeNtBPAygPY8jjPTLyMvG4APAljq2r8YwMU1HtMUj3BYCWCc834cgJXO+58AmGlqV4Mx3w3gI3kdK4BWAH8F8G9QgUVN3t8fqp75B533TU47ymh8EwHcD+BoAPc4E0Aex2kSDrn6zQEMB/C89zvJ2zg9Y/sogIfzOs6BqlaaAOAF1/5651ie2IOZXwIA53V353guxu6oNQ6AejLP1VgdVc3jAF4FcC/UKvENZt5pGMc/x+icfxNAW9pjdPgRgK8D2OXst+V0nAzgf4loGRGd7RzL1W8OpQXYCOBmR013AxENzeE43cwA8HPnfe7GOVCFAxmO1YvbVs3HTkTDAPwSwAXM/I+gpoZjqY+VmfuY+f1QT+YHA3hPwDhqMkYi+jiAV5l5mftwwFhq+bsfxswHAjgewJeI6IiAtrUaZxOUavZ6Zj4AwBYo9YwfNf0/cmxJJwD4RVhTw7FMxjlQhcN6AJNc+xMBbKjRWPx4hYjGAYDz+qpzvKZjJ6JmKMFQZOY78jxWZn4DwINQutqRRKQrH7rH8c8xOudHANiUwfAOA3ACEa0BcAuUaulHORwnmHmD8/oqgDuhBG7efvP1ANYz85+c/duhhEXexqk5HsBfmfkVZz934xyowuEvAPZxPENaoJZ3i2s8Ji+LAcx23s+G0u/r4591vBgOAfCmXo6mDRERgBsB/I2Zf5jHsRLRWCIa6bwfAmAagL8BeADAqT5j1GM/FcDv2FHupgkzX8zME5l5CtTf3++YuZC3cRLRUCLaTb+H0pM/jRz95gDAzC8DeIGI9nMOHQNgRd7G6WImSiolPZ58jTNLA0yeNigvgGeh9NGdNR7LzwG8BGAH1JPCWVD65PsB/N15He20JQDXOeN+CkBHhuM8HGpJ+ySAx51tep7GCuB9AB5zxvg0gEud4+8G8GcAq6CW8oOc44Od/VXO+XfX4Pc/EiVvpVyN0xnPE862XP+v5Ok3d431/QB6nN/+LgCjcjrOVgC9AEa4juVunBIhLQiCIFQwUNVKgiAIQgAiHARBEIQKRDgIgiAIFYhwEARBECoQ4SAIgiBUIMJB6NcQ0eVExM77kc5+dpktK8fzfmcMow3nmIgur8GwBKECEQ5Cf+cGqAR2gMrSeRlU5GyteL8zhgrhADXOG7IdjiCYaQpvIgj1CzOvhwosTAUnaryZmbdX2xczP5rAkAQhEWTlIPRrtFrJySL7vHP4v51jTESfc7X9JBE9SkRbiegNIvoFEU329LeGiLqJ6EwiegbAdgAfc85dQUR/JaI3ieg1Ivqdk/JAX/s5ADc7u393jWGKc75CrUSqKNUjRPS20+9drhQRus2DRPQQEU1z7r+ViJ4mopOq/PqEAYwIB2Gg8BKATzrvvwOlwvkggF8DABGdA5VQcAVU7qIvAtgfwO91biEXR0EV6bkCwHFQ6RoAlUr5agAnAfgcVPK0PxDR+5zzvwbwbef9p1xjMObKIaLjnGs2A/g0gDnOmB4iIm/a5r0AXAPgh87nfAnA7US0d+C3Igg+iFpJGBAw8zYieszZXe1W4TgpyL8L4GZmPtN1/E9Q+bfOgsqYqhkF4CBWyd7c9/i869pGAL+Fykd0FoC5zLyRiJ5zmjzOzKtChv1tAKsBHM9OjQciesQZ04VQAkozBsARzPx3p91foQTEaQCuCrmPIFQgKwdBUE/vwwEUiahJb1C2imegyri6edQrGADAUes8QES9AHZCJVLcF8B+3rZhOBlQD4QqB6qL/4CZnwfwMIAPey75uxYMTrtXoVYukyEIMZCVgyCUqm7d53P+dc9+hRrIcY9dAlXO8yynTR+U99HgGGMaBZWR06Ry0nWH3ZhqO2yLeW9BEOEgCFDpkwFlJ1huOP+WZ9+UyvgUqNXCJ5l5hz5IRKMAvBFjTK8793mX4dy7UBqzIKSCCAdhILHNeR3iOf5HKAGwNzMvjNl3K9RK4Z+Cg4iOhlLrPO9q5zeGMph5CxEtA/ApIrqcmfucPtsBHArg2pjjFAQrRDgIA4lXoJ64ZxDRk1B1hp9n5l4i+ncA1xHRWAC/AfAmlPfRhwE8yMw/C+n7twAuAPA/RHQzlK3hEgAvetqtcF6/REQLoewST/rESVwC5a10DxHNAzAMykPqTQA/iPC5BSEyYpAWBgzMvAvA56H0+fdBlYv9hHPuJ1AF3/cDsAhKQFwB9QD1uEXfSwGcD1Ub+h4AZwL4LFTlNne7JwBc7tz3IWcM4336/C1UDMVIPnNjzwAAAF1JREFUALcBmA9V8vRwduo6C0JaSCU4QRAEoQJZOQiCIAgViHAQBEEQKhDhIAiCIFQgwkEQBEGoQISDIAiCUIEIB0EQBKECEQ6CIAhCBSIcBEEQhApEOAiCIAgV/H9Ssvi8INgErgAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -379,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -390,10 +417,9 @@ "in total: 1 drugs\n", "encoding drug...\n", "unique drugs: 1\n", - "drug encoding finished...\n", "do not do train/test split on the data for already splitted data\n", "predicting...\n", - "The predicted score is [0.009828050620853901]\n" + "The predicted score is [0.003792080795392394]\n" ] } ], @@ -415,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -444,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -456,7 +482,6 @@ "in total: 82 drugs\n", "encoding drug...\n", "unique drugs: 81\n", - "drug encoding finished...\n", "Done.\n", "predicting...\n", "---------------\n", @@ -464,22 +489,22 @@ "+------+----------------------+-------------+-------------+\n", "| Rank | Drug Name | Interaction | Probability |\n", "+------+----------------------+-------------+-------------+\n", - "| 1 | Loviride | YES | 0.92 |\n", - "| 2 | Efavirenz | YES | 0.84 |\n", - "| 3 | Nitazoxanide | YES | 0.80 |\n", - "| 4 | Letermovir | YES | 0.73 |\n", - "| 5 | Imiquimod | YES | 0.55 |\n", - "| 6 | Famciclovir | NO | 0.34 |\n", - "| 7 | Darunavir | NO | 0.14 |\n", - "| 8 | Elvitegravir | NO | 0.08 |\n", - "| 9 | Doravirine | NO | 0.06 |\n", - "| 10 | Podophyllotoxin | NO | 0.05 |\n", + "| 1 | Zidovudine | YES | 0.82 |\n", + "| 2 | Stavudine | NO | 0.49 |\n", + "| 3 | Zalcitabine | NO | 0.39 |\n", + "| 4 | Didanosine | NO | 0.20 |\n", + "| 5 | Nevirapine | NO | 0.19 |\n", + "| 6 | Fosamprenavir | NO | 0.17 |\n", + "| 7 | Amprenavir | NO | 0.10 |\n", + "| 8 | Pyrimidine | NO | 0.07 |\n", + "| 9 | Emtricitabine | NO | 0.06 |\n", + "| 10 | Tromantadine | NO | 0.03 |\n", "checkout ./result/repurposing.txt for the whole list\n" ] } ], "source": [ - "y_pred = property_pred.repurpose(X_repurpose = r, model = model, drug_names = r_name)" + "y_pred = CompoundPred.repurpose(X_repurpose = r, model = model, drug_names = r_name)" ] }, { @@ -491,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -507,22 +532,22 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model = property_pred.model_pretrained(path_dir = './tutorial_model')\n", + "model = CompoundPred.model_pretrained(path_dir = './tutorial_model')\n", "model" ] },